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Development of neuroblastoma tissue diagnostic methods through deep learning-based image analytics

FDA Collaborators: Reena Phillip, PhD; Marc Theoret, MD; Diana Bradford, MD; Prakash Jha, MD; Fengmin Li, PhD; Arpita Roy, PhD, Martha Donoghue, MD

External Collaborators:

  • Stanford University (Awardee): Bill Chiu, MD; Hiroyuki Shimada, MD, PhD; Olivier Gevarert, PhD    

Project Start Date: January 2024

Regulatory Science Challenge

Neuroblastoma (NB), a common pediatric solid tumor, is associated with significant clinical variability based on age and biological factors. Current treatment decisions rely on clinical and molecular prognostic factors to categorize patients into different risk groups. While risk-stratified treatment leads to favorable outcomes for low- and intermediate-risk patients (over 90% 5-year survival), the survival of high-risk patients remains below 50% despite aggressive multimodal therapy.1,2

The International Neuroblastoma Pathology Classification System (based on the Shimada classification) utilizes microscopic examination of tumor specimens to identify specific histologic characteristics and provide the correct diagnosis that correlates with clinical outcome. However, the heterogeneity of neuroblastoma can lead to variations in pathologists’ interpretations, affecting the accuracy in classifying the tumor risk group. 

Project Description & Goals

The goal of this research is to improve neuroblastoma pathologic diagnosis and resulting risk classification by developing a novel diagnostic tool. To achieve this goal, a large collection of neuroblastoma tissue samples from across the US, Canada, Australia, and New Zealand will be analyzed using artificial intelligence (AI) to identify key histopathologic features in whole slide imaging (WSI). The AI-based approach will be used to develop a diagnostic algorithm for improved tumor grading and prognosis evaluation in neuroblastoma.

References:

  1. Whittle SB, Smith V, Doherty E, Zhao S, McCarty S, Zage PE (2017). Overview and recent advances in the treatment of neuroblastoma. Expert review of anticancer therapy, 17(4), 369-386.
  2. Irwin MS, Naranjo A, Zhang FF, Cohn SL, London WB, Gastier-Foster JM, et al. (2021). Revised neuroblastoma risk classification system: a report from the Children's Oncology Group. Journal of Clinical Oncology, 39(29), 3229-3241.

Further Information

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