A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma

• Published in: Radiologia medica Vol. 127; no. 3; pp. 259 - 271
• Main Authors: Gao, Wenyu, Wang, Wentao, Song, Danjun, Yang, Chun, Zhu, Kai, Zeng, Mengsu, Rao, Sheng-xiang, Wang, Manning
• Format: Journal Article
• Language: English
• Published: Milan Springer Milan 01-03-2022; Springer Nature B.V
• Subjects: Abdominal Radiology; Algorithms; Carcinoma, Hepatocellular - diagnostic imaging; Carcinoma, Hepatocellular - pathology; Carcinoma, Hepatocellular - surgery; Diagnostic Radiology; Feature extraction; Humans; Imaging; Interventional Radiology; Liver cancer; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - pathology; Liver Neoplasms - surgery; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Meglumine - analogs & derivatives; Neuroradiology; Organometallic Compounds; Prediction models; Radiology; Radiomics; Retrospective Studies; Training; Ultrasound; Deep learning; Reoccurrence; Hepatocellular carcinoma; Gadoxetic acid-enhanced MRI; Radiomics
• DOI: 10.1007/s11547-021-01445-6
• EISSN: 1826-6983
• ISSN: 1826-6983; 0033-8362

Purpose
Hepatocellular carcinoma (HCC) is the most common liver cancer worldwide, and early recurrence of HCC after curative hepatic resection is indicative of poor prognoses. We aim to develop a predictive model for postoperative early recurrence of HCC based on deep and radiomics features from multi-phasic magnetic resonance imaging (MRI).
Materials and methods
A total of 472 HCC patients were included and divided into the training (
n
 = 378) and validation (
n
 = 94) cohorts in the retrospective study. We separately extracted radiomics features and deep features from eight phases of gadoxetic acid-enhanced MRI and utilized the least absolute shrinkage and selection operator logistic regression algorithm for feature selection and model construction. We integrated the selected two types of features into a combined model and established a radiomics model as well as a deep learning (DL) model for comparison.
Results
In the training and validation cohorts, the combined model demonstrated better performance for stratifying patients at high risk of early recurrence (AUC of 0.911 and 0.840, accuracy of 0.779 and 0.777, sensitivity of 0.927 and 0.769, specificity 0.720 and 0.779) than the radiomics model (AUC of 0.740 and 0.780) and the DL model (AUC of 0.887 and 0.813).
Conclusion
The combined model integrating deep and radiomics features from multi-phasic MRI is efficient for noninvasively stratifying patients at high risk of early HCC recurrence after resection.