Yunpeng Liu1, Justin T Jordan1, Miriam A Bredella1, Serkan Erdin1, James A Walker1, Mark Vangel1, Gordon J Harris1, Scott R Plotkin1, Wenli Cai2. 1. From the Department of Radiology (Y.L., M.A.B., M.V., G.J.H., W.C.), Department of Neurology and Cancer Center (J.T.J., S.R.P.), and Center for Genomic Medicine (S.E., J.A.W.), Massachusetts General Hospital and Harvard Medical School, Boston. 2. From the Department of Radiology (Y.L., M.A.B., M.V., G.J.H., W.C.), Department of Neurology and Cancer Center (J.T.J., S.R.P.), and Center for Genomic Medicine (S.E., J.A.W.), Massachusetts General Hospital and Harvard Medical School, Boston. Cai.Wenli@mgh.harvard.edu.
Abstract
OBJECTIVE: To investigate the genotype-phenotype correlation between neurofibromatosis 1 (NF1) germline mutations and imaging features of neurofibromas on whole-body MRI (WBMRI) by using radiomics image analysis techniques. MATERIALS AND METHODS: Twenty-nine patients with NF1 who had known germline mutations determined by targeted next-generation sequencing were selected from a previous WBMRI study using coronal short tau inversion recovery sequence. Each tumor was segmented in WBMRI and a set of 59 imaging features was calculated using our in-house volumetric image analysis platform, 3DQI. A radiomics heatmap of 59 imaging features was analyzed to investigate the per-tumor and per-patient associations between the imaging features and mutation domains and mutation types. Linear mixed-effect models and one-way analysis of variance tests were performed to assess the similarity of tumor imaging features within mutation groups, between mutation groups, and between randomly selected groups. RESULTS: A total of 218 neurofibromas (97 discrete neurofibromas and 121 plexiform neurofibromas) were identified in 19 of the 29 patients. The unsupervised hierarchical clustering in heatmap analysis revealed 6 major image feature patterns that were significantly correlated with gene mutation domains and types with strong to very strong associations of genotype-phenotype correlations in both per-tumor and per-patient studies (p < 0.05, Cramer V > 0.5), whereas tumor size and locations showed no correlations with imaging features (p = 0.79 and p = 0.42, respectively). The statistical analyses revealed that the number of significantly different features (SDFs) within mutation groups were significantly lower than those between mutation groups (mutation domains: 10.9 ± 9.5% vs 31.9 ± 23.8% and mutation types: 31.8 ± 30.7% vs 52.6 ± 29.3%). The first and second quartile p values of within-patient groups were more than 2 times higher than those between-patient groups. However, the numbers of SDFs between randomly selected groups were much lower (approximately 5.2%). CONCLUSION: This preliminary study identified the NF1 radiogenomics linkage between NF1 causative mutations and MRI radiomic features, i.e., the correlation between NF1 genotype and imaging phenotype on WBMRI.
OBJECTIVE: To investigate the genotype-phenotype correlation between neurofibromatosis 1 (NF1) germline mutations and imaging features of neurofibromas on whole-body MRI (WBMRI) by using radiomics image analysis techniques. MATERIALS AND METHODS: Twenty-nine patients with NF1 who had known germline mutations determined by targeted next-generation sequencing were selected from a previous WBMRI study using coronal short tau inversion recovery sequence. Each tumor was segmented in WBMRI and a set of 59 imaging features was calculated using our in-house volumetric image analysis platform, 3DQI. A radiomics heatmap of 59 imaging features was analyzed to investigate the per-tumor and per-patient associations between the imaging features and mutation domains and mutation types. Linear mixed-effect models and one-way analysis of variance tests were performed to assess the similarity of tumor imaging features within mutation groups, between mutation groups, and between randomly selected groups. RESULTS: A total of 218 neurofibromas (97 discrete neurofibromas and 121 plexiform neurofibromas) were identified in 19 of the 29 patients. The unsupervised hierarchical clustering in heatmap analysis revealed 6 major image feature patterns that were significantly correlated with gene mutation domains and types with strong to very strong associations of genotype-phenotype correlations in both per-tumor and per-patient studies (p < 0.05, Cramer V > 0.5), whereas tumor size and locations showed no correlations with imaging features (p = 0.79 and p = 0.42, respectively). The statistical analyses revealed that the number of significantly different features (SDFs) within mutation groups were significantly lower than those between mutation groups (mutation domains: 10.9 ± 9.5% vs 31.9 ± 23.8% and mutation types: 31.8 ± 30.7% vs 52.6 ± 29.3%). The first and second quartile p values of within-patient groups were more than 2 times higher than those between-patient groups. However, the numbers of SDFs between randomly selected groups were much lower (approximately 5.2%). CONCLUSION: This preliminary study identified the NF1 radiogenomics linkage between NF1 causative mutations and MRI radiomic features, i.e., the correlation between NF1 genotype and imaging phenotype on WBMRI.
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