Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning

Mun, Yechan; Paik, Inyoung; Shin, Su Jin; Kwak, Tae-Yeong; Chang, Hyeyoon

doi:10.1038/s41746-021-00469-6

Cited by 39 publications

(18 citation statements)

References 30 publications

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“…The model and designated pathologist had an AUC of 0.960 for identifying cancer and a mean pairwise kappa of 0.62 for assigning Gleason grades. Mun et al 13 recently published the YAAGGS deep learning system on a large data set from two hospitals. For Gleason grade group prediction, their technique had a 77.5% accuracy and a kappa of 0.65.…”

Section: Discussionmentioning

confidence: 99%

A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies

Singhal¹,

Soni

Bonthu³

et al. 2022

Sci Rep

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Gleason grading, a risk stratification method for prostate cancer, is subjective and dependent on experience and expertise of the reporting pathologist. Deep Learning (DL) systems have shown promise in enhancing the objectivity and efficiency of Gleason grading. However, DL networks exhibit domain shift and reduced performance on Whole Slide Images (WSI) from a source other than training data. We propose a DL approach for segmenting and grading epithelial tissue using a novel training methodology that learns domain agnostic features. In this retrospective study, we analyzed WSI from three cohorts of prostate cancer patients. 3741 core needle biopsies (CNBs) received from two centers were used for training. The κquad (quadratic-weighted kappa) and AUC were measured for grade group comparison and core-level detection accuracy, respectively. Accuracy of 89.4% and κquad of 0.92 on the internal test set of 425 CNB WSI and accuracy of 85.3% and κquad of 0.96 on an external set of 1201 images, was observed. The system showed an accuracy of 83.1% and κquad of 0.93 on 1303 WSI from the third institution (blind evaluation). Our DL system, used as an assistive tool for CNB review, can potentially improve the consistency and accuracy of grading, resulting in better patient outcomes.

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Section: Discussionmentioning

confidence: 99%

A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies

Singhal¹,

Soni

Bonthu³

et al. 2022

Sci Rep

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“…Manual reading of core needle biopsy slides by pathologists is the gold standard in the prostate cancer diagnosis in the clinic. However, it requires the analysis of around 12 (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) biopsy cores, including hundreds of glands. Especially for low-grade and low-volume prostate cancer (GS 3+3 and 3+4), identifying the few malignant glands among vastly benign glands is a tedious and challenging task.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, it has been shown that the assistance of machine learning systems significantly improves the diagnosis and grading of prostate cancer by pathologists [4,5]. A few studies developed successful machine learning systems for prostate cancer diagnosis and grading to assist pathologists [6][7][8][9][10][11][12][13][14]. They covered a broad spectrum of grade groups.…”

Section: Introductionmentioning

confidence: 99%

An AI-assisted Tool For Efficient Prostate Cancer Diagnosis

Öner

Giron

et al. 2022

Preprint

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Pathologists diagnose prostate cancer by core needle biopsy. For low-grade and low-volume cases, the pathologists look for the few malignant glands out of hundreds within a core. They may miss the few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep learning pipeline detecting malignant glands in core needle biopsies to help pathologists effectively and accurately diagnose prostate cancer in low-grade and low-volume cases. The pipeline consisted of two stages: the gland segmentation model detected the glands within the sections and the multi-resolution model classified each detected gland into benign vs. malignant. Analyzing a gland at multiple resolutions provided the classification model to exploit both morphology information (of nuclei and glands) and neighborhood information (for architectural patterns), important in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. The images were made publicly available, becoming the first digital histopathology dataset of prostatic carcinoma patients of Asian ancestry. Our pipeline successfully classified the core needle biopsy parts (81 parts: 50 benign and 31 malignant) into benign vs. malignant. It achieved an AUROC value of 0.997 (95% CI: 0.987 - 1.000). Moreover, it produced heatmaps highlighting the malignancy of each gland in core needle biopsies. Hence, our pipeline can effectively assist pathologists in core needle biopsy analysis.

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“…Therefore, in our proposed system, we extract overlapping patches to maintain the connectivity of the renal tissue and avoid any loss of contextual and spatial information. In [ 23 ], the authors implemented the CLAM model for prostate cancer grading and compared multiclass MIL with CLAM and their proposed system. The multiclass MIL outperformed CLAM in terms of classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images

et al. 2022

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Kidney cancer has several types, with renal cell carcinoma (RCC) being the most prevalent and severe type, accounting for more than 85% of adult patients. The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis. However, the manual identification of RCC is time-consuming and prone to inter-subject variability. In this paper, we aim to distinguish between benign tissue and malignant RCC tumors and identify the tumor subtypes to support medical therapy management. We propose a novel multiscale weakly-supervised deep learning approach for RCC subtyping. Our system starts by applying the RGB-histogram specification stain normalization on the whole slide images to eliminate the effect of the color variations on the system performance. Then, we follow the multiple instance learning approach by dividing the input data into multiple overlapping patches to maintain the tissue connectivity. Finally, we train three multiscale convolutional neural networks (CNNs) and apply decision fusion to their predicted results to obtain the final classification decision. Our dataset comprises four classes of renal tissues: non-RCC renal parenchyma, non-RCC fat tissues, clear cell RCC (ccRCC), and clear cell papillary RCC (ccpRCC). The developed system demonstrates a high classification accuracy and sensitivity on the RCC biopsy samples at the slide level. Following a leave-one-subject-out cross-validation approach, the developed RCC subtype classification system achieves an overall classification accuracy of 93.0% ± 4.9%, a sensitivity of 91.3% ± 10.7%, and a high classification specificity of 95.6% ± 5.2%, in distinguishing ccRCC from ccpRCC or non-RCC tissues. Furthermore, our method outperformed the state-of-the-art Resnet-50 model.

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Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning

Cited by 39 publications

References 30 publications

A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies

A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies

An AI-assisted Tool For Efficient Prostate Cancer Diagnosis

Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images

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