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Updated: May 15, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Delta-radiomics features combined with haematological index predict pathological complete response after neoadjuvant
1Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
Aim:
This study aimed at assessing the value of enhanced computed tomography (CT)-based delta-radiomics features (Delta-RFs) and Delta-RFs combined with haematological dynamic changes in predicting pathological complete response (PCR) after neoadjuvant immunochemotherapy in non-small cell lung cancer (NSCLC).
Materials And Methods:
From January 2021 to August 2023, in total, 165 patients with stage IB-IIIB NSCLC (training, n=115, validation, n=50) who received neoadjuvant immunochemotherapy before surgery, were retrospectively enrolled. Radiomic features were extracted from tumour region of interest on pretreatment and pre-operation enhanced CT images. Delta-RFs are defined as the relative net change in radiomics features between pre-neoadjuvant immunochemotherapy and pre-operation stage. The least absolute shrinkage and selection operator was used to ensure optimal feature selection to calculate the radiomics score (Rad-score) for predicting PCR. Univariate and multivariate logistic regression analyses were performed to screen the factors related to PCR and predictive models were then constructed.
Results:
Forty percent patients showed PCR (66/165) after neoadjuvant immunochemotherapy. Nine Delta-RFs were selected as the most predictive factors for PCR. Logistic regression analysis showed that the Rad-score (OR = 8.542, 95% CI: 3.367-21.673, P<0.001) and ΔLMR (OR = 2.637, 95% CI: 1.094-6.359, P=0.031) were independent factors associated with PCR. With respect to predicting PCR, the Delta-RF model and the combined model both achieved satisfactory areas under the curve in the training (area under the curve [AUC]: 0.74, 0.788) and the validation was found to be cohort (AUC: 0.718, 0.737). The calibration curve showed that the predicted value of Delta-RF combined with haematological dynamic change model was in good agreement with the observed value. Decision curve analysis represented that the model exhibits high clinical practicability.
Conclusions:
The Delta-RF model based on enhanced CT and the combined model can aid in efficient prediction of PCR after neoadjuvant immunochemotherapy in NSCLC, and the combined model can predict PCR performance better than Delta-RF model alone after neoadjuvant immunochemotherapy.
