Pretherapy investigations using highly robust visualized biomarkers from CT imaging by multiple machine-learning
Jingjing Sun1, Feng Li2, Jiantao Yang1
1Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
Purpose:
Molecularly targeted therapy has revolutionized the therapeutic landscape and is emerging as the first-line treatment option for ALK-rearranged non-small-cell lung cancer (NSCLC). In this study, the highly informative and robust biomarkers based on pre-treatment CT images and clinicopathologic features will be developed and validated to predict the prognosis for ALK-inhibitor therapy in NSCLC patients.
Methods:
A total of 161 ALK-positive NSCLC patients treated with ALK inhibitors were retrospectively collected as training, validation and test sets from multi-center institutions. Cox proportional hazard regression (CPH) penalized by LASSO and random survival forest (RSF) coupled with recursive feature elimination (RFE) were used for radiomics and clinical features identification and model construction. An overlapping post-processing method was extra added to training process to investigate the stronger biomarker on the whole set.
Results:
123 of the collected cases progressed after a median follow-up of 15.5 months (IQR, 8.3-25.3). The T and M staging, pericardial effusion, age and ALK inhibitor-alectinib were determined as significant predictors in the survival analysis. Furthermore, we visualized the finally retained 4 radiomics feature. The RSF models built from overlapping-processed clinical and radiomics features respectively reached the maximum C-index of 0.68 and 0.75,but the combination of them,radioclinical signature, improved the score to 0.78. The model on the validation and external test datasets yielded the C-index of 0.73 and 0.79, with the iAUC of 0.76 and 0.83, the IBS of 0.119 and 0.112.
Conclusion:
With respect to a simple selection strategy of overlapping optimal radiomics and clinical features from different survival models may promote better progression-free survival(PFS) prediction than conventional survival analysis, which provides a potential method for guiding personalized pre-treatment options of NSCLC.
Insights
Predicting treatment success in ALK-positive non-small-cell lung cancer (NSCLC) is crucial. This study developed biomarkers from CT scans and patient data to forecast prognosis for ALK-inhibitor therapy, improving personalized treatment strategies.
Area of Science:
- Oncology
- Radiology
- Biomarker Discovery
Background:
- Molecularly targeted therapy, particularly ALK inhibitors, is a first-line treatment for ALK-rearranged non-small-cell lung cancer (NSCLC).
- Predicting patient response to ALK-inhibitor therapy is essential for optimizing treatment outcomes.
Purpose of the Study:
- To develop and validate robust biomarkers using pre-treatment CT images and clinicopathologic features.
- To predict the prognosis for ALK-inhibitor therapy in NSCLC patients.
Main Methods:
- Retrospective collection of 161 ALK-positive NSCLC patients treated with ALK inhibitors.
- Utilized Cox proportional hazard regression (CPH) with LASSO and random survival forest (RSF) with recursive feature elimination (RFE) for feature identification and model construction.
- An overlapping post-processing method was employed to identify stronger biomarkers.
Main Results:
- T and M staging, pericardial effusion, age, and alectinib were significant predictors.
- RSF models combining radiomics and clinical features achieved a C-index of 0.78, with validation and external test sets yielding C-indices of 0.73 and 0.79, respectively.
- The combined radioclinical signature demonstrated superior predictive performance.
Conclusions:
- A strategy using overlapping radiomics and clinical features can improve progression-free survival (PFS) prediction in NSCLC.
- This approach offers a potential method for guiding personalized pre-treatment decisions for NSCLC patients receiving ALK inhibitors.
