A PET/CT-based 3D deep learning model for predicting spread through air spaces in stage I lung adenocarcinoma.

Purpose: This study evaluates a three-dimensional (3D) deep learning (DL) model based on fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for predicting the preoperative status of spread through air spaces (STAS) in patients with clinical stage I lung adenocarcinoma (LUAD).

Methods: A retrospective analysis of 162 patients with stage I LUAD was conducted, splitting data into training and test sets (4:1). Six 3D DL models were developed, and the top-performing PET and CT models (ResNet50) were fused for optimal prediction.

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer.

Objectives: Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM.

Methods: A total of 220 colorectal cancer (CRC) cases were enrolled in this study.

Differential reinforcement of cGAS-STING pathway-involved immunotherapy by biomineralized bacterial outer membrane-sensitized EBRT and RNT.

Radiotherapy (RT), including external beam radiation therapy (EBRT) and radionuclide therapy (RNT), realizes physical killing of local tumors and activates systemic anti-tumor immunity. However, these effects need to be further strengthened and the difference between EBRT and RNT should be discovered. Herein, bacterial outer membrane (OM) was biomineralized with manganese oxide (MnO) to obtain OM@MnO-PEG nanoparticles for enhanced radio-immunotherapy via amplifying EBRT/RNT-induced immunogenic cell death (ICD) and cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) activation.