The Gleason grading system, currently the most powerful prognostic predictor of prostate cancer, is based solely on the tumor’s histological architecture and has high inter-observer variability. We propose an automated Gleason scoring system based on deep neural networks for diagnosis of prostate core needle biopsy samples. To verify its efficacy, the system was trained using 1133 cases of prostate core needle biopsy samples and validated on 700 cases. Further, system-based diagnosis results were compared with reference standards derived from three certified pathologists. In addition, the system’s ability to quantify cancer in terms of tumor length was also evaluated via comparison with pathologist-based measurements. The results showed a substantial diagnostic concordance between the system-grade group classification and the reference standard (0.907 quadratic-weighted Cohen’s kappa coefficient). The system tumor length measurements were also notably closer to the reference standard (correlation coefficient, R = 0.97) than the original hospital diagnoses (R = 0.90). We expect this system to assist pathologists to reduce the probability of over- or under-diagnosis by providing pathologist-level second opinions on the Gleason score when diagnosing prostate biopsy, and to support research on prostate cancer treatment and prognosis by providing reproducible diagnosis based on the consistent standards.

Background: The Continuous Quality Improvement program for cytopathology in 2020 was completed during the coronavirus pandemic. In this study, we report the result of the quality improvement program. Methods: Data related to cytopathology practice from each institute were collected and processed at the web-based portal. The proficiency test was conducted using glass slides and whole-slide images (WSIs). Evaluation of the adequacy of gynecology (GYN) slides from each institution and submission of case glass slides and WSIs for the next quality improvement program were performed. Results: A total of 214 institutions participated in the annual cytopathology survey in 2020. The number of entire cytopathology specimens was 8,220,650, a reduction of 19.0% from the 10,111,755 specimens evaluated in 2019. Notably, the number of respiratory cytopathology specimens, including sputum and bronchial washing/ brushing significantly decreased by 86.9% from 2019, which could be attributed to the global pandemic of coronavirus disease. The ratio of cases with atypical squamous cells to squamous intraepithelial lesions was 4.10. All participating institutions passed the proficiency test and the evaluation of adequacy of GYN slides. Conclusions: Through the Continuous Quality Improvement program, the effect of coronavirus disease 2019 pandemic, manifesting with a reduction in the number of cytologic examinations, especially in respiratory-related specimen has been identified. The Continuous Quality Improvement Program of the Korean Society for Cytopathology can serve as the gold standard to evaluate the current status of cytopathology practice in Korea.

Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas breast invasive carcinoma (BRCA) public dataset for training and validation. We used the Korea University Medical Center, Guro Hospital, BRCA dataset for the final test evaluation. MurSS utilizes both low- and high-resolution patches to leverage multi-resolution features using adaptive instance normalization. This enhances segmentation performance while employing a selective segmentation method to automatically reject ambiguous tissue regions, ensuring stable training. MurSS rejects 5% of WSI regions and achieves a pixel-level accuracy of 96.88% (95% confidence interval (CI): 95.97–97.62%) and mean Intersection over Union of 0.7283 (95% CI: 0.6865–0.7640). In our study, MurSS exhibits superior performance over other deep learning models, showcasing its ability to reject ambiguous areas identified by expert annotations while using multi-resolution inputs.

Choi JE, et al. J Breast Cancer. 2020 Feb;23(1):36-46. https://doi.org/10.4048/jbc.2020.23.e12

There have been several reports of complications of small bowel lymphoma, such as bleeding, obstruction, and perforation, often require emergency surgery. It is hardly showed complications of bleeding and wound dehiscence for diffuse large B cell lymphoma with distal ileum involvement, which needed urgent surgery and medical management. A 65-year-old man with diffuse large B-cell lymphoma with distal ileum involvement experienced both intestinal bleeding and perforation during the course of treatment. As the patient was diagnosed with stage III disease, resection before chemotherapy was not considered due to the resulting delay in chemotherapy, which necessitated sufficient tissue healing. Chemotherapy is important when treating small bowel lymphoma, complications such as bleeding and perforation should always be considered for the treatment of small bowel lymphoma, and surgery is necessary in this situation. After surgery of the small bowel, subsequent chemotherapy could cause wound dehiscence and perforation; therefore, adequate recovery time should be given before chemotherapy.