A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer
The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prost...
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Elsevier
2024-12-01
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| Series: | Journal of Pathology Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353924000208 |
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| author | Sushant Patkar Stephanie Harmon Isabell Sesterhenn Rosina Lis Maria Merino Denise Young G. Thomas Brown Kimberly M. Greenfield John D. McGeeney Sally Elsamanoudi Shyh-Han Tan Cara Schafer Jiji Jiang Gyorgy Petrovics Albert Dobi Francisco J. Rentas Peter A. Pinto Gregory T. Chesnut Peter Choyke Baris Turkbey Joel T. Moncur |
| author_facet | Sushant Patkar Stephanie Harmon Isabell Sesterhenn Rosina Lis Maria Merino Denise Young G. Thomas Brown Kimberly M. Greenfield John D. McGeeney Sally Elsamanoudi Shyh-Han Tan Cara Schafer Jiji Jiang Gyorgy Petrovics Albert Dobi Francisco J. Rentas Peter A. Pinto Gregory T. Chesnut Peter Choyke Baris Turkbey Joel T. Moncur |
| author_sort | Sushant Patkar |
| collection | DOAJ |
| description | The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grading Assessment dataset. A cancer detection model was trained using MobileNetV3 on 0.5 mm × 0.5 mm cancer areas (tiles) captured at 10× magnification. For cancer grading, a Gleason pattern detector was trained on tiles using a ResNet50 convolutional neural network and a selective CutMix training strategy involving a mixture of real and artificial examples. This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. Finally, unsupervised clustering of test RP patient specimens into low-, medium-, and high-risk groups based on AI-estimated proportions of each Gleason pattern resulted in significantly improved RFS and MFS stratification compared with ISUP grading. In summary, deep learning-based quantitative Gleason scoring using a selective CutMix training strategy may improve prognostication after prostate cancer surgery. |
| format | Article |
| id | doaj-art-d4d03e7f2d064afd82948e3605766bf5 |
| institution | Kabale University |
| issn | 2153-3539 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-d4d03e7f2d064afd82948e3605766bf52024-12-15T06:15:16ZengElsevierJournal of Pathology Informatics2153-35392024-12-0115100381A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancerSushant Patkar0Stephanie Harmon1Isabell Sesterhenn2Rosina Lis3Maria Merino4Denise Young5G. Thomas Brown6Kimberly M. Greenfield7John D. McGeeney8Sally Elsamanoudi9Shyh-Han Tan10Cara Schafer11Jiji Jiang12Gyorgy Petrovics13Albert Dobi14Francisco J. Rentas15Peter A. Pinto16Gregory T. Chesnut17Peter Choyke18Baris Turkbey19Joel T. Moncur20Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USAArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; Corresponding author at: 9000 Rockville Pike, Building 10, Room B3B69F, Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.The Joint Pathology Center, Silver Spring, MD 20910, USAArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USALaboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USACenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USAArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USAThe Joint Pathology Center, Silver Spring, MD 20910, USAThe Joint Pathology Center, Silver Spring, MD 20910, USACenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USACenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USACenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USACenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USACenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USACenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USAThe Joint Pathology Center, Silver Spring, MD 20910, USAUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USACenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA; F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA; Urology Service, Walter Reed National Military Medical Center, Bethesda, MD 20814, USAArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USAArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USAThe Joint Pathology Center, Silver Spring, MD 20910, USAThe Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grading Assessment dataset. A cancer detection model was trained using MobileNetV3 on 0.5 mm × 0.5 mm cancer areas (tiles) captured at 10× magnification. For cancer grading, a Gleason pattern detector was trained on tiles using a ResNet50 convolutional neural network and a selective CutMix training strategy involving a mixture of real and artificial examples. This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. Finally, unsupervised clustering of test RP patient specimens into low-, medium-, and high-risk groups based on AI-estimated proportions of each Gleason pattern resulted in significantly improved RFS and MFS stratification compared with ISUP grading. In summary, deep learning-based quantitative Gleason scoring using a selective CutMix training strategy may improve prognostication after prostate cancer surgery.http://www.sciencedirect.com/science/article/pii/S2153353924000208Prostate cancerDigital pathologyGleason gradingArtificial intelligence |
| spellingShingle | Sushant Patkar Stephanie Harmon Isabell Sesterhenn Rosina Lis Maria Merino Denise Young G. Thomas Brown Kimberly M. Greenfield John D. McGeeney Sally Elsamanoudi Shyh-Han Tan Cara Schafer Jiji Jiang Gyorgy Petrovics Albert Dobi Francisco J. Rentas Peter A. Pinto Gregory T. Chesnut Peter Choyke Baris Turkbey Joel T. Moncur A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer Journal of Pathology Informatics Prostate cancer Digital pathology Gleason grading Artificial intelligence |
| title | A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer |
| title_full | A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer |
| title_fullStr | A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer |
| title_full_unstemmed | A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer |
| title_short | A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer |
| title_sort | selective cutmix approach improves generalizability of deep learning based grading and risk assessment of prostate cancer |
| topic | Prostate cancer Digital pathology Gleason grading Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2153353924000208 |
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