Use of a Convolutional Neural Network to Predict the Malignant Potential of Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Images: Visualization of the Focus of the Prediction Model.

Objective: We established a deep convolutional neural network (CNN) model based on ultrasound images (US-CNN) for predicting the malignant potential of gastrointestinal stromal tumors (GISTs).

Methods: A total of 980 ultrasound images from 245 pathology-confirmed GIST patients after surgical operation were retrospectively collected and divided into a low (very-low-risk, low-risk) and a high (medium-risk, high-risk) malignant potential group. Eight pre-trained CNN models were used to extract the features.

Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors.

This study aimed to develop and evaluate a nomogram based on an ultrasound radiomics model to predict the risk grade of gastrointestinal stromal tumors (GISTs). 216 GIST patients pathologically diagnosed between December 2016 and December 2021 were reviewed and divided into a training cohort (n = 163) and a validation cohort (n = 53) in a ratio of 3:1. The tumor region of interest was depicted on each patient's ultrasound image using ITK-SNAP, and the radiomics features were extracted.

Application of ultrasonography in predicting the biological risk of gastrointestinal stromal tumors.

Objectives: To explore and establish a reliable and noninvasive ultrasound model for predicting the biological risk of gastrointestinal stromal tumors (GISTs).

Materials And Methods: We retrospectively reviewed 266 patients with pathologically-confirmed GISTs and 191 patients were included. Data on patient sex, age, tumor location, biological risk classification, internal echo, echo homogeneity, boundary, shape, blood flow signals, presence of necrotic cystic degeneration, long diameter, and short/long (S/L) diameter ratio were collected.