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). Forty had recurrence within 5 years and 22 between 5 and 10 years; thirty-seven were recurrence-free during 5-10 years and 32 were for more than 10 years. We extracted nuclear features from regions of interest (ROIs) using a digital pathology technique and used them to train 5- and 10-year Support Vector Machine models for recurrence prediction. The models predicted recurrence at 5/10 years after surgery with accuracies of 86.4%/74.1% for each ROI and 100%/100% for each case, respectively. By combining the two models, the accuracy of the recurrence prediction within 5 years was 100%. However, recurrence between 5 and 10 years was correctly predicted for only 5 of the 12 test cases. The machine learning models showed good accuracy for recurrence prediction within 5 years after surgery and may be useful for the design of follow-up protocols and patient selection for adjuvant therapy.

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. In general, structural, cellular, and nuclear atypia are evaluated to determine cancer atypia. However, since it is difficult to accurately quantify structural atypia from TUR specimens, in this study, we used only nuclear atypia and analyzed it using feature extraction followed by classification using Support Vector Machine and Random Forest machine learning algorithms. For the analysis, 125 patients diagnosed with NMIBC were used; data from 95 patients were randomly selected for the training set, and data from 30 patients were randomly selected for the test set. The results showed that the support vector machine-based model predicted recurrence within 2 years after TUR with a probability of 90% and the random forest-based model with probability of 86.7%. In the future, the system can be used to objectively predict NMIBC recurrence after TUR.

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. The number of renal cysts was scored and graded. Age of subjects along with histories of smoking, hypertension, and diabetes mellitus were confirmed. Multiple linear regression analyses demonstrated that severity of atherosclerosis and the number of renal cysts were correlated with thoracic aortic circumference, while only the number of renal cysts was correlated with abdominal aortic circumference (p<0.05), which was more predominant in female subjects (p<0.05). Microscopically, significantly more dilated renal tubules (by Student's t-test, p<0.05) along with decreased stainability of basement membrane by Periodic acid-Schiff staining and immunostaining of type IV collagen were noted in background renal tissues in cases with numerous renal cysts than in age- and sex-matched controls without renal cysts (n=10 vs. 10). The present study suggests that a syndrome that affects both aorta and renal tubules may exist.