Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis
Objective Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis...
Uloženo v:
| Vydáno v: | European radiology Ročník 32; číslo 6; s. 3705 - 3715 |
|---|---|
| Hlavní autoři: | , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2022
Springer Nature B.V |
| Témata: | |
| ISSN: | 1432-1084, 0938-7994, 1432-1084 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Objective
Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis.
Methods
In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007–March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions.
Results
A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20–86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71–0.83, independent accuracies 65–79%, and combined accuracies up to 81–88%.
Conclusion
This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases.
Key Points
• An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented.
• The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum).
• The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management. |
|---|---|
| AbstractList | Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis.OBJECTIVEStandard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis.In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions.METHODSIn this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions.A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%.RESULTSA total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%.This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases.CONCLUSIONThis proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases.• An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management.KEY POINTS• An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management. ObjectiveStandard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis.MethodsIn this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007–March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions.ResultsA total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20–86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71–0.83, independent accuracies 65–79%, and combined accuracies up to 81–88%.ConclusionThis proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases.Key Points• An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented.• The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum).• The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management. Objective Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis. Methods In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007–March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions. Results A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20–86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71–0.83, independent accuracies 65–79%, and combined accuracies up to 81–88%. Conclusion This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases. Key Points • An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management. Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis. In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions. A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%. This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases. • An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management. |
| Author | Grussu, Francesco Garcia-Ruiz, Alonso Sarro, Noemi Vidal Bruna, Jordi Perez-Lopez, Raquel Fernandez, Juan Jose Sanchez Cos, Monica Fernandez-Coello, Alejandro Simo, Angels Camins Majos, Carles Pons-Escoda, Albert Naval-Baudin, Pablo |
| Author_xml | – sequence: 1 givenname: Albert orcidid: 0000-0003-4167-8291 surname: Pons-Escoda fullname: Pons-Escoda, Albert email: albert.pons.idi@gencat.cat organization: Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Neurooncology Unit, Institut d’Investigació Biomèdica de Bellvitge- IDIBELL, L’Hospitalet de Llobregat – sequence: 2 givenname: Alonso surname: Garcia-Ruiz fullname: Garcia-Ruiz, Alonso organization: Radiomics Groups, Vall d’Hebron Institut d’Oncologia- VHIO – sequence: 3 givenname: Pablo surname: Naval-Baudin fullname: Naval-Baudin, Pablo organization: Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat – sequence: 4 givenname: Francesco surname: Grussu fullname: Grussu, Francesco organization: Radiomics Groups, Vall d’Hebron Institut d’Oncologia- VHIO – sequence: 5 givenname: Juan Jose Sanchez surname: Fernandez fullname: Fernandez, Juan Jose Sanchez organization: Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat – sequence: 6 givenname: Angels Camins surname: Simo fullname: Simo, Angels Camins organization: Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat – sequence: 7 givenname: Noemi Vidal surname: Sarro fullname: Sarro, Noemi Vidal organization: Neurooncology Unit, Institut d’Investigació Biomèdica de Bellvitge- IDIBELL, L’Hospitalet de Llobregat, Pathology Department, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat – sequence: 8 givenname: Alejandro surname: Fernandez-Coello fullname: Fernandez-Coello, Alejandro organization: Neurosurgery Department, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Pathology and Experimental Therapeutics Department, Anatomy Unit, University of Barcelona, Biomedical Research Networking Centers of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) – sequence: 9 givenname: Jordi surname: Bruna fullname: Bruna, Jordi organization: Neurooncology Unit, Institut d’Investigació Biomèdica de Bellvitge- IDIBELL, L’Hospitalet de Llobregat – sequence: 10 givenname: Monica surname: Cos fullname: Cos, Monica organization: Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat – sequence: 11 givenname: Raquel surname: Perez-Lopez fullname: Perez-Lopez, Raquel organization: Radiomics Groups, Vall d’Hebron Institut d’Oncologia- VHIO, Radiology Department, Hospital Universitari Vall d’Hebron – sequence: 12 givenname: Carles surname: Majos fullname: Majos, Carles organization: Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Neurooncology Unit, Institut d’Investigació Biomèdica de Bellvitge- IDIBELL, L’Hospitalet de Llobregat |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35103827$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kc1u1DAUhSNURH_gBVggS2zYGOzYmTjs0JRCpUog8be07jg3gyvHDrZTMbxSXxKn0wrURSVL8Y3O-XR8z3F14IPHqnrO2WvOWPsmMSYEo6zmlCnZKcofVUdcipryMh_8dz-sjlO6ZIx1XLZPqkPRcCZU3R5V19_Db3TU4RU6Ah7cLtlEwkB8iCM4-wd7cvplTT__OCfZjkitz-iTzTti5niF6S0BMoXyL1twZIse42KDjUMC0xQDmJ8F3BObEyljQZdjgjc4ZWI96W0y0Y7WQ7Z-S7bOho2DlMMIN74Rc5mgxHpaPR7AJXx2-z2pvp29_7r-SC8-fThfv7ugRrRNpjUMXHWyQwWcA-8UU0rKDvoeB9OUldWd2QxGDANIBDU0qhfYik5iz0F2XJxUr_bcEvfXjCnrsWRE58BjmJOuV7VcNbJgi_TlPellmGNZ46JatUzVDVuAL25V82bEXk_lvRB3-q6GIlB7gYkhpYiDNjaXfQSfI1inOdNL43rfuC6N65vG9cKu71nv6A-axN6UithvMf6L_YDrL4y4wJ4 |
| CitedBy_id | crossref_primary_10_1002_acn3_52306 crossref_primary_10_1177_19714009241242596 crossref_primary_10_1007_s00330_024_10686_8 crossref_primary_10_1007_s00330_023_09892_7 crossref_primary_10_1007_s00330_024_10611_z crossref_primary_10_1016_j_acra_2023_10_044 crossref_primary_10_1186_s12885_024_12571_5 crossref_primary_10_3174_ajnr_A8788 crossref_primary_10_1038_s41598_024_83452_x crossref_primary_10_1007_s00330_023_10202_4 crossref_primary_10_1016_j_mri_2023_05_004 crossref_primary_10_1007_s00330_023_09917_1 crossref_primary_10_3390_diagnostics14060618 crossref_primary_10_1007_s00330_023_09729_3 crossref_primary_10_1016_j_rxeng_2024_03_002 crossref_primary_10_1007_s00234_024_03385_0 |
| Cites_doi | 10.4103/2152-7806.111302 10.1007/s00234-020-02522-9 10.1212/WNL.30.9.907 10.1056/NEJM199002223220802 10.1016/j.clineuro.2015.09.017 10.1016/j.neurad.2018.09.006 10.1097/JTO.0b013e3181ec173d 10.1259/bjr/65711810 10.1002/jmri.24648 10.3174/ajnr.A5027 10.1007/s00234-004-1246-7 10.1002/nbm.1016 10.4103/2152-7806.111298 10.3174/ajnr.A2333 10.1002/cncr.30164 10.1212/CON.0000000000000536 10.1093/neuonc/noz150 10.3174/ajnr.A0484 10.1097/PPO.0000000000000126 10.3174/ajnr.A3477 10.1093/neuonc/noaa141 10.1148/radiol.2492071659 10.1016/0360-3016(92)91021-E 10.1007/s11060-010-0240-7 10.1002/nbm.2833 10.3747/co.v16i1.308 10.1102/1470-7330.2012.0038 10.3174/ajnr.A6761 10.3171/jns.1991.75.4.0559 10.1007/s00401-007-0243-4 10.1148/radiol.2223010558 10.3174/ajnr.A4341 10.20517/2394-4722.2019.20 10.3171/2013.3.JNS122226 10.1016/0360-3016(89)90941-3 10.1002/jmri.20707 10.3174/ajnr.A5827 10.1016/j.ejrad.2005.12.032 10.14694/edbook_am.2014.34.e57 10.3174/ajnr.A6153 10.1016/B978-0-12-800945-1.00031-8 10.3174/ajnr.A2441 10.1007/s00234-010-0740-3 10.1155/2017/7064120 10.1007/s00701-010-0774-7 10.1371/journal.pone.0191341 10.1007/s00234-005-0030-7 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to European Society of Radiology 2022 2022. The Author(s), under exclusive licence to European Society of Radiology. The Author(s), under exclusive licence to European Society of Radiology 2022. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to European Society of Radiology 2022 – notice: 2022. The Author(s), under exclusive licence to European Society of Radiology. – notice: The Author(s), under exclusive licence to European Society of Radiology 2022. |
| DBID | AAYXX CITATION NPM 3V. 7QO 7RV 7X7 7XB 88E 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. KB0 LK8 M0S M1P M7P NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS 7X8 |
| DOI | 10.1007/s00330-021-08498-1 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest : Biological Science Collection journals [unlimited simultaneous users] ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Biological Science Database ProQuest Nursing and Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Nursing & Allied Health Source ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Advanced Technologies & Aerospace Database Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic ProQuest Central Student PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1432-1084 |
| EndPage | 3715 |
| ExternalDocumentID | 35103827 10_1007_s00330_021_08498_1 |
| Genre | Journal Article |
| GroupedDBID | --- -53 -5E -5G -BR -EM -Y2 -~C .86 .VR 04C 06C 06D 0R~ 0VY 1N0 1SB 2.D 203 28- 29G 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 36B 3V. 4.4 406 408 409 40D 40E 53G 5GY 5QI 5VS 67Z 6NX 6PF 7RV 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAWTL AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIPD ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABPLI ABQBU ABQSL ABSXP ABTEG ABTKH ABTMW ABULA ABUWG ABUWZ ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHVE ACHXU ACIHN ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACREN ACUDM ACZOJ ADBBV ADHHG ADHIR ADIMF ADINQ ADJJI ADKNI ADKPE ADOJX ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFJLC AFKRA AFLOW AFQWF AFRAH AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGVAE AGWIL AGWZB AGYKE AHAVH AHBYD AHIZS AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKMHD ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AZFZN B-. BA0 BBNVY BBWZM BDATZ BENPR BGLVJ BGNMA BHPHI BKEYQ BMSDO BPHCQ BSONS BVXVI CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EBD EBLON EBS ECF ECT EIHBH EIOEI EJD EMB EMOBN EN4 ESBYG EX3 F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GRRUI GXS H13 HCIFZ HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ IMOTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW KPH LAS LK8 LLZTM M1P M4Y M7P MA- N2Q N9A NAPCQ NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9S PF0 PQQKQ PROAC PSQYO PT4 PT5 Q2X QOK QOR QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RRX RSV RZK S16 S1Z S26 S27 S28 S37 S3B SAP SCLPG SDE SDH SDM SHX SISQX SJYHP SMD SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZ9 SZN T13 T16 TEORI TSG TSK TSV TT1 TUC U2A U9L UDS UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WJK WK8 WOW YLTOR Z45 Z7R Z7U Z7X Z7Y Z7Z Z82 Z83 Z85 Z87 Z88 Z8M Z8O Z8R Z8S Z8T Z8V Z8W Z8Z Z91 Z92 ZMTXR ZOVNA ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PQGLB NPM PUEGO 7QO 7XB 8FD 8FK AZQEC DWQXO FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI PRINS 7X8 |
| ID | FETCH-LOGICAL-c375t-2af18949e8a11a198088449addefc503329cbfc3ffa4ea8f58d3e7394ed1a4913 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 21 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000749416400004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1432-1084 0938-7994 |
| IngestDate | Thu Sep 04 19:58:39 EDT 2025 Tue Dec 02 15:53:56 EST 2025 Tue Sep 30 00:37:07 EDT 2025 Sat Nov 29 04:37:29 EST 2025 Tue Nov 18 22:43:36 EST 2025 Fri Feb 21 02:45:29 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | Brain neoplasms Magnetic resonance imaging Perfusion imaging Glioblastoma Metastases |
| Language | English |
| License | 2022. The Author(s), under exclusive licence to European Society of Radiology. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c375t-2af18949e8a11a198088449addefc503329cbfc3ffa4ea8f58d3e7394ed1a4913 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-4167-8291 |
| PMID | 35103827 |
| PQID | 2667082501 |
| PQPubID | 54162 |
| PageCount | 11 |
| ParticipantIDs | proquest_miscellaneous_2624654088 proquest_journals_2667082501 pubmed_primary_35103827 crossref_citationtrail_10_1007_s00330_021_08498_1 crossref_primary_10_1007_s00330_021_08498_1 springer_journals_10_1007_s00330_021_08498_1 |
| PublicationCentury | 2000 |
| PublicationDate | 20220600 2022-06-00 2022-Jun 20220601 |
| PublicationDateYYYYMMDD | 2022-06-01 |
| PublicationDate_xml | – month: 6 year: 2022 text: 20220600 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Germany – name: Heidelberg |
| PublicationTitle | European radiology |
| PublicationTitleAbbrev | Eur Radiol |
| PublicationTitleAlternate | Eur Radiol |
| PublicationYear | 2022 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
| References | Wen, Huse (CR44) 2017; 23 Willats, Calamante (CR15) 2013; 26 Mandrekar (CR49) 2010; 5 CR39 CR38 CR35 Vallée, Guillevin, Wager, Delwail, Guillevin, JV (CR26) 2018; 39 CR33 CR32 Louis, Ohgaki, Wiestler (CR43) 2007; 114 Pons-Escoda, Garcia-Ruiz, Naval-Baudin (CR42) 2020; 41 Liang, Thornton, Sandler, Greenberg (CR48) 1991; 75 Gaspar, Fisher, Macdonald (CR47) 1992; 24 CR4 CR5 Ostrom, Cioffi, Gittleman (CR2) 2019; 21 Cha, Lupo, Chen (CR31) 2007; 28 Upadhyay, Waldman (CR9) 2011; 84 CR46 CR41 CR40 Patchell, Tibbs, Walsh (CR8) 1990; 322 Klekner, Hutóczki, Virga (CR23) 2015; 139 Wang, Kim, Chawla (CR34) 2011; 32 Fuentes-Raspall, Vilardell, Perez-Bueno (CR7) 2011; 101 Boxerman, Paulson, Prah, Schmainda (CR17) 2013; 34 Shiroishi, Castellazzi, Boxerman (CR13) 2015; 41 Tsougos, Svolos, Kousi (CR28) 2012; 12 Boxerman, Quarles, Hu (CR16) 2020; 22 CR19 CR14 Hochberg, Pruitt (CR45) 1980; 30 CR12 Pekmezci, Perry (CR21) 2013; 4 CR11 Cindil, Sendur, Cerit (CR30) 2021; 63 Fink, Fink (CR10) 2013; 4 Altwairgi, Raja, Manzoor (CR3) 2017; 11 Askaner, Rydelius, Engelholm (CR37) 2019; 46 CR29 Chiang, Kuo, Lu (CR36) 2004; 46 CR27 Fidler (CR24) 2015; 21 CR25 Leu, Boxerman, Ellingson (CR18) 2017 CR22 Tate, Underwood, Acosta (CR50) 2006; 19 Donin, Filson, Drakaki (CR6) 2016; 122 Paulson, Schmainda (CR20) 2008; 249 Campos, Davey, Hird (CR1) 2009; 16 K Leu (8498_CR18) 2017 LE Gaspar (8498_CR47) 1992; 24 S Campos (8498_CR1) 2009; 16 8498_CR5 8498_CR4 JL Boxerman (8498_CR17) 2013; 34 8498_CR11 8498_CR14 8498_CR12 8498_CR19 L Willats (8498_CR15) 2013; 26 A Pons-Escoda (8498_CR42) 2020; 41 I Tsougos (8498_CR28) 2012; 12 S Cha (8498_CR31) 2007; 28 JL Boxerman (8498_CR16) 2020; 22 MS Shiroishi (8498_CR13) 2015; 41 8498_CR40 IC Chiang (8498_CR36) 2004; 46 8498_CR41 DN Louis (8498_CR43) 2007; 114 8498_CR46 AR Tate (8498_CR50) 2006; 19 Á Klekner (8498_CR23) 2015; 139 K Askaner (8498_CR37) 2019; 46 AK Altwairgi (8498_CR3) 2017; 11 K Fink (8498_CR10) 2013; 4 A Vallée (8498_CR26) 2018; 39 8498_CR32 8498_CR33 JN Mandrekar (8498_CR49) 2010; 5 IJ Fidler (8498_CR24) 2015; 21 8498_CR35 8498_CR38 8498_CR39 BC Liang (8498_CR48) 1991; 75 E Cindil (8498_CR30) 2021; 63 M Pekmezci (8498_CR21) 2013; 4 FH Hochberg (8498_CR45) 1980; 30 QT Ostrom (8498_CR2) 2019; 21 N Donin (8498_CR6) 2016; 122 R Fuentes-Raspall (8498_CR7) 2011; 101 8498_CR22 S Wang (8498_CR34) 2011; 32 ES Paulson (8498_CR20) 2008; 249 8498_CR25 PY Wen (8498_CR44) 2017; 23 N Upadhyay (8498_CR9) 2011; 84 8498_CR29 RA Patchell (8498_CR8) 1990; 322 8498_CR27 |
| References_xml | – volume: 4 start-page: 245 year: 2013 ident: CR21 article-title: Neuropathology of brain metastases publication-title: Surg Neurol Int doi: 10.4103/2152-7806.111302 – ident: CR22 – volume: 63 start-page: 331 year: 2021 end-page: 342 ident: CR30 article-title: Validation of combined use of DWI and percentage signal recovery-optimized protocol of DSC-MRI in differentiation of high-grade glioma, metastasis, and lymphoma publication-title: Neuroradiology doi: 10.1007/s00234-020-02522-9 – volume: 30 start-page: 907 year: 1980 end-page: 907 ident: CR45 article-title: Assumptions in the radiotherapy of glioblastoma publication-title: Neurology doi: 10.1212/WNL.30.9.907 – volume: 322 start-page: 494 year: 1990 end-page: 500 ident: CR8 article-title: A randomized trial of surgery in the treatment of single metastases to the brain publication-title: N Engl J Med doi: 10.1056/NEJM199002223220802 – ident: CR4 – volume: 139 start-page: 138 year: 2015 end-page: 143 ident: CR23 article-title: Expression pattern of invasion-related molecules in the peritumoral brain publication-title: Clin Neurol Neurosurg doi: 10.1016/j.clineuro.2015.09.017 – ident: CR39 – ident: CR12 – volume: 46 start-page: 367 year: 2019 end-page: 372 ident: CR37 article-title: Differentiation between glioblastomas and brain metastases and regarding their primary site of malignancy using dynamic susceptibility contrast MRI at 3T publication-title: J Neuroradiol doi: 10.1016/j.neurad.2018.09.006 – volume: 5 start-page: 1315 year: 2010 end-page: 1316 ident: CR49 article-title: Receiver operating characteristic curve in diagnostic test ssessment publication-title: J Thorac Oncol doi: 10.1097/JTO.0b013e3181ec173d – ident: CR35 – volume: 84 start-page: S107 year: 2011 end-page: S111 ident: CR9 article-title: Conventional MRI evaluation of gliomas publication-title: Br J Radiol doi: 10.1259/bjr/65711810 – volume: 41 start-page: 296 year: 2015 end-page: 313 ident: CR13 article-title: Principles of T 2 *-weighted dynamic susceptibility contrast MRI technique in brain tumor imaging publication-title: J Magn Reson Imaging doi: 10.1002/jmri.24648 – ident: CR29 – volume: 11 start-page: 54 year: 2017 end-page: 62 ident: CR3 article-title: Management and treatment recommendations for World Health Organization Grade III and IV gliomas publication-title: Int J Health Sci (Qassim) – ident: CR25 – year: 2017 ident: CR18 article-title: Effects of MRI protocol parameters, preload injection dose, fractionation strategies, and leakage correction algorithms on the fidelity of dynamic-susceptibility contrast MRI estimates of relative cerebral blood volume in gliomas publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A5027 – ident: CR46 – ident: CR19 – volume: 46 start-page: 619 year: 2004 end-page: 627 ident: CR36 article-title: Distinction between high-grade gliomas and solitary metastases using peritumoral 3-T magnetic resonance spectroscopy, diffusion, and perfusion imagings publication-title: Neuroradiology doi: 10.1007/s00234-004-1246-7 – volume: 19 start-page: 411 year: 2006 end-page: 434 ident: CR50 article-title: Development of a decision support system for diagnosis and grading of brain tumours usingin vivo magnetic resonance single voxel spectra publication-title: NMR Biomed doi: 10.1002/nbm.1016 – ident: CR11 – ident: CR32 – ident: CR5 – volume: 4 start-page: 209 year: 2013 ident: CR10 article-title: Imaging of brain metastases publication-title: Surg Neurol Int doi: 10.4103/2152-7806.111298 – volume: 32 start-page: 507 year: 2011 end-page: 514 ident: CR34 article-title: Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A2333 – volume: 39 start-page: 1423 year: 2018 end-page: 1431 ident: CR26 article-title: Added value of spectroscopy to perfusion mrı in the differential diagnostic performance of common malignant brain tumors publication-title: AJNR Am J Neuroradiol – volume: 122 start-page: 3075 year: 2016 end-page: 3086 ident: CR6 article-title: Risk of second primary malignancies among cancer survivors in the United States, 1992 through 2008 publication-title: Cancer doi: 10.1002/cncr.30164 – volume: 23 start-page: 1531 year: 2017 end-page: 1547 ident: CR44 article-title: 2016 World Health Organization classification of central nervous system tumors publication-title: Contin Lifelong Learn Neurol doi: 10.1212/CON.0000000000000536 – ident: CR14 – ident: CR33 – volume: 21 start-page: v1 year: 2019 end-page: v100 ident: CR2 article-title: CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012–2016 publication-title: Neuro Oncol doi: 10.1093/neuonc/noz150 – ident: CR40 – volume: 28 start-page: 1078 year: 2007 end-page: 1084 ident: CR31 article-title: Differentiation of glioblastoma multiforme and single brain metastasis by peak height and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A0484 – ident: CR27 – volume: 21 start-page: 284 year: 2015 end-page: 293 ident: CR24 article-title: The biology of brain metastasis publication-title: Cancer J doi: 10.1097/PPO.0000000000000126 – volume: 34 start-page: 1364 year: 2013 end-page: 1369 ident: CR17 article-title: The effect of pulse sequence parameters and contrast agent dose on percentage signal recovery in DSC-MRI: implications for clinical applications publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A3477 – volume: 22 start-page: 1262 year: 2020 end-page: 1275 ident: CR16 article-title: Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas publication-title: Neuro Oncol doi: 10.1093/neuonc/noaa141 – volume: 249 start-page: 601 year: 2008 end-page: 613 ident: CR20 article-title: Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors publication-title: Radiology doi: 10.1148/radiol.2492071659 – volume: 24 start-page: 55 year: 1992 end-page: 57 ident: CR47 article-title: Supratentorial malignant glioma: patterns of recurrence and implications for external beam local treatment publication-title: Int J Radiat Oncol doi: 10.1016/0360-3016(92)91021-E – volume: 101 start-page: 117 year: 2011 end-page: 123 ident: CR7 article-title: Population-based incidence and survival of central nervous system (CNS) malignancies in Girona (Spain) 1994–2005 publication-title: J Neurooncol doi: 10.1007/s11060-010-0240-7 – volume: 26 start-page: 913 year: 2013 end-page: 931 ident: CR15 article-title: The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI publication-title: NMR Biomed doi: 10.1002/nbm.2833 – ident: CR38 – volume: 16 start-page: 62 year: 2009 end-page: 66 ident: CR1 article-title: Brain metastasis from an unknown primary, or primary brain tumour? A diagnostic dilemma publication-title: Curr Oncol doi: 10.3747/co.v16i1.308 – volume: 12 start-page: 423 year: 2012 end-page: 436 ident: CR28 article-title: Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T publication-title: Cancer Imaging doi: 10.1102/1470-7330.2012.0038 – volume: 41 start-page: 1816 year: 2020 end-page: 1824 ident: CR42 article-title: Presurgical identification of primary central nervous system lymphoma with normalized time-intensity curve: a pilot study of a new method to analyze DSC-PWI publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A6761 – volume: 75 start-page: 559 year: 1991 end-page: 563 ident: CR48 article-title: Malignant astrocytomas: focal tumor recurrence after focal external beam radiation therapy publication-title: J Neurosurg doi: 10.3171/jns.1991.75.4.0559 – volume: 114 start-page: 97 year: 2007 end-page: 109 ident: CR43 article-title: The 2007 WHO classification of tumours of the central nervous system publication-title: Acta Neuropathol doi: 10.1007/s00401-007-0243-4 – ident: CR41 – ident: 8498_CR32 doi: 10.1148/radiol.2223010558 – ident: 8498_CR14 doi: 10.3174/ajnr.A4341 – ident: 8498_CR4 doi: 10.20517/2394-4722.2019.20 – volume: 16 start-page: 62 year: 2009 ident: 8498_CR1 publication-title: Curr Oncol doi: 10.3747/co.v16i1.308 – volume: 39 start-page: 1423 year: 2018 ident: 8498_CR26 publication-title: AJNR Am J Neuroradiol – volume: 19 start-page: 411 year: 2006 ident: 8498_CR50 publication-title: NMR Biomed doi: 10.1002/nbm.1016 – volume: 4 start-page: 245 year: 2013 ident: 8498_CR21 publication-title: Surg Neurol Int doi: 10.4103/2152-7806.111302 – volume: 28 start-page: 1078 year: 2007 ident: 8498_CR31 publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A0484 – ident: 8498_CR22 doi: 10.3171/2013.3.JNS122226 – ident: 8498_CR46 doi: 10.1016/0360-3016(89)90941-3 – volume: 41 start-page: 296 year: 2015 ident: 8498_CR13 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.24648 – year: 2017 ident: 8498_CR18 publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A5027 – volume: 24 start-page: 55 year: 1992 ident: 8498_CR47 publication-title: Int J Radiat Oncol doi: 10.1016/0360-3016(92)91021-E – volume: 22 start-page: 1262 year: 2020 ident: 8498_CR16 publication-title: Neuro Oncol doi: 10.1093/neuonc/noaa141 – volume: 101 start-page: 117 year: 2011 ident: 8498_CR7 publication-title: J Neurooncol doi: 10.1007/s11060-010-0240-7 – volume: 11 start-page: 54 year: 2017 ident: 8498_CR3 publication-title: Int J Health Sci (Qassim) – ident: 8498_CR39 doi: 10.1002/jmri.20707 – ident: 8498_CR19 doi: 10.3174/ajnr.A5827 – volume: 32 start-page: 507 year: 2011 ident: 8498_CR34 publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A2333 – volume: 84 start-page: S107 year: 2011 ident: 8498_CR9 publication-title: Br J Radiol doi: 10.1259/bjr/65711810 – volume: 23 start-page: 1531 year: 2017 ident: 8498_CR44 publication-title: Contin Lifelong Learn Neurol doi: 10.1212/CON.0000000000000536 – ident: 8498_CR40 doi: 10.1016/j.ejrad.2005.12.032 – volume: 41 start-page: 1816 year: 2020 ident: 8498_CR42 publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A6761 – ident: 8498_CR5 doi: 10.14694/edbook_am.2014.34.e57 – ident: 8498_CR38 doi: 10.3174/ajnr.A6153 – volume: 26 start-page: 913 year: 2013 ident: 8498_CR15 publication-title: NMR Biomed doi: 10.1002/nbm.2833 – ident: 8498_CR12 doi: 10.1016/B978-0-12-800945-1.00031-8 – volume: 34 start-page: 1364 year: 2013 ident: 8498_CR17 publication-title: AJNR Am J Neuroradiol doi: 10.3174/ajnr.A3477 – volume: 12 start-page: 423 year: 2012 ident: 8498_CR28 publication-title: Cancer Imaging doi: 10.1102/1470-7330.2012.0038 – volume: 322 start-page: 494 year: 1990 ident: 8498_CR8 publication-title: N Engl J Med doi: 10.1056/NEJM199002223220802 – volume: 114 start-page: 97 year: 2007 ident: 8498_CR43 publication-title: Acta Neuropathol doi: 10.1007/s00401-007-0243-4 – volume: 75 start-page: 559 year: 1991 ident: 8498_CR48 publication-title: J Neurosurg doi: 10.3171/jns.1991.75.4.0559 – ident: 8498_CR25 doi: 10.3174/ajnr.A2441 – volume: 63 start-page: 331 year: 2021 ident: 8498_CR30 publication-title: Neuroradiology doi: 10.1007/s00234-020-02522-9 – volume: 4 start-page: 209 year: 2013 ident: 8498_CR10 publication-title: Surg Neurol Int doi: 10.4103/2152-7806.111298 – ident: 8498_CR35 doi: 10.1007/s00234-010-0740-3 – volume: 46 start-page: 367 year: 2019 ident: 8498_CR37 publication-title: J Neuroradiol doi: 10.1016/j.neurad.2018.09.006 – volume: 122 start-page: 3075 year: 2016 ident: 8498_CR6 publication-title: Cancer doi: 10.1002/cncr.30164 – volume: 46 start-page: 619 year: 2004 ident: 8498_CR36 publication-title: Neuroradiology doi: 10.1007/s00234-004-1246-7 – volume: 30 start-page: 907 year: 1980 ident: 8498_CR45 publication-title: Neurology doi: 10.1212/WNL.30.9.907 – ident: 8498_CR11 doi: 10.1155/2017/7064120 – volume: 21 start-page: v1 year: 2019 ident: 8498_CR2 publication-title: Neuro Oncol doi: 10.1093/neuonc/noz150 – volume: 21 start-page: 284 year: 2015 ident: 8498_CR24 publication-title: Cancer J doi: 10.1097/PPO.0000000000000126 – volume: 139 start-page: 138 year: 2015 ident: 8498_CR23 publication-title: Clin Neurol Neurosurg doi: 10.1016/j.clineuro.2015.09.017 – volume: 5 start-page: 1315 year: 2010 ident: 8498_CR49 publication-title: J Thorac Oncol doi: 10.1097/JTO.0b013e3181ec173d – volume: 249 start-page: 601 year: 2008 ident: 8498_CR20 publication-title: Radiology doi: 10.1148/radiol.2492071659 – ident: 8498_CR27 doi: 10.1007/s00701-010-0774-7 – ident: 8498_CR29 doi: 10.1371/journal.pone.0191341 – ident: 8498_CR33 doi: 10.1007/s00234-005-0030-7 – ident: 8498_CR41 |
| SSID | ssj0009147 |
| Score | 2.4761598 |
| Snippet | Objective
Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and... Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels... ObjectiveStandard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and... |
| SourceID | proquest pubmed crossref springer |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 3705 |
| SubjectTerms | Blood volume Brain Brain cancer Cerebral blood flow Diagnostic Radiology Glioblastoma Histograms Imaging Information processing Internal Medicine Interventional Radiology Mathematical models Medicine Medicine & Public Health Metastases Metastasis Neuro Neuroradiology Parameters Perfusion Radiology Signal reconstruction Substantia alba Tumors Ultrasound |
| SummonAdditionalLinks | – databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1fi9QwEA96ivji_9PqKRF802DTZNvEF5HTQ0GPBfW8t5JNk6PQa9dt91C_kl_SmTTdRQ7vRehLaJJOmUwyk5n5DSHPeAUSlMkKzJIqZ9LLjC106piWNs9nHo6okEh79LE4PFTHx3oeL9z6GFY57Ylho646i3fkL-EgKdCcSfnr5XeGVaPQuxpLaFwmVxAlIQuhe_Mt6C4PBcbAaFes0FrGpJmQOodFzFKGAQqpkhosqb8PpnPa5jlPaTiADm7-L-m3yI2oetI341q5TS659g659ik61--S30fdD9ewBsOIqIlgJbTztEW9tql_uYq-_bzP5t8-UCxJz-ox_H34Se16deb6V9TQZTdg_BF852QEtMaoscbRCbscJq5oPfQUmjA1PHZMnKR1SzFFeCwzhsHY9KSpuwVo90N3asK4UzdAywBZ98jXg3df9t-zWMuBWVHMBpYZz5WW2inDueFawe4mpcbd1Vt0pWbaLrwV3hvpjPIzVQlXCC1dxY3UXOySnbZr3QNCC5kjjJ6o8pmVwugF6CypycHu9MY4kSaET4wsbQQ6x3obTbmBaA7ML4H5ZWB-yRPyfDNmOcJ8XNh7b2J0GUW-L7dcTsjTzWsQVvTAmNZ1a-yTIX4d_HtC7o_ravM5gdiGKisS8mJaaNvJ_03Lw4tpeUSuZ5iwEe6N9sjOsFq7x-SqPRvqfvUkiMsffxQbJA priority: 102 providerName: ProQuest |
| Title | Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis |
| URI | https://link.springer.com/article/10.1007/s00330-021-08498-1 https://www.ncbi.nlm.nih.gov/pubmed/35103827 https://www.proquest.com/docview/2667082501 https://www.proquest.com/docview/2624654088 |
| Volume | 32 |
| WOSCitedRecordID | wos000749416400004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1432-1084 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: P5Z dateStart: 20210101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1432-1084 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: M7P dateStart: 20210101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1432-1084 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: 7X7 dateStart: 20210101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1432-1084 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: 7RV dateStart: 19970101 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1432-1084 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: BENPR dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK customDbUrl: eissn: 1432-1084 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009147 issn: 1432-1084 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3di9QwEA_enYgvfn_seS4RfNNA06Ztcm935x0KupQ9XRdfSjZNjkKvPbbdQ_2X_Ced6dchp4JCCZQkk5TJJJPOzG8IeckzkCBfZHAtySImnPDZSnmWKWGiKHRwRLWBtIv38Wwml0uV9EFh9eDtPpgk2516DHbDtGMeQ5cCTwoFd58tsgO0JCZsmJ8urqB2uYj78Jjf9_v1CLqmV16zibZHzcnd_5vkPXKnVy3pQbcW7pMbtnxAbn3ojecPyY9F9dUWrEA3Iap7MBJaOVqi3lrk321G35weseTzO4op51neubc336jZrC9tvU81vaga9C-Ccc46wGr0CissHbDJgXBG86am8Aqk4TFdYCTNS4ohwF0aMXS2pmdFXq1Ae2-qc932O7cNvGmY1iPy6eT449Fb1udqYCaIw4b52nGphLJSc645cEVKIRTuns6gqdRXZuVM4JwWVksXyiywcaCEzbgWigePyXZZlfYpobGIECYvyKLQiECrFegkno7gXum0toE3IXxgX2p6IHPMp1GkIwRzy4UUuJC2XEj5hLwa-1x0MB5_bb03rIq0F-k6BU0mxvu0B9UvxmoQRrSw6NJWG2zjIz4dfPuEPOlW0zhcgNiF0o8n5PWwdK6I_3kuu__W_Bm57WOARvufaI9sN-uNfU5umssmr9dTshXPF1gu47aUU7JzeDxL5lP0eU2gTMIv01acfgIB-BWQ |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgoAL70eggJHgBBZ5OA8jIYRaqq66Xa1EaSsuwes4VaRssmyyhfKXkPiNzOSxK1TRWw9IuURxHCf5Zjxjz3wD8MJJUIJckaBbkgRcpMLlE2kbLoUOAj_FKapJpD0YhqNRdHQkx2vwu8-FobDKXic2ijopNa2Rv8GJJCR3xnbez75xqhpFu6t9CY0WFrvm9Du6bNW7wRb-35euu_1xf3OHd1UFuPZCv-auSp1ICmki5TgKfW6UMyEkyXmqaVPPlXqSai9NlTAqSv0o8UzoSWESRwnpeNjvJbgshGuTFI39LyuSX6cpaGZLVCKhlKJL0mlS9ahoms0pIMKOhETP7e-J8Ix1e2Zntpnwtm_-b5_qFtzoTGv2oZWF27Bmijtwda8LHrgLvw7KHybnOYVJMdWRsbAyZQXZ7Xn20yRs69MmHx8OWJ1NDc_a8P76lOnF_MRUb5lis7Km-Cp8znFL2E1RcblhPTc7dpywrK4YnmLXeOg2MZRlBaMU6LaMGgWbs-M8KyfovdTlVDX3TU2NZwqHdQ8-X8inug_rRVmYh8BCERBNoJcEvhaekhO0yWwVoF-dKmU82wKnB06sOyJ3qieSx0sK6gZsMYItbsAWOxa8Wt4za2lMzm290QMr7lRaFa9QZcHz5WVURrTDpApTLqiNS_x8-O4WPGhxvHycR9yNkRta8LoH9qrzf4_l0fljeQbXdvb3hvFwMNp9DNddSk5p1sg2YL2eL8wTuKJP6qyaP21ElcHXiwb8H64NeT0 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3bbtNAEB2VgipeuF8CBRYJnmBVXza2Fwkh1BARtUSRgFL1xWzWu5Ulxw6xUyi_xB_wdcz4kghV9K0PSH6xbK_X9pnZHe-ZMwDP3AQtyBMJhiVJwIUVHp9Kx3ApdBD0LQ5RdSLtwX44HkeHh3KyAb-7XBiiVXY-sXbUSaHpH_kODiQhhTOOu2NbWsRkMHwz_8apghSttHblNBqI7JnT7xi-la9HA_zWzz1v-O7T7nveVhjg2g_7FfeUdSMppImU6yqMv9HmhJBk81bTAp8n9dRq31oljIpsP0p8E_pSmMRVQro-tnsJLocYYxKdcNI_Wgv-unVxM0eiQwmlFG3CTp22RwXUHE7kCCcSEqO4vwfFMzPdM6u09eA3vP4_v7YbcK2dcrO3jY3chA2T34KtDy2p4Db8Oih-mIxnRJ9iqhVpYYVlOc3ns_SnSdjg4y6ffBmxKp0Znja0_-qU6eXixJSvmGLzoiLeFd7nuBHyJrZcZlin2Y4NJyytSoa72DRuukkYZWnOKDW6Ka9GJHR2nKXFFKOaqpip-rqZqXBPYbfuwOcLeVV3YTMvcnMfGKKM5AP9JOhr4Ss5xbmaowKMt61Sxnd64HYginUr8E51RrJ4JU1dAy9G4MU18GK3By9W18wbeZNzz97uQBa3rq6M1wjrwdPVYXRStPKkclMs6RyPdPvw2Xtwr8H06nY-aTpGXtiDlx3I143_uy8Pzu_LE9hCnMf7o_HeQ7jqUc5K_etsGzarxdI8giv6pErLxePaahl8vWi8_wHcqIIH |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Voxel-level+analysis+of+normalized+DSC-PWI+time-intensity+curves%3A+a+potential+generalizable+approach+and+its+proof+of+concept+in+discriminating+glioblastoma+and+metastasis&rft.jtitle=European+radiology&rft.au=Pons-Escoda%2C+Albert&rft.au=Garcia-Ruiz%2C+Alonso&rft.au=Naval-Baudin%2C+Pablo&rft.au=Grussu%2C+Francesco&rft.date=2022-06-01&rft.eissn=1432-1084&rft.volume=32&rft.issue=6&rft.spage=3705&rft_id=info:doi/10.1007%2Fs00330-021-08498-1&rft_id=info%3Apmid%2F35103827&rft.externalDocID=35103827 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1432-1084&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1432-1084&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1432-1084&client=summon |