Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach.

Background: Treatment of locally advanced rectal cancer (LARC) involves neoadjuvant chemoradiotherapy (nCRT), followed by total mesorectal excision. Examining the response to treatment is one of the most important factors in the follow-up of patients; therefore, in this study, radiomics patterns derived from pretreatment computed tomography images in rectal cancer and its relationship with treatment response measurement criteria have been investigated.

Methods: Fifty patients with rectal adenocarcinoma who were candidates for nCRT and surgery were included.

Apigenin's Influence on Inflammatory and Epigenetic Responses in Rat Lungs After Radiotherapy.

Introduction: The lung is a moderately radio-sensitive organ. When cells are damaged due to accidental radiation exposure or treatment, they release molecules that lead to the recruitment of immune cells, accumulating inflammatory cytokines at the site of damage. Apigenin (Api) is a natural flavonoid known for its anti-inflammatory properties.

Prediction of auditory brain stem responses damage in patients with head-and-neck cancers receiving radiotherapy using the functional assays of normal tissue complication probability models.

Aim: The purpose of this study was to set four NTCP models on clinical data and develop a model that calculates the possibility of hearing damage due to irradiation of healthy and at-risk brainstem tissue.

Materials And Methods: ABR tests were performed on 50 head-and-neck cancer patients three years after radiotherapy for evaluation of lesions in a part of the auditory nerve or the auditory pathway in the brainstem.

Results: It indicated a significant difference in the latency of the waves assessed by the ABR test between the two groups.

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach.

Purpose: To establish the early prediction models of radiation-induced oral mucositis (RIOM) based on baseline CT-based radiomic features (RFs), dosimetric data, and clinical features by machine learning models for head and neck cancer (HNC) patients.

Methods: In this single-center prospective study, 49 HNCs treated with curative intensity modulated radiotherapy (IMRT) were enrolled. Baseline CT images ( CT simulation), dosimetric, and clinical features were collected.