https://www.selleckchem.com/products/ikk-16.html 66). Moreover, a shorter dilator tip (r = 0.52), lower flexibility ratio (r = 0.52), and lower frictional force (r = 0.50) were correlated with a lower insertion force at the proximal ureter. A UAS with a rigid base and flexible tip parts, a smoother surface, and a shorter dilator tip would be preferable for reducing the insertion force. These findings may be crucial for selecting or developing an ideal UAS that can decrease the risk of ureteral injury. A UAS with a rigid base and flexible tip parts, a smoother surface, and a shorter dilator tip would be preferable for reducing the insertion force. These findings may be crucial for selecting or developing an ideal UAS that can decrease the risk of ureteral injury. To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning. Data of 104 patients with esophagus cancer, who underwent chemoradiotherapy followed by surgery at the Hiroshima University hospital from 2003 to 2016 were analyzed. The treatment outcomes of these tumors were known prior to the study. The data were split into 3 sets 57/16 tumors for the training/validation and 31 tumors for model testing. The degree of differentiation of squamous cell carcinoma was classified into two groups. The first group (Group I) was a poorly differentiated (POR) patients. The second group (Group II) was well and moderately differentiated patients. The radiomics feature was extracted in the tumor and around the tumor regions. A total number of 3480 radiomics features per patient image were extracted from radiotherapy planning CT scan. Models were built with the least absolute shrinkage and selection oper For esophageal cancer, the differentiation of degree is the import indexes reflecting the aggressiveness. The current study proposed the prediction model for the differentiation of degree with radiomics ana