CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy.

Abstract

OBJECTIVES:

The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both.

MATERIALS AND METHODS:

This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient's CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients.

RESULTS:

The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619-0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646-0.846).

CONCLUSIONS:

Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future.

KEY POINTS:

The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.

• Authors: Fengchang Yang, Jiayi Zhang, Liu Zhou, Wei Xia, Rui Zhang, Haifeng Wei, Jinxue Feng, Xingyu Zhao, Junming Jian, Xin Gao, Shuanghu Yuan
• Journal: European radiology (Eur Radiol) Vol. 32 Issue 3 Pg. 1538-1547 (Mar 2022) ISSN: 1432-1084 [Electronic] Germany
• PMID: 34564744 (Publication Type: Journal Article, Randomized Controlled Trial)
• Copyright: © 2021. European Society of Radiology.

Topics

• Carcinoma, Non-Small-Cell Lung (diagnostic imaging, drug therapy)
• Humans
• Lung
• Lung Neoplasms (diagnostic imaging, drug therapy)
• Retrospective Studies
• Tomography, X-Ray Computed