Publications by authors named "Yahan Tong"
Article Synopsis
- The study aimed to develop and validate a radio-clinical model that predicts microvascular invasion (MI) in gastric cancer using pre-surgery radiological and clinical data from 534 patients.
- The model utilized advanced techniques like radiomics, principal component analysis, and logistic regression to analyze and extract important features from CT images and clinical characteristics.
- The results showed the model had good predictive ability with AUC values of 0.88 for the training set and 0.80 for the test set, indicating its potential utility in clinical settings.
Purpose: Non-invasive methods are urgently needed to assess the efficacy of transarterial chemoembolization (TACE) and to identify patients with hepatocellular carcinoma (HCC) who may benefit from this procedure. This study, therefore, aimed to investigate the predictive ability of tumor growth patterns and radiomics features from contrast-enhanced magnetic resonance imaging (CE-MRI) in predicting tumor response to TACE among patients with HCC.
Patients And Methods: A retrospective study was conducted on 133 patients with HCC who underwent TACE at three centers between January 2015 and April 2023.
Article Synopsis
- This study focuses on developing and validating a diagnostic model to predict RAS mutation status in patients with colorectal liver metastases (CRLMs), which is important for determining the efficacy of EGFR therapy.
- The research involved a multivariate prediction model, utilizing clinical, demographic, and imaging data from patients who underwent liver surgery between 2014 and 2020, with validation through external patient cohorts.
- The model's effectiveness was evaluated using statistical measures like area under the curve (AUC) and calibration plots, with additional analyses to assess its clinical relevance.
Purpose: To establish and validate a radiomics nomogram for predicting recurrence of esophageal squamous cell carcinoma (ESCC) after esophagectomy with curative intent.
Materials And Methods: The medical records of 155 patients who underwent surgical treatment for pathologically confirmed ESCC were collected. Patients were randomly divided into a training group (n=109) and a validation group (n=46) in a 7:3 ratio.
Background: Early noninvasive screening of patients who would benefit from neoadjuvant chemotherapy (NCT) is essential for personalized treatment of locally advanced gastric cancer (LAGC). The aim of this study was to identify radio-clinical signatures from pretreatment oversampled computed tomography (CT) images to predict the response to NCT and prognosis of LAGC patients.
Methods: LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021.
Objectives: DNA mismatch repair deficiency (dMMR) status has served as a positive predictive biomarker for immunotherapy and long-term prognosis in gastric cancer (GC). The aim of the present study was to develop a computed tomography (CT)-based nomogram for preoperatively predicting mismatch repair (MMR) status in GC.
Methods: Data from a total of 159 GC patients between January 2020 and July 2021 with dMMR GC (n=53) and MMR-proficient (pMMR) GC (n=106) confirmed by postoperative immunohistochemistry (IHC) staining were retrospectively analyzed.
Purpose: To develop and validate a radiomics nomogram integrated with clinic-radiological features for preoperative prediction of DNA mismatch repair deficiency (dMMR) in gastric adenocarcinoma.
Materials And Methods: From March 2014 to August 2020, 161 patients with pathologically confirmed gastric adenocarcinoma were included from two centers (center 1 as the training and internal testing sets, n = 101; center 2 as the external testing sets, n = 60). All patients underwent preoperative contrast-enhanced computerized tomography (CT) examination.