| Autor: |
Mayampurath, Anoop, Ramesh, Siddhi, Michael, Diana, Liu, Liu, Feinberg, Nicholas, Granger, Meaghan, Naranjo, Arlene, Cohn, Susan L., Volchenboum, Samuel L., Applebaum, Mark A. |
| Předmět: |
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| Zdroj: |
JCO Clinical Cancer Informatics; 12/9/2021, Vol. 5, p1181-1188, 8p |
| Abstrakt: |
PURPOSE: Metaiodobenzylguanidine (MIBG) scans are a radionucleotide imaging modality that undergo Curie scoring to semiquantitatively assess neuroblastoma burden, which can be used as a marker of therapy response. We hypothesized that a convolutional neural network (CNN) could be developed that uses diagnostic MIBG scans to predict response to induction chemotherapy. METHODS: We analyzed MIBG scans housed in the International Neuroblastoma Risk Group Data Commons from patients enrolled in the Children's Oncology Group high-risk neuroblastoma study ANBL12P1. The primary outcome was response to upfront chemotherapy, defined as a Curie score ≤ 2 after four cycles of induction chemotherapy. We derived and validated a CNN using two-dimensional whole-body MIBG scans from diagnosis and evaluated model performance using area under the receiver operating characteristic curve (AUC). We also developed a clinical classification model to predict response on the basis of age, stage, and MYCN amplification. RESULTS: Among 103 patients with high-risk neuroblastoma included in the final cohort, 67 (65%) were responders. Performance in predicting response to upfront chemotherapy was equivalent using the CNN and the clinical model. Class-activation heatmaps verified that the CNN used areas of disease within the MIBG scans to make predictions. Furthermore, integrating predictions using a geometric mean approach improved detection of responders to upfront chemotherapy (geometric mean AUC 0.73 v CNN AUC 0.63, P <.05; v clinical model AUC 0.65, P <.05). CONCLUSION: We demonstrate feasibility in using machine learning of diagnostic MIBG scans to predict response to induction chemotherapy for patients with high-risk neuroblastoma. We highlight improvements when clinical risk factors are also integrated, laying the foundation for using a multimodal approach to guiding treatment decisions for patients with high-risk neuroblastoma. In this study of patients with high-risk neuroblastoma, we demonstrate feasibility in using machine learning of diagnostic MIBG scans to predict response to induction chemotherapy. @ComerChildrens [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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