Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence
Background Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progre...
Saved in:
| Published in: | BMC cancer Vol. 24; no. 1; pp. 1523 - 12 |
|---|---|
| Main Authors: | , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
18.12.2024
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1471-2407, 1471-2407 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Background
Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm.
Methods
A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient’s tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs.
Results
The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms.
Conclusion
The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation. |
|---|---|
| AbstractList | Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm. A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient's tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs. The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms. The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation. Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm.BACKGROUNDRectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm.A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient's tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs.METHODSA total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient's tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs.The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms.RESULTSThe accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms.The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation.CONCLUSIONThe developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation. Background Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm. Methods A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient's tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs. Results The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms. Conclusion The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation. Keywords: Rectal cancer, Unsupervised learning, Whole slide image, Prognosis prediction, Bioinformatics Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm. A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient's tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs. The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms. The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation. BackgroundRectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm.MethodsA total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient’s tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs.ResultsThe accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms.ConclusionThe developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation. Abstract Background Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm. Methods A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient’s tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs. Results The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms. Conclusion The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation. Background Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm. Methods A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient’s tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs. Results The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms. Conclusion The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation. |
| ArticleNumber | 1523 |
| Audience | Academic |
| Author | Dai, Jing Yu, Yi Wang, Chang Gao, Zhixian Lu, Yizhan Wang, Chong Zhou, Xuezhi Zhao, Yandong Liu, Yong Zhao, Zongya Zhao, Qingqing Cao, Wuteng |
| Author_xml | – sequence: 1 givenname: Xuezhi surname: Zhou fullname: Zhou, Xuezhi organization: College of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design – sequence: 2 givenname: Jing surname: Dai fullname: Dai, Jing organization: Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University – sequence: 3 givenname: Yizhan surname: Lu fullname: Lu, Yizhan organization: College of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design – sequence: 4 givenname: Qingqing surname: Zhao fullname: Zhao, Qingqing organization: College of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design – sequence: 5 givenname: Yong surname: Liu fullname: Liu, Yong organization: College of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design – sequence: 6 givenname: Chang surname: Wang fullname: Wang, Chang organization: College of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design – sequence: 7 givenname: Zongya surname: Zhao fullname: Zhao, Zongya organization: College of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design – sequence: 8 givenname: Chong surname: Wang fullname: Wang, Chong organization: College of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design – sequence: 9 givenname: Zhixian surname: Gao fullname: Gao, Zhixian organization: College of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design – sequence: 10 givenname: Yi surname: Yu fullname: Yu, Yi email: yuyi@xxmu.edu.cn organization: College of Medical Engineering, Xinxiang Medical University, Engineering Technology Research Center of Neurosense and Control of Henan Province, Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design – sequence: 11 givenname: Yandong surname: Zhao fullname: Zhao, Yandong email: zhaoyd6@mail.sysu.edu.cn organization: Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University – sequence: 12 givenname: Wuteng surname: Cao fullname: Cao, Wuteng email: caowteng@mail.sysu.edu.cn organization: Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39696090$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kstq3DAYhU1JaS7tC3RRDIXSLpzqZllahRB6GQgUeqFLIcu_PQoeaSrJoX37ypmkGYcSvJAsfedYOj7HxYHzDoriJUanGAv-PmIiRF0hwipMiSRV_aQ4wqzBFWGoOdibHxbHMV4hhBuBxLPikEouOZLoqFj_XPsRyjjaDkq70QOUrY7QldvgB-ejjXkGnTXJeldaVwYwSY-l0c5AKKdo3VBOLk5bCNd2FuqQbG-NzZB1CcbRDpDZ58XTXo8RXtyOJ8WPjx--X3yuLr98Wl2cX1aGNzRVUnDKaVvnNy1a2dQIia7tDWVtTYwWmAGnhjVdL1pqUKN7YwwTsqVCGIR7elKsdr6d11dqG_Kdwh_ltVU3Cz4Maj6hGUEhDshwyRhuEDMUNO4ZaMlko6HlosteZzuv7dRuoDPgUtDjwnS54-xaDf5aYcxrwZjMDm9vHYL_NUFMamOjyaFoB36Kis6_iJK6mdHXD9ArPwWXs8pUTbiQQtb31KDzDazrff6wmU3VuSCoJgRxkqnT_1D56WBjTW5Rb_P6QvBuIchMgt9p0FOMavXt65J9s8euQY9pHf04zQ2JS_DVfnz_crtrXwbEDjDBxxigV8YmPfvk49pRYaTmoqtd0VUuuropuppzIA-kd-6PiuhOFDPsBgj3ET-i-gslRw43 |
| CitedBy_id | crossref_primary_10_1186_s12885_025_13740_w |
| Cites_doi | 10.3390/e24111669 10.1371/journal.pmed.1002730 10.1109/ISBI.2009.5193250 10.1038/s41374-020-00514-0 10.1101/2024.02.26.582106 10.1155/2022/5844846 10.1148/radiol.2021203281 10.6004/jnccn.2022.0051 10.1016/j.ejso.2024.108369 10.3390/ijms19123733 10.1109/CVPR.2017.725 10.3389/fmed.2019.00264 10.1038/s41591-019-0583-3 10.1038/s41467-023-41195-9 10.1080/00401706.1970.10488634 10.1016/j.media.2020.101789 10.1109/ISBI.2019.8759512 10.1016/S0140-6736(19)32998-8 10.1038/s41598-018-21758-3 10.1245/s10434-022-12926-x 10.1016/j.ebiom.2021.103583 10.1158/1078-0432.CCR-04-0713 10.1016/j.radonc.2018.10.019 10.1038/s41698-023-00451-3 10.1038/nature14539 10.1186/s12859-023-05262-8 10.1016/j.clcc.2020.11.001 10.3322/caac.21565 10.1177/2632010X21989686 10.1073/pnas.1717139115 10.1016/j.cmpb.2022.106914 10.1007/s00330-020-07590-2 10.1038/s41591-021-01343-4 10.3322/caac.21660 10.1016/S2589-7500(23)00208-X 10.1111/den.14547 10.1245/s10434-010-0985-4 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). COPYRIGHT 2024 BioMed Central Ltd. 2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2024 2024 |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: COPYRIGHT 2024 BioMed Central Ltd. – notice: 2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2024 2024 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7TO 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH H94 K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.1186/s12885-024-13292-5 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Oncogenes and Growth Factors Abstracts ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) AIDS and Cancer Research Abstracts ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection PML(ProQuest Medical Library) ProQuest Central Premium ProQuest One Academic Publicly Available Content Database 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 Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Oncogenes and Growth Factors Abstracts ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection AIDS and Cancer Research Abstracts ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1471-2407 |
| EndPage | 12 |
| ExternalDocumentID | oai_doaj_org_article_06e0c69441704c3ea1f4ea9497aeb68d PMC11658449 A820522062 39696090 10_1186_s12885_024_13292_5 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: Henan Province Key Research and Development and Promotion Projects grantid: 232102310009; 232102310315 – fundername: Major Science and Technology Projects of Henan Province grantid: 221100310500 – fundername: National Natural Science Foundation of China grantid: 82302298; 82201709 – fundername: National Key Clinical Discipline, and the program of Guangdong Provincial Clinical Research Center for Digestive Diseases grantid: 2020B1111170004 – fundername: Innovative Research Team (in Science and Technology) in University of Henan Province grantid: 24IRTSTHN042 – fundername: National Natural Science Foundation of China grantid: 82201709 – fundername: National Natural Science Foundation of China grantid: 82302298 – fundername: Henan Province Key Research and Development and Promotion Projects grantid: 232102310009 – fundername: Henan Province Key Research and Development and Promotion Projects grantid: 232102310315 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 6PF 7X7 88E 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABUWG ACGFO ACGFS ACIHN ACMJI ACPRK ACUHS ADBBV ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GROUPED_DOAJ GX1 HMCUK HYE IAO IHR IHW INH INR ISR ITC KQ8 LGEZI LOTEE M1P M48 M~E NADUK NXXTH O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS U2A UKHRP W2D WOQ WOW XSB AAYXX AFFHD CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7TO 7XB 8FK AZQEC DWQXO H94 K9. PKEHL PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c673t-986363b5c67a8b975008dbfc34b52ca814e63c47df8b3c07afccc489b388c01f3 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001381025900005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2407 |
| IngestDate | Fri Oct 03 12:39:26 EDT 2025 Tue Nov 04 02:03:24 EST 2025 Fri Sep 05 14:34:39 EDT 2025 Tue Oct 07 05:19:04 EDT 2025 Tue Nov 11 10:50:22 EST 2025 Tue Nov 04 18:25:34 EST 2025 Thu Nov 13 15:57:36 EST 2025 Mon Dec 01 06:32:06 EST 2025 Sun Mar 30 02:12:47 EDT 2025 Tue Nov 18 22:18:46 EST 2025 Sat Nov 29 06:43:08 EST 2025 Sat Sep 06 07:29:13 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Whole slide image Prognosis prediction Rectal cancer Bioinformatics Unsupervised learning |
| Language | English |
| License | 2024. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c673t-986363b5c67a8b975008dbfc34b52ca814e63c47df8b3c07afccc489b388c01f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/3152689895?pq-origsite=%requestingapplication% |
| PMID | 39696090 |
| PQID | 3152689895 |
| PQPubID | 44074 |
| PageCount | 12 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_06e0c69441704c3ea1f4ea9497aeb68d pubmedcentral_primary_oai_pubmedcentral_nih_gov_11658449 proquest_miscellaneous_3147132579 proquest_journals_3152689895 gale_infotracmisc_A820522062 gale_infotracacademiconefile_A820522062 gale_incontextgauss_ISR_A820522062 gale_healthsolutions_A820522062 pubmed_primary_39696090 crossref_citationtrail_10_1186_s12885_024_13292_5 crossref_primary_10_1186_s12885_024_13292_5 springer_journals_10_1186_s12885_024_13292_5 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-12-18 |
| PublicationDateYYYYMMDD | 2024-12-18 |
| PublicationDate_xml | – month: 12 year: 2024 text: 2024-12-18 day: 18 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC cancer |
| PublicationTitleAbbrev | BMC Cancer |
| PublicationTitleAlternate | BMC Cancer |
| PublicationYear | 2024 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | JN Kather (13292_CR11) 2019; 16 M Cui (13292_CR7) 2021; 101 S Wang (13292_CR33) 2019; 132 B Liu (13292_CR37) 2024 KD Miller (13292_CR2) 2019; 69 P Mobadersany (13292_CR15) 2018; 115 O-J Skrede (13292_CR12) 2020; 395 W Wang (13292_CR24) 2023; 14 X Jiang (13292_CR28) 2024; 6 Y Takashina (13292_CR36) 2023; 35 Q Sun (13292_CR38) 2021; 301 RL Camp (13292_CR27) 2004; 10 DR C (13292_CR25) 1972; 34 P Courtiol (13292_CR16) 2019; 25 13292_CR23 Y LeCun (13292_CR8) 2015; 521 13292_CR22 S Chen (13292_CR19) 2022; 2022 AE Hoerl (13292_CR26) 1970; 12 X Zhou (13292_CR17) 2024; 50 Y Zhao (13292_CR40) 2021; 31 FC Staal (13292_CR29) 2021; 20 AB Benson (13292_CR4) 2022; 20 C Sun (13292_CR20) 2022; 221 D Bychkov (13292_CR30) 2018; 8 X Li (13292_CR32) 2022; 24 H Sung (13292_CR1) 2021; 71 J Van der Laak (13292_CR9) 2021; 27 RM Souza da Silva (13292_CR5) 2021; 14 R Nakanishi (13292_CR13) 2023; 30 J Yan (13292_CR39) 2021; 72 C Molinari (13292_CR6) 2018; 19 N Dimitriou (13292_CR10) 2019; 6 13292_CR14 J Höhn (13292_CR31) 2023; 7 M Wysocka (13292_CR21) 2023; 24 13292_CR18 13292_CR34 J Yao (13292_CR35) 2020; 65 SB Edge (13292_CR3) 2010; 17 |
| References_xml | – volume: 24 start-page: 1669 year: 2022 ident: 13292_CR32 publication-title: Entropy doi: 10.3390/e24111669 – volume: 16 start-page: e1002730 year: 2019 ident: 13292_CR11 publication-title: PLoS Med doi: 10.1371/journal.pmed.1002730 – ident: 13292_CR22 doi: 10.1109/ISBI.2009.5193250 – volume: 101 start-page: 412 year: 2021 ident: 13292_CR7 publication-title: Lab Invest doi: 10.1038/s41374-020-00514-0 – volume-title: Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer year: 2024 ident: 13292_CR37 doi: 10.1101/2024.02.26.582106 – volume: 2022 start-page: 5844846 year: 2022 ident: 13292_CR19 publication-title: Comput Math Methods Med doi: 10.1155/2022/5844846 – volume: 301 start-page: 654 issue: 3 year: 2021 ident: 13292_CR38 publication-title: Radiology doi: 10.1148/radiol.2021203281 – volume: 20 start-page: 1139 year: 2022 ident: 13292_CR4 publication-title: J Natl Compr Canc Netw doi: 10.6004/jnccn.2022.0051 – volume: 50 start-page: 108369 year: 2024 ident: 13292_CR17 publication-title: Eur J Surg Oncol doi: 10.1016/j.ejso.2024.108369 – volume: 19 start-page: 3733 year: 2018 ident: 13292_CR6 publication-title: Int J Mol Sci doi: 10.3390/ijms19123733 – ident: 13292_CR14 doi: 10.1109/CVPR.2017.725 – ident: 13292_CR34 – volume: 6 start-page: 264 year: 2019 ident: 13292_CR10 publication-title: Front Med doi: 10.3389/fmed.2019.00264 – volume: 25 start-page: 1519 year: 2019 ident: 13292_CR16 publication-title: Nat Med doi: 10.1038/s41591-019-0583-3 – volume: 14 start-page: 6359 issue: 1 year: 2023 ident: 13292_CR24 publication-title: Nat Commun doi: 10.1038/s41467-023-41195-9 – volume: 12 start-page: 55 year: 1970 ident: 13292_CR26 publication-title: Technometrics doi: 10.1080/00401706.1970.10488634 – volume: 65 start-page: 101789 year: 2020 ident: 13292_CR35 publication-title: Med Image Anal doi: 10.1016/j.media.2020.101789 – ident: 13292_CR18 doi: 10.1109/ISBI.2019.8759512 – volume: 395 start-page: 350 year: 2020 ident: 13292_CR12 publication-title: Lancet doi: 10.1016/S0140-6736(19)32998-8 – volume: 34 start-page: 248 year: 1972 ident: 13292_CR25 publication-title: JR Stat Soc – volume: 8 start-page: 3395 year: 2018 ident: 13292_CR30 publication-title: Sci Rep doi: 10.1038/s41598-018-21758-3 – volume: 30 start-page: 3506 year: 2023 ident: 13292_CR13 publication-title: Ann Surg Oncol doi: 10.1245/s10434-022-12926-x – ident: 13292_CR23 – volume: 72 start-page: 103583 year: 2021 ident: 13292_CR39 publication-title: EBioMedicine doi: 10.1016/j.ebiom.2021.103583 – volume: 10 start-page: 7252 year: 2004 ident: 13292_CR27 publication-title: Clin Cancer Res doi: 10.1158/1078-0432.CCR-04-0713 – volume: 132 start-page: 171 year: 2019 ident: 13292_CR33 publication-title: Radiother Oncol doi: 10.1016/j.radonc.2018.10.019 – volume: 7 start-page: 98 year: 2023 ident: 13292_CR31 publication-title: NPJ Precis Oncol doi: 10.1038/s41698-023-00451-3 – volume: 521 start-page: 436 year: 2015 ident: 13292_CR8 publication-title: Nature doi: 10.1038/nature14539 – volume: 24 start-page: 198 year: 2023 ident: 13292_CR21 publication-title: BMC Bioinformatics doi: 10.1186/s12859-023-05262-8 – volume: 20 start-page: 52 year: 2021 ident: 13292_CR29 publication-title: Clin Colorectal Cancer doi: 10.1016/j.clcc.2020.11.001 – volume: 69 start-page: 363 year: 2019 ident: 13292_CR2 publication-title: CA Cancer J Clin doi: 10.3322/caac.21565 – volume: 14 start-page: 2632010X2198968 year: 2021 ident: 13292_CR5 publication-title: Clin Pathol doi: 10.1177/2632010X21989686 – volume: 115 start-page: E2970 year: 2018 ident: 13292_CR15 publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.1717139115 – volume: 221 start-page: 106914 year: 2022 ident: 13292_CR20 publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2022.106914 – volume: 31 start-page: 5032 issue: 7 year: 2021 ident: 13292_CR40 publication-title: Eur Radiol doi: 10.1007/s00330-020-07590-2 – volume: 27 start-page: 775 year: 2021 ident: 13292_CR9 publication-title: Nat Med doi: 10.1038/s41591-021-01343-4 – volume: 71 start-page: 209 year: 2021 ident: 13292_CR1 publication-title: CA Cancer J Clin doi: 10.3322/caac.21660 – volume: 6 start-page: e33 issue: 1 year: 2024 ident: 13292_CR28 publication-title: Lancet Digit Health doi: 10.1016/S2589-7500(23)00208-X – volume: 35 start-page: 902 year: 2023 ident: 13292_CR36 publication-title: Dig Endosc doi: 10.1111/den.14547 – volume: 17 start-page: 1471 year: 2010 ident: 13292_CR3 publication-title: Ann Surg Oncol doi: 10.1245/s10434-010-0985-4 |
| SSID | ssj0017808 |
| Score | 2.4380944 |
| Snippet | Background
Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology... Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has... Background Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology... BackgroundRectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology... Abstract Background Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1523 |
| SubjectTerms | Accuracy Aged Algorithms Artificial Intelligence Bioinformatics Biomarkers Biomarkers, Tumor Biomedical and Life Sciences Biomedicine Cancer Cancer Research Clinical outcomes Colorectal cancer Datasets Decision making Deep learning Diagnostic imaging Female Health aspects Health Promotion and Disease Prevention Humans Male Medical prognosis Medicine/Public Health Methods Middle Aged Oncology Patients Prognosis Prognosis prediction Progression-Free Survival Rectal cancer Rectal Neoplasms - mortality Rectal Neoplasms - pathology Rectum Surgical Oncology Survival Technology application Transfer learning Tumors Unsupervised learning Unsupervised Machine Learning Whole slide image |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Nb9UwDI_QhBAXxDdlAwJC4gDV0iYvTY4DMYEEE-Jztyhf3SpB3_T6ur8fO20fr0PAhVvbOFVrO46t2D8T8tRyyWwA62dD6SFAqTysuRjzwior7YKFkOq4v76rjo7U8bH-sNXqC3PCBnjggXH7TEbmpcZOWUx4Hm1Ri2i10JWNTqqA1pdVegqmxvODSjE1lcgoud-BFVZYiSxybKwO4ddsG0po_b_b5K1N6WLC5IVT07QZHV4n10Yvkh4MX3-DXIrtTXLl_XhOfoucfsO2txR8yBBp8wNsBsXtKlBMx2qXXdPBFVKjWGjTUrR78EKPOrCimAx_Qvu268_QlOBEZNQANkGbLRTP2-TL4evPr97kY0-F3MuKr3OtJJfcLeDOKqfBX2AquNpz4Ralt6oQUXIvqlArxz2rbO29F0o7rpRnRc3vkJ122cZ7hFpXAec9oht6UbjaRhYjBFCh1o5VQWakmFhs_Ag4jn0vvpsUeChpBrEYEItJYjGLjDzfzDkb4Db-Sv0SJbehRKjs9AAUyIwKZP6lQBl5hHI3Q93pZsGbA_CNwDllsszIk0SBcBkt5uOc2L7rzNtPH2dEz0aiegl_6e1Y3gC8QoStGeXejBLWs58PTwpoRnvSGV4gLI9WGv758WYYZ2KOXBuXPdKAo8HBBOuM3B30dcMZjiBITLOMqJkmz1g3H2mb04Q2jvhMSgh46YtJ6X99159lc_9_yGaXXC3Toi3zQu2RnfWqjw_IZX--brrVw7TkfwLyJlkW priority: 102 providerName: Directory of Open Access Journals – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELagIMSF9yNQwCAkDhDhxF7HPhZEBRJUqIXSm-XYzjYSJNVml9_PjPOgKQ8JbrvrcbQZz3weyzPfEPLUcsmsB_SzPndwQCkc-FwIaWaVlXbBvI913Ifvi709dXSkPw5FYd2Y7T5eSUakjm6t5MsOkFRhNbFIsTk6HKHOkwuw3Sls2LB_cDjdHRSKqbE85rfzZltQZOr_FY9PbUhnkyXP3JjGjWj36v-9wjVyZQg86U5vKdfJudDcIJc-DFfrN8nxF-yUSyHs9IHW3wBmKO5wnmIGV9N2dQefUBpXktYNRaiEBzo0mxXF_Pkl3TTd5gTRByeiUfb8FLQ-Rfx5i3zeffPp9dt0aMOQOlnwdaqV5JKXC_hmVakhxGDKl5XjolzkzqpMBMmdKHylSu5YYSvnnFC65Eo5llX8Ntlq2ibcJdSWRQ7mgISITmRlZQMLAc5cvtIlK7xMSDaujHEDRzm2yvhq4llFSdOr0IAKTVShWSTk-TTnpGfo-Kv0K1zwSRLZteMP7WppBmc1TAbmpMbubEw4HmxWiWC10IUNpVQ-IY_QXExfqjphhNmBcAriWSbzhDyJEsiw0WAKz9Juus68O9ifCT0bhKoW3tLZoSICdIWkXDPJ7ZkkQICbD492awYI6gzPkMlHKw3v_HgaxpmYVteEdoMyEJtwQG2dkDu9mU-a4cibxDRLiJo5wEx185GmPo4E5UjppISAh74Y_eDn__rz2tz7N_H75HIeXSlPM7VNttarTXhALrrv67pbPYyY8AMGkV0z priority: 102 providerName: Springer Nature |
| Title | Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence |
| URI | https://link.springer.com/article/10.1186/s12885-024-13292-5 https://www.ncbi.nlm.nih.gov/pubmed/39696090 https://www.proquest.com/docview/3152689895 https://www.proquest.com/docview/3147132579 https://pubmed.ncbi.nlm.nih.gov/PMC11658449 https://doaj.org/article/06e0c69441704c3ea1f4ea9497aeb68d |
| Volume | 24 |
| WOSCitedRecordID | wos001381025900005&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: PRVADU databaseName: BioMedCentral customDbUrl: eissn: 1471-2407 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017808 issn: 1471-2407 databaseCode: RBZ dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2407 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017808 issn: 1471-2407 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2407 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017808 issn: 1471-2407 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2407 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017808 issn: 1471-2407 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2407 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017808 issn: 1471-2407 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1471-2407 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017808 issn: 1471-2407 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: Springer Nature Link Contemporary 1997-Present customDbUrl: eissn: 1471-2407 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017808 issn: 1471-2407 databaseCode: RSV dateStart: 20011201 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/eLvHCXMwpV3db9MwELdgQ4gXvj8CowSExANEc-LUsZ_QhjYxiVVVB6N7shzb6SJBUpqWv587J-2WIfbCS9XU56j2nX8-2-ffEfJWM061BfTTNjGwQMkMjDnnolgLzfWQWuvvcZ9-yUYjMZ3Kcbfh1nRhlWtM9EBta4N75LssRmISKeTw4_xXhFmj8HS1S6Fxk2xj2my082y6WXDFmaBifVFG8N0GsFjgfeQ0wvTqsAjrTUaes_9vZL40NV0Nm7xyduqnpMN7_9uY--Ru54yGe631PCA3XPWQ3D7ujtsfkfPvmD03BFfUurD8CdAT4qxnQ4zqquqmbOAbSqN2w7IKET7hhQZNaRFiTP0sXFXNao6IhBXRUFvOirC8RAb6mHw7PPj66XPUpWaIDM_YMpKCM87yITxpkUtwO6iweWFYmg8To0WcOs5MmtlC5MzQTBfGmFTInAlhaFywJ2Srqiv3jIQ6zxIwESRJNGmcF9pR52AdZguZ08zygMRrHSnT8ZZj-owfyq9fBFetXhXoVXm9qmFA3m_qzFvWjmul91H1G0lk3PY_1IuZ6gawotxRwyVmbKOpYU7HReq0TGWmXc6FDcgrNBzVXl_d4IbaAxcLfFzKk4C88RLIulFhWM9Mr5pGHZ1MekLvOqGihlYa3d2SgL5Coq6e5E5PEmDB9IvXpqc6WGrUhd0F5PWmGGtiqF3l6hXKgL_CAMllQJ62Br_pGYZcSlTSgIjeUOh1Xb-kKs89aTnSPIk0hZd-WI-ai__1b908v74ZL8idxI_nJIrFDtlaLlbuJbllfi_LZjHwaOA_xYBs7x-MxpOB33SBp_HR8fgMniYnp38APT5tZA |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQcCF9yNQqEEgDhDVibOOc0CoPKquuq0QtLA349jONhIky2YXxJ_iNzKTx7YporceuO2ux1HszHzj2cx8Q8gTzQXTFtBP29BAgBIbsDnn_EBLLfSAWVvXcX8axXt7cjxO3q-Q310tDKZVdphYA7UtDf5HvsEDJCZJZDJ4Nf3uY9cofLvatdBo1GLH_foJIVv1cvgWnu_TMNx6t_9m22-7CvhGxHzuJ1JwwdMBfNMyTcBjMmnTzPAoHYRGyyBygpsotplMuWGxzowxkUxSLqVhQcbhuufIecDxGIO9eLwM8IJYMtkV5kixUQH2S6x_jnxs5w5BX8_51T0C_vYEx1zhyTTNE-9qaxe4dfV_27xr5Ep72KabjXVcJyuuuEEu7rbpBDfJ4WfsDkzhqG0dzb8BtFL06pZi1lpRVnkFn1AatZfmBUX3ABc0aCozijUDE7ooqsUUERcnoiE2nBw0P0Z2eoscnMk6b5PVoizcXUJ1GodgAkgCaaIgzbRjzkGcabMkZbEVHgk6nVCm5WXH9iBfVR2fSaEaPVKgR6rWIzXwyPPlnGnDSnKq9GtUtaUkMorXP5SziWoBSjHhmBEJdqRjkeFOB1nkdBIlsXapkNYj66ioqinPXeKi2oQjJJzhmQg98riWQFaRAtOWJnpRVWr48UNP6FkrlJWwSqPbKhDYKyQi60mu9SQB9kx_uFN11cJupY703COPlsM4E1MJC1cuUAbOYxw8VeKRO42BLXeGI1cUS5hHZM_0elvXHynyw5qUHWmsZBTBRV90Vnp0X_9-NvdOX8Y6ubS9vztSo-Hezn1yOayxJPQDuUZW57OFe0AumB_zvJo9rJGIki9nbb1_AHZtxIc |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwELdgoIkXvj8CgwWExAOL5sSuYz-Oj4qJUU0Mxt4sx3a6SJBWTcvfz52ThmZ8SIi3ND5H9fnu57N89zMhzw0T1DhAP-MyCxuU3ILPeZ-kRhphRtS5UMd9epRPJvLsTB1vVPGHbPf1kWRb04AsTfVyf-7K1sWl2G8AVSVWFvMEL0qH7dRlcoVjIj3u109O-3OEXFK5LpX5bb_BchRY-3_F5o3F6WLi5IXT07AojW_8_3BukutdQBoftBZ0i1zy9W2y_aE7cr9Dzr_gDboxhKPOx9U3gJ8YVz4XY2ZXPWuqBp5QGmc4ruoYIRQ-aNGcFjHm1U_jVd2s5ohK2BGNteWtiKsNQtC75PP47afX75LueobEipwtEyUFE6wYwS8jCwWhB5WuKC3jxSizRqbcC2Z57kpZMEtzU1pruVQFk9LStGT3yFY9q_0DEpsiz8BMkCjR8rQojafew17MlaqguRMRSdezpG3HXY5XaHzVYQ8jhW5VqEGFOqhQjyLysu8zb5k7_ir9Cie_l0TW7fBitpjqzok1FZ5aofDWNsot8yYtuTeKq9z4QkgXkV00Hd2WsPbYoQ8gzII4l4osIs-CBDJv1JjaMzWrptGHJx8HQi86oXIGo7Smq5QAXSFZ10ByZyAJ0GCHzWsb1h00NZqlyPCjpIIxP-2bsSem29V-tkIZiFkYoLmKyP3W5HvNMORToopGRA6cYaC6YUtdnQficqR6kpzDR_fWPvHzf_15bh7-m_gu2T5-M9ZHh5P3j8i1LHhVlqRyh2wtFyv_mFy135dVs3gSoOIH7U9o-w |
| 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=Whole+slide+image+based+prognosis+prediction+in+rectal+cancer+using+unsupervised+artificial+intelligence&rft.jtitle=BMC+cancer&rft.au=Zhou%2C+Xuezhi&rft.au=Dai%2C+Jing&rft.au=Lu%2C+Yizhan&rft.au=Zhao%2C+Qingqing&rft.date=2024-12-18&rft.issn=1471-2407&rft.eissn=1471-2407&rft.volume=24&rft.issue=1&rft.spage=1523&rft_id=info:doi/10.1186%2Fs12885-024-13292-5&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2407&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2407&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2407&client=summon |