Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?

Cancers

Honghai Zhang¹  NamH. Le¹  Yanan Liu¹  Milan Sonka¹  Caitlin Ward²  Varun Monga³  Sarv Priya⁴  Ravishankar Pillenahalli Maheshwarappa⁴  Neetu Soni⁴  Girish Bathla⁴ 

[1] College of Engineering, University of Iowa, Iowa City, IA 52242, USA;Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA;Department of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;

关键词: radiomics;    MRI;    glioblastoma;    metastases;    CNS lymphoma;    machine learning;   

DOI  :  10.3390/cancers13112568

来源: DOAJ

【 摘 要 】

Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311–0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods.

【 授权许可】

Unknown