Clinical utility of artificial intelligence assistance in histopathologic review of lymph node metastasis for gastric adenocarcinoma.

BACKGROUND: Advances in whole-slide image capture and computer image analyses using deep learning technologies have enabled the development of computer-assisted diagnostics in pathology. Herein, we built a deep learning algorithm to detect lymph node (LN) metastasis on whole-slide images of LNs retrieved from patients with gastric adenocarcinoma and evaluated its performance in clinical settings. METHODS: We randomly selected 18 patients with gastric adenocarcinoma who underwent surgery with curative intent and were positive for LN metastasis at Chiba University Hospital. A ResNet-152-based assistance system was established to detect LN metastases and to outline regions that are highly probable for metastasis in LN images. Reference standards comprising 70 LN images from two different institutions were reviewed by six pathologists with or without algorithm assistance, and their diagnostic performances were compared between the two settings. RESULTS: No statistically significant differences were observed between these two settings regarding sensitivity, review time, or confidence levels in classifying macrometastases, isolated tumor cells, and metastasis-negative. Meanwhile, the sensitivity for detecting micrometastases significantly improved with algorithm assistance, although the review time was significantly longer than that without assistance. Analysis of the algorithm's sensitivity in detecting metastasis in the reference standard indicated an area under the curve of 0.869, whereas that for the detection of micrometastases was 0.785. CONCLUSIONS: A wide variety of histological types in gastric adenocarcinoma could account for these relatively low performances; however, this level of algorithm performance could suffice to help pathologists improve diagnostic accuracy.

[Intravascular large B-cell lymphoma diagnosed by total hysterectomy].

Intravascular large B-cell lymphoma (IVLBCL) is a rare form of non-Hodgkin B-cell lymphoma which occurs mainly in capillaries and small blood vessels. Successful diagnosis of IVLBCL is challenging since it lacks tumor formation and presents various clinical manifestations. An 82-year-old Asian female patient presented to our emergency department with a history of general fatigue, weight loss, and fever for two weeks. The patient's random skin biopsy was negative, and her bone marrow biopsy revealed hemophagocytic syndrome with no obvious involvement of lymphoma cells. Gallium scintigraphy showed mild uptake in the uterus, pelvis, and spine. The repetitive bone marrow biopsy result and the endometrial cytology/biopsy were negative; however, the pelvic MRI was compatible with lymphoma, revealing lesions in the corpus uteri, pelvis, and vertebral body. After laparoscopic-assisted vaginal total hysterectomy and bilateral salpingo-oophorectomy, the diagnosis of the Asian variant of IVLBCL was made. Although total hysterectomy remains controversial for elderly patients with declining performance status, we could successfully diagnose the condition and initiate the treatment. The patient's general condition improved soon after starting rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone regimen on day 26, and she was discharged on day 45.

The Use of Deep Learning-Based Computer Diagnostic Algorithm for Detection of Lymph Node Metastases of Gastric Adenocarcinoma.

Objectives. The diversifying modalities of treatment for gastric cancer raise urgent demands for the rapid and precise diagnosis of metastases in regional lymph nodes, thereby significantly impact the workload of pathologists. Meanwhile, the recent advent of whole-slide scanners and deep-learning techniques have enabled the computer-assisted analysis of histopathological images, which could help to alleviate this impact. Thus, we developed a deep learning-based diagnostic algorithm to detect lymph node metastases of gastric adenocarcinoma and evaluated its performance. Methods. We randomly selected 20 patients with gastric adenocarcinoma who underwent surgery as definitive treatment and were found to be node metastasis-positive. HEMATOXYLIN-eosin (HE) stained glass slides, including a total of 51 metastasis-positive nodes, were retrieved from the specimens of these cases. Other slides with 776 metastasis-negative nodes were also retrieved from other twenty cases with the same disease that were diagnosed as metastasis-negative by the final pathological examinations. All glass slides were digitized using a whole-slide scanner. A deep-learning algorithm to detect metastases was developed using the data in which metastasis-positive parts of the images were annotated by a well-trained pathologist, and its performance in detecting metastases was evaluated. Results. Cross-validation analysis indicated an area of 0.9994 under the receiver operating characteristic curve. Free-response receiver operating characteristic curve (FROC) analysis indicated a sensitivity of 1.00 with three false positives. Further evaluation using an independent dataset also showed similar level of accuracies. Conclusion. This deep learning-based diagnosis-aid system is a promising tool that can assist pathologists involved in gastric cancer care and reduce their workload.