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A potential biomarker based on clinical-radiomics nomogram for predicting survival and adjuvant chemotherapy benefit in resected node-negative, early-stage lung adenocarcinoma.

ABSTRACT:

Background

We aimed to construct a clinical-radiomics nomogram to predict disease-free survival (DFS) and the added survival benefit of adjuvant chemotherapy (ACT) for node-negative, early-stage (I-II) lung adenocarcinoma (ADC).

Methods

In this retrospective study including 310 patients from two independent cohorts, the CT-derived radiomics features were selected by least absolute shrinkage and selection operator Cox regression to generate a radiomics signature associated with DFS. The radiomics signature was incorporated to construct a clinical-radiomics nomogram along with the independent clinical risk predictors. The model performance was evaluated with reference to discrimination quantified by Harrell concordance index (C-index), integrated discrimination improvement (IDI) and net reclassification index (NRI), calibration and clinical utility. The risk score (RS) for clinical-radiomics nomogram was calculated. The association between ACT and survival benefit was assessed in high and low RS subgroup.

Results

The clinical-radiomics nomogram achieved the highest C-index of 0.822 [95% confidence interval (CI): 0.769, 0.876] in training cohort and 0.802 (95% CI: 0.716, 0.888) in validation cohort. The incorporation of radiomics signature into clinical-radiomics nomogram showed an incremental benefit over clinical nomogram according to the improved NRI and IDI. The calibration curves and decision curve analysis further verified the clinical utility of clinical-radiomics nomogram. Further, patients with high RS based on clinical-radiomics nomogram were more prone to benefit from ACT.

Conclusions

The clinical-radiomics nomogram approach can feasibly conduct risk prediction and have potential to identify the beneficiaries of ACT among patients with node-negative, early-stage ADC, which might serve as a helpful tool in informing therapeutic decision-making.

SUBMITTER: Ma X

PROVIDER: S-EPMC8828517 | biostudies-literature |

REPOSITORIES: biostudies-literature

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