The pathologic and clinical outcomes of risk-reducing salpingo-oophorectomy in asymptomatic carriers of homologous recombination repair gene mutation.

OBJECTIVE: To investigate the prevalence of pathological findings and clinical outcomes of risk-reducing salpingo-oophorectomy (RRSO) in asymptomatic carriers with germline homologous recombination repair (HRR) gene pathogenic/likely pathogenic variants (PV/LPV). METHODS: This retrospective study enrolled asymptomatic carriers with germline HR gene PV/LPV who underwent RRSO between 2006 and 2022 at the National Cancer Center in Korea. Clinical characteristics, including history of breast cancer, family history of ovarian/breast cancer, parity, and oral contraceptive use, were analyzed. RESULTS: Of the 255 women who underwent RRSO, 129 (50.6%) had PV/LPV in BRCA1, 121 (47.5%) in BRCA2, and 2 (0.7%) had both BRCA1 and BRCA2 PV/LPV. In addition, 1 carried PV/LPV in RAD51D, and 2 in BRIP1. Among the BRCA1/2 PV/LPV carriers, occult neoplasms were identified in 3.5% of patients: serous tubal intraepithelial carcinoma (1.1%, n=3), fallopian tubal cancers (0.8%, n=2), ovarian cancer (1.2%, n=3), and breast cancer (0.4%, n=1). Of the 9 patients with occult neoplasms, 5 (2.0%) were identified from the 178 breast cancer patients, and 4 (1.6%) were detected in 65 healthy mutation carriers. During the median follow-up period of 36.7 months (interquartile range, 25.9-71.4), 1 (0.4%) BRCA1 PV carrier with no precursor lesions at RRSO developed primary peritoneal carcinomatosis after 30.1 months. CONCLUSION: Women with HRR gene mutations PV/LPV who undergo RRSO are at a risk of detecting occult neoplasms, with a of 3.5%. Even in the absence of precursor lesions during RRSO, there was a cumulative risk of peritoneal carcinomatosis development, emphasizing the need for continued surveillance.

Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology.

Background: Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whole slide images (WSIs) of thyroid fine-needle aspiration cytology (FNA), incorporating z-stacking, from institutions across the nation to develop an AI model. Methods: We conducted a multicenter retrospective diagnostic accuracy study using thyroid FNA dataset from the Open AI Dataset Project that consists of digitalized images samples collected from 3 university hospitals and 215 Korean institutions through extensive quality check during the case selection, scanning, labeling, and reviewing process. Multiple z-layer images were captured using three different scanners and image patches were extracted from WSIs and resized after focus fusion and color normalization. We pretested six AI models, determining Inception ResNet v2 as the best model using a subset of dataset, and subsequently tested the final model with total datasets. Additionally, we compared the performance of AI and cytopathologists using randomly selected 1031 image patches and reevaluated the cytopathologists' performance after reference to AI results. Results: A total of 10,332 image patches from 306 thyroid FNAs, comprising 78 malignant (papillary thyroid carcinoma) and 228 benign from 86 institutions were used for the AI training. Inception ResNet v2 achieved highest accuracy of 99.7%, 97.7%, and 94.9% for training, validation, and test dataset, respectively (sensitivity 99.9%, 99.6%, and 100% and specificity 99.6%, 96.4%, and 90.4% for training, validation, and test dataset, respectively). In the comparison between AI and human, AI model showed higher accuracy and specificity than the average expert cytopathologists beyond the two-standard deviation (accuracy 99.71% [95% confidence interval (CI), 99.38-100.00%] vs. 88.91% [95% CI, 86.99-90.83%], sensitivity 99.81% [95% CI, 99.54-100.00%] vs. 87.26% [95% CI, 85.22-89.30%], and specificity 99.61% [95% CI, 99.23-99.99%] vs. 90.58% [95% CI, 88.80-92.36%]). Moreover, after referring to the AI results, the performance of all the experts (accuracy 96%, 95%, and 96%, respectively) and the diagnostic agreement (from 0.64 to 0.84) increased. Conclusions: These results suggest that the application of AI technology to thyroid FNA cytology may improve the diagnostic accuracy as well as intra- and inter-observer variability among pathologists. Further confirmatory research is needed.

Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid.

Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network. Datasets were collected from The OPEN AI Dataset Project, a nationwide cytology dataset for AI research. The numbers of patch images used for training, validation, and testing were 56,560, 7068, and 6534, respectively. We evaluated 1041 patch images of benign and metastatic CRC in the ascitic fluid to compare the performance of pathologists and an AI algorithm, and to examine whether the diagnostic accuracy of pathologists improved with the assistance of AI. This AI method showed an accuracy, a sensitivity, and a specificity of 93.74%, 87.76%, and 99.75%, respectively, for the differential diagnosis of malignant and benign ascites. The diagnostic accuracy and sensitivity of the pathologist with the assistance of the proposed AI method increased from 86.8% to 90.5% and from 73.3% to 79.3%, respectively. The proposed deep learning method may assist pathologists with different levels of experience in diagnosing metastatic CRC cells of ascites.