Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning

Introduction: The excellent soft-tissue contrast of magnetic resonance imaging (MRI) is appealing for delineation of organs-at-risk (OARs) as it is required for radiation therapy planning (RTP). In the last decade there has been an increasing interest in using deep-learning (DL) techniques to shorten the labor-intensive manual work and increase reproducibility. This paper focuses on the automatic segmentation of 27 head-and-neck and 10 male pelvis OARs with deep-learning methods based on T2-weighted MR images.Method: The proposed method uses 2D U-Nets for localization and 3D U-Net for segmentation of the various structures. The models were trained using public and private datasets and evaluated on private datasets only.Results and discussion: Evaluation with ground-truth contours demonstrated that the proposed method can accurately segment the majority of OARs and indicated similar or superior performance to state-of-the-art models. Furthermore, the auto-contours were visually rated by clinicians using Likert score and on average, 81% of them was found clinically acceptable

Clear cell renal cell carcinoma and papillary renal cell carcinoma: differentiation of distinct histological types with multiphase CT

PURPOSE: Conventional clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) have different behavioral characteristics and clinical management strategies (nephrectomy vs. nephron-sparing surgery). Our aim was to retrospectively evaluate the contrast enhancement pattern of ccRCC and pRCC and evaluate its possible diagnostic role for preoperative differentiation using a standardized protocol. MATERIALS AND METHODS: Quadriphasic multidetector computed tomography (CT) images (unenhanced, corticomedullary, nephrographic, and excretory phases) of 19 patients with 20 ccRCC and 14 patients with 15 pRCC lesions (mean ages, 62.3+/-14.1 and 61.4+/-13.7 years, respectively) were reviewed retrospectively. The attenuation characteristics were compared with the attenuation of the normal renal cortex using either multiple 10-mm2 regions of interest or whole tumor attenuation measurements. The degree of contrast enhancement was also compared. RESULTS: Univariate analysis revealed that ccRCC lesions showed higher mean attenuation values on the corticomedullary and nephrographic phases compared with pRCC masses (P < 0.05) using both measurement techniques. CONCLUSION: The findings underscore the importance of multiphase CT in the differentiation of these two subtypes of RCC using standard assessment techniques. The measurement of the degree of enhancement on contrast-enhanced multidetector CT may be a simple and useful method to radiologically differentiate between the two histological types of RCC