Recurrence prediction in clear cell renal cell carcinoma using machine learning of quantitative nuclear features.

The recurrence of non-metastatic renal cell carcinoma (RCC) may occur early or late after surgery. This study aimed to develop a recurrence prediction machine learning model based on quantitative nuclear morphologic features of clear cell RCC (ccRCC). We investigated 131 ccRCC patients who underwent nephrectomy (T1-3N0M0).

Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features.

Non-muscle invasive bladder cancer (NMIBC) generally has a good prognosis; however, recurrence after transurethral resection (TUR), the standard primary treatment, is a major problem. Clinical management after TUR has been based on risk classification using clinicopathological factors, but these classifications are not complete. In this study, we attempted to predict early recurrence of NMIBC based on machine learning of quantitative morphological features.

Correlation between number of renal cysts and aortic circumferences measured using autopsy material.

Although the presence of renal cysts has been reported to be associated with aortic aneurysm or dissection by imaging studies, an autopsy study has not been performed. Therefore, in our institute, recent consecutive adult autopsy cases (n=108, 64 males and 44 females) were reviewed. The circumferences and atherosclerosis ratios of both thoracic and abdominal aorta were individually measured and graded.