Tumor growth prediction with reaction-diffusion and hyperelastic biomechanical model by physiological data fusion
•Tumor growth prediction with physiological data fusion.•A tumor growth model with reaction-diffusion and hyperelastic biomechanical model.•A derivative-free global optimization algorithm for model parameter estimation.•Physiological data fusion of contrast-enhanced CT and FDG-PET images.•Average pr...
Saved in:
| Published in: | Medical image analysis Vol. 25; no. 1; pp. 72 - 85 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Netherlands
Elsevier B.V
01.10.2015
|
| Subjects: | |
| ISSN: | 1361-8415, 1361-8423 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •Tumor growth prediction with physiological data fusion.•A tumor growth model with reaction-diffusion and hyperelastic biomechanical model.•A derivative-free global optimization algorithm for model parameter estimation.•Physiological data fusion of contrast-enhanced CT and FDG-PET images.•Average prediction performance: Dice = 84.6%, relative volume difference = 14.2%.
[Display omitted]
The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by physiological modeling and model personalization from clinical measurements. Although image-driven frameworks have been proposed with promising results, several issues such as infinitesimal strain assumptions, complicated personalization procedures, and the lack of functional information, may limit their prediction accuracy. In view of these issues, we propose a framework for pancreatic neuroendocrine tumor growth prediction, which comprises a FEM-based tumor growth model with coupled reaction-diffusion equation and nonlinear biomechanics. Physiological data fusion of structural and functional images is used to improve the subject-specificity of model personalization, and a derivative-free global optimization algorithm is adopted to facilitate the complicated model and accommodate flexible choices of objective functions. With this flexibility, we propose an objective function accounting for both the tumor volume difference and the root-mean-squared error of intracellular volume fractions. Experiments were performed on synthetic and clinical data to verify the parameter estimation capability and the prediction performance. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results of eight patient data sets, the average recall, precision, Dice coefficient, and relative volume difference between predicted and measured tumor volumes were 84.5 ± 6.9%, 85.8 ± 8.2%, 84.6 ± 1.7%, and 14.2 ± 8.4%, respectively. |
|---|---|
| AbstractList | The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by physiological modeling and model personalization from clinical measurements. Although image-driven frameworks have been proposed with promising results, several issues such as infinitesimal strain assumptions, complicated personalization procedures, and the lack of functional information, may limit their prediction accuracy. In view of these issues, we propose a framework for pancreatic neuroendocrine tumor growth prediction, which comprises a FEM-based tumor growth model with coupled reaction-diffusion equation and nonlinear biomechanics. Physiological data fusion of structural and functional images is used to improve the subject-specificity of model personalization, and a derivative-free global optimization algorithm is adopted to facilitate the complicated model and accommodate flexible choices of objective functions. With this flexibility, we propose an objective function accounting for both the tumor volume difference and the root-mean-squared error of intracellular volume fractions. Experiments were performed on synthetic and clinical data to verify the parameter estimation capability and the prediction performance. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results of eight patient data sets, the average recall, precision, Dice coefficient, and relative volume difference between predicted and measured tumor volumes were 84.5 ± 6.9%, 85.8 ± 8.2%, 84.6 ± 1.7%, and 14.2 ± 8.4%, respectively. •Tumor growth prediction with physiological data fusion.•A tumor growth model with reaction-diffusion and hyperelastic biomechanical model.•A derivative-free global optimization algorithm for model parameter estimation.•Physiological data fusion of contrast-enhanced CT and FDG-PET images.•Average prediction performance: Dice = 84.6%, relative volume difference = 14.2%. [Display omitted] The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by physiological modeling and model personalization from clinical measurements. Although image-driven frameworks have been proposed with promising results, several issues such as infinitesimal strain assumptions, complicated personalization procedures, and the lack of functional information, may limit their prediction accuracy. In view of these issues, we propose a framework for pancreatic neuroendocrine tumor growth prediction, which comprises a FEM-based tumor growth model with coupled reaction-diffusion equation and nonlinear biomechanics. Physiological data fusion of structural and functional images is used to improve the subject-specificity of model personalization, and a derivative-free global optimization algorithm is adopted to facilitate the complicated model and accommodate flexible choices of objective functions. With this flexibility, we propose an objective function accounting for both the tumor volume difference and the root-mean-squared error of intracellular volume fractions. Experiments were performed on synthetic and clinical data to verify the parameter estimation capability and the prediction performance. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results of eight patient data sets, the average recall, precision, Dice coefficient, and relative volume difference between predicted and measured tumor volumes were 84.5 ± 6.9%, 85.8 ± 8.2%, 84.6 ± 1.7%, and 14.2 ± 8.4%, respectively. The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by physiological modeling and model personalization from clinical measurements. Although image-driven frameworks have been proposed with promising results, several issues such as infinitesimal strain assumptions, complicated personalization procedures, and the lack of functional information, may limit their prediction accuracy. In view of these issues, we propose a framework for pancreatic neuroendocrine tumor growth prediction, which comprises a FEM-based tumor growth model with coupled reaction-diffusion equation and nonlinear biomechanics. Physiological data fusion of structural and functional images is used to improve the subject-specificity of model personalization, and a derivative-free global optimization algorithm is adopted to facilitate the complicated model and accommodate flexible choices of objective functions. With this flexibility, we propose an objective function accounting for both the tumor volume difference and the root-mean-squared error of intracellular volume fractions. Experiments were performed on synthetic and clinical data to verify the parameter estimation capability and the prediction performance. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results of eight patient data sets, the average recall, precision, Dice coefficient, and relative volume difference between predicted and measured tumor volumes were 84.5±6.9%, 85.8±8.2%, 84.6±1.7%, and 14.2±8.4%, respectively. |
| Author | Yao, Jianhua Summers, Ronald M. Wong, Ken C.L. Kebebew, Electron |
| AuthorAffiliation | b Endocrine Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA a Clinical Image Processing Service, Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA |
| AuthorAffiliation_xml | – name: b Endocrine Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA – name: a Clinical Image Processing Service, Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA |
| Author_xml | – sequence: 1 givenname: Ken C.L. surname: Wong fullname: Wong, Ken C.L. email: ken.wong@nih.gov organization: Clinical Image Processing Service, Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA – sequence: 2 givenname: Ronald M. surname: Summers fullname: Summers, Ronald M. email: rsummers@cc.nih.gov organization: Clinical Image Processing Service, Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA – sequence: 3 givenname: Electron surname: Kebebew fullname: Kebebew, Electron email: kebebewe@mail.nih.gov organization: Endocrine Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA – sequence: 4 givenname: Jianhua surname: Yao fullname: Yao, Jianhua email: jyao@cc.nih.gov organization: Clinical Image Processing Service, Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25962846$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9Uctq3DAUFSWlebRfUChedmPnSrJsz6KFEvoIBLpJ10KP67EG23IkO2H-PvJMEtouspLuvecB55yTk9GPSMhHCgUFWl3uigGtUwUDKgooCwD2hpxRXtG8KRk_eflTcUrOY9wBQF2W8I6cMrGpWFNWZ-Tudhl8yLbBP8xdNoWkaGbnx-zBpTmgOky5dW27xHWvRpt1-wkD9irOzmTa-QFNp0ZnVJ8N3mKf6X02dfuE7_32sLZqVtlR4T1526o-4oen94L8-fH99upXfvP75_XVt5vcCLaZ87atwVisoeG12dRGMdUwBE51JURla624aQRyW2JrDQeutaCK6VagblfgBfl61J0WnYIyOM5B9XIKblBhL71y8t_L6Dq59feyFCVUNU8Cn58Egr9bMM5ycNFg36sR_RIlrUFAyv4A_fS314vJc84JsDkCTPAxBmylcbNao03WrpcU5Nqp3MlDp3LtVEIpU6eJy__jPsu_zvpyZGHK-N5hkNE4HE0CBjSztN69yn8EghvAjg |
| CitedBy_id | crossref_primary_10_1016_j_camwa_2019_03_047 crossref_primary_10_1109_TMI_2017_2774044 crossref_primary_10_1109_TMI_2025_3533038 crossref_primary_10_1016_j_mechrescom_2020_103539 crossref_primary_10_3390_diagnostics12112639 crossref_primary_10_1016_j_biosystems_2021_104377 crossref_primary_10_1016_j_compbiomed_2022_105922 crossref_primary_10_1109_ACCESS_2018_2839681 crossref_primary_10_1146_annurev_bioeng_062117_121105 crossref_primary_10_1016_j_cma_2018_12_008 crossref_primary_10_1371_journal_pone_0260108 crossref_primary_10_1007_s00466_019_01744_w crossref_primary_10_1016_j_asoc_2019_04_034 crossref_primary_10_1016_j_media_2024_103240 crossref_primary_10_1016_j_neo_2020_10_011 crossref_primary_10_1038_s41598_020_77397_0 crossref_primary_10_1109_TMI_2016_2597313 crossref_primary_10_1088_0031_9155_61_21_R344 crossref_primary_10_1109_TBME_2021_3085523 crossref_primary_10_1038_s41596_021_00617_y crossref_primary_10_1016_j_cma_2016_07_010 |
| Cites_doi | 10.1016/j.neuroimage.2006.01.015 10.1109/TMI.2003.815867 10.1109/10.310090 10.1007/s00330-008-0924-y 10.1016/S0720-048X(99)00012-1 10.1148/radiol.10090908 10.1093/comjnl/7.4.308 10.1007/s002590050256 10.1007/s00285-007-0139-x 10.1001/archsurg.141.8.765 10.1038/nature04648 10.1038/35098076 10.1016/S0022-5193(03)00244-3 10.1007/s00259-004-1687-6 10.1109/TMI.2009.2026413 10.1038/ncb2548 10.1109/TBME.2012.2222027 10.1634/theoncologist.2008-0259 10.1158/1078-0432.CCR-04-2626 10.1016/j.surg.2007.09.012 10.1016/j.mechrescom.2012.02.007 10.1109/TMI.2005.857217 10.1002/jcp.22766 10.1148/121.2.379 10.1016/j.media.2014.02.005 10.1016/j.jtbi.2008.04.011 10.1016/S0020-7225(02)00014-9 10.1109/42.790458 10.1529/biophysj.105.060640 10.1148/radiol.12112458 10.1038/nrd3181 10.1073/pnas.1213353109 10.1109/TMI.2011.2181857 10.1023/A:1017930332101 10.1038/nrc2808 |
| ContentType | Journal Article |
| Copyright | 2015 Published by Elsevier B.V. |
| Copyright_xml | – notice: 2015 – notice: Published by Elsevier B.V. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM |
| DOI | 10.1016/j.media.2015.04.002 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| 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: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering |
| EISSN | 1361-8423 |
| EndPage | 85 |
| ExternalDocumentID | PMC4540673 25962846 10_1016_j_media_2015_04_002 S1361841515000523 |
| Genre | Journal Article Research Support, N.I.H., Intramural |
| GrantInformation_xml | – fundername: Intramural NIH HHS grantid: Z99 CL999999 |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1~. 1~5 29M 4.4 457 4G. 53G 5GY 5VS 7-5 71M 8P~ AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABBQC ABJNI ABLVK ABMAC ABMZM ABXDB ABYKQ ACDAQ ACGFS ACIUM ACIWK ACNNM ACPRK ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFRAH AFTJW AFXIZ AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV AJRQY ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ANZVX AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC BNPGV C45 CAG COF CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HX~ HZ~ IHE J1W JJJVA KOM LCYCR M41 MO0 N9A O-L O9- OAUVE OVD OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SDP SEL SES SEW SPC SPCBC SSH SST SSV SSZ T5K TEORI UHS ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACIEU ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD AGCQF AGRNS CGR CUY CVF ECM EIF NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c529t-ff70cde70837c97ca2a82e031b6556d7ba3c85e3d4efdc303bb51a2bf5ebf2e03 |
| ISICitedReferencesCount | 30 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000360864700008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1361-8415 |
| IngestDate | Tue Sep 30 16:39:10 EDT 2025 Thu Sep 25 08:36:47 EDT 2025 Mon Jul 21 05:57:27 EDT 2025 Sat Nov 29 04:06:01 EST 2025 Tue Nov 18 21:49:40 EST 2025 Fri Feb 23 02:28:19 EST 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Nonlinear solid mechanics Model personalization Tumor growth prediction Physiological data fusion Derivative-free optimization |
| Language | English |
| License | Published by Elsevier B.V. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c529t-ff70cde70837c97ca2a82e031b6556d7ba3c85e3d4efdc303bb51a2bf5ebf2e03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | http://doi.org/10.1016/j.media.2015.04.002 |
| PMID | 25962846 |
| PQID | 1705001573 |
| PQPubID | 23479 |
| PageCount | 14 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_4540673 proquest_miscellaneous_1705001573 pubmed_primary_25962846 crossref_citationtrail_10_1016_j_media_2015_04_002 crossref_primary_10_1016_j_media_2015_04_002 elsevier_sciencedirect_doi_10_1016_j_media_2015_04_002 |
| PublicationCentury | 2000 |
| PublicationDate | 2015-10-01 |
| PublicationDateYYYYMMDD | 2015-10-01 |
| PublicationDate_xml | – month: 10 year: 2015 text: 2015-10-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | Medical image analysis |
| PublicationTitleAlternate | Med Image Anal |
| PublicationYear | 2015 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Gatenby, Gawlinski (bib0017) 1996; 56 Massoptier, Casciaro (bib0033) 2008; 18 Han, Bayouth, Song, Taurani, Sonka, Buatti, Wu (bib0021) 2011 Fung (bib0015) 1993 Kam, Rejniak, Anderson (bib0025) 2012; 227 Hogea, Davatzikos, Biros (bib0022) 2008; 56 Menzel, Kuhl (bib0035) 2012; 42 Menze, Van Leemput, Honkela, Konukoglu, Weber, Ayache (bib0034) 2011 Alarcón, Byrne, Maini (bib0001) 2003; 225 Kazanjian, Reber, Hines (bib0026) 2006; 141 Konukoglu, Clatz, Menze, Stieltjes, Weber, Mandonnet (bib0028) 2010; 29 Marino, Hogue, Ray, Kirschner (bib0032) 2008; 254 Kormano, Dean (bib0029) 1976; 121 Pluim, Maintz, Viergever (bib0039) 2003; 22 Fang, Boas (bib0013) 2009 Sadato, Tsuchida, Nakaumra, Waki, Uematsu, Takahashi (bib0045) 1998; 25 Hamamci, Kucuk, Karaman, Engin, Unal (bib0020) 2012; 31 West, Brown, Enquist (bib0049) 2001; 413 Clatz, Sermesant, Bondiau, Delingette, Warfield, Malandain, Ayache (bib0009) 2005; 24 Nelder, Mead (bib0038) 1965; 7 Been, Suurmeijer, Cobben, Jager, Hoekstra, Elsinga (bib0005) 2004; 31 Nacif, Kawel, Lee, Chen, Yao, Zavodni (bib0037) 2012; 264 Kelloff, Hoffman, Johnson, Scher, Siegel, Cheng (bib0027) 2005; 11 Clayton, Lindon, Cloarec, Antti, Charuel, Hanton (bib0010) 2006; 440 (bib0042) 2007 Friedl, Locker, Sahai, Segall (bib0014) 2012; 14 Conn, Scheinberg, Vicente (bib0011) 2009; Vol. 8 Yushkevich, Piven, Hazlett, Smith, Ho, Gee (bib0050) 2006; 31 Ambrosi, Mollica (bib0002) 2002; 40 Liu, Sadowski, Weisbrod, Kebebew, Summers, Yao (bib0031) 2014; 18 Graziano, Preziosi (bib0019) 2007 Blansfield, Choyke, Morita, Choyke, Pingpank, Alexander (bib0006) 2007; 142 Miles (bib0036) 1999; 30 Bae (bib0003) 2010; 256 Rogers, McCulloch (bib0043) 1994; 41 Byrne (bib0007) 2010; 10 Stylianopoulos, Martin, Chauhan, Jain, Diop-Frimpong, Bardeesy (bib0047) 2012; 109 Thie (bib0048) 2004; 45 Bathe (bib0004) 1996 Chen, Summers, Yao (bib0008) 2013; 60 Rowan (bib0044) 1990 Powell (bib0040) 2009 Kyriacou, Davatzikos, Zinreich, Bryan (bib0030) 1999; 18 Gablonsky, Kelley (bib0016) 2001; 21 Germann, Stanfield (bib0018) 2005 Ehehalt, Saeger, Schmidt, Grützmann (bib0012) 2009; 14 Holzapfel (bib0023) 2000 Schilsky (bib0046) 2010; 9 Rao (bib0041) 2009 Jiang, Pjesivac-Grbovic, Cantrell, Freyer (bib0024) 2005; 89 Graziano (10.1016/j.media.2015.04.002_bib0019) 2007 Nacif (10.1016/j.media.2015.04.002_bib0037) 2012; 264 Stylianopoulos (10.1016/j.media.2015.04.002_bib0047) 2012; 109 Marino (10.1016/j.media.2015.04.002_bib0032) 2008; 254 Conn (10.1016/j.media.2015.04.002_bib0011) 2009; Vol. 8 Holzapfel (10.1016/j.media.2015.04.002_bib0023) 2000 Fang (10.1016/j.media.2015.04.002_bib0013) 2009 Kam (10.1016/j.media.2015.04.002_bib0025) 2012; 227 Miles (10.1016/j.media.2015.04.002_bib0036) 1999; 30 Nelder (10.1016/j.media.2015.04.002_bib0038) 1965; 7 (10.1016/j.media.2015.04.002_bib0042) 2007 Schilsky (10.1016/j.media.2015.04.002_bib0046) 2010; 9 Bae (10.1016/j.media.2015.04.002_bib0003) 2010; 256 Been (10.1016/j.media.2015.04.002_bib0005) 2004; 31 Kazanjian (10.1016/j.media.2015.04.002_bib0026) 2006; 141 Germann (10.1016/j.media.2015.04.002_bib0018) 2005 Clayton (10.1016/j.media.2015.04.002_bib0010) 2006; 440 Ambrosi (10.1016/j.media.2015.04.002_bib0002) 2002; 40 Gatenby (10.1016/j.media.2015.04.002_bib0017) 1996; 56 Kelloff (10.1016/j.media.2015.04.002_bib0027) 2005; 11 Bathe (10.1016/j.media.2015.04.002_bib0004) 1996 Alarcón (10.1016/j.media.2015.04.002_bib0001) 2003; 225 Jiang (10.1016/j.media.2015.04.002_bib0024) 2005; 89 Hamamci (10.1016/j.media.2015.04.002_bib0020) 2012; 31 Byrne (10.1016/j.media.2015.04.002_bib0007) 2010; 10 Fung (10.1016/j.media.2015.04.002_bib0015) 1993 Rao (10.1016/j.media.2015.04.002_bib0041) 2009 Rowan (10.1016/j.media.2015.04.002_bib0044) 1990 Clatz (10.1016/j.media.2015.04.002_bib0009) 2005; 24 Sadato (10.1016/j.media.2015.04.002_bib0045) 1998; 25 Kyriacou (10.1016/j.media.2015.04.002_bib0030) 1999; 18 Menze (10.1016/j.media.2015.04.002_bib0034) 2011 Yushkevich (10.1016/j.media.2015.04.002_bib0050) 2006; 31 Kormano (10.1016/j.media.2015.04.002_bib0029) 1976; 121 Pluim (10.1016/j.media.2015.04.002_bib0039) 2003; 22 Powell (10.1016/j.media.2015.04.002_bib0040) 2009 Blansfield (10.1016/j.media.2015.04.002_bib0006) 2007; 142 Massoptier (10.1016/j.media.2015.04.002_bib0033) 2008; 18 Han (10.1016/j.media.2015.04.002_bib0021) 2011 Liu (10.1016/j.media.2015.04.002_bib0031) 2014; 18 Ehehalt (10.1016/j.media.2015.04.002_bib0012) 2009; 14 Menzel (10.1016/j.media.2015.04.002_bib0035) 2012; 42 Konukoglu (10.1016/j.media.2015.04.002_bib0028) 2010; 29 Rogers (10.1016/j.media.2015.04.002_bib0043) 1994; 41 Thie (10.1016/j.media.2015.04.002_bib0048) 2004; 45 Hogea (10.1016/j.media.2015.04.002_bib0022) 2008; 56 West (10.1016/j.media.2015.04.002_bib0049) 2001; 413 Chen (10.1016/j.media.2015.04.002_bib0008) 2013; 60 Friedl (10.1016/j.media.2015.04.002_bib0014) 2012; 14 Gablonsky (10.1016/j.media.2015.04.002_bib0016) 2001; 21 |
| References_xml | – start-page: 1142 year: 2009 end-page: 1145 ident: bib0013 article-title: Tetrahedral mesh generation from volumetric binary and grayscale images publication-title: IEEE International Symposium on Biomedical Imaging 2009 – volume: 254 start-page: 178 year: 2008 end-page: 196 ident: bib0032 article-title: A methodology for performing global uncertainty and sensitivity analysis in systems biology publication-title: J. Theor. Biol. – year: 2005 ident: bib0018 publication-title: Principles of Human Physiology – year: 1990 ident: bib0044 publication-title: Functional Stability Analysis of Numerical Algorithms Ph.D. thesis – volume: 60 start-page: 169 year: 2013 end-page: 173 ident: bib0008 article-title: Kidney tumor growth prediction by coupling reaction-diffusion and biomechanical model publication-title: IEEE Trans. Biomed. Eng. – year: 1993 ident: bib0015 publication-title: Biomechanics: Mechanical Properties of Living Tissues – volume: 121 start-page: 379 year: 1976 end-page: 382 ident: bib0029 article-title: Extravascular contrast material: the major component of contrast enhancement publication-title: Radiology – volume: 225 start-page: 257 year: 2003 end-page: 274 ident: bib0001 article-title: A cellular automaton model for tumour growth in inhomogeneous environment publication-title: J. Theor. Biol. – volume: 56 start-page: 793 year: 2008 end-page: 825 ident: bib0022 article-title: An image-driven parameter estimation problem for a reaction-diffusion glioma growth model with mass effects publication-title: J. Math. Biol. – volume: 142 start-page: 814 year: 2007 end-page: 818.e2 ident: bib0006 article-title: Clinical, genetic and radiographic analysis of 108 patients with von Hippel-Lindau disease (VHL) manifested by pancreatic neuroendocrine tumors (PNETs) publication-title: Surgery – volume: 31 start-page: 1659 year: 2004 end-page: 1672 ident: bib0005 article-title: [18F] FLT-PET in oncology: Current status and opportunities publication-title: Eur. J. Nucl. Med. Mol. Imag. – volume: 7 start-page: 308 year: 1965 end-page: 313 ident: bib0038 article-title: A simplex method for function minimization publication-title: Comput. J. – volume: Vol. 8 year: 2009 ident: bib0011 publication-title: Introduction to Derivative-Free Optimization – volume: 109 start-page: 15101 year: 2012 end-page: 15108 ident: bib0047 article-title: Causes, consequences, and remedies for growth-induced solid stress in murine and human tumors publication-title: Proc. Natl. Acad. Sci. USA – volume: 413 start-page: 628 year: 2001 end-page: 631 ident: bib0049 article-title: A general model for ontogenetic growth publication-title: Nature – volume: 56 start-page: 5745 year: 1996 end-page: 5753 ident: bib0017 article-title: A reaction-diffusion model of cancer invasion publication-title: Cancer Res. – volume: 29 start-page: 77 year: 2010 end-page: 95 ident: bib0028 article-title: Image guided personalization of reaction-diffusion type tumor growth models using modified anisotropic eikonal equations publication-title: IEEE Trans. Med. Imag. – volume: 18 start-page: 580 year: 1999 end-page: 592 ident: bib0030 article-title: Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model publication-title: IEEE Trans. Med. Imag. – volume: 10 start-page: 221 year: 2010 end-page: 230 ident: bib0007 article-title: Dissecting cancer through mathematics: from the cell to the animal model publication-title: Nat. Rev. Cancer – volume: 141 start-page: 765 year: 2006 end-page: 770 ident: bib0026 article-title: Resection of pancreatic neuroendocrine tumors: results of 70 cases publication-title: Arch. Surg. – volume: 9 start-page: 363 year: 2010 end-page: 366 ident: bib0046 article-title: Personalized medicine in oncology: The future is now publication-title: Nat. Rev. Drug Discov. – volume: 24 start-page: 1334 year: 2005 end-page: 1346 ident: bib0009 article-title: Realistic simulation of the 3D growth of brain tumors in MR images coupling diffusion with biomechanical deformation publication-title: IEEE Trans. Med. Imag. – year: 2009 ident: bib0040 publication-title: The BOBYQA algorithm for bound constrained optimization without derivatives. Cambridge NA Report NA2009/06 – volume: 18 start-page: 1658 year: 2008 end-page: 1665 ident: bib0033 article-title: A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans publication-title: European Radiology – volume: 14 start-page: 777 year: 2012 end-page: 783 ident: bib0014 article-title: Classifying collective cancer cell invasion publication-title: Nat. Cell Biol. – year: 2000 ident: bib0023 publication-title: Nonlinear Solid Mechanics: A Continuum Approach for Engineering – volume: 18 start-page: 555 year: 2014 end-page: 566 ident: bib0031 article-title: Patient specific tumor growth prediction using multimodal images publication-title: Med. Image Anal. – volume: 264 start-page: 876 year: 2012 end-page: 883 ident: bib0037 article-title: Interstitial myocardial fibrosis assessed as extracellular volume fraction with low-radiation-dose cardiac CT publication-title: Radiology – volume: 440 start-page: 1073 year: 2006 end-page: 1077 ident: bib0010 article-title: Pharmaco-metabonomic phenotyping and personalized drug treatment publication-title: Nature – year: 2007 ident: bib0042 publication-title: SEER Survival Monograph: Cancer Survival Among Adults: U.S. SEER Program, 1988–2001, Patient and Tumor Characteristics – volume: 30 start-page: 198 year: 1999 end-page: 205 ident: bib0036 article-title: Tumour angiogenesis and its relation to contrast enhancement on computed tomography: A review publication-title: Eur. J. Radiol. – volume: 31 start-page: 790 year: 2012 end-page: 804 ident: bib0020 article-title: Tumor-cut: Segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications publication-title: IEEE Trans. Med. Imag. – start-page: 263 year: 2007 end-page: 321 ident: bib0019 article-title: Mechanics in tumor growth publication-title: Modeling of Biological Materials – volume: 22 start-page: 986 year: 2003 end-page: 1004 ident: bib0039 article-title: Mutual-information-based registration of medical images: A survey publication-title: IEEE Trans. Med. Imag. – volume: 89 start-page: 3884 year: 2005 end-page: 3894 ident: bib0024 article-title: A multiscale model for avascular tumor growth publication-title: Biophys. J. – volume: 41 start-page: 743 year: 1994 end-page: 757 ident: bib0043 article-title: A collocation-Galerkin finite element model of cardiac action potential propagation publication-title: IEEE Trans. Biomed. Eng. – volume: 14 start-page: 456 year: 2009 end-page: 467 ident: bib0012 article-title: Neuroendocrine tumors of the pancreas publication-title: Oncologist – start-page: 735 year: 2011 end-page: 747 ident: bib0034 article-title: A generative approach for image-based modeling of tumor growth publication-title: Information Processing in Medical Imaging – volume: 45 start-page: 1431 year: 2004 end-page: 1434 ident: bib0048 article-title: Understanding the standardized uptake value, its methods, and implications for usage publication-title: J. Nucl. Med. – volume: 256 start-page: 32 year: 2010 end-page: 61 ident: bib0003 article-title: Intravenous contrast medium administration and scan timing at CT: Considerations and approaches publication-title: Radiology – volume: 31 start-page: 1116 year: 2006 end-page: 1128 ident: bib0050 article-title: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability publication-title: NeuroImage – year: 1996 ident: bib0004 publication-title: Finite Element Procedures – volume: 227 start-page: 431 year: 2012 end-page: 438 ident: bib0025 article-title: Cellular modeling of cancer invasion: integration of publication-title: J. Cell Physiol. – start-page: 245 year: 2011 end-page: 256 ident: bib0021 article-title: Globally optimal tumor segmentation in PET-CT images: A graph-based co-segmentation method publication-title: Information Processing in Medical Imaging – volume: 25 start-page: 559 year: 1998 end-page: 564 ident: bib0045 article-title: Non-invasive estimation of the net influx constant using the standardized uptake value for quantification of FDG uptake of tumours publication-title: Eur. J. Nucl. Med. – volume: 40 start-page: 1297 year: 2002 end-page: 1316 ident: bib0002 article-title: On the mechanics of a growing tumor publication-title: Int. J. Eng. Sci. – year: 2009 ident: bib0041 publication-title: Engineering Optimization: Theory and Practice – volume: 21 start-page: 27 year: 2001 end-page: 37 ident: bib0016 article-title: A locally-biased form of the DIRECT algorithm publication-title: J. Global Optim. – volume: 11 start-page: 2785 year: 2005 end-page: 2808 ident: bib0027 article-title: Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development publication-title: Clin. Cancer Res. – volume: 42 start-page: 1 year: 2012 end-page: 14 ident: bib0035 article-title: Frontiers in growth and remodeling publication-title: Mech. Res. Commun. – volume: 31 start-page: 1116 issue: 3 year: 2006 ident: 10.1016/j.media.2015.04.002_bib0050 article-title: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability publication-title: NeuroImage doi: 10.1016/j.neuroimage.2006.01.015 – volume: 22 start-page: 986 issue: 8 year: 2003 ident: 10.1016/j.media.2015.04.002_bib0039 article-title: Mutual-information-based registration of medical images: A survey publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.2003.815867 – year: 1990 ident: 10.1016/j.media.2015.04.002_bib0044 – volume: 41 start-page: 743 issue: 8 year: 1994 ident: 10.1016/j.media.2015.04.002_bib0043 article-title: A collocation-Galerkin finite element model of cardiac action potential propagation publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/10.310090 – volume: 18 start-page: 1658 issue: 8 year: 2008 ident: 10.1016/j.media.2015.04.002_bib0033 article-title: A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans publication-title: European Radiology doi: 10.1007/s00330-008-0924-y – volume: 30 start-page: 198 issue: 3 year: 1999 ident: 10.1016/j.media.2015.04.002_bib0036 article-title: Tumour angiogenesis and its relation to contrast enhancement on computed tomography: A review publication-title: Eur. J. Radiol. doi: 10.1016/S0720-048X(99)00012-1 – volume: 256 start-page: 32 issue: 1 year: 2010 ident: 10.1016/j.media.2015.04.002_bib0003 article-title: Intravenous contrast medium administration and scan timing at CT: Considerations and approaches publication-title: Radiology doi: 10.1148/radiol.10090908 – year: 1996 ident: 10.1016/j.media.2015.04.002_bib0004 – start-page: 245 year: 2011 ident: 10.1016/j.media.2015.04.002_bib0021 article-title: Globally optimal tumor segmentation in PET-CT images: A graph-based co-segmentation method – volume: 7 start-page: 308 issue: 4 year: 1965 ident: 10.1016/j.media.2015.04.002_bib0038 article-title: A simplex method for function minimization publication-title: Comput. J. doi: 10.1093/comjnl/7.4.308 – volume: 25 start-page: 559 issue: 6 year: 1998 ident: 10.1016/j.media.2015.04.002_bib0045 article-title: Non-invasive estimation of the net influx constant using the standardized uptake value for quantification of FDG uptake of tumours publication-title: Eur. J. Nucl. Med. doi: 10.1007/s002590050256 – start-page: 1142 year: 2009 ident: 10.1016/j.media.2015.04.002_bib0013 article-title: Tetrahedral mesh generation from volumetric binary and grayscale images – volume: 56 start-page: 793 issue: 6 year: 2008 ident: 10.1016/j.media.2015.04.002_bib0022 article-title: An image-driven parameter estimation problem for a reaction-diffusion glioma growth model with mass effects publication-title: J. Math. Biol. doi: 10.1007/s00285-007-0139-x – volume: 141 start-page: 765 issue: 8 year: 2006 ident: 10.1016/j.media.2015.04.002_bib0026 article-title: Resection of pancreatic neuroendocrine tumors: results of 70 cases publication-title: Arch. Surg. doi: 10.1001/archsurg.141.8.765 – volume: 440 start-page: 1073 issue: 7087 year: 2006 ident: 10.1016/j.media.2015.04.002_bib0010 article-title: Pharmaco-metabonomic phenotyping and personalized drug treatment publication-title: Nature doi: 10.1038/nature04648 – volume: 413 start-page: 628 issue: 6856 year: 2001 ident: 10.1016/j.media.2015.04.002_bib0049 article-title: A general model for ontogenetic growth publication-title: Nature doi: 10.1038/35098076 – volume: 225 start-page: 257 issue: 2 year: 2003 ident: 10.1016/j.media.2015.04.002_bib0001 article-title: A cellular automaton model for tumour growth in inhomogeneous environment publication-title: J. Theor. Biol. doi: 10.1016/S0022-5193(03)00244-3 – volume: 31 start-page: 1659 issue: 12 year: 2004 ident: 10.1016/j.media.2015.04.002_bib0005 article-title: [18F] FLT-PET in oncology: Current status and opportunities publication-title: Eur. J. Nucl. Med. Mol. Imag. doi: 10.1007/s00259-004-1687-6 – volume: 29 start-page: 77 issue: 1 year: 2010 ident: 10.1016/j.media.2015.04.002_bib0028 article-title: Image guided personalization of reaction-diffusion type tumor growth models using modified anisotropic eikonal equations publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.2009.2026413 – volume: 14 start-page: 777 issue: 8 year: 2012 ident: 10.1016/j.media.2015.04.002_bib0014 article-title: Classifying collective cancer cell invasion publication-title: Nat. Cell Biol. doi: 10.1038/ncb2548 – start-page: 263 year: 2007 ident: 10.1016/j.media.2015.04.002_bib0019 article-title: Mechanics in tumor growth – volume: 60 start-page: 169 issue: 1 year: 2013 ident: 10.1016/j.media.2015.04.002_bib0008 article-title: Kidney tumor growth prediction by coupling reaction-diffusion and biomechanical model publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2012.2222027 – volume: 45 start-page: 1431 issue: 9 year: 2004 ident: 10.1016/j.media.2015.04.002_bib0048 article-title: Understanding the standardized uptake value, its methods, and implications for usage publication-title: J. Nucl. Med. – volume: 14 start-page: 456 issue: 5 year: 2009 ident: 10.1016/j.media.2015.04.002_bib0012 article-title: Neuroendocrine tumors of the pancreas publication-title: Oncologist doi: 10.1634/theoncologist.2008-0259 – volume: 11 start-page: 2785 issue: 8 year: 2005 ident: 10.1016/j.media.2015.04.002_bib0027 article-title: Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development publication-title: Clin. Cancer Res. doi: 10.1158/1078-0432.CCR-04-2626 – volume: 142 start-page: 814 issue: 6 year: 2007 ident: 10.1016/j.media.2015.04.002_bib0006 article-title: Clinical, genetic and radiographic analysis of 108 patients with von Hippel-Lindau disease (VHL) manifested by pancreatic neuroendocrine tumors (PNETs) publication-title: Surgery doi: 10.1016/j.surg.2007.09.012 – volume: 42 start-page: 1 year: 2012 ident: 10.1016/j.media.2015.04.002_bib0035 article-title: Frontiers in growth and remodeling publication-title: Mech. Res. Commun. doi: 10.1016/j.mechrescom.2012.02.007 – start-page: 735 year: 2011 ident: 10.1016/j.media.2015.04.002_bib0034 article-title: A generative approach for image-based modeling of tumor growth – year: 2009 ident: 10.1016/j.media.2015.04.002_bib0041 – volume: 24 start-page: 1334 issue: 10 year: 2005 ident: 10.1016/j.media.2015.04.002_bib0009 article-title: Realistic simulation of the 3D growth of brain tumors in MR images coupling diffusion with biomechanical deformation publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.2005.857217 – volume: 227 start-page: 431 issue: 2 year: 2012 ident: 10.1016/j.media.2015.04.002_bib0025 article-title: Cellular modeling of cancer invasion: integration of in silico and in vitro approaches publication-title: J. Cell Physiol. doi: 10.1002/jcp.22766 – year: 2007 ident: 10.1016/j.media.2015.04.002_bib0042 – volume: 121 start-page: 379 issue: 2 year: 1976 ident: 10.1016/j.media.2015.04.002_bib0029 article-title: Extravascular contrast material: the major component of contrast enhancement publication-title: Radiology doi: 10.1148/121.2.379 – volume: 18 start-page: 555 issue: 3 year: 2014 ident: 10.1016/j.media.2015.04.002_bib0031 article-title: Patient specific tumor growth prediction using multimodal images publication-title: Med. Image Anal. doi: 10.1016/j.media.2014.02.005 – volume: 254 start-page: 178 issue: 1 year: 2008 ident: 10.1016/j.media.2015.04.002_bib0032 article-title: A methodology for performing global uncertainty and sensitivity analysis in systems biology publication-title: J. Theor. Biol. doi: 10.1016/j.jtbi.2008.04.011 – volume: 40 start-page: 1297 issue: 12 year: 2002 ident: 10.1016/j.media.2015.04.002_bib0002 article-title: On the mechanics of a growing tumor publication-title: Int. J. Eng. Sci. doi: 10.1016/S0020-7225(02)00014-9 – volume: 18 start-page: 580 issue: 7 year: 1999 ident: 10.1016/j.media.2015.04.002_bib0030 article-title: Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model publication-title: IEEE Trans. Med. Imag. doi: 10.1109/42.790458 – year: 1993 ident: 10.1016/j.media.2015.04.002_bib0015 – volume: 89 start-page: 3884 issue: 6 year: 2005 ident: 10.1016/j.media.2015.04.002_bib0024 article-title: A multiscale model for avascular tumor growth publication-title: Biophys. J. doi: 10.1529/biophysj.105.060640 – volume: 264 start-page: 876 issue: 3 year: 2012 ident: 10.1016/j.media.2015.04.002_bib0037 article-title: Interstitial myocardial fibrosis assessed as extracellular volume fraction with low-radiation-dose cardiac CT publication-title: Radiology doi: 10.1148/radiol.12112458 – volume: 56 start-page: 5745 year: 1996 ident: 10.1016/j.media.2015.04.002_bib0017 article-title: A reaction-diffusion model of cancer invasion publication-title: Cancer Res. – year: 2000 ident: 10.1016/j.media.2015.04.002_bib0023 – volume: 9 start-page: 363 issue: 5 year: 2010 ident: 10.1016/j.media.2015.04.002_bib0046 article-title: Personalized medicine in oncology: The future is now publication-title: Nat. Rev. Drug Discov. doi: 10.1038/nrd3181 – year: 2005 ident: 10.1016/j.media.2015.04.002_bib0018 – volume: 109 start-page: 15101 issue: 38 year: 2012 ident: 10.1016/j.media.2015.04.002_bib0047 article-title: Causes, consequences, and remedies for growth-induced solid stress in murine and human tumors publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1213353109 – volume: 31 start-page: 790 issue: 3 year: 2012 ident: 10.1016/j.media.2015.04.002_bib0020 article-title: Tumor-cut: Segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.2011.2181857 – volume: 21 start-page: 27 issue: 1 year: 2001 ident: 10.1016/j.media.2015.04.002_bib0016 article-title: A locally-biased form of the DIRECT algorithm publication-title: J. Global Optim. doi: 10.1023/A:1017930332101 – volume: Vol. 8 year: 2009 ident: 10.1016/j.media.2015.04.002_bib0011 – volume: 10 start-page: 221 issue: 3 year: 2010 ident: 10.1016/j.media.2015.04.002_bib0007 article-title: Dissecting cancer through mathematics: from the cell to the animal model publication-title: Nat. Rev. Cancer doi: 10.1038/nrc2808 – year: 2009 ident: 10.1016/j.media.2015.04.002_bib0040 |
| SSID | ssj0007440 |
| Score | 2.3004441 |
| Snippet | •Tumor growth prediction with physiological data fusion.•A tumor growth model with reaction-diffusion and hyperelastic biomechanical model.•A derivative-free... The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by physiological modeling and model personalization from... |
| SourceID | pubmedcentral proquest pubmed crossref elsevier |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 72 |
| SubjectTerms | Algorithms Biomechanical Phenomena Contrast Media Derivative-free optimization Fluorodeoxyglucose F18 Humans Model personalization Neoplasms - pathology Nonlinear solid mechanics Physiological data fusion Positron-Emission Tomography - methods Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted - methods Radiopharmaceuticals Sensitivity and Specificity Tomography, X-Ray Computed - methods Tumor growth prediction |
| Title | Tumor growth prediction with reaction-diffusion and hyperelastic biomechanical model by physiological data fusion |
| URI | https://dx.doi.org/10.1016/j.media.2015.04.002 https://www.ncbi.nlm.nih.gov/pubmed/25962846 https://www.proquest.com/docview/1705001573 https://pubmed.ncbi.nlm.nih.gov/PMC4540673 |
| Volume | 25 |
| WOSCitedRecordID | wos000360864700008&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1361-8423 dateEnd: 20160831 omitProxy: false ssIdentifier: ssj0007440 issn: 1361-8415 databaseCode: AIEXJ dateStart: 19960301 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLa6DiF4QDBu5TIZibcSlDhxnDxOaIiLmHgYUt8iO3FopzUtbTPGv-Anc44dp8mqTfDAS9QmjtP2-3p8bJ_zHUJeK5mzlEvfE4UovSiSgSd1GHqpYFroIPaL0jfFJsTJSTKZpF8Hg98uF-biXFRVcnmZLv8r1HAOwMbU2X-Au-0UTsBrAB2OADsc_w74er5Yjb_D9HozRQmAYmargZsVV3ARzTsPC6PUaxeLPIXZKGa1oGjz2GTkY0Kwwc-UykEn1ayBtKYSI0vHtoeuf-v2fWZzjAWSjeJJa_mb-F-w7mOX72A2pMzquQ30xqXq7RLtZ0Bf6Z82AM0W7GntlLSbRsDvaS27qxcBb-PgYPCxFjeMAy-JbNKxM8k2F7pHPWtfbZmfHbNvVyDO3ppsG4zX40a_1u-1BpiWcwM6w5JDSXRFgtsM6u7SHtlngqfJkOwffTyefGpHdxRUdOpVJk5w55moL930cp2zszuZuRqT23FyTu-Te83shB5ZVj0gA10dkLsdzcoDcvtLE43xkPwwVKOWanRLNYpUo7tUo0A12qUa7VGNGqpR9Yv2qEaRatT28Ih8e398-u6D15Tw8HLO0o1XlsLPCy3A0Rd5KnLJZMI0DCQq5jwuhJJhnnAdFpEuixzcKaV4IJkquVYlNnxMhtWi0k8JDYs4BD8L2obg9bJARZIrvywTAbYlkuGIMPdbZ3mjb49lVs4zF8h4lhmsMsQq86MMsBqRN-1NSyvvcnPz2IGYNR6q9TwzYOLNN75ykGdgv3FTTlZ6Ua8zlLPCiYuAL_DEUqD9JI5GIyJ65GgboDZ8_0o1mxqNeBTWjEX47No-n5M72__jCzLcrGr9ktzKLzaz9eqQ7IlJcthw_w_vTNwX |
| linkProvider | Elsevier |
| 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=Tumor+growth+prediction+with+reaction-diffusion+and+hyperelastic+biomechanical+model+by+physiological+data+fusion&rft.jtitle=Medical+image+analysis&rft.au=Wong%2C+Ken+C+L&rft.au=Summers%2C+Ronald+M&rft.au=Kebebew%2C+Electron&rft.au=Yao%2C+Jianhua&rft.date=2015-10-01&rft.eissn=1361-8423&rft.volume=25&rft.issue=1&rft.spage=72&rft_id=info:doi/10.1016%2Fj.media.2015.04.002&rft_id=info%3Apmid%2F25962846&rft.externalDocID=25962846 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1361-8415&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1361-8415&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1361-8415&client=summon |