Narrative review of prostate cancer grading systems: will the Gleason scores be replaced by the Grade Groups?

The Gleason grading system, proposed by Dr. Donald F. Gleason in 1966, is one of the most important prognostic factors in men with prostate cancer (PCa). At consensus conferences held in 2005 and 2014, organized by the International Society of Urological Pathology (ISUP), the system was modified to reflect the current diagnostic and therapeutic approaches. In particular, in the 2014 Conference, it was recognized that there were weaknesses with the original and the 2005 ISUP modified Gleason systems. Based on the results of a research conducted by Prof. JI Epstein and his group, a new grading system was proposed by the ISUP in order to address some of such deficiencies: i.e., the five distinct Grade Groups (GGs). Since 2014, results of studies have been published by different groups and societies, including the Genitourinary Pathology Society (GUPS), giving additional support to the prognostic role of the architectural Gleason patterns and, in particular, of the GGs. A revised GG system, taking into account the percentage of Gleason pattern (GP) 4, cribriform and intraductal carcinoma, tertiary GP 5, and reactive stroma grade, has shown to have some advantages, however not ready for adoption in the current practice. The aim of this contribution was to review the major updates and recommendations regarding the GPs and GSs, as well as the GGs, trying to give an answer to the following questions: “How has the grade group system been used in the routine?” and “will the Gleason scoring system be replace by the grade groups?” We also discussed the potential implementation in the future of molecular pathology and artificial intelligence in grading to further define risk groups in patients with PCa

Mosaic-Based Color-Transform Optimization for Lossy and Lossy-to-Lossless Compression of Pathology Whole-Slide Images

Altres ajuts: This work has been funded by the EU Marie Curie CIG Programme under Grant PIMCO, the Engineering and Physical Sciences Research Council (EPSRC), UKThe use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loève Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes

Machine Learning for Prostate Histopathology Assessment

Pathology reporting on radical prostatectomy (RP) specimens is essential to post-surgery patient care. However, current pathology interpretation of RP sections is typically qualitative and subject to intra- and inter-observer variability, which challenges quantitative and repeatable reporting of lesion grade, size, location, and spread. Therefore, we developed and validated a software platform that can automatically detect and grade cancerous regions on whole slide images (WSIs) of whole-mount RP sections to support quantitative and visual reporting. Our study used hæmatoxylin- and eosin-stained WSIs from 299 whole-mount RP sections from 71 patients, comprising 1.2 million 480μm×480μm regions-of-interest (ROIs) covering benign and cancerous tissues which contain all clinically relevant grade groups. Each cancerous region was annotated and graded by an expert genitourinary pathologist. We used a machine learning approach with 7 different classifiers (3 non-deep learning and 4 deep learning) to classify: 1) each ROI as cancerous vs. non-cancerous, and 2) each cancerous ROI as high- vs. low-grade. Since recent studies found some subtypes beyond Gleason grade to have independent prognostic value, we also used one deep learning method to classify each cancerous ROI from 87 RP sections of 25 patients as each of eight subtypes to support further clinical pathology research on this topic. We cross-validated each system against the expert annotations. To compensate for the staining variability across different WSIs from different patients, we computed the tissue component map (TCM) using our proposed adaptive thresholding algorithm to label nucleus pixels, global thresholding to label lumen pixels, and assigning the rest as stroma/other. Fine-tuning AlexNet with ROIs of the TCM yielded the best results for prostate cancer (PCa) detection and grading, with areas under the receiver operating characteristic curve (AUCs) of 0.98 and 0.93, respectively, followed by fine-tuned AlexNet with ROIs of the raw image. For subtype grading, fine-tuning AlexNet with ROIs of the raw image yielded AUCs ≥ 0.7 for seven of eight subtypes. To conclude, deep learning approaches outperformed non-deep learning approaches for PCa detection and grading. The TCMs provided the primary cues for PCa detection and grading. Machine learning can be used for subtype grading beyond the Gleason grading system