Clinical Research Directory

Risk Model for Metastasis Detection of Neuroblastoma

Neuroblastoma (NB) is the most common extracranial solid tumor in children, accounting for about 15% of tumor-related mortality. NB patients in high-risk group are prone to bone marrow and/or bone metastases with low five-year overall survival rate. The artificial intelligence (AI) and deep learning technologies have potential to identifying morphological characteristics of bone marrow cytology in clinical practice. In this study, the investigators construct and evaluate the bone marrow cytology-based AI model for detection and prognosis of NB. The main questions of the study as follows: The question 1: Dose bone marrow cytology-based AI model work for prediction of bone marrow metastasis in NB? The question 2: Dose bone marrow cytology-based AI model work for prediction of bone metastasis in NB? The question 3: Dose bone marrow cytology-based AI model have potential to assist doctors in making individualized predictions of survival outcome? The investigators will retrospectively obtain the participants with NB between January 2019 and June 2024. The follow-up date ended on June 30, 2024. The internal cohort including participants from Xinhua Hospital, Shanghai Jiao Tong University School of Medicine. The independent external cohorts including participants form Children's Hospital, Zhejiang University School of Medicine and Shenzhen Children's Hospital. The investigators collect the clinical data of enrolled participants at the time of the patients' initial admission to the hospital, prior to receiving treatment. The clinical information including age, gender, primary tumor location, tumor grade, bone marrow metastasis state, bone metastasis state, genetic aberrations (MYCN amplification, Chromosome 1p deletion, Chromosome 11q deletion) and lab variables (peripheral blood cell count, bone marrow cytology indicators, the serum concentration of lactate dehydrogenase, neuron specific enolase). This study is a non-interventional observational study, there is no risk to the participants and investigators. Participants get these benefits: 1. Early Detection: The model helps in early risk identification and personalize treatment. 2. Convenience: Because the model relies on general lab tests, it is easy to carry out can reduce invasive diagnostic procedures. 3. Cost-Effective: Using existing clinical data from routine tests can make the prediction process more cost-effective. 4. Data-Driven Decisions: The AI model improve diagnostic efficiency and support the medical decision.