Machine learning-based image analysis for accelerating the diagnosis of complicated preneoplastic and neoplastic ductal lesions in breast biopsy tissues.

PURPOSE: Diagnosis of breast preneoplastic and neoplastic lesions is difficult due to their similar morphology in breast biopsy specimens. To diagnose these lesions, pathologists perform immunohistochemical analysis and consult with expert breast pathologists. These additional examinations are time-consuming and expensive. Artificial intelligence (AI)-based image analysis has recently improved, and may help in ordinal pathological diagnosis. Here, we showed the significance of machine learning-based image analysis of breast preneoplastic and neoplastic lesions for facilitating high-throughput diagnosis. METHODS: Images were obtained from normal mammary glands, hyperplastic lesions, preneoplastic lesions and neoplastic lesions, such as usual ductal hyperplasia (UDH), columnar cell lesion (CCL), ductal carcinoma in situ (DCIS), and DCIS with comedo necrosis (comedo DCIS) in breast biopsy specimens. The original enhanced convoluted neural network (CNN) system was used for analyzing the pathological images. RESULTS: The AI-based image analysis provided the following area under the curve values (AUC): normal lesion versus DCIS, 0.9902; DCIS versus comedo DCIS, 0.9942; normal lesion versus CCL, 0.9786; and UDH versus DCIS, 1.000. Multiple comparison analysis showed precision and recall scores similar to those of single comparison analysis. Based on the gradient-weighted class activation mapping (Grad-CAM) used to visualize the important regions reflecting the result of CNN analysis, the ratio of stromal tissue in the whole weighted area was significantly higher in UDH and CCL than that in DCIS. CONCLUSIONS: These analyses may provide a more accurate and rapid pathological diagnosis of patients. Moreover, Grad-CAM identifies uncharted important histological characteristics for newer pathological findings and targets of research for understanding diseases.

[A CASE OF METASTATIC KIDNEY CANCER FOR WHICH THE EFFICACY OF NIVOLUMAB THERAPY WAS MAINTAINED EVEN AFTER THE DEVELOPMENT OF INTERSTITIAL PNEUMONIA].

The patient was a 52-year old man who underwent laparoscopic radical nephrectomy for kidney cancer. Left adrenal and lung metastases occurred 5 and 11 years after the surgery, respectively. Various molecular-targeted therapies were ineffective, so nivolumab treatment was started 12 years after the surgery. Treatment was discontinued when the patient developed interstitial pneumonia after three courses of nivolumab treatment. After steroid treatment for interstitial pneumonia, both the symptoms and findings of the imaging tests improved quickly. On the other hand, while the effect of Partial Response (PR) was evident in the lungs and adrenal glands, on the basis of the image assessments performed after three courses of treatment, the effect was maintained without regrowth even at the last follow-up, 10 months after discontinuing the treatment.