» Articles » PMID: 36471884

Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges

Overview
Journal J Oncol
Specialty Oncology
Date 2022 Dec 6
PMID 36471884
Authors
Affiliations
Soon will be listed here.
Abstract

Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.

Citing Articles

Radiomics features of computed tomography and magnetic resonance imaging for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis.

Feng L, Chen Q, Huang L, Long L Front Oncol. 2023; 13:1194200.

PMID: 37519801 PMC: 10374837. DOI: 10.3389/fonc.2023.1194200.

References
1.
van Griethuysen J, Lambregts D, Trebeschi S, Lahaye M, Bakers F, Vliegen R . Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer. Abdom Radiol (NY). 2019; 45(3):632-643. DOI: 10.1007/s00261-019-02321-8. View

2.
Zhang X, Wang L, Zhu H, Li Z, Ye M, Li X . Predicting Rectal Cancer Response to Neoadjuvant Chemoradiotherapy Using Deep Learning of Diffusion Kurtosis MRI. Radiology. 2020; 296(1):56-64. DOI: 10.1148/radiol.2020190936. View

3.
van Helden E, Vacher Y, van Wieringen W, van Velden F, Verheul H, Hoekstra O . Radiomics analysis of pre-treatment [F]FDG PET/CT for patients with metastatic colorectal cancer undergoing palliative systemic treatment. Eur J Nucl Med Mol Imaging. 2018; 45(13):2307-2317. PMC: 6208805. DOI: 10.1007/s00259-018-4100-6. View

4.
Kumar V, Gu Y, Basu S, Berglund A, Eschrich S, Schabath M . Radiomics: the process and the challenges. Magn Reson Imaging. 2012; 30(9):1234-48. PMC: 3563280. DOI: 10.1016/j.mri.2012.06.010. View

5.
Fu J, Zhong X, Li N, van Dams R, Lewis J, Sung K . Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer. Phys Med Biol. 2020; 65(7):075001. DOI: 10.1088/1361-6560/ab7970. View