Gd-EOB-DTPA-enhanced MRI radiological and deep learning radiomics models for predicting dual-phenotype hepatocellular carcinoma and patient prognosis

Abstract

Aim: To develop a deep learning radiomics (DLR) model based on Gd-EOB-DTPA- enhanced magnetic resonance imaging for preoperative prediction of dual-phenotype hepatocellular carcinoma (DPHCC) and patient prognosis.

Methods: This study included 331 patients from two centers: 228 in the training cohort (center I, 93 DPHCC, 135 non-DPHCC) and 103 in the external validation cohort (center II, 47 DPHCC, 56 non-DPHCC). Conventional radiological features were analyzed to build a radiological model. In total, 1,316 radiomics features and 768 deep learning (DL) features were extracted from the volumes of interest in arterial phase (AP), portal venous phase (PP), and hepatobiliary phase images, respectively. Three machine learning classifiers were applied to develop radiomics, DL, and DLR models using single-phase and combined-phase (CP) data for DPHCC prediction. Recurrence-free survival (RFS) was assessed by the Kaplan-Meier method.

Results: In the external validation cohort, rim AP hyperenhancement and non-smooth tumor margins were independent predictors of DPHCC (both P < 0.05), yielding a radiological model with an area under the curve (AUC) of 0.607 [95% confidence interval (CI), 0.511-0.704]. Among radiomics and DL models, the PP-DLR model achieved the highest discrimination (AUC: 0.805; 95%CI, 0.722-0.887), outperforming the radiological model, PP-Radiomics (AUC: 0.714; 95%CI, 0.615-0.813), CP-Radiomics (AUC: 0.639; 95%CI, 0.532-0.746), and CP-DLR models (AUC: 0.650; 95%CI, 0.543-0.758) (all P < 0.05). Patients classified as DPHCC by the PP-DLR model had significantly shorter median RFS than those classified as non-DPHCC (P < 0.05).

Conclusion: The PP-DLR model, which integrates radiomics and DL features, may aid in the preoperative prediction of DPHCC. DPHCC may be associated with poorer postoperative outcomes.

Keywords

Dual-phenotype hepatocellular carcinoma / deep learning radiomics / Gd-EOB-DTPA-enhanced MRI / diagnosis / prognosis

References

[1] Kim TH,Kim H,Joo I,Lee JM. Combined hepatocellular-cholangiocarcinoma: changes in the 2019 world health organization histological classification system and potential impact on imaging-based diagnosis Korean J Radiol. 2020 21 1115 PMC7458861

[2] Siegel RL,Miller KD,Wagle NS,Jemal A. Cancer statistics, 2023 CA Cancer J Clin. 2023 73 17 48

[3] Stroescu C,Herlea V,Dragnea A.et al. The diagnostic value of cytokeratins and carcinoembryonic antigen immunostaining in differentiating hepatocellular carcinomas from intrahepatic cholangiocarcinomas J Gastrointestin Liver Dis. 2006 15 9 14

[4] Lin DC,Mayakonda A,Dinh HQ.et al. Genomic and epigenomic heterogeneity of hepatocellular carcinoma Cancer Res. 2017 77 2255 65 PMC5413372

[5] Lu XY,Xi T,Lau WY.et al. Hepatocellular carcinoma expressing cholangiocyte phenotype is a novel subtype with highly aggressive behavior Ann Surg Oncol. 2011 18 2210 7

[6] Cong WM,Bu H,Chen J.et al. Practice guidelines for the pathological diagnosis of primary liver cancer: 2015 update World J Gastroenterol. 2016 22 9279 PMC5107692

[7] Wang Y,Wang X,Huang X.et al. Integrated Genomic and Transcriptomic Analysis reveals key genes for predicting dual-phenotype Hepatocellular Carcinoma Prognosis J. Cancer. 2021 12 2993 3010 PMC8040886

[8] Takano M,Shimada K,Fujii T.et al. Keratin 19 as a key molecule in progression of human hepatocellular carcinomas through invasion and angiogenesis BMC Cancer. 2016 16 903 PMC5116168

[9] Kawai T,Yasuchika K,Ishii T.et al. Keratin 19, a cancer stem cell marker in human hepatocellular carcinoma Clin Cancer Res. 2015 21 3081 91

[10] Lee JI. Prognosis of hepatocellular carcinoma expressing cytokeratin 19: comparison with other liver cancers World J Gastroenterol. 2012 18 4751 PMC3442214

[11] Govaere O,Komuta M,Berkers J.et al. Keratin 19: a key role player in the invasion of human hepatocellular carcinomas Gut. 2014 63 674 85 PMC3963546

[12] Liu MT,Zhang JY,Xu L.et al. A multivariate model based on gadoxetic acid-enhanced MRI using Li-RADS v2018 and other imaging features for preoperative prediction of dual‑phenotype hepatocellular carcinoma Radiol med. 2023 128 1333 46

[13] Chen S,Feng S,Wei J.et al. Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging Eur Radiol. 2019 29 4177 87

[14] Gu HX,Huang XS,Xu JX,Zhu P,Xu JF,Fan SF. Diagnostic value of MRI features in dual-phenotype hepatocellular carcinoma: a preliminary study J Digit Imaging. 2023 36 2554 66 PMC10584802

[15] Feng Z,Li H,Liu Q.et al. CT radiomics to predict macrotrabecular-massive subtype and immune status in hepatocellular carcinoma Radiology. 2023 307 e221291

[16] Ali MU,Kim KS,Khalid M,Farrash M,Zafar A,Lee SW. Enhancing Alzheimer’s disease diagnosis and staging: a multistage CNN framework using MRI Front. Psychiatry. 2024 15 1395563 PMC11228270

[17] Li M,Fan Y,You H.et al. Dual-energy CT deep learning radiomics to predict macrotrabecular-massive hepatocellular carcinoma Radiology. 2023 308 e230255

[18] Chernyak V,Fowler KJ,Kamaya A.et al. Liver Imaging Reporting and Data System (LI-RADS) version 2018: imaging of hepatocellular carcinoma in at-risk patients Radiology. 2018 289 816 30 PMC6677371

[19] Wu Q,Yu YX,Zhang T.et al. Preoperative diagnosis of dual-phenotype hepatocellular carcinoma using enhanced MRI radiomics models J Magn Reson Imaging. 2022 57 1185 96

[20] Rhee H,Kim H,Park YN. Clinico-radio-pathological and molecular features of hepatocellular carcinomas with keratin 19 expression Liver Cancer. 2020 9 663 81 PMC7768132

[21] Fatourou E,Koskinas J,Karandrea D.et al. Keratin 19 protein expression is an independent predictor of survival in human hepatocellular carcinoma Eur J Gastroenterol Hepatol. 2015 27 1094 102

[22] Huang X,Long L,Wei J.et al. Radiomics for diagnosis of dual-phenotype hepatocellular carcinoma using Gd-EOB-DTPA-enhanced MRI and patient prognosis J Cancer Res Clin Oncol. 2019 145 2995 3003 PMC6861194

[23] Cai Y,Long Y,Han Z.et al. Swin Unet3D: a three-dimensional medical image segmentation network combining vision transformer and convolution BMC Med Inform Decis Mak. 2023 23 33 PMC9926542

[24] Wu M,Qian Y,Liao X,Wang Q,Heng PA. Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention BMC Med Imaging. 2023 23 91 PMC10329304

[25] Shamshad F,Khan S,Zamir SW.et al. Transformers in medical imaging: a survey Med Image Anal. 2023 88 102802

[26] Liu Z,Lin Y,Cao Y.et al. Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV); 2021 Oct 10-17; Montreal, Canada. IEEE; 2021. pp. 9992-10002

[27] He X,Xu Y,Zhou C.et al. Prediction of microvascular invasion and pathological differentiation of hepatocellular carcinoma based on a deep learning model Eur J Radiol. 2024 172 111348

[28] Wang W,Wang Y,Song D.et al. A Transformer-Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation Liver Int. 2024 44 894 906

[29] Zhao L,Bao J,Wang X.et al. Detecting adverse pathology of prostate cancer with a deep learning approach based on a 3D swin-transformer Model and biparametric MRI: a multicenter retrospective study J Magn Reson Imaging. 2023 59 2101 12

[30] Ali MU,Zafar A,Kim S,Kim KS,Lee SW. From task-specific to foundation models: a paradigm shift in medical vision-language analysis Comput Sci Rev. 2026 59 100831

[31] Li L,Zhou X,Cui W.et al. Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT J Cancer Res Clin Oncol. 2023 149 15469 78 PMC11797261

[32] Yang L,Xu P,Zhang Y.et al. A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma Neuroradiology. 2022 64 1373 82

[33] Kim H,Choi GH,Na DC.et al. Human hepatocellular carcinomas with “Stemness”-related marker expression: keratin 19 expression and a poor prognosis Hepatology. 2011 54 1707 17

[34] Durnez A,Verslype C,Nevens F.et al. The clinicopathological and prognostic relevance of cytokeratin 7 and 19 expression in hepatocellular carcinoma. A possible progenitor cell origin. Histopathology. 2006;49:138-51