Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

ABSTRACT:

Purpose

Whether radiomics methods are useful in prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) is unclear. This study aimed to investigate multiple magnetic resonance imaging (MRI) sequence-based radiomics methods in evaluating therapeutic response to nCRT in patients with locally advanced rectal cancer (LARC).

Methods

This retrospective study enrolled patients with LARC (06/2014-08/2017) and divided them into nCRT-sensitive and nCRT-resistant groups according to postoperative tumor regression grading results. Radiomics features from preoperative MRI were extracted, followed by dimension reduction using the minimum redundancy maximum relevance filter. Three machine-learning classifiers and an ensemble classifier were used for therapeutic response prediction. Radiomics nomogram incorporating clinical parameters were constructed using logistic regression. The receiver operating characteristic (ROC), decision curves analysis (DCA) and calibration curves were also plotted to evaluate the prediction performance.

Results

The machine learning classifiers showed good prediction performance for therapeutic responses in LARC patients (n=189). The ROC curve showed satisfying performance (area under the curve [AUC], 0.830; specificity, 0.794; sensitivity, 0.815) in the validation group. The radiomics signature included 30 imaging features derived from axial T1-weighted imaging with contrast and sagittal T2-weighted imaging and exhibited good predictive power for nCRT. A radiomics nomogram integrating carcinoembryonic antigen levels and tumor diameter showed excellent performance with an AUC of 0.949 (95% confidence interval, 0.892-0.997; specificity, 0.909; sensitivity, 0.879) in the validation group. DCA confirmed the clinical usefulness of the nomogram model.

Conclusion

The radiomics method using multiple MRI sequences can be used to achieve individualized prediction of nCRT in patients with LARC before treatment.

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC8136527 | biostudies-literature |

REPOSITORIES: biostudies-literature