Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions

• Published in: European journal of nuclear medicine and molecular imaging Vol. 49; no. 8; pp. 2917 - 2928
• Main Authors: Sun, Kui, Shi, Liting, Qiu, Jianfeng, Pan, Yuteng, Wang, Ximing, Wang, Haiyan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022; Springer Nature B.V
• Subjects: Advanced Image Analyses (Radiomics and Artificial Intelligence); Cardiology; Classification; Cysts; Feature extraction; Hemangioma; Hepatocellular carcinoma; Image contrast; Image enhancement; Image registration; Imaging; Kernel functions; Lesions; Liver; Liver cancer; Machine learning; Magnetic resonance imaging; Medical imaging; Medicine; Medicine & Public Health; Multiphase; Nuclear Medicine; Oncology; Original; Original Article; Orthopedics; Phase contrast; Radiology; Radiomics; Support vector machines; Hepatocellular carcinoma; Magnetic resonance imaging; Machine learning; Radiomics
• ISSN: 1619-7070; 1619-7089; 1619-7089
• DOI: 10.1007/s00259-022-05742-8

Purpose This study aimed to investigate whether models built from radiomics features based on multiphase contrast-enhanced MRI can identify microscopic pre-hepatocellular carcinoma lesions. Methods We retrospectively studied 54 small hepatocellular carcinoma (SHCC, diameter < 2 cm) patients and 70 patients with hepatocellular cysts or haemangiomas from September 2018 to June 2021. For the former, two MRI scans were collected within 12 months of each other; the 2 nd scan was used to confirm the diagnosis. The volumes of interest (VOIs), including SHCCs and normal liver tissues, were delineated on the 2 nd scans, mapped to the 1 st scans via image registration, and enrolled into the SHCC and internal-control cohorts, respectively, while those of normal liver tissues from patients with hepatocellular cysts or haemangioma were enrolled in the external-control cohort. We extracted 1132 radiomics features from each VOI and analysed their discriminability between the SHCC and internal-control cohorts for intra-group classification and the SHCC and external-control cohorts for inter-group classification. Five radial basis-function, kernel-based support vector machine (SVM) models (four corresponding single-phase models and one integrated from the four-phase MR images) were established. Results Among the 124 subjects, the multiphase models yielded better performance on the testing set for intra-group and inter-group classification, with areas under the receiver operating characteristic curves of 0.93 (95% CI, 0.85–1.00) and 0.97 (95% CI, 0.92–1.00), accuracies of 86.67% and 94.12%, sensitivities of 87.50% and 94.12%, and specificities of 85.71% and 94.12%, respectively. Conclusion The combined multiphase MRI-based radiomics feature model revealed microscopic pre-hepatocellular carcinoma lesions.