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Wang L, Zhang Y, Li J, Guo S, Ren J, Li Z, Zhuang X, Xue J, Lei J. A Nomogram of Magnetic Resonance Imaging for Preoperative Assessment of Microvascular Invasion and Prognosis of Hepatocellular Carcinoma. Dig Dis Sci 2023; 68:4521-4535. [PMID: 37794295 DOI: 10.1007/s10620-023-08022-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 06/23/2023] [Indexed: 10/06/2023]
Abstract
BACKGROUND Microvascular invasion (MVI) is a predictor of recurrence and overall survival in hepatocellular carcinoma (HCC), the preoperative diagnosis of MVI through noninvasive methods play an important role in clinical treatment. AIMS To investigate the effectiveness of radiomics features in evaluating MVI in HCC before surgery. METHODS We included 190 patients who had undergone contrast-enhanced MRI and curative resection for HCC between September 2015 and November 2021 from two independent institutions. In the training cohort of 117 patients, MVI-related radiomics models based on multiple sequences and multiple regions from MRI were constructed. An independent cohort of 73 patients was used to validate the proposed models. A final Clinical-Imaging-Radiomics nomogram for preoperatively predicting MVI in HCC patients was generated. Recurrence-free survival was analyzed using the log-rank test. RESULTS For tumor-extracted features, the performance of signatures in fat-suppressed T1-weighted images and hepatobiliary phase was superior to that of other sequences in a single-sequence model. The radiomics signatures demonstrated better discriminatory ability than that of the Clinical-Imaging model for MVI. The nomogram incorporating clinical, imaging and radiomics signature showed excellent predictive ability and achieved well-fitted calibration curves, outperforming both the Radiomics and Clinical-Radiomics models in the training and validation cohorts. CONCLUSIONS The Clinical-Imaging-Radiomics nomogram model of multiple regions and multiple sequences based on serum alpha-fetoprotein, three MRI characteristics, and 12 radiomics signatures achieved good performance for predicting MVI in HCC patients, which may help clinicians select optimal treatment strategies to improve subsequent clinical outcomes.
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Affiliation(s)
- Lili Wang
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Yanyan Zhang
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, Beijing, 100069, China
| | - Junfeng Li
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Infectious Diseases, Institute of Infectious Diseases, First Hospital of Lanzhou University, Chengguan District, Donggang Road No. 1, Lanzhou, 730000, China
| | - Shunlin Guo
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Jialiang Ren
- GE Healthcare China, Daxing District, Tongji South Road No. 1, Beijing, 100176, China
| | - Zhihao Li
- GE Healthcare China, Yanta District, 12th Jinye Road, Xi'an, 710076, Shanxi, China
| | - Xin Zhuang
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Jingmei Xue
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Junqiang Lei
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.
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Zhang K, Zhang L, Li WC, Xie SS, Cui YZ, Lin LY, Shen ZW, Zhang HM, Xia S, Ye ZX, He K, Shen W. Radiomics nomogram for the prediction of microvascular invasion of HCC and patients' benefit from postoperative adjuvant TACE: a multi-center study. Eur Radiol 2023; 33:8936-8947. [PMID: 37368104 DOI: 10.1007/s00330-023-09824-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/15/2023] [Accepted: 03/26/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVES To evaluate the performance of a radiomics nomogram developed based on gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) MRI for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization (PA-TACE). METHODS A total of 260 eligible patients were retrospectively enrolled from three hospitals (140, 65, and 55 in training, standardized external, and non-standardized external validation cohort). Radiomics features and image characteristics were extracted from Gd-EOB-DTPA MRI image before hepatectomy for each lesion. In the training cohort, a radiomics nomogram which incorporated the radiomics signature and radiological predictors was developed. The performance of the radiomics nomogram was assessed with respect to discrimination calibration, and clinical usefulness with external validation. A score (m-score) was constructed to stratify the patients and explored whether it could accurately predict patient who benefit from PA-TACE. RESULTS A radiomics nomogram integrated with the radiomics signature, max-D(iameter) > 5.1 cm, peritumoral low intensity (PTLI), incomplete capsule, and irregular morphology had favorable discrimination in the training cohort (AUC = 0.982), the standardized external validation cohort (AUC = 0.969), and the non-standardized external validation cohort (AUC = 0.981). Decision curve analysis confirmed the clinical usefulness of the novel radiomics nomogram. The log-rank test revealed that PA-TACE significantly decreased the early recurrence in the high-risk group (p = 0.006) with no significant effect in the low-risk group (p = 0.270). CONCLUSIONS The novel radiomics nomogram combining the radiomics signature and clinical radiological features achieved preoperative non-invasive MVI risk prediction and patient benefit assessment after PA-TACE, which may help clinicians implement more appropriate interventions. CLINICAL RELEVANCE STATEMENT Our radiomics nomogram could represent a novel biomarker to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization, which may help clinicians to implement more appropriate interventions and perform individualized precision therapies. KEY POINTS • The novel radiomics nomogram developed based on Gd-EOB-DTPA MRI achieved preoperative non-invasive MVI risk prediction. • An m-score based on the radiomics nomogram could stratify HCC patients and further identify individuals who may benefit from the PA-TACE. • The radiomics nomogram could help clinicians to implement more appropriate interventions and perform individualized precision therapies.
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Affiliation(s)
- Kun Zhang
- Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, 300192, China
| | - Lei Zhang
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China
| | - Wen-Cui Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Shuang-Shuang Xie
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Ying-Zhu Cui
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China
| | - Li-Ying Lin
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Zhi-Wei Shen
- Philips Healthcare, Beijing, The World Profit Centre, No. 16 Tianze Road, Chaoyang District, Beijing, 100125, China
| | - Hui-Mao Zhang
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China
| | - Shuang Xia
- Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, 300192, China
| | - Zhao-Xiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
| | - Kan He
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China.
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China.
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Zheng R, Zhang X, Liu B, Zhang Y, Shen H, Xie X, Li S, Huang G. Comparison of non-radiomics imaging features and radiomics models based on contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma within 5 cm. Eur Radiol 2023; 33:6462-6472. [PMID: 37338553 DOI: 10.1007/s00330-023-09789-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/15/2023] [Accepted: 03/30/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVES The purpose of this study is to establish microvascular invasion (MVI) prediction models based on preoperative contrast-enhanced ultrasound (CEUS) and ethoxybenzyl-enhanced magnetic resonance imaging (EOB-MRI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm. METHODS Patients with a single HCC ≤ 5 cm and accepting CEUS and EOB-MRI before surgery were enrolled in this study. Totally, 85 patients were randomly divided into the training and validation cohorts in a ratio of 7:3. Non-radiomics imaging features, the CEUS and EOB-MRI radiomics scores were extracted from the arterial phase, portal phase and delayed phase images of CEUS and the hepatobiliary phase images of EOB-MRI. Different MVI predicting models based on CEUS and EOB-MRI were constructed and their predictive values were evaluated. RESULTS Since univariate analysis revealed that arterial peritumoral enhancement on the CEUS image, CEUS radiomics score, and EOB-MRI radiomics score were significantly associated with MVI, three prediction models, namely the CEUS model, the EOB-MRI model, and the CEUS-EOB model, were developed. In the validation cohort, the areas under the receiver operating characteristic curve of the CEUS model, the EOB-MRI model, and the CEUS-EOB model were 0.73, 0.79, and 0.86, respectively. CONCLUSIONS Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm. CLINICAL RELEVANCE STATEMENT Radiomics models based on CEUS and EOB-MRI are effective for MVI predicting and conducive to pretreatment decision-making in patients with a single HCC within 5 cm. KEY POINTS • Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. • There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm.
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Affiliation(s)
- Ruiying Zheng
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaoer Zhang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Baoxian Liu
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yi Zhang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hui Shen
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaoyan Xie
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Shurong Li
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
| | - Guangliang Huang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
- Department of Medical Ultrasonics, Guangxi Hospital Division of the First Affiliated Hospital, Sun Yat-Sen University, Guangxi, China.
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Zhang S, Duan C, Zhou X, Liu F, Wang X, Shao Q, Gao Y, Duan F, Zhao R, Wang G. Radiomics nomogram for prediction of microvascular invasion in hepatocellular carcinoma based on MR imaging with Gd-EOB-DTPA. Front Oncol 2022; 12:1034519. [PMID: 36387156 PMCID: PMC9663997 DOI: 10.3389/fonc.2022.1034519] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 10/17/2022] [Indexed: 01/25/2023] Open
Abstract
OBJECTIVE To develop a radiomics nomogram for predicting microvascular invasion (MVI) before surgery in hepatocellular carcinoma (HCC) patients. MATERIALS AND METHODS The data from a total of 189 HCC patients (training cohort: n = 141; validation cohort: n = 48) were collected, involving the clinical data and imaging characteristics. Radiomics features of all patients were extracted from hepatobiliary phase (HBP) in 15 min. Least absolute shrinkage selection operator (LASSO) regression and logistic regression were utilized to reduce data dimensions, feature selection, and to construct a radiomics signature. Clinicoradiological factors were identified according to the univariate and multivariate analyses, which were incorporated into the final predicted nomogram. A nomogram was developed to predict MVI of HCC by combining radiomics signatures and clinicoradiological factors. Radiomics nomograms were evaluated for their discrimination capability, calibration, and clinical usefulness. RESULTS In the clinicoradiological factors, gender, alpha-fetoprotein (AFP) level, tumor shape and halo sign served as the independent risk factors of MVI, with which the area under the curve (AUC) is 0.802. Radiomics signatures covering 14 features at HBP 15 min can effectively predict MVI in HCC, to construct radiomics signature model, with the AUC of 0.732. In the final nomogram model the clinicoradiological factors and radiomics signatures were integrated, outperforming the clinicoradiological model (AUC 0.884 vs. 0.802; p <0.001) and radiomics signatures model (AUC 0.884 vs. 0.732; p < 0.001) according to Delong test results. A robust calibration and discrimination were demonstrated in the nomogram model. The results of decision curve analysis (DCA) showed more significantly clinical efficiency of the nomogram model in comparison to the clinicoradiological model and the radiomic signature model. CONCLUSIONS Depending on the clinicoradiological factors and radiological features on HBP 15 min images, nomograms can effectively predict MVI status in HCC patients.
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Affiliation(s)
- Shuai Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chongfeng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaoming Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Fang Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xin Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qiulin Shao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yuanxiang Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Feng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ruirui Zhao
- Operating Room, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Gang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China,*Correspondence: Gang Wang,
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Lu XY, Zhang JY, Zhang T, Zhang XQ, Lu J, Miao XF, Chen WB, Jiang JF, Ding D, Du S. Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI. BMC Med Imaging 2022; 22:157. [PMID: 36057576 PMCID: PMC9440540 DOI: 10.1186/s12880-022-00855-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives We aimed to investigate the value of performing gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) radiomics for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on multiple sequences. Methods We randomly allocated 165 patients with HCC who underwent partial hepatectomy to training and validation sets. Stepwise regression and the least absolute shrinkage and selection operator algorithm were used to select significant variables. A clinicoradiological model, radiomics model, and combined model were constructed using multivariate logistic regression. The performance of the models was evaluated, and a nomogram risk-prediction model was built based on the combined model. A concordance index and calibration curve were used to evaluate the discrimination and calibration of the nomogram model. Results The tumour margin, peritumoural hypointensity, and seven radiomics features were selected to build the combined model. The combined model outperformed the radiomics model and the clinicoradiological model and had the highest sensitivity (90.89%) in the validation set. The areas under the receiver operating characteristic curve were 0.826, 0.755, and 0.708 for the combined, radiomics, and clinicoradiological models, respectively. The nomogram model based on the combined model exhibited good discrimination (concordance index = 0.79) and calibration. Conclusions The combined model based on radiomics features of Gd-EOB-DTPA enhanced MRI, tumour margin, and peritumoural hypointensity was valuable for predicting HCC microvascular invasion. The nomogram based on the combined model can intuitively show the probabilities of MVI. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00855-w.
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Affiliation(s)
- Xin-Yu Lu
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China.,The First People's Hospital of Taicang, Taicang, Suzhou, Jiangsu, China
| | - Ji-Yun Zhang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Tao Zhang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China.
| | - Xue-Qin Zhang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China.
| | - Jian Lu
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Xiao-Fen Miao
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | | | - Ji-Feng Jiang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Ding Ding
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Sheng Du
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
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Sun BY, Gu PY, Guan RY, Zhou C, Lu JW, Yang ZF, Pan C, Zhou PY, Zhu YP, Li JR, Wang ZT, Gao SS, Gan W, Yi Y, Luo Y, Qiu SJ. Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma. World J Surg Oncol 2022; 20:189. [PMID: 35676669 PMCID: PMC9178852 DOI: 10.1186/s12957-022-02645-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/17/2022] [Indexed: 12/24/2022] Open
Abstract
Background Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC. Methods We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated, and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression. Results Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and a-fetoprotein (AFP) were independently associated with MVI: DL-MVI (odds ratio [OR] = 35.738; 95% confidence interval [CI] 14.027–91.056; p < 0.001), AFP (OR = 4.634, 95% CI 2.576–8.336; p < 0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824. Conclusions Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC. Supplementary Information The online version contains supplementary material available at 10.1186/s12957-022-02645-8.
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Affiliation(s)
- Bao-Ye Sun
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Pei-Yi Gu
- School of Software Engineering, Tongji University, Shanghai, People's Republic of China
| | - Ruo-Yu Guan
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Cheng Zhou
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Jian-Wei Lu
- School of Software Engineering, Tongji University, Shanghai, People's Republic of China
| | - Zhang-Fu Yang
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Chao Pan
- School of Software Engineering, Tongji University, Shanghai, People's Republic of China
| | - Pei-Yun Zhou
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Ya-Ping Zhu
- School of Software Engineering, Tongji University, Shanghai, People's Republic of China
| | - Jia-Rui Li
- School of Software Engineering, Tongji University, Shanghai, People's Republic of China
| | - Zhu-Tao Wang
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Shan-Shan Gao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, 200032, People's Republic of China
| | - Wei Gan
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
| | - Yong Yi
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
| | - Ye Luo
- School of Software Engineering, Tongji University, Shanghai, People's Republic of China.
| | - Shuang-Jian Qiu
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
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Zhong X, Long H, Su L, Zheng R, Wang W, Duan Y, Hu H, Lin M, Xie X. Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY) 2022; 47:2071-2088. [PMID: 35364684 DOI: 10.1007/s00261-022-03496-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/09/2022] [Accepted: 03/14/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE To assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS Publications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models. RESULTS Twenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82-0.89), 0.87 (95% CI 0.83-0.92), and 0.74 (95% CI 0.67-0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region. CONCLUSION Radiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.
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Affiliation(s)
- Xian Zhong
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Haiyi Long
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Liya Su
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Ruiying Zheng
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Wei Wang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Yu Duan
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Hangtong Hu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Manxia Lin
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China.
| | - Xiaoyan Xie
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China.
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Qu C, Wang Q, Li C, Xie Q, Cai P, Yan X, Sparrelid E, Zhang L, Ma K, Brismar TB. A Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma ≤ 5 cm. Front Oncol 2022; 12:831795. [PMID: 35664790 PMCID: PMC9160991 DOI: 10.3389/fonc.2022.831795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Aim The aim of this study is to establish and validate a radiomics-based model using preoperative Gd-EOB-DTPA-enhanced MRI to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma ≤ 5 cm. Methods Clinicopathologic and MRI data of 178 patients with solitary hepatocellular carcinoma (HCC) (≤5 cm) were retrospectively collected from a single medical center between May 2017 and November 2020. Patients were randomly assigned into training and test subsets by a ratio of 7:3. Imaging features were extracted from the segmented tumor volume of interest with 1-cm expansion on arterial phase (AP) and hepatobiliary phase (HBP) images. Different models based on the significant clinical risk factors and/or selected imaging features were established and the predictive performance of the models was evaluated. Results Three radiomics models, the AP_model, the HBP_model, and the AP+HBP_model, were constructed for MVI prediction. Among them, the AP+HBP_model outperformed the other two. When it was combined with a clinical model, consisting of tumor size and alpha-fetoprotein (AFP), the combined model (AP+HBP+Clin_model) showed an area under the curve of 0.90 and 0.70 in the training and test subsets, respectively. Its sensitivity and specificity were 0.91 and 0.76 in the training subset and 0.60 and 0.79 in the test subset, respectively. The calibration curve illustrated that the combined model possessed a good agreement between the predicted and the actual probabilities. Conclusions The radiomics-based model combining imaging features from the arterial and hepatobiliary phases of Gd-EOB-DTPA-enhanced MRI and clinical risk factors provides an effective and reliable tool for the preoperative prediction of MVI in patients with HCC ≤ 5 cm.
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Affiliation(s)
- Chengming Qu
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China
| | - Qiang Wang
- Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Changfeng Li
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China
| | - Qiao Xie
- Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Ping Cai
- Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Xiaochu Yan
- Department of Pathology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Ernesto Sparrelid
- Division of Surgery, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Leida Zhang
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China
| | - Kuansheng Ma
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China
| | - Torkel B. Brismar
- Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
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Zhang H, Guo D, Liu H, He X, Qiao X, Liu X, Liu Y, Zhou J, Zhou Z, Liu X, Fang Z. MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018. Diagnostics (Basel) 2022; 12:diagnostics12051043. [PMID: 35626199 PMCID: PMC9139717 DOI: 10.3390/diagnostics12051043] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 02/04/2023] Open
Abstract
Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study aimed to evaluate whether magnetic resonance imaging (MRI) models based on radiomics features could further improve the ability to classify LR-M tumour subtypes. A total of 102 liver tumours were defined as LR-M by two radiologists based on LI-RADS and were confirmed to be HCC (n = 31) and non-HCC (n = 71) by surgery. A radiomics signature was constructed based on reproducible features using the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression algorithms with tenfold cross-validation. Logistic regression modelling was applied to establish different models based on T2-weighted imaging (T2WI), arterial phase (AP), portal vein phase (PVP), and combined models. These models were verified independently in the validation cohort. The area under the curve (AUC) of the models based on T2WI, AP, PVP, T2WI + AP, T2WI + PVP, AP + PVP, and T2WI + AP + PVP were 0.768, 0.838, 0.778, 0.880, 0.818, 0.832, and 0.884, respectively. The combined model based on T2WI + AP + PVP showed the best performance in the training cohort and validation cohort. The discrimination efficiency of each radiomics model was significantly better than that of junior radiologists’ visual assessment (p < 0.05; Delong). Therefore, the MRI-based radiomics models had a good ability to discriminate between HCC and non-HCC in LR-M tumours, providing more options to improve the accuracy of LI-RADS classification.
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Affiliation(s)
- Haiping Zhang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Dajing Guo
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Huan Liu
- GE Healthcare, Shanghai 201203, China;
| | - Xiaojing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Xiaofeng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Xinjie Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Yangyang Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Jun Zhou
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Zhiming Zhou
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Xi Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
| | - Zheng Fang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (H.Z.); (D.G.); (X.H.); (X.Q.); (X.L.); (Y.L.); (J.Z.); (Z.Z.); (X.L.)
- Correspondence: ; Tel.: +86-23-63693238
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Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment. Cancers (Basel) 2021. [DOI: 10.3390/cancers13225864
expr 925508420 + 988274397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Abstract
Preoperative prediction of microvascular invasion (MVI) is of importance in hepatocellular carcinoma (HCC) patient treatment management. Plenty of radiomics models for MVI prediction have been proposed. This study aimed to elucidate the role of radiomics models in the prediction of MVI and to evaluate their methodological quality. The methodological quality was assessed by the Radiomics Quality Score (RQS), and the risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Twenty-two studies using CT, MRI, or PET/CT for MVI prediction were included. All were retrospective studies, and only two had an external validation cohort. The AUC values of the prediction models ranged from 0.69 to 0.94 in the test cohort. Substantial methodological heterogeneity existed, and the methodological quality was low, with an average RQS score of 10 (28% of the total). Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. In conclusion, a radiomics model could be an accurate and effective tool for MVI prediction in HCC patients, although the methodological quality has so far been insufficient. Future prospective studies with an external validation cohort in accordance with a standardized radiomics workflow are expected to supply a reliable model that translates into clinical utilization.
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Wang Q, Li C, Zhang J, Hu X, Fan Y, Ma K, Sparrelid E, Brismar TB. Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment. Cancers (Basel) 2021; 13:5864. [PMID: 34831018 PMCID: PMC8616379 DOI: 10.3390/cancers13225864] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/13/2021] [Accepted: 11/17/2021] [Indexed: 12/12/2022] Open
Abstract
Preoperative prediction of microvascular invasion (MVI) is of importance in hepatocellular carcinoma (HCC) patient treatment management. Plenty of radiomics models for MVI prediction have been proposed. This study aimed to elucidate the role of radiomics models in the prediction of MVI and to evaluate their methodological quality. The methodological quality was assessed by the Radiomics Quality Score (RQS), and the risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Twenty-two studies using CT, MRI, or PET/CT for MVI prediction were included. All were retrospective studies, and only two had an external validation cohort. The AUC values of the prediction models ranged from 0.69 to 0.94 in the test cohort. Substantial methodological heterogeneity existed, and the methodological quality was low, with an average RQS score of 10 (28% of the total). Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. In conclusion, a radiomics model could be an accurate and effective tool for MVI prediction in HCC patients, although the methodological quality has so far been insufficient. Future prospective studies with an external validation cohort in accordance with a standardized radiomics workflow are expected to supply a reliable model that translates into clinical utilization.
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Affiliation(s)
- Qiang Wang
- Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 14186 Stockholm, Sweden;
- Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, 14186 Stockholm, Sweden
| | - Changfeng Li
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China; (C.L.); (K.M.)
| | - Jiaxing Zhang
- Department of Pharmacy, Guizhou Provincial People’s Hospital, Guiyang 550002, China;
| | - Xiaojun Hu
- Hepatobiliary Surgery, The Fifth Affiliated Hospital, Southern Medical University, Guangzhou 510999, China;
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China;
| | - Yingfang Fan
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China;
- Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
| | - Kuansheng Ma
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China; (C.L.); (K.M.)
| | - Ernesto Sparrelid
- Division of Surgery, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, 14186 Stockholm, Sweden;
| | - Torkel B. Brismar
- Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 14186 Stockholm, Sweden;
- Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, 14186 Stockholm, Sweden
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Gong XQ, Tao YY, Wu YK, Liu N, Yu X, Wang R, Zheng J, Liu N, Huang XH, Li JD, Yang G, Wei XQ, Yang L, Zhang XM. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol 2021; 11:698373. [PMID: 34616673 PMCID: PMC8488263 DOI: 10.3389/fonc.2021.698373] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/31/2021] [Indexed: 02/05/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively. Objective This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC. Methods A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis. Results Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients. Conclusion Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.
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Affiliation(s)
- Xue-Qin Gong
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yao-Kun Wu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xi Yu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ran Wang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Nian Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Hua Huang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jing-Dong Li
- Department of Hepatocellular Surgery, Institute of Hepato-Biliary-Intestinal Disease, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Gang Yang
- Department of Hepatocellular Surgery, Institute of Hepato-Biliary-Intestinal Disease, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Qin Wei
- School of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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Chong H, Gong Y, Pan X, Liu A, Chen L, Yang C, Zeng M. Peritumoral Dilation Radiomics of Gadoxetate Disodium-Enhanced MRI Excellently Predicts Early Recurrence of Hepatocellular Carcinoma without Macrovascular Invasion After Hepatectomy. J Hepatocell Carcinoma 2021; 8:545-563. [PMID: 34136422 PMCID: PMC8200148 DOI: 10.2147/jhc.s309570] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/06/2021] [Indexed: 01/27/2023] Open
Abstract
Background Whether peritumoral dilation radiomics can excellently predict early recrudescence (≤2 years) in hepatocellular carcinoma (HCC) remains unclear. Methods Between March 2012 and June 2018, 323 pathologically confirmed HCC patients without macrovascular invasion, who underwent liver resection and preoperative gadoxetate disodium (Gd-EOB-DTPA) MRI, were consecutively recruited into this study. Multivariate logistic regression identified independent clinicoradiologic predictors of 2-year recrudescence. Peritumoral dilation (tumor and peritumoral zones within 1cm) radiomics extracted features from 7-sequence images for modeling and achieved average but robust predictive performance through 5-fold cross validation. Independent clinicoradiologic predictors were then incorporated with the radiomics model for constructing a comprehensive nomogram. The predictive discrimination was quantified with the area under the receiver operating characteristic curve (AUC) and net reclassification improvement (NRI). Results With the median recurrence-free survival (RFS) reaching 60.43 months, 28.2% (91/323) and 16.4% (53/323) patients suffered from early and delay relapse, respectively. Microvascular invasion, tumor size >5 cm, alanine aminotransferase >50 U/L, γ-glutamyltransferase >60 U/L, prealbumin ≤250 mg/L, and peritumoral enhancement independently impaired 2-year RFS in the clinicoradiologic model with AUC of 0.694 (95% CI 0.628–0.760). Nevertheless, these indexes were paucity of robustness (P >0.05) when integrating with 38 most recurrence-related radiomics signatures for developing the comprehensive nomogram. The peritumoral dilation radiomics—the ultimate prediction model yielded satisfactory mean AUCs (training cohort: 0.939, 95% CI 0.908–0.973; validation cohort: 0.842, 95% CI 0.736–0.951) after 5-fold cross validation and fitted well with the actual relapse status in the calibration curve. Besides, our radiomics model obtained the best clinical net benefits, with significant improvements of NRI (35.9%-66.1%, P <0.001) versus five clinical algorithms: the clinicoradiologic model, the tumor-node-metastasis classification, the Barcelona Clinic Liver Cancer stage, the preoperative and postoperative risks of Early Recurrence After Surgery for Liver tumor. Conclusion Gd-EOB-DTPA MRI-based peritumoral dilation radiomics is a potential preoperative biomarker for early recurrence of HCC patients without macrovascular invasion.
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Affiliation(s)
- Huanhuan Chong
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China.,Shanghai Institute of Medical Imaging, Shanghai, 200032, People's Republic of China
| | - Yuda Gong
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, People's Republic of China
| | - Aie Liu
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, People's Republic of China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, People's Republic of China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China.,Shanghai Institute of Medical Imaging, Shanghai, 200032, People's Republic of China.,Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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