Jordan S Taylor1, Lingdao Sha2, Naohiko Ikegaki3, Jasmine Zeki1, Ryan Deaton4, Jamie Harris5, Jeannine Coburn6, Burcin Yavuz7, Amit Sethi3, Hiroyuki Shimada8, David L Kaplan7, Peter Gann4, Bill Chiu9. 1. Department of Surgery, Stanford University, Stanford, CA. 2. Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL. 3. Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL. 4. Department of Pathology, University of Illinois at Chicago, Chicago, IL. 5. Department of Surgery, Rush University Medical Center, Chicago, IL. 6. Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA; Department of Biomedical Engineering, Tufts University, Medford, MA. 7. Department of Biomedical Engineering, Tufts University, Medford, MA. 8. Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA. 9. Department of Surgery, Stanford University, Stanford, CA. Electronic address: bhsc@stanford.edu.
Abstract
PURPOSE: Large cell neuroblastomas (LCN) are frequently seen in recurrent, high-risk neuroblastoma but are rare in primary tumors. LCN, characterized by large nuclei with prominent nucleoli, predict a poor prognosis. We hypothesize that LCN can be created with high-dose intra-tumoral chemotherapy and identified by a digital analysis system. METHODS: Orthotopic mouse xenografts were created using human neuroblastoma and treated with high-dose chemotherapy delivered locally via sustained-release silk platforms, inducing tumor remission. After recurrence, LCN populations were identified on H&E sections manually. Clusters of typical LCN and non-LCN cells were divided equally into training and test sets for digital analysis. Marker-controlled watershed segmentation was used to identify nuclei and characterize their features. Logistic regression was developed to distinguish LCN from non-LCN. RESULTS: Image analysis identified 15,000 nuclei and characterized 70 nuclear features. A 19-feature model provided AUC >0.90 and 100% accuracy when >30% nuclei/cluster were predicted as LCN. Overall accuracy was 87%. CONCLUSIONS: We recreated LCN using high-dose chemotherapy and developed an automated method for defining LCN histologically. Features in the model provide insight into LCN nuclear phenotypic changes that may be related to increased activity. This model could be adapted to identify LCN in human tumors and correlated with clinical outcomes.
PURPOSE: Large cell neuroblastomas (LCN) are frequently seen in recurrent, high-risk neuroblastoma but are rare in primary tumors. LCN, characterized by large nuclei with prominent nucleoli, predict a poor prognosis. We hypothesize that LCN can be created with high-dose intra-tumoral chemotherapy and identified by a digital analysis system. METHODS: Orthotopic mouse xenografts were created using humanneuroblastoma and treated with high-dose chemotherapy delivered locally via sustained-release silk platforms, inducing tumor remission. After recurrence, LCN populations were identified on H&E sections manually. Clusters of typical LCN and non-LCN cells were divided equally into training and test sets for digital analysis. Marker-controlled watershed segmentation was used to identify nuclei and characterize their features. Logistic regression was developed to distinguish LCN from non-LCN. RESULTS: Image analysis identified 15,000 nuclei and characterized 70 nuclear features. A 19-feature model provided AUC >0.90 and 100% accuracy when >30% nuclei/cluster were predicted as LCN. Overall accuracy was 87%. CONCLUSIONS: We recreated LCN using high-dose chemotherapy and developed an automated method for defining LCN histologically. Features in the model provide insight into LCN nuclear phenotypic changes that may be related to increased activity. This model could be adapted to identify LCN in humantumors and correlated with clinical outcomes.
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