A comparative study of the inter-observer variability on Gleason grading against Deep Learning-based approaches for prostate cancer. (June 2023)

• Record Type: Journal Article
• Authors: Marrón-Esquivel, José M.; Duran-Lopez, L.; Linares-Barranco, A.; Dominguez-Morales, Juan P.
• Is Part Of: Computers in biology and medicine. Volume 159(2023)
• Publication Date: 2023-06
• Subjects: Prostate cancer -- Computational pathology -- Deep Learning -- Convolutional neural networks -- Inter-observer variability -- Medical image analysis; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106856
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: : Among all the cancers known today, prostate cancer is one of the most commonly diagnosed in men. With modern advances in medicine, its mortality has been considerably reduced. However, it is still a leading type of cancer in terms of deaths. The diagnosis of prostate cancer is mainly conducted by biopsy test. From this test, Whole Slide Images are obtained, from which pathologists diagnose the cancer according to the Gleason scale. Within this scale from 1 to 5, grade 3 and above is considered malignant tissue. Several studies have shown an inter-observer discrepancy between pathologists in assigning the value of the Gleason scale. Due to the recent advances in artificial intelligence, its application to the computational pathology field with the aim of supporting and providing a second opinion to the professional is of great interest. Method: In this work, the inter-observer variability of a local dataset of 80 whole-slide images annotated by a team of 5 pathologists from the same group was analyzed at both area and label level. Four approaches were followed to train six different Convolutional Neural Network architectures, which were evaluated on the same dataset on which the inter-observer variability was analyzed. Results: : An inter-observer variability of 0.6946 κ was obtained, with 46% discrepancy in terms of area size of the annotations performed by the pathologists. The best trained models achieved 0.826 ± 0 . 014 κ on the test set when trained with data from the same source. Conclusions: The obtained results show that deep learning-based automatic diagnosis systems could help reduce the widely-known inter-observer variability that is present among pathologists and support them in their decision, serving as a second opinion or as a triage tool for medical centers. Graphical abstract: Highlights: Analyzing prostate cancer WSIs is subjective and time-consuming for pathologists. AI could be used as a support, reducing subjectivity on Gleason grading. Four DL-based training approaches were evaluated, including transfer learning. 240 CNNs were trained and compared with pathologists' inter-observer variability. The best results achieved (0.826 k), improve inter-observer variability on two tenths.