Integrating multimodality CT-MRI deep learning with clinical-radiological features for preoperative hepatocellular carcinoma grading: a multicenter study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen 529030, PR China.
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin 541000, PR China.
- Guangdong Medical University, 2 Wenming East Road, Zhanjiang 524000, PR China.
- Department of Radiology, Yue Bei People's Hosptial, 133 Huimin South Road, Shaoguan 512000, PR China.
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin 541000, PR China. Electronic address: [email protected].
- Department of Radiology, The Fifth Affiliated Hospital, Sun Yat-sen University, No. 52 Meihua Dong Road, Zhuhai 519000, Guangdong, PR China. Electronic address: [email protected].
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen 529030, PR China; Guangdong Medical University, 2 Wenming East Road, Zhanjiang 524000, PR China; Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application, 23 Beijie Haibang Street, Jiangmen 529030, PR China. Electronic address: [email protected].
Abstract
To develop and externally validate an integrated model that combines multimodality CT-MRI deep learning with clinical and radiological features for noninvasive preoperative hepatocellular carcinoma (HCC) histologic grading, and to evaluate whether this integration outperforms single-modality imaging, the multimodality imaging model alone, and a clinical-radiological model. In this multicentre retrospective study, 668 patients with pathologically confirmed HCC from three institutions (January 2010-December 2023) were included. Single-modality cohorts from Centre 1 (CT-only, n=283; MRI-only, n=135) were used for modality-specific pretraining via staged transfer learning. A total of 250 patients with paired preoperative CT and MRI were allocated to a training cohort (n=88), an internal validation cohort (n=57), and two independent external validation cohort (n=62, n=43). Single-modality multiphase CT (mpCT) and multisequence MRI (msMRI) models, a combined multimodality CT-MRI deep learning model (TL-CMDLM), a clinical-radiological signature model (CRSM), and an integrated hybrid fusion model (TL-HFM) combining the CT-MRI deep learning signature with clinical and radiological features were developed. Performance was assessed using the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement, and decision curve analysis. A total of 250 patients with paired CT and MRI (218 men; mean age, 57 ± 10 years) were evaluated. The integrated TL-HFM achieved the highest discrimination, with AUCs of 0.89, 0.88, and 0.90 in the internal validation cohort and two external validation cohort, respectively, and significantly outperformed both the multimodality CT-MRI deep learning model alone (TL-CMDLM: 0.82, 0.72, 0.81) and the clinical-radiological model (CRSM: 0.62, 0.73, 0.59) across all cohorts (all p < 0.05). The multimodality CT-MRI model in turn outperformed each best single-modality model (mpCT: 0.76, 0.58, 0.61; msMRI: 0.72, 0.62, 0.62). Decision curve analysis confirmed that the integrated TL-HFM provided the greatest net benefit across the internal validation and two external validation cohort. An integrated model combining multimodality CT-MRI deep learning with clinical and radiological features provided the most accurate noninvasive preoperative grading of HCC, outperforming single-modality imaging, multimodality imaging alone, and a clinical-radiological model, and delivered the greatest net clinical benefit across multicentre cohorts.