Journal article
Patient Outcome Prognosis for External Beam Radiation Therapy Using CBCT-based Radiomics: A Systematic Review
Biomedical physics & engineering express, Vol.12(1), 012002
02/2026
DOI: 10.1088/2057-1976/ae308b
PMCID: PMC13193058
PMID: 41435426
Abstract
This review investigates the use of cone-beam computed tomography (CBCT) in conjunction with radiomics for external beam radiation therapy (EBRT) in cancer treatment. CBCT, which provides high-resolution, volumetric images, offers a promising tool for precision treatment delivery. By integrating radiomics and quantitative features extracted from CBCT, this review explores potential advancements in tumor characterization, treatment planning, and monitoring treatment responses in personalized cancer therapy.
We conducted this systematic review using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) framework. This study focused on CBCT-only radiomics applications, examining publications in PubMed, Embase, and Scopus databases. The inclusion criteria were strictly peer-reviewed journal articles, resulting in 29 studies being selected for analysis. These studies were divided into two main categories: 1) method development for treatment outcome prediction; 2) verification, validation, and uncertainty quantification (VVUQ) for CBCT-based radiomics.
The literature encompasses a range of investigations into CBCT-based radiomics for EBRT, covering different cancer types such as head-and-neck squamous cell carcinoma, non-small cell lung cancer, esophageal squamous cell cancer, hepatocellular carcinoma, prostate cancer, and rectal cancer. These studies used radiomics to predict outcomes including tumor response, local failure, tissue toxicity, and patient survival. VVUQ studies addressed the robustness and reproducibility of radiomic features. Furthermore, the emerging field of 4D-CBCT radiomics shows potential in improving image quality.
CBCT-based radiomics presents a promising advancement in personalized radiotherapy, allowing for enhanced cancer prognosis and treatment adaptation. However, challenges of imaging quality and acquisition need to be addressed to ensure consistency and reliability. Future research should focus on standardizing imaging protocols and incorporating multi-institutional collaborations to further validate the clinical applicability of CBCT-based radiomics. Integration of this technology can potentially induce a paradigm shift in personalized cancer radiotherapy. New technologies promise to make CBCT even more valuable in the future.
Details
- Title: Subtitle
- Patient Outcome Prognosis for External Beam Radiation Therapy Using CBCT-based Radiomics: A Systematic Review
- Creators
- Chih-Wei Chang - Emory UniversityTonghe Wang - Memorial Sloan Kettering Cancer CenterRichard L J Qiu - Emory UniversityJochen Cammin - University of Maryland, College ParkKailin Yang - University of Iowa Holden Comprehensive Cancer CenterJeffrey Buchsbaum - National Cancer InstituteXiang Li - Memorial Sloan Kettering Cancer CenterYue-Houng Hu - Harvard Medical SchoolLei Ren - University of Maryland School of MedicinePing Xia - Cleveland ClinicAmit Sawant - University of Maryland School of MedicineJacob G Scott - Cleveland ClinicXiaofeng Yang - Emory University
- Resource Type
- Journal article
- Publication Details
- Biomedical physics & engineering express, Vol.12(1), 012002
- DOI
- 10.1088/2057-1976/ae308b
- PMID
- 41435426
- PMCID
- PMC13193058
- NLM abbreviation
- Biomed Phys Eng Express
- ISSN
- 2057-1976
- eISSN
- 2057-1976
- Publisher
- IOP; Bristol
- Grant note
- National Institutes of Health: U54CA274513, U54CA273956, R01CA272991, R01CA279013, R01DE033512, R01EB032680, P30CA008748
This research is supported in part by the National Institutes of Health under Award Number U54CA274513, U54CA273956, R01CA272991, R01DE033512, R01EB032680, R01CA279013, and P30CA008748. The authors would like to thank Dr Nam T. Dinh for sharing his perspectives on validation, verification, and uncertainty quantification for modeling and simulation. The authors would like to thank Dr Ceferino Obcemea for sharing his expertise on AI applications for radiation oncology.
- Language
- English
- Electronic publication date
- 12/23/2025
- Date published
- 02/2026
- Academic Unit
- Radiation Oncology
- Record Identifier
- 9985093882702771
Metrics
1 Record Views