Deep learning dosiomics in grade 4 radiation-induced lymphopenia prediction in radiotherapy for esophageal cancer: a multi-center study.

Purpose: To investigate the feasibility and accuracy of using deep learning and dosiomics features, as well as their combination with dose-volume histogram (DVH) parameters and clinical factors to predict grade 4 radiation-induced lymphopenia (G4RIL) for patients with esophageal cancer (EC) who undergoing radiotherapy (RT).

Methods: This retrospective study enrolled 545 patients with EC who underwent RT between January 2015 and December 2023 from five medical centers, and divided them into a training set, an internal validation set, an external test set 1, and an external test set 2, respectively. Dosiomics (D) and deep learning dosiomics (DLD) models were built to predict the probability of G4RIL based on radiation dose distributions using five-fold cross-validation.

Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics.

Background: Preoperative stratification is critical for the management of patients with esophageal cancer (EC). To investigate the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stages for patients with EC.

Methods: Histologically confirmed 100 EC patients with preoperative PET-CT images were enrolled retrospectively and randomly divided into training and validation cohorts at a ratio of 7:3.

Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer.

Background: Noninvasive and accurate prediction of lymph node metastasis (LNM) is very important for patients with early-stage cervical cancer (ECC). Our study aimed to investigate the accuracy and sensitivity of radiomics models with features extracted from both intra- and peritumoral regions in magnetic resonance imaging (MRI) with T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) for predicting LNM.

Methods: A total of 247 ECC patients with confirmed lymph node status were enrolled retrospectively and randomly divided into training (n = 172) and testing sets (n = 75).

Differentiating Primary Tumors for Brain Metastasis with Integrated Radiomics from Multiple Imaging Modalities.

Objectives: To differentiate the primary site of brain metastases (BMs) is of high clinical value for the successful management of patients with BM. The purpose of this study is to investigate a combined radiomics model with computer tomography (CT) and magnetic resonance imaging (MRI) images in differentiating BMs originated from lung and breast cancer.

Methods: Pretreatment cerebral contrast enhanced CT and T1-weighted MRI images of 78 patients with 179 BMs from primary lung and breast cancer were retrospectively analyzed.

The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer.

To investigate the effects of different ultrasonic machines on the performance of radiomics models using ultrasound (US) images in the prediction of lymph node metastasis (LNM) for patients with cervical cancer (CC) preoperatively. A total of 536 CC patients with confirmed histological characteristics and lymph node status after radical hysterectomy and pelvic lymphadenectomy were enrolled. Radiomics features were extracted and selected with US images acquired with ATL HDI5000, Voluson E8, MyLab classC, ACUSON S2000, and HI VISION Preirus to build radiomics models for LNM prediction using support vector machine (SVM) and logistic regression, respectively.