Frontiers in oncology · 2025
Radiomic approach to support multidisciplinary tumor board decision-making in locally advanced non-small cell lung cancer.
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Pasello Giulia, Kotler Harel, Ferro Alessandra, Bergamin Luca, Scagliori Elena, Grassi Angela, Aiolli Fabio, De Nuzzo Mattia, Schiavon Marco, Sepulcri Matteo, Krengli Marco, Guarneri Valentina, Caumo Francesca, Gennaro Gisella
Аннотация
BACKGROUND AND OBJECTIVE: Selecting the optimal treatment for locally advanced non-small cell lung cancer (LA-NSCLC) is complex and typically requires multidisciplinary tumor board (MTB) evaluation. This study investigated whether machine learning (ML) models trained on MTB decisions could support treatment selection by integrating clinicopathological characteristics with radiomic features from both the primary tumor and mediastinal lymph nodes (LN).
MATERIALS AND METHODS: We retrospectively analyzed patients with LA-NSCLC whose treatments had been decided by an expert MTB. Patients were categorized into three pathways: (A) upfront surgery, (B) neoadjuvant systemic treatment followed by surgery, (C) concurrent chemoradiotherapy. Baseline CT scans were segmented to extract radiomic features from primary tumors and mediastinal LNs. Two ML models were developed based on clinicopathological and radiomic data, using MTB decisions as ground truth: (1) A vs. Rest and (2) B vs. C. Performance was assessed in independent training and test cohorts using the area under the receiver operating characteristic curve (AUC) and accuracy.
RESULTS: In the training cohort, the A vs. Rest achieved an AUC of 0.847 and accuracy of 0.795 with 13 features, while the B vs. C model reached an AUC of 0.740 and accuracy of 0.700 with 9 features. In the test cohort, results remained robust, with an AUC of 0.808 (accuracy 0.700) for A vs. Rest and an AUC of 0.754 (accuracy 0.740) for B vs. C.
CONCLUSIONS: ML models combining clinicopathological and radiomic features can reproduce MTB treatment recommendations for LA-NSCLC with good accuracy. This approach may provide decision in settings with limited MTB expertise and promote more consistent treatment allocation.
MATERIALS AND METHODS: We retrospectively analyzed patients with LA-NSCLC whose treatments had been decided by an expert MTB. Patients were categorized into three pathways: (A) upfront surgery, (B) neoadjuvant systemic treatment followed by surgery, (C) concurrent chemoradiotherapy. Baseline CT scans were segmented to extract radiomic features from primary tumors and mediastinal LNs. Two ML models were developed based on clinicopathological and radiomic data, using MTB decisions as ground truth: (1) A vs. Rest and (2) B vs. C. Performance was assessed in independent training and test cohorts using the area under the receiver operating characteristic curve (AUC) and accuracy.
RESULTS: In the training cohort, the A vs. Rest achieved an AUC of 0.847 and accuracy of 0.795 with 13 features, while the B vs. C model reached an AUC of 0.740 and accuracy of 0.700 with 9 features. In the test cohort, results remained robust, with an AUC of 0.808 (accuracy 0.700) for A vs. Rest and an AUC of 0.754 (accuracy 0.740) for B vs. C.
CONCLUSIONS: ML models combining clinicopathological and radiomic features can reproduce MTB treatment recommendations for LA-NSCLC with good accuracy. This approach may provide decision in settings with limited MTB expertise and promote more consistent treatment allocation.