Combined with the semantic features of CT and selected clinical variables, a machine learning model for accurately predicting the prognosis of Omicron was established.

Objectives: To efficiently use medical resources and offer optimal personalized treatment for individuals with Omicron infection, it is vital to predict the disease's outcome early on. This research developed three machine learning models to foresee the results for Omicron-infected patients.

Methods: Data from 253 Omicron-infected patients, including their CT scans, clinical details, and relevant laboratory values, were studied.

Delta dual‑region DCE-MRI radiomics from breast masses predicts axillary lymph node response after neoadjuvant therapy for breast cancer.

Objectives: This study was designed to develop and validate models based on delta intratumoral and peritumoral radiomics features from breast masses on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the prediction of axillary lymph node (ALN) pathological complete response (pCR) after neoadjuvant therapy (NAT) in patients with breast cancer (BC).

Methods: We retrospectively collected data from 187 BC patients with ALN metastases. Radiomics features were extracted from the intratumoral and 3 mm-peritumoral regions on DCE-MRI at baseline and after the 2nd course of NAT to calculate delta intratumoral and peritumoral radiomics features, respectively.