Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Purpose: Managing chemorefractory metastatic colorectal cancer (mCRC) requires a meticulous equilibrium between the efficacy and toxicity of interventions, a task compounded by the constrained life expectancy of the patient. While existing prognostic tools, such as the Colon Life nomogram, primarily focus on general patient conditions or a single diagnostic modality, they do not fully integrate the potential predictive value of multimodal data. This study aims to develop and validate an Imaging Score, integrating clinical and imaging features derived from whole-body F-fluorodeoxyglucose (F-FDG) positron emission tomography-computed tomography (PET-CT), predicting death probability within 12 weeks from treatment initiation for refractory disease.

Materials And Methods: The development cohort comprises 254 patients from three clinical trials. Nine clinical variables and six imaging variables were assessed. After optimal subset selection through recursive Feature Elimination with cross-validation, a support vector classifier-trained machine learning model generated the Imaging Score. Validation was performed on a real-life patient cohort (n = 74). Model performance was assessed on discrimination (Harrell C-index) and calibration.

Results: Final prognostic features included whole-body metabolically active tumor volume, Eastern Cooperative Oncology Group performance status, visceral fat density, number of metastatic sites, body mass index, maximum standardized distance, and months since diagnosis. The Imaging Score demonstrated robust discriminative ability in both the development (C-index, 0.797) and validation (C-index, 0.714) sets, outperforming the Colon Life nomogram that tended to overestimate 12-week mortality.

Conclusion: The Imaging Score, integrating F-FDG PET-CT imaging with clinical parameters, is an effective prognostic tool for patients with chemorefractory mCRC. This combination of imaging biomarkers with clinical factors improves discrimination, enhancing its potential for clinical decision making, patient stratification for chemorefractory treatments, and trial eligibility.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12058364PMC
http://dx.doi.org/10.1200/CCI-24-00207DOI Listing

Publication Analysis

Top Keywords

imaging score
20
imaging
9
positron emission
8
emission tomography-computed
8
life expectancy
8
colorectal cancer
8
colon life
8
life nomogram
8
score integrating
8
clinical
6

Similar Publications

Background: Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath-hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath-hold compliance often requires external monitoring equipment.

View Article and Find Full Text PDF

Background: Tumefactive demyelination (TD) is a rare variant of multiple sclerosis (MS) characterized by tumor-like lesions that often require aggressive management. Genome-wide association studies (GWAS) identified variants associated with MS; similar analyses in TD are lacking.

Objective: A GWAS was performed to identify variants associated with TD.

View Article and Find Full Text PDF

Background: Endometrial carcinoma (EC) represents a significant clinical challenge due to its pronounced molecular heterogeneity, directly influencing prognosis and therapeutic responses. Accurate classification of molecular subtypes (CNV-high, CNV-low, MSI-H, POLE) and precise tumor mutational burden (TMB) assessment is crucial for guiding personalized therapeutic interventions. Integrating proteomics data with advanced machine learning (ML) techniques offers a promising strategy for achieving precise, clinically actionable classification and biomarker discovery in EC.

View Article and Find Full Text PDF

Genome-wide association study reveals candidate loci for resistance to anthracnose in blueberry.

G3 (Bethesda)

September 2025

Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA.

Anthracnose, caused by Colletotrichum gloeosporioides, poses a significant threat to blueberries, necessitating a deeper understanding of the genetic mechanisms underlying resistance to develop efficient breeding strategies. Here, we conducted a genome-wide association study on 355 advanced selections of southern highbush blueberry from the University of Florida Blueberry Breeding and Genomics Program. Visual scores and image analyses were used for assessing disease severity.

View Article and Find Full Text PDF

Background: Automated cardiac MR segmentation enables accurate and reproducible ventricular function assessment in Tetralogy of Fallot (ToF), whereas manual segmentation remains time-consuming and variable.

Purpose: To evaluate the deep learning (DL)-based models for automatic left ventricle (LV), right ventricle (RV), and LV myocardium segmentation in ToF, compared with manual reference standard annotations.

Study Type: Retrospective.

View Article and Find Full Text PDF