Non-invasive classification of non-neoplastic and neoplastic gallbladder polyps based on clinical imaging and ultrasound radiomics features: An interpretable machine learning model.

Background: Gallbladder (GB) adenomas, precancerous lesions for gallbladder carcinoma (GBC), lack reliable non-invasive tools for preoperative differentiation of neoplastic polyps from cholesterol polyps. This study aimed to evaluate an interpretable machine learning (ML) combined model for the precise differentiation of the pathological nature of gallbladder polyps (GPs).

Methods: This study consecutively enrolled 744 patients from Xi'an Jiaotong University First Affiliated Hospital between January 2017 and December 2023 who were pathologically diagnosed postoperatively with cholesterol polyps, adenomas or T1-stage GBC.

Machine learning model based on preoperative contrast-enhanced CT and clinical features to predict perineural invasion in gallbladder carcinoma patients.

Background: Perineural invasion (PNI) is an independent prognostic risk factor for gallbladder carcinoma (GBC). However, there is currently no reliable method for the preoperative noninvasive prediction of PNI.

Methods: This retrospective study included 180 patients with pathologically diagnosed GBC who underwent preoperative contrast-enhanced CT between January 2022 to December 2023 at one high-volume medical center from China.

Primary repair and reconstruction of tumor defects in parotid masseter region: a report of 58 cases.

Background: The purpose of this study was to investigate the one-stage reconstruction of primary or secondary neoplastic defects in the parotid masseter area.

Methods: Fifty-eight cases of soft tissue defects reconstruction caused by tumors in the parotid masseter area from 2014 to 2018 were analyzed. The minimum area of defect was 4 cm × 5 cm, and the maximum area was 12 cm × 12 cm.