Multimodal prediction of response to neoadjuvant nivolumab and chemotherapy for surgically resectable stage IIIA non–small cell lung cancer.
Authors: Ferrer Loïc , Nadal Ernest , Guidel Floriane , Insa Amelia , Menu Philippe , Casal Joaquin , Domine Manuel , Massuti Bartomeu , Majem Margarita , Martinez-Marti Alex , Campelo Rosario Garcia , de Castro Carpeño Javier , Cobo Manuel , Lopez Vivanco Guillermo , del Barco Edel , Bernabé Reyes , Vinolas Nuria , Barneto Isidoro , Colin Thierry , Provencio-Pulla Mariano 
Published: June 2022

8542 Background: The NADIM trial (NCT03081689), led by the Spanish Lung Cancer Group, assessed the antitumor activity and safety of neoadjuvant chemoimmunotherapy for resectable stage IIIA NSCLC. Patients received neoadjuvant nivolumab and paclitaxel-carboplatin for three cycles before surgical resection, followed by one year of adjuvant nivolumab. At 24 months, progression-free survival (PFS) was 77%, suggesting that neoadjuvant chemoimmunotherapy represents a promising option in this setting. Pathological complete response (pCR) could potentially be used as an important surrogate endpoint for survival. We present here a re-analysis of the NADIM cohort aiming to develop a machine learning algorithm to predict the pCR status based on multimodal baseline data. Methods: We combined baseline clinical data (e.g., age, smoking status), biological data (e.g., tumor histology, mutations), radiology reports and radiomics analysis of the baseline CT scan in a multimodal analysis. While 46 patients were enrolled in the NADIM trial, only 28 had a complete set of data available for this retrospective study. For each patient, tumors were segmented on the baseline CT-scan in 3D by a Deep Learning algorithm. Radiomics features were extracted following the IBSI standards and combined with the other data modalities. A filter-based variable selection method was applied before training several machine learning algorithms. The optimization criterion was the Area Under the ROC Curve (AUC). Due to the small size of the cohort, a leave-one-out cross-validation approach was used to properly estimate the model performance. For a sub-cohort of 20 patients for which data have been collected longitudinally during the neoadjuvant treatment, an additional Delta-radiomics model was used to predict the pCR status. Results: An XGBoost algorithm with a linear base learner displayed an AUC of 0.69, a precision of 75%, a sensitivity of 83% and a specificity of 50%. Features with highest weight in the algorithm were a mix of radiological, radiomics, biological and clinical features (including the neutrophils to lymphocytes ratio, mutations and histology) highlighting the importance of a truly multimodal analysis. Indeed, withdrawing a specific data modality (e.g., radiomics or biological features), led to a decrease of ̃15% of the AUC. Inclusion of the Delta-radiomics analysis on the data collected longitudinally prior to surgery led to an improved AUC of 0.76 in that patient sub-cohort. Conclusions: This study is, to our knowledge, the first to offer a multimodal analysis of the response to neoadjuvant treatment for surgically resectable stage IIIA NSCLC and is a proof of concept that a machine learning algorithm can be used to predict the pCR in this context. These preliminary results are being confirmed in the ongoing NADIM II trial. Clinical trial information: NCT03838159.