Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

INTRODUCTION: The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) images. METHODS: Fifty-nine tumors with stage 2 or 3 rectal cancer that received nCRT were retrospectively evaluated. Pathological tumor regression grading was carried out using the Dworak (Dw-TRG) guidelines and served as the ground truth for response predictions. Imaging-based tumor regression grading was performed according to the MERCURY group guidelines from pre-treatment and post-treatment para-axial T2-weighted MR images (MR-TRG). Tumor signal intensity signatures were extracted by segmenting the tumors volumetrically on the images. Normalized histograms of the signatures were used as input to a deep neural network (DNN) housing long short-term memory (LSTM) units. The output of the network was the tumor regression grading prediction, DNN-TRG. RESULTS: In predicting complete or good response, DNN-TRG demonstrated modest agreement with Dw-TRG (Cohen's kappa= 0.79) and achieved 84.6% sensitivity, 93.9% specificity, and 89.8% accuracy. MR-TRG revealed 46.2% sensitivity, 100% specificity, and 76.3% accuracy. In predicting a complete response, DNN-TRG showed slight agreement with Dw-TRG (Cohen's kappa= 0.75) with 71.4% sensitivity, 97.8% specificity, and 91.5% accuracy. MR-TRG provided 42.9% sensitivity, 100% specificity, and 86.4% accuracy. DNN-TRG benefited from higher sensitivity but lower specificity, leading to higher accuracy than MR-TRG in predicting tumor response. CONCLUSION: The use of deep LSTM neural networks is a promising approach for evaluating the tumor response to nCRT in rectal cancer.

Encapsulated Papillary Carcinoma in A Man with Gynecomastia: Ultrasonography, Mammography and Magnetic Resonance Imaging Features with Pathologic Correlation.

Male breast cancer is an uncommon disease that constitutes 1% of all breast cancers and encapsulated papillary carcinoma (EPC) is a rare subtype of malignant male diseases. Gynecomastia is the most common disease of the male breast. We report a 63-year-old male patient with EPC accompanied by gynecomastia that was diagnosed and treated at our breast center. Mammography showed an oval-shaped dense mass with circumscribed margins on the ground of nodular gynecomastia. On ultrasonographic exam, we saw a well-circumscribed complex mass with a solid component which was vascular on Doppler ultrasonography. Magnetic resonance imaging revealed a complex cystic mass containing solid components. Dynamic images showed enhancement of the cystic mass wall and mural components. Tumor stage was evaluated as T2N0. The lesion's histologic examination and immunohistochemical analysis by showing no myoepithelial layer revealed an encapsulated papillary carcinoma. To our knowledge, this is the first case report which describes MR imaging findings of male breast encapsulated papillary cancer.