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Gross M, Haider SP, Ze'evi T, Huber S, Arora S, Kucukkaya AS, Iseke S, Gebauer B, Fleckenstein F, Dewey M, Jaffe A, Strazzabosco M, Chapiro J, Onofrey JA. Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning. Eur Radiol 2024:10.1007/s00330-024-10624-8. [PMID: 38536464 DOI: 10.1007/s00330-024-10624-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/18/2023] [Accepted: 01/08/2024] [Indexed: 07/03/2024]
Abstract
BACKGROUND Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective. PURPOSE To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI). METHODS This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution. Patients were censored at their last date of follow-up, end-of-observation, or liver transplantation date. The data were randomly sampled into independent cohorts, with 85% for development and 15% for independent validation. An automated liver segmentation framework was adopted for radiomic feature extraction. A random survival forest combined clinical and radiomic variables to predict overall survival (OS), and performance was evaluated using Harrell's C-index. RESULTS A total of 555 treatment-naïve HCC patients (mean age, 63.8 years ± 8.9 [standard deviation]; 118 females) with MRI at the time of diagnosis were included, of which 287 (51.7%) died after a median time of 14.40 (interquartile range, 22.23) months, and had median followed up of 32.47 (interquartile range, 61.5) months. The developed risk prediction framework required 1.11 min on average and yielded C-indices of 0.8503 and 0.8234 in the development and independent validation cohorts, respectively, outperforming conventional clinical staging systems. Predicted risk scores were significantly associated with OS (p < .00001 in both cohorts). CONCLUSIONS Machine learning reliably, rapidly, and reproducibly predicts mortality risk in patients with hepatocellular carcinoma from data routinely acquired in clinical practice. CLINICAL RELEVANCE STATEMENT Precision mortality risk prediction using routinely available standard-of-care clinical data and automated MRI radiomic features could enable personalized follow-up strategies, guide management decisions, and improve clinical workflow efficiency in tumor boards. KEY POINTS • Machine learning enables hepatocellular carcinoma mortality risk prediction using standard-of-care clinical data and automated radiomic features from multiphasic contrast-enhanced MRI. • Automated mortality risk prediction achieved state-of-the-art performances for mortality risk quantification and outperformed conventional clinical staging systems. • Patients were stratified into low, intermediate, and high-risk groups with significantly different survival times, generalizable to an independent evaluation cohort.
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Affiliation(s)
- Moritz Gross
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | - Stefan P Haider
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany
| | - Tal Ze'evi
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Steffen Huber
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Sandeep Arora
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Ahmet S Kucukkaya
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simon Iseke
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Bernhard Gebauer
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Florian Fleckenstein
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Marc Dewey
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ariel Jaffe
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Mario Strazzabosco
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - John A Onofrey
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Urology, Yale University School of Medicine, New Haven, CT, USA
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Wang X, Zhao M, Zhang C, Chen H, Liu X, An Y, Zhang L, Guo X. Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review. J Hepatocell Carcinoma 2023; 10:1389-1398. [PMID: 37637500 PMCID: PMC10460189 DOI: 10.2147/jhc.s417123] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 08/17/2023] [Indexed: 08/29/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, accounting for approximately 90% of all primary liver cancers, with high mortality and a poor prognosis. A large number of predictive models have been applied that integrate multiple clinical factors and biomarkers to predict the prognosis of HCC. Nomograms, as easy-to-use prognostic predictive models, are widely used to predict the probability of clinical outcomes. We searched PubMed with the keywords "hepatocellular carcinoma" and "nomogram", and 974 relative literatures were retrieved. According to the construction methodology and the real validity of the nomograms, in this study, 97 nomograms for HCC were selected in 77 publications. These 97 nomograms were established based on more than 100,000 patients, covering seven main prognostic outcomes. The research data of 56 articles are from hospital-based HCC patients, and 13 articles provided external validation results of the nomogram. In addition to AFP, tumor size, tumor number, stage, vascular invasion, age, and other common prognostic risk factors are included in the HCC-related nomogram, more and more biomarkers, including gene mRNA expression, gene polymorphisms, and gene signature, etc. were also included in the nomograms. The establishment, assessment and validation of these nomograms are also discussed in depth. This study would help clinicians construct and select appropriate nomograms to guide precise judgment and appropriate treatments.
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Affiliation(s)
- Xiangze Wang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, People’s Republic of China
| | - Minghui Zhao
- Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, People’s Republic of China
| | - Chensheng Zhang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, People’s Republic of China
| | - Haobo Chen
- Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, People’s Republic of China
| | - Xingyu Liu
- School of Computer and Information Engineering, Henan University, Kaifeng, 475004, People’s Republic of China
| | - Yang An
- Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, People’s Republic of China
| | - Lu Zhang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, People’s Republic of China
| | - Xiangqian Guo
- Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, People’s Republic of China
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Ji Y, Yang S, Yan X, Zhu L, Yang W, Yang X, Yu F, Shi L, Zhu X, Lu Y, Zhang C, Lu H, Zhang F. CircCRIM1 Promotes Hepatocellular Carcinoma Proliferation and Angiogenesis by Sponging miR-378a-3p and Regulating SKP2 Expression. Front Cell Dev Biol 2021; 9:796686. [PMID: 34869393 PMCID: PMC8634842 DOI: 10.3389/fcell.2021.796686] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 10/28/2021] [Indexed: 12/17/2022] Open
Abstract
Mounting evidence has demonstrated that circular RNAs have an important function in tumorigenesis and cancer evolvement. CircCRIM1 has been shown to be a poor prognostic element in multiple human malignancies. However, the clinical significance and mechanism of circCRIM1 in hepatocellular carcinoma (HCC) is still unclear. The present study confirmed the expression level of circCRIM1 using quantitative real-time PCR. In addition, circCRIM1 siRNA and overexpression vectors were used for transfection into LM3 or Huh7 cells to down- or up-regulate the expression of circCRIM1. In vitro and in vivo experiments were performed to explore the function of circCRIM1 in HCC. RNA pull-down, RNA immunoprecipitation, fluorescent in situ hybridization, and luciferase reporter assays were conducted to confirm the relationship between miR-378a-3p and circCRIM1 or S-phase kinase-associated protein 2 (SKP2) in HCC. Then, circCRIM1 was up-regulated in HCC and its expression level was significantly associated with poor prognosis and clinicopathologic characteristics. CircCRIM1 enhanced the proliferation and angiogenesis of HCC cells in vitro and promoted xenograft growth in vivo. Moreover, circCRIM1 upregulated the expression of SKP2 by functioning as a sponge for miR-378a-3p. These findings suggest that circCRIM1 boosts the HCC progression via the miR-378-3p/SKP2 axis and may act as a crucial epigenetic therapeutic molecule target in HCC.
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Affiliation(s)
- Yang Ji
- Key Laboratory of Liver Transplantation, Hepatobiliary/Liver Transplantation Center, Chinese Academy of Medical Sciences, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shikun Yang
- Key Laboratory of Liver Transplantation, Hepatobiliary/Liver Transplantation Center, Chinese Academy of Medical Sciences, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xueqi Yan
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Li Zhu
- Department of Hepatobiliary Surgery, The Third Hospital Affiliated to Soochow University, Changzhou, China
| | - Wenjie Yang
- Key Laboratory of Liver Transplantation, Hepatobiliary/Liver Transplantation Center, Chinese Academy of Medical Sciences, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xinchen Yang
- Key Laboratory of Liver Transplantation, Hepatobiliary/Liver Transplantation Center, Chinese Academy of Medical Sciences, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei Yu
- Key Laboratory of Liver Transplantation, Hepatobiliary/Liver Transplantation Center, Chinese Academy of Medical Sciences, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Longqing Shi
- Department of Hepatobiliary Surgery, The Third Hospital Affiliated to Soochow University, Changzhou, China
| | - Xi Zhu
- Department of Infectious Disease, The First People's Hospital of Kunshan Affliated with Jiangsu University, Zhenjiang, China
| | - Yunjie Lu
- Department of Hepatobiliary Surgery, The Third Hospital Affiliated to Soochow University, Changzhou, China
| | - Chuanyong Zhang
- Key Laboratory of Liver Transplantation, Hepatobiliary/Liver Transplantation Center, Chinese Academy of Medical Sciences, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hao Lu
- Key Laboratory of Liver Transplantation, Hepatobiliary/Liver Transplantation Center, Chinese Academy of Medical Sciences, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Feng Zhang
- Key Laboratory of Liver Transplantation, Hepatobiliary/Liver Transplantation Center, Chinese Academy of Medical Sciences, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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