Integration of Deep Learning Radiomics and Counts of Circulating Tumor Cells Improves Prediction of Outcomes of Early Stage NSCLC Patients Treated With Stereotactic Body Radiation Therapy.

Purpose: We develop a deep learning (DL) radiomics model and integrate it with circulating tumor cell (CTC) counts as a clinically useful prognostic marker for predicting recurrence outcomes of early-stage (ES) non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).

Methods And Materials: A cohort of 421 NSCLC patients was used to train a DL model for gleaning informative imaging features from computed tomography (CT) data. The learned imaging features were optimized on a cohort of 98 ES-NSCLC patients treated with SBRT for predicting individual patient recurrence risks by building DL models on CT data and clinical measures. These DL models were validated on the third cohort of 60 ES-NSCLC patients treated with SBRT to predict recurrent risks and stratify patients into subgroups with distinct outcomes in conjunction with CTC counts.

Results: The DL model obtained a concordance-index of 0.880 (95% confidence interval, 0.879-0.881). Patient subgroups with low and high DL risk scores had significantly different recurrence outcomes (P = 3.5e-04). The integration of DL risk scores and CTC measures identified 4 subgroups of patients with significantly different risks of recurrence (χ = 20.11, P = 1.6e-04). Patients with positive CTC measures were associated with increased risks of recurrence that were significantly different from patients with negative CTC measures (P = 0.0447).

Conclusions: In this first-ever study integrating DL radiomics models and CTC counts, our results suggested that this integration improves patient stratification compared with either imagining data or CTC measures alone in predicting recurrence outcomes for patients treated with SBRT for ES-NSCLC.