A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer.

Purpose: To evaluate the potential of radiomics approach for predicting No. 14v station lymph node metastasis (14vM) in gastric cancer (GC).

Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 288 GC patients were retrospectively collected.

Accuracy of cone-beam breast computed tomography for assessing breast cancer tumor size-comparison with breast magnetic resonance imaging.

Background: Accurate preoperative assessment of tumor size is important in developing a surgical plan for breast cancer. The purpose of this study was to evaluate the accuracy of cone-beam breast computed tomography (CBBCT) and magnetic resonance imaging (MRI) in the assessment of tumor size and to analyze the factors influencing the discordance.

Methods: In this retrospective study, patients with breast cancer who underwent preoperative contrast-enhanced CBBCT (CE-CBBCT) and dynamic contrast-enhanced MRI (DCE-MRI) and received a complete pathologic diagnosis from August 2020 to December 2021 were included, using the pathological result as the gold standard.

Feasibility analysis of magnetic resonance imaging-based radiomics features for preoperative prediction of nuclear grading of ductal carcinoma in situ.

Background: The nuclear grading of ductal carcinoma in situ (DCIS) affects its clinical risk. The aim of this study was to investigate the possibility of predicting the nuclear grading of DCIS, by magnetic resonance imaging (MRI)-based radiomics features. And to develop a nomogram combining radiomics features and MRI semantic features to explore the potential role of MRI radiomic features in the assessment of DCIS nuclear grading.