RISK STRATIFICATION IN NEUROBLASTOMA PATIENTS THROUGH MACHINE LEARNING IN THE MULTICENTER PRIMAGE COHORT

Jose Lozano-Montoya ^1* Ana Jimenez-Pastor ^1* Almudena Fuster-Matanzo ¹ Glen J Weiss ² Leonor Cerda-Alberich ³ Diana Veiga-Canuto ⁴ Blanca Martínez-de-las-Heras ⁴ Adela Cañete-Nieto ⁴ Sabine Taschner-Mandl ⁵ Barbara Hero ⁶ Thorsten Simon ⁶ Ruth Ladenstein ⁵ Luis Marti-Bonmati ³ Angel Alberich-Bayarri ¹

• ¹ Quibim, Valencia, Spain
• ² Quibim, New York, United States
• ³ La Fe Health Research Institute, Valencia, Valencia, Spain
• ⁴ Section of Pediatric Oncology and Hematology, La Fe University and Polytechnic Hosptial, Valencia, Spain
• ⁵ St. Anna Children’s Cancer Research Institute (CCRI), Vienna, Vienna, Austria
• ⁶ Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical Faculty, University of Cologne, Cologne, North Rhine-Westphalia, Germany

Introduction: Neuroblastoma, the most prevalent solid cancer in children, presents significant biological and clinical heterogeneity. This inherent heterogeneity underscores the need for more precise prognostic markers at the time of diagnosis to enhance patient stratification, allowing for more personalized treatment strategies. In response, this investigation developed a machine learning model using clinical, molecular, and magnetic resonance (MR) radiomics features at diagnosis to predict patient’s overall survival (OS) and improve their risk stratification. Methods: PRIMAGE database, including 513 patients (discovery cohort), was used for model training, validation, and testing. Additional 22 patients from different hospitals served as an external independent cohort. Primary tumor segmentation on T2-weighted MR images was semi-automatically edited by an experienced radiologist. From this area, 107 radiomics features were extracted. For the development of the prediction model, radiomics features were harmonized following the nested ComBat methodology and nested cross-validation approach was employed to determine the optimal preprocessing and model configuration. Results: The discovery cohort yielded a 78.8 ± 4.9 and 77.7 ± 6.1 of C index and time-dependent area under de curve (AUC), respectively, over the test set, with a random survival forest exhibiting the best performance. In the independent cohort, a C-index of 93.4 and a time-dependent AUC of 95.4 were achieved. Interpretability analysis identified lesion heterogeneity, size, and molecular variables as crucial factors in OS prediction. The model stratified neuroblastoma patients into low-, intermediate-, and high-risk categories, demonstrating a superior stratification compared to standard-of-care classification system in both cohorts. Discussion: Our results suggested that radiomics features improve current risk stratification systems in patients with neuroblastoma.