Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes.

Quantitative image features, also known as radiomic features, have shown potential for predicting treatment outcomes in several body sites. We quantitatively analyzed Fluorine-fluorodeoxyglucose (F-FDG) Positron Emission Tomography (PET) uptake heterogeneity in the Metabolic Tumor Volume (MTV) of eighty cervical cancer patients to investigate the predictive performance of radiomic features for two treatment outcomes: the development of distant metastases (DM) and loco-regional recurrent disease (LRR). We aimed to fit the highest predictive features in multiple logistic regression models (MLRs).

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from Flourine-fluorodeoxyglucose ( F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients.

Fiducial markers coupled with 3D PET/CT offer more accurate radiation treatment delivery for locally advanced esophageal cancer.

Background And Aims:  The role of three-dimensional positron emission tomography/computed tomography (3 D PET/CT) in esophageal tumors that move with respiration and have potential for significant mucosal inflammation is unclear. The aim of this study was to determine the correlation between gross tumor volumes derived from 3 D PET/CT and endoscopically placed fiducial markers.

Methods:  This was a retrospective, IRB approved analysis of 40 patients with esophageal cancer with fiducials implanted and PET/CT.