Evidence map›Paper›PMID 38953042›Full record

ArticleJournal of pathology informatics2024

A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.

Sushant Patkar, Stephanie Harmon, Isabell Sesterhenn, Rosina Lis, Maria Merino, Denise Young, G Thomas Brown, Kimberly M Greenfield, John D McGeeney, Sally Elsamanoudi and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pathology informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Sushant PatkarArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Stephanie HarmonArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Isabell SesterhennThe Joint Pathology Center, Silver Spring, MD 20910, USA.
Rosina LisArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Maria MerinoLaboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Denise YoungCenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA.
G Thomas BrownArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Kimberly M GreenfieldThe Joint Pathology Center, Silver Spring, MD 20910, USA.
John D McGeeneyThe Joint Pathology Center, Silver Spring, MD 20910, USA.
Sally ElsamanoudiCenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA.
Shyh-Han TanCenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA.
Cara SchaferCenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA.
Jiji JiangCenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA.
Gyorgy PetrovicsCenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA.
Albert DobiCenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA.
Francisco J RentasThe Joint Pathology Center, Silver Spring, MD 20910, USA.
Peter A PintoUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Gregory T ChesnutCenter for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20817, USA.
Peter ChoykeArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Baris TurkbeyArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Joel T MoncurThe Joint Pathology Center, Silver Spring, MD 20910, USA.

Funding

Prostate SectionZIABC011081 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI PINTO, PETER · 2009 to 2025
$15.2M
Development and Validation of Artificial Intelligence Algorithms in Digital PathologyZIABC012163 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI HARMON, STEPHANIE · 2024 to 2025
$1.7M
6 · The paper itself

Abstract

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.

Indexed as

Artificial intelligenceDigital pathologyGleason gradingProstate cancer

Identifiers

PMID38953042
PMCPMC11215954

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.