Discrimination of macrotrabecular-massive hepatocellular carcinoma based on fusion of multi-phase contrast-enhanced computed tomography radiomics features_Journal of Biomedical Engineering

Authors：

ZHANG Zhenyang ^1,2 , XIE Jincheng ² , ZHONG Weixiong ² , LIANG Fangrong ³ , ZHANG Wanli ³ , SUN Yan ⁴ ,  ZHEN Xin ^2,5 ,  YANG Ruimeng ^1,3

1. Department of Radiology, the Second Affiliated Hospital, South China University of Technology, Guangzhou 510180, P. R. China;
2. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, P. R. China;
3. School of Medicine, South China University of Technology, Guangzhou 510006, P. R. China;
4. School of Life Sciences, Nankai University, Tianjin 300350, P. R. China;
5. Medical Big Data Center, Guangdong Provincial People’s Hospital, Southern Medical University, Guangzhou 510080, P. R. China;

Corresponding?author：

ZHEN Xin, Email: xinzhen@smu.edu.cn; YANG Ruimeng, Email: eyruimengyang@scut.edu.cn

Keywords：

Macrotrabecular-massive; Hepatocellular carcinoma; Multi-phase contrast-enhanced computed tomography; Radiomics; Feature fusion

DOI：

10.7507/1001-5515.202401031

Video：

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The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is a histological variant with higher malignant potential. Non-invasive preoperative identification of MTM-HCC is crucial for precise treatment. Current radiomics-based diagnostic models often integrate multi-phase features by simple feature concatenation, which may inadequately explore the latent complementary information between phases. This study proposes a feature fusion-based radiomics model using multi-phase contrast-enhanced computed tomography (mpCECT) images. Features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) CT images of 121 HCC patients. The fusion model was constructed and compared against the traditional concatenation model. Five-fold cross-validation demonstrated that the feature fusion model combining AP and PVP features achieved the best classification performance, with an area under the receiver operating characteristic curve (AUC) of 0.839. Furthermore, for any combination of two phases, the feature fusion model consistently outperformed the traditional feature concatenation approach. In conclusion, the proposed feature fusion model effectively enhances the discrimination capability compared to traditional models, providing a new tool for clinical practice.

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27.	Shinde P P, Shah S. A review of machine learning and deep learning applications// Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation. Pune: IEEE, 2018: 1-6.
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29.	Chen J, Xia C, Duan T, et al. Macrotrabecular-massive hepatocellular carcinoma: imaging identification and prediction based on gadoxetic acid–enhanced magnetic resonance imaging. Eur Radio, 2021, 31: 7696-7704.

1. Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca-Cancer J Clin, 2021, 71(3): 209-249.
2. Yuen M, Hou J, Chutaputti A. Asia Pacific working party on prevention of hepatocellular C. Hepatocellular carcinoma in the Asia pacific region. J Gastroenterol Hepatol, 2009, 24(3): 346-353.
3. Asia-Pacific Working Party on Prevention of Hepatocellular Carcinoma. Prevention of hepatocellular carcinoma in the Asia–Pacific region: Consensus statements. J Gastroen Hepatol, 2010, 25(4): 657-663.
4. Yang J D, Hainaut P, Gores G J, et al. A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol, 2019, 16(10): 589-604.
5. Ally A, Balasundaram M, Carlsen R, et al. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell, 2017, 169(7): 1327-1341.e23.
6. Nagtegaal I D, Odze R D, Klimstra D, et al. The 2019 WHO classification of tumours of the digestive system. Histopathology, 2020, 76(2): 182.
7. Ziol M, Poté N, Amaddeo G, et al. Macrotrabecular‐massive hepatocellular carcinoma: a distinctive histological subtype with clinical relevance. Hepatology, 2018, 68(1): 103-112.
8. Jeon Y, Benedict M, Taddei T, et al. Macrotrabecular hepatocellular carcinoma. Am J Surg Pathol, 2019, 43(7): 943-948.
9. Rhee H, Cho E-S, Nahm J H, et al. Gadoxetic acid-enhanced MRI of macrotrabecular-massive hepatocellular carcinoma and its prognostic implications. J Hepatol, 2021, 74(1): 109-121.
10. Galle P R, Forner A, Llovet J M, et al. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol, 2018, 69(1): 182-236.
11. Association K L C. 2022 KLCA-NCC Korea practice guidelines for the management of hepatocellular carcinoma. Korean J Radiol, 2022, 23(12): 1126.
12. Xie D-Y, Ren Z-G, Zhou J, et al. 2019 Chinese clinical guidelines for the management of hepatocellular carcinoma: updates and insights. Hepatobil Surg Nutr, 2020, 9(4): 452.
13. Zhu Y, Weng S, Li Y, et al. A radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma. Abdom Radiol, 2021, 46: 3139-3148.
14. Feng Z, Li H, Liu Q, et al. CT radiomics to predict macrotrabecular-massive subtype and immune status in hepatocellular carcinoma. Radiology, 2022, 307(1): e221291.
15. He X, Li K, Wei R, et al. A multitask deep learning radiomics model for predicting the macrotrabecular-massive subtype and prognosis of hepatocellular carcinoma after hepatic arterial infusion chemotherapy. Radiol Med, 2023, 128: 1508-1520.
16. Li M, Fan Y, You H, et al. Dual-energy CT deep learning radiomics to predict macrotrabecular-massive hepatocellular carcinoma. Radiology, 2023, 308(2): e230255.
17. Zhang Y, He D, Liu J, et al. Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma through dynamic contrast-enhanced magnetic resonance imaging-based radiomics. World J Gastroentero, 2023, 29(13): 2001.
18. Lambin P, Leijenaar R T, Deist T M, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
19. Gillies R J, Kinahan P E, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
20. Dercle L, Lu L, Schwartz L H, et al. Radiomics response signature for identification of metastatic colorectal cancer sensitive to therapies targeting EGFR pathway. Natl Cancer I, 112(9): 902-912.
21. Hu S, Kang Y, Xie Y, et al. ¹⁸F-FDG PET/CT-based radiomics nomogram for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma: a two-center study. Abdom Radiol, 2023, 48(2): 532-542.
22. Hu S, Xie Y, Yang T, et al. Tumor metabolism derived from ¹⁸F-FDG PET/CT in predicting the macrotrabecular-massive subtype of hepatocellular carcinoma. Quant Imaging Med Surg, 2023, 13(1): 309.
23. Feng Z, Li H, Zhao H, et al. Preoperative CT for characterization of aggressive macrotrabecular-massive subtype and vessels that encapsulate tumor clusters pattern in hepatocellular carcinoma. Radiology, 2021, 300(1): 219-229.
24. Mulé S, Galletto Pregliasco A, Tenenhaus A, et al. Multiphase liver MRI for identifying the macrotrabecular-massive subtype of hepatocellular carcinoma. Radiology, 2020, 295(3): 562-571.
25. Ramachandram D, Taylor G W. Deep multimodal learning: A survey on recent advances and trends. IEEE Signal Process Mag, 2017, 34(6): 96-108.
26. Stahlschmidt S R, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Briefings Bioinf, 2022, 23(2): bbab569.
27. Shinde P P, Shah S. A review of machine learning and deep learning applications// Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation. Pune: IEEE, 2018: 1-6.
28. Van Griethuysen J J, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res, 2017, 77(21): e104-e107.
29. Chen J, Xia C, Duan T, et al. Macrotrabecular-massive hepatocellular carcinoma: imaging identification and prediction based on gadoxetic acid–enhanced magnetic resonance imaging. Eur Radio, 2021, 31: 7696-7704.

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