Automatic renal carcinoma biopsy guidance using forward-viewing endoscopic optical coherence tomography and deep learning.

Percutaneous renal biopsy is commonly used for kidney cancer diagnosis. However, the biopsy procedure remains challenging in sampling accuracy. Here we introduce a forward-viewing optical coherence tomography probe for differentiating tumor and normal tissues, aiming at precise biopsy guidance. Totally, ten human kidney samples, nine of which had malignant renal carcinoma and one had benign oncocytoma, were used for system evaluation. Based on their distinct imaging features, carcinoma could be efficiently distinguished from normal renal tissues. Additionally, oncocytoma could be differentiated from carcinoma. We developed convolutional neural networks for tissue recognition. Compared to the conventional attenuation coefficient method, convolutional neural network models provided more accurate carcinoma predictions. These models reached a tissue recognition accuracy of 99.1% on a hold-out set of four kidney samples. Furthermore, they could efficiently distinguish oncocytoma from carcinoma. In conclusion, our convolutional neural network-aided endoscopic imaging platform could enhance carcinoma diagnosis during percutaneous renal biopsy procedures.

Automatic renal carcinoma biopsy guidance using forward-viewing endoscopic optical coherence tomography and deep learning.

Percutaneous renal biopsy (PRB) is commonly used for kidney cancer diagnosis. However, current PRB remains challenging in sampling accuracy. This study introduces a forward-viewing optical coherence tomography (OCT) probe for differentiating tumor and normal tissues, aiming at precise PRB guidance. Five human kidneys and renal carcinoma samples were used to evaluate the performance of our probe. Based on their distinct OCT imaging features, tumor and normal renal tissues can be accurately distinguished. We examined the attenuation coefficient for tissue classification and achieved 98.19% tumor recognition accuracy, but underperformed for distinguishing normal tissues. We further developed convolutional neural networks (CNN) and evaluated two CNN architectures: ResNet50 and InceptionV3, yielding 99.51% and 99.48% accuracies for tumor recognition, and over 98.90% for normal tissues recognition. In conclusion, combining OCT and CNN significantly enhanced the PRB guidance, offering a promising guidance technology for improved kidney cancer diagnosis.

Yttrium-90 radioembolization in desmoplastic small round cell tumor with recurrent hepatic metastasis following hyperthermic intraperitoneal chemotherapy.

We report a case of a 26-year-old male who was diagnosed with metastatic desmoplastic small round cell tumor initially treated with systemic chemotherapy followed by tumor debulking and hyperthermic intra-peritoneal chemotherapy. The patient was in complete remission by clinical and imaging criteria for 11 months, until he developed bi-lobar hepatic disease, which was successfully treated with selective internal radiation therapy by Yttrium-90. The patient demonstrated liver-specific complete response on follow-up imaging obtained 18 months after the procedure.