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Xi Z, Ye Y, Yang Y, He Y, Song Z, Ma Q, Zeng H, Shao G. Radiomics analysis based on contrast-enhanced MRI for predicting short-term efficacy of drug-eluting beads transarterial chemoembolization in hepatocellular carcinoma. Abdom Radiol (NY) 2024:10.1007/s00261-024-04319-3. [PMID: 39030402 DOI: 10.1007/s00261-024-04319-3] [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: 10/07/2023] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 07/21/2024]
Abstract
OBJECTIVE We developed and validated a clinical-radiomics model for preoperative prediction of the short-term efficacy of initial drug-eluting beads transarterial chemoembolization (D-TACE) treatment in patients with hepatocellular carcinoma (HCC). METHODS In this retrospective cohort study of 113 patients with intermediate and advanced HCC, 5343 features were extracted based on three sequences of the arterial phase (AP), diffusion-weighted imaging, and T2-weighted images based on contrast-enhanced magnetic resonance imaging, and minimum redundancy maximum correlation and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and model construction. Multifactor logistic regression was used to build a clinical-imaging model based on clinical factors and a clinical-radiomics model. The area under the curve (AUC) and calibration curves were used to assess model performance, and the clinical value of the model was analyzed using decision curve analysis. The relationship between the actual and predicted short-term efficacy of the combined model and progression-free survival (PFS) was evaluated using Kaplan-Meier survival curves and log-rank tests. RESULTS A total of 34 radiomics features were selected by LASSO, and the clinical-radiomics model had the best predictive performance (AUC = 0.902 and AUC = 0.845 for the training and testing sets, respectively), and the model based on AP had the best predictive performance among the four radiomics models (AUC = 0.89 for the training set and AUC = 0.85 for the testing set); the multifactorial logistic regression results showed that microsphere type (p = 0.042) and AP Rad-score (p = 0.01) were associated with short-term efficacy. In addition, a difference in PFS was observed in patients with HCC with different short-term efficacies predicted by the combined model. Moreover, prognosis was better in the objective versus non-objective response group. CONCLUSIONS The combined clinical-radiomics model is an effective predictor of the short-term efficacy of initial D-TACE in patients with HCC, contributing to clinical and economic benefits for patients.
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Affiliation(s)
- Zihan Xi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, 325035, China.
| | - Yuanxin Ye
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Yongbo Yang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Yiwei He
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Ziyang Song
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Qian Ma
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, 325035, China
| | - Hui Zeng
- Department of Intervention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Guoliang Shao
- Department of Intervention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
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Hu Y, Zhang L, Zhang H, Zhang B, Yang J, Li R. Prediction power of radiomics in early recurrence of hepatocellular carcinoma: A systematic review and meta-analysis. Medicine (Baltimore) 2024; 103:e38721. [PMID: 38968499 PMCID: PMC11224803 DOI: 10.1097/md.0000000000038721] [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: 06/28/2023] [Accepted: 06/06/2024] [Indexed: 07/07/2024] Open
Abstract
BACKGROUND Raiomics is an emerging auxiliary diagnostic tool, but there are still differences in whether it can be applied to predict early recurrence of hepatocellular carcinoma (HCC). The purpose of this meta-analysis was to systematically evaluate the predictive power of radiomics in the early recurrence (ER) of HCC. METHODS Comprehensive studies on the application of radiomics to predict ER in HCC patients after hepatectomy or curative ablation were systematically screened in Embase, PubMed, and Web of Science. RESULTS Ten studies which is involving a total of 1929 patients were reviewed. The overall estimates of radiomic models for sensitivity and specificity in predicting the ER of HCC were 0.79 (95% confidence interval [CI]: 0.68-0.87) and 0.83 (95% CI: 0.73-0.90), respectively. The area under the summary receiver operating characteristic curve (SROC) was 0.88 (95% CI: 0.85-0.91). CONCLUSIONS The imaging method is a reliable method for diagnosing HCC. Radiomics, which is based on medical imaging, has excellent power in predicting the ER of HCC. With the help of radiomics, we can predict the recurrence of HCC after surgery more effectively and provide a useful reference for clinical practice.
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Affiliation(s)
- Yanzi Hu
- Department of Radiology, Yuhuan Second People’s Hospital, Zhejiang, China
| | - Limin Zhang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huangqi Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Binhao Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Jiawen Yang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Renzhan Li
- Department of Radiology, Sanmen People’s Hospital, Zhejiang Province, China
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Wei H, Zheng T, Zhang X, Wu Y, Chen Y, Zheng C, Jiang D, Wu B, Guo H, Jiang H, Song B. MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma. Insights Imaging 2024; 15:120. [PMID: 38763975 PMCID: PMC11102894 DOI: 10.1186/s13244-024-01679-8] [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: 01/13/2024] [Accepted: 03/23/2024] [Indexed: 05/21/2024] Open
Abstract
OBJECTIVES To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC). METHODS This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems. RESULTS Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with 5 mm tumor border extension and liver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor "capsule," a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001). CONCLUSION A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor "capsule" accurately predicted early postsurgical recurrence of a single HCC. CRITICAL RELEVANCE STATEMENT The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor "capsule" hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC. KEY POINTS A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS.
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Affiliation(s)
- Hong Wei
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Tianying Zheng
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | | | - Yuanan Wu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610000, China
| | - Yidi Chen
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Chao Zheng
- Shukun Technology Co., Ltd, Beijing, 100102, China
| | - Difei Jiang
- Shukun Technology Co., Ltd, Beijing, 100102, China
| | - Botong Wu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100102, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100102, China
| | - Hanyu Jiang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
| | - Bin Song
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, 572000, China.
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Lu M, Wang C, Zhuo Y, Gou J, Li Y, Li J, Dong X. Preoperative prediction power of radiomics and non-radiomics methods based on MRI for early recurrence in hepatocellular carcinoma: a systemic review and meta-analysis. Abdom Radiol (NY) 2024:10.1007/s00261-024-04356-y. [PMID: 38704783 DOI: 10.1007/s00261-024-04356-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 05/07/2024]
Abstract
OBJECTIVE To compare radiomics and non-radiomics in predicting early recurrence (ER) in patients with hepatocellular carcinoma (HCC) after curative surgery. METHODS We systematically searched PubMed and Embase databases. Studies with clear reference criteria were selected. Data were extracted and assessed for quality using the quality in prognosis studies tool (QUIPS) by two independent authors. All included radiomics studies underwent radiomics quality score (RQS) assessment. We calculated sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) using random or fixed models with a 95%CI. Forest maps visualized the data, and summary receiver operating characteristic (sROC) curves with the area under the curve (AUC) were generated. Meta-regression and subgroup analyses explored sources of heterogeneity. We compared sensitivity, specificity, PLR, and NLR using the z-test and compared AUC values using the Delong test. RESULTS Our meta-analysis included 10 studies comprising 1857 patients. For radiomics, the pooled sensitivity, specificity, AUC of sROC, PLR and NLR were 0.84(95%CI: 0.78-0.89), 0.80(95%CI: 0.75-0.85), 0.89(95%CI: 0.86-0.91), 4.28(95%CI: 3.48-5.27) and 0.20(95%CI: 0.14-0.27), respectively, but with significant heterogeneity (I2 = 60.78% for sensitivity, I2 = 55.79% for specificity) and potential publication bias (P = 0.04). The pooled sensitivity, specificity, AUC of sROC, PLR, NLR for non-radiomics were 0.75(95%CI:0.68-0.81), 0.78(95%CI:0.72-0.83), 0.83(95%CI: 0.80-0.86), 3.45(95%CI: 2.68-4.44) and 0.32(95%CI: 0.24-0.41), respectively. There was no significant heterogeneity in this group (I2 = 0% for sensitivity, I2 = 17.27% for specificity). Radiomics showed higher diagnostic accuracy (AUC: 0.89 vs. 0.83, P = 0.0456), higher sensitivity (0.84 vs. 0.75, P = 0.0385) and lower NLR (0.20 vs. 0.32, P = 0.0287). CONCLUSION The radiomics from preoperative MRI effectively predicts ER of HCC and has higher diagnostic accuracy than non-radiomics. Due to potential publication bias and suboptimal RQS scores in radiomics, these results should be interpreted cautiously.
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Affiliation(s)
- Mingjie Lu
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Chen Wang
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Yi Zhuo
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Junjiu Gou
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Yingfeng Li
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Jingqi Li
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Xue Dong
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
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Liu L, Zhao L, Jing Y, Li D, Linghu H, Wang H, Zhou L, Fang Y, Li Y. Exploring a multiparameter MRI-based radiomics approach to predict tumor proliferation status of serous ovarian carcinoma. Insights Imaging 2024; 15:74. [PMID: 38499907 PMCID: PMC10948697 DOI: 10.1186/s13244-024-01634-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 01/27/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVES To develop a multiparameter magnetic resonance imaging (MRI)-based radiomics approach that can accurately predict the tumor cell proliferation status of serous ovarian carcinoma (SOC). MATERIALS AND METHODS A total of 134 patients with SOC who met the inclusion and exclusion criteria were retrospectively screened from institution A, spanning from January 2016 to March 2022. Additionally, an external validation set comprising 42 SOC patients from institution B was also included. The region of interest was determined by drawing each ovarian mass boundaries manually slice-by-slice on T2-weighted imaging fat-suppressed fast spin-echo (T2FSE) and T1 with contrast enhancement (T1CE) images using ITK-SNAP software. The handcrafted radiomic features were extracted, and then were selected using variance threshold algorithm, SelectKBest algorithm, and least absolute shrinkage and selection operator. The optimal radiomic scores and the clinical/radiological independent predictors were integrated as a combined model. RESULTS Compared with the area under the curve (AUC) values of each radiomic signature of T2FSE and T1CE, respectively, the AUC value of the radiomic signature (T1CE-T2FSE) was the highest in the training set (0.999 vs. 0.965 and 0.860). The homogeneous solid component of the ovarian mass was considered the only independent predictor of tumor cell proliferation status among the clinical/radiological variables. The AUC of the radiomic-radiological model was 0.999. CONCLUSIONS The radiomic-radiological model combining radiomic scores and the homogeneous solid component of the ovarian mass can accurately predict tumor cell proliferation status of SOC which has high repeatability and may enable more targeted and effective treatment strategies. CRITICAL RELEVANCE STATEMENT The proposed radiomic-radiological model combining radiomic scores and the homogeneous solid component of the ovarian mass can predict tumor cell proliferation status of SOC which has high repeatability and may guide individualized treatment programs. KEY POINTS • The radiomic-radiological nomogram may guide individualized treatment programs of SOC. • This radiomic-radiological nomogram showed a favorable prediction ability. • Homogeneous slightly higher signal intensity on T2FSE is vital for Ki-67.
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Affiliation(s)
- Li Liu
- Department of Radiology, The People's Hospital of Yubei District of Chongqing City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, Yuanjiagang, China
| | - Ling Zhao
- Department of Radiology, The People's Hospital of Yubei District of Chongqing City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China
| | - Yang Jing
- Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Room A206, B2Haidian District, Beijing, 100192, China
| | - Dan Li
- Department of Pathology, Chongqing Medical University, No.1 Medical College Road, Yuzhong District, Chongqing, 400016, China
| | - Hua Linghu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road Yuzhong District, Chongqing, 400016, Yuanjiagang, China
| | - Haiyan Wang
- Department of Radiology, The People's Hospital of Yubei District of Chongqing City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China
| | - Linyi Zhou
- Department of Radiology, Army Medical Center, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing, 40024, China
| | - Yuan Fang
- Department of Radiology, The People's Hospital of Yubei District of Chongqing City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China.
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, Yuanjiagang, China.
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Mao B, Ren Y, Yu X, Liang X, Ding X. Preoperative prediction for early recurrence of hepatocellular carcinoma using machine learning-based radiomics. Front Oncol 2024; 14:1346124. [PMID: 38559563 PMCID: PMC10978579 DOI: 10.3389/fonc.2024.1346124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Objective To develop a contrast-enhanced computed tomography (CECT) based radiomics model using machine learning method and assess its ability of preoperative prediction for the early recurrence of hepatocellular carcinoma (HCC). Methods A total of 297 patients confirmed with HCC were assigned to the training dataset and test dataset based on the 8:2 ratio, and the follow-up period of the patients was from May 2012 to July 2017. The lesion sites were manually segmented using ITK-SNAP, and the pyradiomics platform was applied to extract radiomic features. We established the machine learning model to predict the early recurrence of HCC. The accuracy, AUC, standard deviation, specificity, and sensitivity were applied to evaluate the model performance. Results 1,688 features were extracted from the arterial phase and venous phase images, respectively. When arterial phase and venous phase images were employed correlated with clinical factors to train a prediction model, it achieved the best performance (AUC with 95% CI 0.8300(0.7560-0.9040), sensitivity 89.45%, specificity 79.07%, accuracy 82.67%, p value 0.0064). Conclusion The CECT-based radiomics may be helpful to non-invasively reveal the potential connection between CECT images and early recurrence of HCC. The combination of radiomics and clinical factors could boost model performance.
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Affiliation(s)
- Bing Mao
- Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital; Henan University People’s Hospital, Zhengzhou, Henan, China
| | - Yajun Ren
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xuan Yu
- Department of Medical Imaging, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinliang Liang
- Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital; Henan University People’s Hospital, Zhengzhou, Henan, China
| | - Xiangming Ding
- Department of Gastroenterology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Li J, Ma Y, Yang C, Qiu G, Chen J, Tan X, Zhao Y. Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy. Front Oncol 2024; 14:1277698. [PMID: 38463221 PMCID: PMC10920317 DOI: 10.3389/fonc.2024.1277698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 02/09/2024] [Indexed: 03/12/2024] Open
Abstract
Objectives This study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy. Methods We conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram's discrimination, calibration, and clinical utility were thoroughly evaluated. Results Multivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808. Conclusion The nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.
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Affiliation(s)
- Jia Li
- Department of Oncology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
| | - Yunhui Ma
- Department of Oncology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
| | - Chunyu Yang
- Department of Radiology, The First School of Clinical Medicine, Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Ganbin Qiu
- Imaging Department of Zhaoqing Medical College, Zhaoqing, China
| | - Jingmu Chen
- Department of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
| | - Xiaoliang Tan
- Department of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
| | - Yue Zhao
- Department of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
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Maino C, Vernuccio F, Cannella R, Franco PN, Giannini V, Dezio M, Pisani AR, Blandino AA, Faletti R, De Bernardi E, Ippolito D, Gatti M, Inchingolo R. Radiomics and liver: Where we are and where we are headed? Eur J Radiol 2024; 171:111297. [PMID: 38237517 DOI: 10.1016/j.ejrad.2024.111297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/03/2024] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.
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Affiliation(s)
- Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Michele Dezio
- Department of Radiology, Miulli Hospital, Acquaviva delle Fonti 70021, Bari, Italy
| | - Antonio Rosario Pisani
- Nuclear Medicine Unit, Interdisciplinary Department of Medicine, University of Bari, Bari 70121, Italy
| | - Antonino Andrea Blandino
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Elisabetta De Bernardi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano Bicocca, Milano 20100, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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10
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Ebrahimi B, Gandhi D, Alsaeedi MH, Lerman LO. Patterns of cortical oxygenation may predict the response to stenting in subjects with renal artery stenosis: A radiomics-based model. J Cardiovasc Magn Reson 2024; 26:100993. [PMID: 38218433 PMCID: PMC11211233 DOI: 10.1016/j.jocmr.2024.100993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 01/03/2024] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Percutaneous-transluminal renal angioplasty (PTRA) and stenting aim to halt the progression of kidney disease in patients with renal artery stenosis (RAS), but its outcome is often suboptimal. We hypothesized that a model incorporating markers of renal function and oxygenation extracted using radiomics analysis of blood oxygenation-level dependent (BOLD)-MRI images may predict renal response to PTRA in swine RAS. MATERIALS AND METHODS Twenty domestic pigs with RAS were scanned with CT and BOLD MRI before and 4 weeks after PTRA. Stenotic (STK) and contralateral (CLK) kidney volume, blood flow (RBF), and glomerular filtration rate (GFR) were determined, and BOLD-MRI R2 * maps were generated before and after administration of furosemide, a tubular reabsorption inhibitor. Radiomics features were extracted from pre-PTRA BOLD maps and Robust features were determined by Intraclass correlation coefficients (ICC). Prognostic models were developed to predict post-PTRA renal function based on the baseline functional and BOLD-radiomics features, using Lasso-regression for training, and testing with resampling. RESULTS Twenty-six radiomics features passed the robustness test. STK oxygenation distribution pattern did not respond to furosemide, whereas in the CLK radiomics features sensitive to oxygenation heterogeneity declined. Radiomics-based model predictions of post-PTRA GFR (r = 0.58, p = 0.007) and RBF (r = 0.68; p = 0.001) correlated with actual measurements with sensitivity and specificity of 92% and 67%, respectively. Models were unsuccessful in predicting post-PTRA systemic measures of renal function. CONCLUSIONS Several radiomics features are sensitive to cortical oxygenation patterns and permit estimation of post-PTRA renal function, thereby distinguishing subjects likely to respond to PTRA and stenting.
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Affiliation(s)
- Behzad Ebrahimi
- Department of Radiation and Cellular Oncology, University of Chicago, IL, 60637, USA
| | - Deep Gandhi
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
| | - Mina H Alsaeedi
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
| | - Lilach O Lerman
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA.
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11
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Wu J, Liu W, Qiu X, Li J, Song K, Shen S, Huo L, Chen L, Xu M, Wang H, Jia N, Chen L. A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:549-564. [PMID: 38223688 PMCID: PMC10781918 DOI: 10.1007/s43657-023-00136-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/21/2023] [Accepted: 10/13/2023] [Indexed: 01/16/2024]
Abstract
It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-023-00136-8.
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Affiliation(s)
- Jianmin Wu
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438 China
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
| | - Wanmin Liu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200333 China
| | - Xinyao Qiu
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 China
| | - Jing Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Kairong Song
- Department of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438 China
| | - Siyun Shen
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
| | - Lei Huo
- Department of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438 China
| | - Lu Chen
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
| | - Mingshuang Xu
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
| | - Hongyang Wang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438 China
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 China
| | - Ningyang Jia
- Department of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438 China
| | - Lei Chen
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
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12
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Zhao J, Zhang Q, Liu M, Zhao X. MRI-based radiomics approach for the prediction of recurrence-free survival in triple-negative breast cancer after breast-conserving surgery or mastectomy. Medicine (Baltimore) 2023; 102:e35646. [PMID: 37861556 PMCID: PMC10589522 DOI: 10.1097/md.0000000000035646] [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: 06/23/2023] [Accepted: 09/22/2023] [Indexed: 10/21/2023] Open
Abstract
To explore the value of a radiomics signature and develop a nomogram combined with a radiomics signature and clinical factors for predicting recurrence-free survival in triple-negative breast cancer patients. We enrolled 151 patients from the cancer imaging archive who underwent preoperative contrast-enhanced magnetic resonance imaging. They were assigned to training, validation and external validation cohorts. Image features with coefficients not equal to zero in the 10-fold cross-validation were selected to generate a radiomics signature. Based on the optimal cutoff value of the radiomics signature determined by maximally selected log-rank statistics, patients were stratified into high- and low-risk groups in the training and validation cohorts. Kaplan-Meier survival analysis was performed for both groups. Kaplan-Meier survival distributions in these groups were compared using log-rank tests. Univariate and multivariate Cox regression analyses were used to construct clinical and combined models. Concordance index was used to assess the predictive performance of the 3 models. Calibration of the combined model was assessed using calibration curves. Four image features were selected to generate the radiomics signature. The Kaplan-Meier survival distributions of patients in the 2 groups were significantly different in the training (P < .001) and validation cohorts (P = .001). The C-indices of the radiomics model, clinical model, and combined model in the training and validation cohorts were 0.772, 0.700, 0.878, and 0.744, 0.574, 0.777, respectively. The C-indices of the radiomics model, clinical model, and combined model in the external validation cohort were 0.778, 0.733, 0.822, respectively. The calibration curves of the combined model showed good calibration. The radiomics signature can predict recurrence-free survival of patients with triple-negative breast cancer and improve the predictive performance of the clinical model.
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Affiliation(s)
- Jingwei Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qi Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Muqing Liu
- Department of Radiology, Chaoyang Central Hospital, Chaoyang, China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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13
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Li C, Wang Q, Zou M, Cai P, Li X, Feng K, Zhang L, Sparrelid E, Brismar TB, Ma K. A radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma. Front Oncol 2023; 13:1164739. [PMID: 37476376 PMCID: PMC10354521 DOI: 10.3389/fonc.2023.1164739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 06/19/2023] [Indexed: 07/22/2023] Open
Abstract
Background Post-hepatectomy liver failure (PHLF) is a fatal complication after liver resection in patients with hepatocellular carcinoma (HCC). It is of clinical importance to estimate the risk of PHLF preoperatively. Aims This study aimed to develop and validate a prediction model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging to estimate the risk of PHLF in patients with HCC. Methods A total of 276 patients were retrospectively included and randomly divided into training and test cohorts (194:82). Clinicopathological variables were assessed to identify significant indicators for PHLF prediction. Radiomics features were extracted from the normal liver parenchyma at the hepatobiliary phase and the reproducible, robust and non-redundant ones were filtered for modeling. Prediction models were developed using clinicopathological variables (Clin-model), radiomics features (Rad-model), and their combination. Results The PHLF incidence rate was 24% in the whole cohort. The combined model, consisting of albumin-bilirubin (ALBI) score, indocyanine green retention test at 15 min (ICG-R15), and Rad-score (derived from 16 radiomics features) outperformed the Clin-model and the Rad-model. It yielded an area under the receiver operating characteristic curve (AUC) of 0.84 (95% confidence interval (CI): 0.77-0.90) in the training cohort and 0.82 (95% CI: 0.72-0.91) in the test cohort. The model demonstrated a good consistency by the Hosmer-Lemeshow test and the calibration curve. The combined model was visualized as a nomogram for estimating individual risk of PHLF. Conclusion A model combining clinicopathological risk factors and radiomics signature can be applied to identify patients with high risk of PHLF and serve as a decision aid when planning surgery treatment in patients with HCC.
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Affiliation(s)
- Changfeng Li
- 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
| | - Mengda Zou
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China
| | - Ping Cai
- Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Xuesong Li
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China
| | - Kai Feng
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China
| | - Leida Zhang
- Institute of Hepatobiliary Surgery, 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
| | - 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
| | - Kuansheng Ma
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China
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14
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Yan M, Zhang X, Zhang B, Geng Z, Xie C, Yang W, Zhang S, Qi Z, Lin T, Ke Q, Li X, Wang S, Quan X. Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy. Eur Radiol 2023; 33:4949-4961. [PMID: 36786905 PMCID: PMC10289921 DOI: 10.1007/s00330-023-09419-0] [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: 05/25/2022] [Revised: 12/26/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence. METHODS In this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction. RESULTS MVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram. CONCLUSION The proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy. KEY POINTS • Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.
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Affiliation(s)
- Meng Yan
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
| | - Xiao Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Artificial Intelligence and Clinical Innovation Research, Guangzhou, 510000, Guangdong, People's Republic of China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
| | - Zhijun Geng
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, No. 651, Dongfeng East Road, Yuexiu District, Guangzhou, 510060, People's Republic of China
| | - Chuanmiao Xie
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, No. 651, Dongfeng East Road, Yuexiu District, Guangzhou, 510060, People's Republic of China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, No. 1023, Shatai Road, Baiyun District, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
| | - Zhendong Qi
- Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Haizhu District, Guangzhou, 510282, People's Republic of China
| | - Ting Lin
- Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Haizhu District, Guangzhou, 510282, People's Republic of China
| | - Qiying Ke
- Medical Imaging Center, the First Affiliated Hospital of Guangzhou University of Chinese Medicine, No. 16, Airport Road, Baiyun District, Guangzhou, 510405, Guangdong, People's Republic of China
| | - Xinming Li
- Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Haizhu District, Guangzhou, 510282, People's Republic of China.
| | - Shutong Wang
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-Sen University, No. 58, Zhong Shan Road 2, Yuexiu District, Guangzhou, 510080, Guangdong, People's Republic of China.
| | - Xianyue Quan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Haizhu District, Guangzhou, 510282, People's Republic of China.
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15
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Wang Q, Sheng Y, Jiang Z, Liu H, Lu H, Xing W. What Imaging Modality Is More Effective in Predicting Early Recurrence of Hepatocellular Carcinoma after Hepatectomy Using Radiomics Analysis: CT or MRI or Both? Diagnostics (Basel) 2023; 13:2012. [PMID: 37370907 DOI: 10.3390/diagnostics13122012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/22/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND It is of great importance to predict the early recurrence (ER) of hepatocellular carcinoma (HCC) after hepatectomy using preoperative imaging modalities. Nevertheless, no comparative studies have been conducted to determine which modality, CT or MRI with radiomics analysis, is more effective. METHODS We retrospectively enrolled 119 HCC patients who underwent preoperative CT and MRI. A total of 3776 CT features and 4720 MRI features were extracted from the whole tumor. The minimum redundancy and maximum relevance algorithm (MRMR) and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection, then support vector machines (SVMs) were applied for model construction. Multivariable logistic regression analysis was employed to construct combined models that integrate clinical-radiological-pathological (CRP) traits and radscore. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to compare the efficacy of CT, MRI, and CT and MRI models in the test cohort. RESULTS The CT model and MRI model showed no significant difference in the prediction of ER in HCC patients (p = 0.911). RadiomicsCT&MRI demonstrated a superior predictive performance than either RadiomicsCT or RadiomicsMRI alone (p = 0.032, 0.039). The combined CT and MRI model can significantly stratify patients at high risk of ER (area under the curve (AUC) of 0.951 in the training set and 0.955 in the test set) than the CT model (AUC of 0.894 and 0.784) and the MRI model (AUC of 0.856 and 0.787). DCA demonstrated that the CT and MRI model provided a greater net benefit than the models without radiomics analysis. CONCLUSIONS No significant difference was found in predicting the ER of HCC between CT models and MRI models. However, the multimodal radiomics model derived from CT and MRI can significantly improve the prediction of ER in HCC patients after resection.
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Affiliation(s)
- Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou 213200, China
| | - Ye Sheng
- Department of Interventional Radiology, Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou 213200, China
| | - Zhenxing Jiang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou 213200, China
| | - Haifeng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou 213200, China
| | - Haitao Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou 213200, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou 213200, China
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16
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Tian H, Xie Y, Wang Z. Radiomics for preoperative prediction of early recurrence in hepatocellular carcinoma: a meta-analysis. Front Oncol 2023; 13:1114983. [PMID: 37350952 PMCID: PMC10282764 DOI: 10.3389/fonc.2023.1114983] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 05/18/2023] [Indexed: 06/24/2023] Open
Abstract
Background/Objective Early recurrence (ER) affects the long-term survival prognosis of patients with hepatocellular carcinoma (HCC). Many previous studies have utilized CT/MRI-based radiomics to predict ER after radical treatment, achieving high predictive value. However, the diagnostic performance of radiomics for the preoperative identification of ER remains uncertain. Therefore, we aimed to perform a meta-analysis to investigate the predictive performance of radiomics for ER in HCC. Methods A systematic literature search was conducted in PubMed, Web of Science (including MEDLINE), EMBASE and the Cochrane Central Register of Controlled Trials to identify studies that utilized radiomics methods to assess ER in HCC. Data were extracted and quality assessed for retrieved studies. Statistical analyses included pooled data, tests for heterogeneity, and publication bias. Meta-regression and subgroup analyses were performed to investigate potential sources of heterogeneity. Results The analysis included fifteen studies involving 3,281 patients focusing on preoperative CT/MRI-based radiomics for the prediction of ER in HCC. The combined sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic were 75% (95% CI: 65-82), 78% (95% CI: 68-85), and 83% (95% CI: 79-86), respectively. The combined positive likelihood ratio, negative likelihood ratio, diagnostic score, and diagnostic odds ratio were 3.35 (95% CI: 2.41-4.65), 0.33 (95% CI: 0.25-0.43), 2.33 (95% CI: 1.91-2.75), and 10.29 (95% CI: 6.79-15.61), respectively. Substantial heterogeneity was observed among the studies (I²=99%; 95% CI: 99-100). Meta-regression showed imaging equipment contributed to the heterogeneity of specificity in subgroup analysis (P= 0.03). Conclusion Preoperative CT/MRI-based radiomics appears to be a promising and non-invasive predictive approach with moderate ER recognition performance.
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Affiliation(s)
- Huan Tian
- Department of Radiology, Aerospace Center Hospital, Beijing, China
| | - Yong Xie
- Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing, China
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17
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Jiang C, Cai YQ, Yang JJ, Ma CY, Chen JX, Huang L, Xiang Z, Wu J. Radiomics in the diagnosis and treatment of hepatocellular carcinoma. Hepatobiliary Pancreat Dis Int 2023:S1499-3872(23)00044-9. [PMID: 37019775 DOI: 10.1016/j.hbpd.2023.03.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/20/2023] [Indexed: 04/07/2023]
Abstract
Hepatocellular carcinoma (HCC) is a common malignant tumor. At present, early diagnosis of HCC is difficult and therapeutic methods are limited. Radiomics can achieve accurate quantitative evaluation of the lesions without invasion, and has important value in the diagnosis and treatment of HCC. Radiomics features can predict the development of cancer in patients, serve as the basis for risk stratification of HCC patients, and help clinicians distinguish similar diseases, thus improving the diagnostic accuracy. Furthermore, the prediction of the treatment outcomes helps determine the treatment plan. Radiomics is also helpful in predicting the HCC recurrence, disease-free survival and overall survival. This review summarized the role of radiomics in the diagnosis, treatment and prognosis of HCC.
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Affiliation(s)
- Chun Jiang
- Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China
| | - Yi-Qi Cai
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Jia-Jia Yang
- Department of Infection Management, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China
| | - Can-Yu Ma
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Jia-Xi Chen
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Lan Huang
- Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China
| | - Ze Xiang
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Jian Wu
- Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China.
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18
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Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy. Cancers (Basel) 2023; 15:cancers15041105. [PMID: 36831445 PMCID: PMC9954441 DOI: 10.3390/cancers15041105] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/27/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.
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Wei J, Jiang H, Zhou Y, Tian J, Furtado FS, Catalano OA. Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma. Dig Liver Dis 2023:S1590-8658(22)00863-5. [PMID: 36641292 DOI: 10.1016/j.dld.2022.12.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 01/16/2023]
Abstract
The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China.
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, PR. China
| | - Yu Zhou
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; School of Life Science and Technology, Xidian University, Xi'an, PR. China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, PR. China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR. China.
| | - Felipe S Furtado
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States
| | - Onofrio A Catalano
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States.
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20
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Miranda J, Horvat N, Fonseca GM, Araujo-Filho JDAB, Fernandes MC, Charbel C, Chakraborty J, Coelho FF, Nomura CH, Herman P. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023; 29:43-60. [PMID: 36683711 PMCID: PMC9850949 DOI: 10.3748/wjg.v29.i1.43] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/27/2022] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-010, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | | | - Maria Clara Fernandes
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
| | - Paulo Herman
- Department of Gastroenterology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
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21
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Tao YY, Shi Y, Gong XQ, Li L, Li ZM, Yang L, Zhang XM. Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:cancers15020365. [PMID: 36672315 PMCID: PMC9856314 DOI: 10.3390/cancers15020365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/01/2023] [Accepted: 01/03/2023] [Indexed: 01/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the sixth most common malignant tumour and the third leading cause of cancer death in the world. The emerging field of radiomics involves extracting many clinical image features that cannot be recognized by the human eye to provide information for precise treatment decision making. Radiomics has shown its importance in HCC identification, histological grading, microvascular invasion (MVI) status, treatment response, and prognosis, but there is no report on the preoperative prediction of programmed death ligand-2 (PD-L2) expression in HCC. The purpose of this study was to investigate the value of MRI radiomic features for the non-invasive prediction of immunotherapy target PD-L2 expression in hepatocellular carcinoma (HCC). A total of 108 patients with HCC confirmed by pathology were retrospectively analysed. Immunohistochemical analysis was used to evaluate the expression level of PD-L2. 3D-Slicer software was used to manually delineate volumes of interest (VOIs) and extract radiomic features on preoperative T2-weighted, arterial-phase, and portal venous-phase MR images. Least absolute shrinkage and selection operator (LASSO) was performed to find the best radiomic features. Multivariable logistic regression models were constructed and validated using fivefold cross-validation. The area under the receiver characteristic curve (AUC) was used to evaluate the predictive performance of each model. The results show that among the 108 cases of HCC, 50 cases had high PD-L2 expression, and 58 cases had low PD-L2 expression. Radiomic features correlated with PD-L2 expression. The T2-weighted, arterial-phase, and portal venous-phase and combined MRI radiomics models showed AUCs of 0.789 (95% CI: 0.702-0.875), 0.727 (95% CI: 0.632-0.823), 0.770 (95% CI: 0.682-0.875), and 0.871 (95% CI: 0.803-0.939), respectively. The combined model showed the best performance. The results of this study suggest that prediction based on the radiomic characteristics of MRI could noninvasively predict the expression of PD-L2 in HCC before surgery and provide a reference for the selection of immune checkpoint blockade therapy.
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Affiliation(s)
- Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Yue Shi
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Xue-Qin Gong
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Li Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Zu-Mao Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
- Correspondence:
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
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22
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Yang X, Yuan C, Zhang Y, Li K, Wang Z. Predicting hepatocellular carcinoma early recurrence after ablation based on magnetic resonance imaging radiomics nomogram. Medicine (Baltimore) 2022; 101:e32584. [PMID: 36596081 PMCID: PMC9803514 DOI: 10.1097/md.0000000000032584] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The aim of this study is to investigate a model for predicting the early recurrence of hepatocellular carcinoma (HCC) after ablation. METHODS A total of 181 patients with HCC after ablation (train group was 119 cases; validation group was 62 cases) were enrolled. The cases of early recurrence in the set of train and validation were 63 and 31, respectively. Radiomics features were extracted from the enhanced magnetic resonance imaging scanning, including pre-contrast injection, arterial phase, late arterial phase, portal venous phase, and delayed phase. The least absolute shrinkage and selection operator cox proportional hazards regression after univariate and multivariate analysis was used to screen radiomics features and build integrated models. The nomograms predicting recurrence and survival of patients of HCC after ablation were established based on the clinical, imaging, and radiomics features. The area under the curve (AUC) of the receiver operating characteristic curve and C-index for the train and validation group was used to evaluate model efficacy. RESULTS Four radiomics features were selected out of 34 texture features to formulate the rad-score. Multivariate analyses suggested that the rad-score, number of lesions, integrity of the capsule, pathological type, and alpha-fetoprotein were independent influencing factors. The AUC of predicting early recurrence at 1, 2, and 3 years in the train group was 0.79 (95% CI: 0.72-0.88), 0.72 (95% CI: 0.63-0.82), and 0.71 (95% CI: 0.61-0.83), respectively. The AUC of predicting early recurrence at 1, 2, and 3 years in the validation group was 0.72 (95% CI: 0.58-0.84), 0.61 (95% CI: 0.45-0.78) and 0.64 (95% CI: 0.40-0.87). CONCLUSION The model for early recurrence of HCC after ablation based on the clinical, imaging, and radiomics features presented good predictive performance. This may facilitate the early treatment of patients.
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Affiliation(s)
- Xiaozhen Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Department of Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Chunwang Yuan
- Department of Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Yinghua Zhang
- Department of Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Kang Li
- Biomedical Information Center, Beijing You’An Hospital, Capital Medical University, Beijing, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- * Correspondence: Zhenchang Wang, Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95, Yong An Road, Xicheng District, Beijing 100050, China (e-mail: )
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23
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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24
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Liu Y, Wei X, Zhang X, Pang C, Xia M, Du Y. CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy. Transl Oncol 2022; 26:101536. [PMID: 36115077 PMCID: PMC9483805 DOI: 10.1016/j.tranon.2022.101536] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/29/2022] [Accepted: 09/04/2022] [Indexed: 11/18/2022] Open
Abstract
PURPOSE To establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features. METHODS This study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models. RESULTS A total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1-3 years AUC:0.774-0.837; validation cohort 1-3 years AUC:0.754-0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups. CONCLUSIONS The CR model could predict the OS of the HCC patients after hepatectomy.
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Affiliation(s)
- Ying Liu
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Xiaoqin Wei
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Xinrui Zhang
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Caifeng Pang
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Mingkai Xia
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Yong Du
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China.
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25
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Wang S, Zheng W, Zhang Z, Zhang GH, Huang DJ. Microvascular invasion risk scores affect the estimation of early recurrence after resection in patients with hepatocellular carcinoma: a retrospective study. BMC Med Imaging 2022; 22:204. [PMID: 36419016 PMCID: PMC9682687 DOI: 10.1186/s12880-022-00940-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 11/15/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Microvascular invasion (MVI) is a histological factor that is closely related to the early recurrence of hepatocellular carcinoma (HCC) after resection. To investigate whether a noninvasive risk score system based on MVI status can be established to estimate early recurrence of HCC after resection. METHODS Between January 2018 to March 2021, a total of 108 patients with surgically treated single HCC was retrospectively included in our study. Fifty-one patients were pathologically confirmed with MVI and 57 patients were absent of MVI. Univariate and multivariate logistic regression analysis of preoperative laboratory and magnetic resonance imaging (MRI) features were used to screen noninvasive risk factors in association with MVI in HCC. Risk scores based on the odds ratio (OR) values of MVI-related risk factors were calculated to estimate the early recurrence after resection of HCC. RESULTS In multivariate logistic regression analysis, tumor size > 2 cm (P = 0.024, OR 3.05, 95% CI 1.19-11.13), Prothrombin induced by vitamin K absence-II > 32 mAU/ml (P = 0.001, OR 4.13, 95% CI 1.23-11.38), irregular tumor margin (P = 0.018, OR 3.10, 95% CI 1.16-8.31) and apparent diffusion coefficient value < 1007 × 10- 3mm2/s (P = 0.035, OR 2.27, 95% CI 1.14-7.71) were independent risk factors correlated to MVI in HCC. Risk scores of patients were calculated and were then categorized into high or low-risk levels. In multivariate cox survival analysis, only high-risk score of MVI was the independent risk factor of early recurrence (P = 0.009, OR 2.11, 95% CI 1.20-3.69), with a sensitivity and specificity of 0.52, 0.88, respectively. CONCLUSION A risk score system based on MVI status can help stratify patients in high-risk of early recurrence after resection of HCC.
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Affiliation(s)
- Sheng Wang
- grid.469601.cDepartment of Radiology, Taizhou First People’s Hospital, 218 Hengjie Rd., Dongcheng Street, Huangyan District, Taizhou City, 318020 Zhejiang Province China
| | - Weizhi Zheng
- grid.469601.cDepartment of Pathology, Taizhou First People’s Hospital, Taizhou City, 318020 Zhejiang Province China
| | - Zhencheng Zhang
- grid.469601.cDepartment of Laboratory, Taizhou First People’s Hospital, Taizhou City, 318020 Zhejiang Province China
| | - Guo-hua Zhang
- grid.469601.cDepartment of Radiology, Taizhou First People’s Hospital, 218 Hengjie Rd., Dongcheng Street, Huangyan District, Taizhou City, 318020 Zhejiang Province China
| | - Dan-jiang Huang
- grid.469601.cDepartment of Radiology, Taizhou First People’s Hospital, 218 Hengjie Rd., Dongcheng Street, Huangyan District, Taizhou City, 318020 Zhejiang Province China
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Zhang X, Wang C, Zheng D, Liao Y, Wang X, Huang Z, Zhong Q. Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation. Front Oncol 2022; 12:1013770. [PMID: 36439458 PMCID: PMC9686343 DOI: 10.3389/fonc.2022.1013770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 10/24/2022] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND There are few studies on the application of radiomics in the risk prediction of early recurrence (ER) after radiofrequency ablation (RFA). This study evaluated the value of a multi-parametric magnetic resonance imaging (MRI, mpMRI)-based radiomics nomogram in predicting ER of small hepatocellular carcinoma (HCC) after RFA. MATERIALS AND METHODS A retrospective analysis was performed on 90 patients with small HCC who were treated with RFA. Patients were divided into two groups according to recurrence within 2 years: the ER group (n=38) and the non-ER group (n=52). Preoperative T1WI, T2WI, and contrast-enhanced MRI (CE-MRI) were used for radiomic analysis. Tumor segmentation was performed on the images and applied to extract 1316 radiomics features. The most predictive features were selected using analysis of variance + Mann-Whitney, Spearman's rank correlation test, random forest (importance), and least absolute shrinkage and selection operator analysis. Radiomics models based on each sequence or combined sequences were established using logistic regression analysis. A predictive nomogram was constructed based on the radiomics score (rad-score) and clinical predictors. The predictive efficiency of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to evaluate the clinical efficacy of the nomogram. RESULTS The radiomics model mpMRI, which is based on T1WI, T2WI, and CE-MRI sequences, showed the best predictive performance, with an AUC of 0.812 for the validation cohort. Combined with the clinical risk factors of albumin level, number of tumors, and rad-score of mpMRI, the AUC of the preoperative predictive nomogram in the training and validation cohorts were 0.869 and 0.812, respectively. DCA demonstrated that the combined nomogram is clinically useful. CONCLUSIONS The multi-parametric MRI-based radiomics nomogram has a high predictive value for ER of small HCC after RFA, which could be helpful for personalized risk stratification and further treatment decision-making for patients with small HCC.
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Affiliation(s)
- Xiaojuan Zhang
- Department of Radiology, Fujian Medical University Xiamen Humanity Hospital, Xiamen, China
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Chuandong Wang
- Department of Thyroid and Breast Surgery, Fujian Medical University Xiamen Humanity Hospital, Xiamen, China
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Dan Zheng
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Radiology, 900th Hospital of Joint Logistics Support Force, Fuzhou, China
| | - Yuting Liao
- Institute of Precision Medicine, GE Healthcare, Shanghai, China
| | - Xiaoyang Wang
- Department of Radiology, 900th Hospital of Joint Logistics Support Force, Fuzhou, China
| | - Zhifeng Huang
- Department of Radiology, 900th Hospital of Joint Logistics Support Force, Fuzhou, China
| | - Qun Zhong
- Department of Radiology, The Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
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He Y, Liang T, Chen Z, Mo S, Liao Y, Gao Q, Huang K, Peng T, Zhou W, Han C. Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:7261786. [PMID: 36238647 PMCID: PMC9553367 DOI: 10.1155/2022/7261786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/07/2022] [Accepted: 09/12/2022] [Indexed: 11/27/2022]
Abstract
Background Early stage hepatocellular carcinoma (HCC) has a high recurrence rate after surgery and lacks reliable predictive tools. We explored the potential of combining enhanced CT with gut microbiome to develop a predictive model for recurrence after early HCC surgery. Methods A total of 112 patients with early HCC who underwent hepatectomy from September 2018 to December 2020 were included in this study, and the machine learning method was divided into a training group (N = 71) and a test group (N = 41) with the observed endpoint of recurrence-free survival (RFS). Features were extracted from the arterial and portal phases of enhanced computed tomography (CT) images and gut microbiome, and features with minimum absolute contraction and selection operator regression were created, and the extracted features were scored to create a preoperative prediction model by using the multivariate Cox regression analysis with risk stratification analysis. Results In the study cohort, the model constructed by combining radiological and gut flora features provided good predictive performance (C index, 0.811 (0.650-0.972)). The combined radiology and gut flora-based model constructed risk strata with high, intermediate, or low risk of recurrence and different characteristics of recurrent tumor imaging and gut flora. Recurrence of early stage hepatocellular carcinoma may be associated with oxidative stress in the intestinal flora. Conclusions This study successfully constructs a risk model integrating enhanced CT and gut microbiome characteristics that can be used for the risk of postoperative recurrence in patients with early HCC. In addition, intestinal flora associated with HCC recurrence may be involved in oxidative stress.
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Affiliation(s)
- Yongfei He
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Tianyi Liang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Zijun Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Shutian Mo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Yuan Liao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Qiang Gao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Ketuan Huang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Tao Peng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Weijie Zhou
- Deputy Chief Technician of Laboratory, Baise People's Hospital, Baise, Guangxi Zhuang Autonomous Region, China
| | - Chuangye Han
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
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Zhang H, Huo F. Prediction of early recurrence of HCC after hepatectomy by contrast-enhanced ultrasound-based deep learning radiomics. Front Oncol 2022; 12:930458. [PMID: 36248986 PMCID: PMC9554932 DOI: 10.3389/fonc.2022.930458] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/07/2022] [Indexed: 12/07/2022] Open
Abstract
Objective This study aims to evaluate the predictive model based on deep learning (DL) and radiomics features from contrast-enhanced ultrasound (CEUS) to predict early recurrence (ER) in patients with hepatocellular carcinoma (HCC). Methods One hundred seventy-two patients with HCC who underwent hepatectomy and followed up for at least 1 year were included in this retrospective study. The data were divided according to the 7:3 ratios of training and test data. The ResNet-50 architecture, CEUS-based radiomics, and the combined model were used to predict the early recurrence of HCC after hepatectomy. The receiver operating characteristic (ROC) curve and calibration curve were drawn to evaluate its diagnostic efficiency. Results The CEUS-based radiomics ROCs of the “training set” and “test set” were 0.774 and 0.763, respectively. The DL model showed increased prognostic value, the ROCs of the “training set” and “test set” were 0.885 and 0.834, respectively. The combined model ROCs of the “training set” and “test set” were 0.943 and 0.882, respectively. Conclusion The deep learning radiomics model integrating DL and radiomics features from CEUS was used to predict ER and achieve satisfactory performance. Its diagnostic efficiency is significantly better than that of the single model.
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Affiliation(s)
- Hui Zhang
- Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Fanding Huo
- Department of Medical Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
- *Correspondence: Fanding Huo,
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Wang L, Feng B, Wang S, Hu J, Liang M, Li D, Wang S, Ma X, Zhao X. Diagnostic value of whole-tumor apparent diffusion coefficient map radiomics analysis in predicting early recurrence of solitary hepatocellular carcinoma ≤ 5 cm. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3290-3300. [PMID: 35776146 DOI: 10.1007/s00261-022-03582-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To evaluate the role of whole-tumor radiomics analysis of apparent diffusion coefficient (ADC) maps in predicting early recurrence (ER) of solitary hepatocellular carcinoma (HCC) ≤ 5 cm and compare the diagnostic efficiency of whole-tumor and single-slice ADC measurements. METHODS One hundred and seventy patients with primary HCC were randomly divided into the training set (n = 119) and the test set (n = 51). The diagnostic efficiency was compared between the whole-tumor and single-slice ADC measurements. The clinical-radiological model was established by selected significant clinical characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The significant clinical-radiological risk factors and radiomics features were integrated to develop the combined model. Receiver operating characteristic (ROC) curves were used for evaluating the predictive performance. RESULTS Cirrhosis, age, and albumin were significantly associated with ER in the clinical-radiological model selected by the random forest classifier. The diagnostic efficiency of the whole-tumor ADC measurements was slight higher than that of the single-slice (AUC = 0.602 and 0.586, respectively). The clinical-radiological model (AUC = 0.84 and 0.82 in the training and test sets, respectively) showed better diagnostic performance than the radiomics model (AUC = 0.70 and 0.69 in the training and test sets, respectively) in predicting ER. The combined model showed optimal predictive performance with the highest AUC values of 0.88 and 0.85 in the training and test sets, respectively. CONCLUSIONS The whole-tumor ADC measurements performed better than the single-slice ADC measurements. The clinical-radiological model performed better than the radiomics model for predicting ER in patients with solitary HCC ≤ 5 cm.
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Affiliation(s)
- Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing, 100176, China
| | - Jiesi Hu
- Institute of Electronical and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, 518055, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Shuang Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Wu C, Chen J, Fan Y, Zhao M, He X, Wei Y, Ge W, Liu Y. Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma. Front Oncol 2022; 12:943942. [PMID: 35875154 PMCID: PMC9299359 DOI: 10.3389/fonc.2022.943942] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 06/07/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives The study developed and validated a radiomics nomogram based on a combination of computed tomography (CT) radiomics signature and clinical factors and explored the ability of radiomics for individualized prediction of Ki-67 expression in hepatocellular carcinoma (HCC). Methods First-order, second-order, and high-order radiomics features were extracted from preoperative enhanced CT images of 172 HCC patients, and the radiomics features with predictive value for high Ki-67 expression were extracted to construct the radiomic signature prediction model. Based on the training group, the radiomics nomogram was constructed based on a combination of radiomic signature and clinical factors that showed an independent association with Ki-67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram. Results Sixteen higher-order radiomic features that were associated with Ki-67 expression were used to construct the radiomics signature (AUC: training group, 0.854; validation group, 0.744). In multivariate logistic regression, alfa-fetoprotein (AFP) and Edmondson grades were identified as independent predictors of Ki-67 expression. Thus, the radiomics signature was combined with AFP and Edmondson grades to construct the radiomics nomogram (AUC: training group, 0.884; validation group, 0.819). The calibration curve and DCA showed good clinical application of the nomogram. Conclusion The radiomics nomogram developed in this study based on the high-order features of CT images can accurately predict high Ki-67 expression and provide individualized guidance for the treatment and clinical monitoring of HCC patients.
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Affiliation(s)
- Cuiyun Wu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Junfa Chen
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Yuqian Fan
- Department of Clinical Pathology, Graduate School, Hebei Medical University, Shijiazhuang, China
| | - Ming Zhao
- Cancer Center, Department of Pathology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Xiaodong He
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Yuguo Wei
- Precision Health Institution, General Electrical Healthcare, Hangzhou, China
| | - Weidong Ge
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Yang Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
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Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma. Cancers (Basel) 2022; 14:cancers14133201. [PMID: 35804973 PMCID: PMC9264891 DOI: 10.3390/cancers14133201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/23/2022] [Accepted: 06/23/2022] [Indexed: 12/04/2022] Open
Abstract
Simple Summary In the past, radiomics studies of nasopharyngeal carcinoma (NPC) were only based on basic MR sequences. Previous studies have shown that radiomics methods based on T2-weighted imaging and contrast-enhanced T1-weighted imaging have been successfully used to improve the prognosis of patients with nasopharyngeal carcinoma. The purpose of this study was to explore the predictive efficacy of radiomics analyses based on readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI) which quantitatively reflects the diffusion motion of water molecules for prognosis evaluation in nasopharyngeal carcinoma. Several prognostic radiomics models were established by using diffusion-weighted imaging, apparent diffusion coefficient maps, T2-weighted and contrast-enhanced T1-weighted imaging to predict the risk of recurrence or metastasis of nasopharyngeal carcinoma, and the predictive effects of different models were compared. The results show that the model based on MRI DWI can successfully predict the prognosis of patients with nasopharyngeal carcinoma and has higher predictive efficiency than the model based on the conventional sequence, which suggests MRI DWI-radiomics can provide a useful and alternative approach for survival estimation. Abstract Purpose: This study aimed to explore the predictive efficacy of radiomics analyses based on readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI) for prognosis evaluation in nasopharyngeal carcinoma in order to provide further information for clinical decision making and intervention. Methods: A total of 154 patients with untreated NPC confirmed by pathological examination were enrolled, and the pretreatment magnetic resonance image (MRI)—including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI)—was collected. The Random Forest (RF) algorithm selected radiomics features and established the machine-learning models. Five models, namely model 1 (DWI + ADC), model 2 (T2WI + CE-T1WI), model 3 (DWI + ADC + T2WI), model 4 (DWI + ADC + CE-T1WI), and model 5 (DWI + ADC + T2WI + CE-T1WI), were constructed. The average area under the curve (AUC) of the validation set was determined in order to compare the predictive efficacy for prognosis evaluation. Results: After adjusting the parameters, the RF machine learning models based on extracted imaging features from different sequence combinations were obtained. The invalidation sets of model 1 (DWI + ADC) yielded the highest average AUC of 0.80 (95% CI: 0.79–0.81). The average AUCs of the model 2, 3, 4, and 5 invalidation sets were 0.72 (95% CI: 0.71–0.74), 0.66 (95% CI: 0.64–0.68), 0.74 (95% CI: 0.73–0.75), and 0.75 (95% CI: 0.74–0.76), respectively. Conclusion: A radiomics model derived from the MRI DWI of patients with nasopharyngeal carcinoma was generated in order to evaluate the risk of recurrence and metastasis. The model based on MRI DWI can provide an alternative approach for survival estimation, and can reveal more information for clinical decision-making and intervention.
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Wang L, Ma X, Feng B, Wang S, Liang M, Li D, Wang S, Zhao X. Multi-Sequence MR-Based Radiomics Signature for Predicting Early Recurrence in Solitary Hepatocellular Carcinoma ≤5 cm. Front Oncol 2022; 12:899404. [PMID: 35756618 PMCID: PMC9213728 DOI: 10.3389/fonc.2022.899404] [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: 03/18/2022] [Accepted: 05/17/2022] [Indexed: 01/27/2023] Open
Abstract
Purpose To investigate the value of radiomics features derived from preoperative multi-sequence MR images for predicting early recurrence (ER) in patients with solitary hepatocellular carcinoma (HCC) ≤5 cm. Methods One hundred and ninety HCC patients were enrolled and allocated to training and validation sets (n = 133:57). The clinical–radiological model was established by significant clinical risk characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm in the training set. The combined model was formed by integrating the clinical–radiological risk factors and selected radiomics features. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC). Results Arterial peritumoral hyperenhancement, non-smooth tumor margin, satellite nodules, cirrhosis, serosal invasion, and albumin showed a significant correlation with ER. The AUC of the clinical–radiological model was 0.77 (95% CI: 0.69–0.85) and 0.76 (95% CI: 0.64–0.88) in the training and validation sets, respectively. The radiomics model constructed using 12 radiomics features selected by LASSO regression had an AUC of 0.85 (95% CI: 0.79–0.91) and 0.84 (95% CI: 0.73–0.95) in the training and validation sets, respectively. The combined model further improved the prediction performance compared with the clinical–radiological model, increasing AUC to 0.90 (95% CI: 0.85–0.95) in the training set and 0.88 (95% CI: 0.80–0.97) in the validation set (p < 0.001 and p = 0.012, respectively). The calibration curve fits well with the standard curve. Conclusions The predictive model incorporated the clinical–radiological risk factors and radiomics features that could adequately predict the individualized ER risk in patients with solitary HCC ≤5 cm.
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Affiliation(s)
- Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuang Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Sheng R, Zeng M, Jin K, Zhang Y, Wu D, Sun H. MRI-based Nomogram Predicts the Risk of Progression of Unresectable Hepatocellular Carcinoma After Combined Lenvatinib and anti-PD-1 Antibody Therapy. Acad Radiol 2022; 29:819-829. [PMID: 34649778 DOI: 10.1016/j.acra.2021.09.004] [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: 07/27/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES Combined immune and anti-angiogenic treatment has shown promising results for unresectable hepatocellular carcinoma (HCC), but with a high risk of early progression. In this study, we aimed to investigate whether pre-treatment magnetic resonance imaging (MRI) features and MRI-based nomogram could predict the risk of disease progression of unresectable HCC after first-line lenvatinib/anti-PD-1 antibody therapy. MATERIALS AND METHODS Thirty-seven HCC participants with qualified pre-treatment contrast-enhanced MRI were enrolled. All patients received combined lenvatinib and anti-PD-1 antibody treatment. Progression free survival rate was analyzed using the Kaplan-Meier method. Potential clinical-radiological risk factors for progression were analyzed using the log-rank tests and Cox regression model. The performance of MRI-based nomogram was evaluated based on C-index, calibration, and decision curve analyses. RESULTS The 6-month and 12-month cumulative progression free survival rates were 59.5% (95% confidence interval (CI), 43.6%-75.4%) and 48.0% (95% CI, 31.7%-64.3%). On multivariate analysis, no or incomplete tumor capsule (hazard ratio (HR) = 15.215 [95% CI 2.707-85.529], p = 0.002), heterogeneous signal on T2-weighted imaging (HR = 28.179 [95% CI 2.437-325.838]; p = 0.008) and arterial contrast-to-noise ratio ≤95.45 (HR = 5.113 [95% CI 1.538-17.00]; p = 0.008) were independent risk factors for disease progression. Satisfactory predictive performance of the nomogram incorporating the three independent imaging features was obtained with a C-index value of 0.880 (95% CI 0.824-0.937), and the combined nomogram had more favorable clinical prediction performance than any single feature. CONCLUSION MRI features can be considered effective predictors of disease progression for unresectable HCC with first-line lenvatinib plus anti-PD-1 antibody therapy, and the combined MRI-based nomogram achieved a superior prognostic model, which may help to identify appropriate candidates for the therapy.
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Zhao QY, Liu SS, Fan MX. Prediction of early recurrence of hepatocellular carcinoma after resection based on Gd-EOB-DTPA enhanced magnetic resonance imaging: a preliminary study. J Gastrointest Oncol 2022; 13:792-801. [PMID: 35557582 PMCID: PMC9086065 DOI: 10.21037/jgo-22-224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/11/2022] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Early recurrence (ER) after radical resection of hepatocellular carcinoma (HCC) affects the prognosis of patients. Gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) can improve the detection rate of small HCC. This study innovatively introduces a new quantitative index combined with qualitative index to compare the differences in clinical and imaging characteristics between ER and non-ER groups and evaluate the feasibility of Gd-EOB-DTPA-enhanced MRI in predicting ER. METHODS A total of 68 patients with HCC confirmed by operation and pathology in the Shandong Cancer Hospital and Institute were included retrospectively. All participants were examined by Gd-EOB-DTPA-enhanced MRI within 3 weeks before surgery. Regular follow-up was performed every 2 months within 1 year after operation. Among them, 18 cases with new lesions were in ER group, and 50 cases without new lesions were in non-ER group. The clinical and imaging data of the 2 groups were collected, and the differences of clinical data and preoperative MRI signs between the ER group and non-ER group were compared. The predictive factors of ER after HCC were analyzed by multivariate logistic regression. RESULTS The quantitative parameter lesion-to-liver contrast enhancement ratio (LLCER) can predict the pathological grade of HCC (P=0.023). The results of univariate analysis between the ER group and non-ER group showed that there were significant differences in pathological grade (P=0.008), lesion morphology (P=0.011), peritumoral low signal intensity in hepatobiliary phase (HBP) (P<0.001), satellite nodules (P<0.001), and LLCER (P<0.001) between the 2 groups. Multivariate logistic regression analysis showed that HBP peritumoral low signal intensity [odds ratio (OR) =7.214, 95% confidence interval (CI): 1.230-42.312, P=0.029], satellite nodules (OR =9.198, 95% CI: 1.402-60.339, P=0.021), and parameter LLCER value (OR =0.906, 95% CI: 0.826-0.995, P=0.039) were independent predictors of ER of HCC after resection. CONCLUSIONS Preoperative Gd-EOB-DTPA enhanced MRI has important predictive value for early recurrence after radical resection of hepatocellular carcinoma.
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Affiliation(s)
- Qi-Yu Zhao
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shi-Shun Liu
- Medical Imaging Department, Jinan Second People’s Hospital, Jinan, China
| | - Ming-Xin Fan
- Department of Radiology, The Third Affiliated Hospital of Shandong First Medical University (Affiliated Hospital of Shandong Academy of Medical Sciences), Jinan, China
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Yang Y, Zou X, Zhou W, Yuan G, Hu D, Shen Y, Xie Q, Zhang Q, Kuang D, Hu X, Li Z. DWI-based radiomic signature: potential role for individualized adjuvant chemotherapy in intrahepatic cholangiocarcinoma after partial hepatectomy. Insights Imaging 2022; 13:37. [PMID: 35244793 PMCID: PMC8897536 DOI: 10.1186/s13244-022-01179-7] [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: 10/24/2021] [Accepted: 02/07/2022] [Indexed: 11/10/2022] Open
Abstract
Objectives To develop a diffusion-weighted imaging (DWI) based radiomic signature for predicting early recurrence (ER) (i.e., recurrence within 1 year after surgery), and to explore the potential value for individualized adjuvant chemotherapy. Methods A total of 124 patients with intrahepatic cholangiocarcinoma (ICC) were randomly divided into the training (n = 87) and the validation set (n = 37). Radiomic signature was built using radiomic features extracted from DWI with random forest. An integrated radiomic nomogram was constructed with multivariate logistic regression analysis to demonstrate the incremental value of the radiomic signature beyond clinicopathological-radiographic factors. A clinicopathological-radiographic (CPR) model was constructed as a reference. Results The radiomic signature showed a comparable discrimination performance for predicting ER to CPR model in the validation set (AUC, 0.753 vs. 0.621, p = 0.274). Integrating the radiomic signature with clinicopathological-radiographic factors further improved prediction performance compared with CPR model, with an AUC of 0.821 (95%CI 0.684–0.959) in the validation set (p = 0.01). The radiomic signature succeeded to stratify patients into distinct survival outcomes according to their risk index of ER, and remained an independent prognostic factor in multivariable analysis (disease-free survival (DFS), p < 0.0001; overall survival (OS), p = 0.029). Furthermore, adjuvant chemotherapy improved prognosis in high-risk patients defined by the radiomic signature (DFS, p = 0.029; OS, p = 0.088) and defined by the nomogram (DFS, p = 0.031; OS, p = 0.023), whereas poor chemotherapy efficacy was detected in low-risk patients. Conclusions The preoperative DWI-based radiomic signature could improve prognostic prediction and help to identify ICC patients who may benefit from postoperative adjuvant chemotherapy. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01179-7.
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Affiliation(s)
- Yang Yang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China
| | - Xianlun Zou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China
| | - Wei Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China
| | - Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China
| | - Qingguo Xie
- Department of Biomedical Engineering, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Dong Kuang
- Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China.
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China.
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China
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Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions. Eur J Nucl Med Mol Imaging 2022; 49:2917-2928. [PMID: 35230493 PMCID: PMC9206604 DOI: 10.1007/s00259-022-05742-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/17/2022] [Indexed: 12/17/2022]
Abstract
Purpose
This study aimed to investigate whether models built from radiomics features based on multiphase contrast-enhanced MRI can identify microscopic pre-hepatocellular carcinoma lesions. Methods We retrospectively studied 54 small hepatocellular carcinoma (SHCC, diameter < 2 cm) patients and 70 patients with hepatocellular cysts or haemangiomas from September 2018 to June 2021. For the former, two MRI scans were collected within 12 months of each other; the 2nd scan was used to confirm the diagnosis. The volumes of interest (VOIs), including SHCCs and normal liver tissues, were delineated on the 2nd scans, mapped to the 1st scans via image registration, and enrolled into the SHCC and internal-control cohorts, respectively, while those of normal liver tissues from patients with hepatocellular cysts or haemangioma were enrolled in the external-control cohort. We extracted 1132 radiomics features from each VOI and analysed their discriminability between the SHCC and internal-control cohorts for intra-group classification and the SHCC and external-control cohorts for inter-group classification. Five radial basis-function, kernel-based support vector machine (SVM) models (four corresponding single-phase models and one integrated from the four-phase MR images) were established. Results Among the 124 subjects, the multiphase models yielded better performance on the testing set for intra-group and inter-group classification, with areas under the receiver operating characteristic curves of 0.93 (95% CI, 0.85–1.00) and 0.97 (95% CI, 0.92–1.00), accuracies of 86.67% and 94.12%, sensitivities of 87.50% and 94.12%, and specificities of 85.71% and 94.12%, respectively. Conclusion The combined multiphase MRI-based radiomics feature model revealed microscopic pre-hepatocellular carcinoma lesions. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05742-8.
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Gao W, Wang W, Song D, Yang C, Zhu K, Zeng M, Rao SX, Wang M. A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma. Radiol Med 2022; 127:259-271. [PMID: 35129757 DOI: 10.1007/s11547-021-01445-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/30/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Hepatocellular carcinoma (HCC) is the most common liver cancer worldwide, and early recurrence of HCC after curative hepatic resection is indicative of poor prognoses. We aim to develop a predictive model for postoperative early recurrence of HCC based on deep and radiomics features from multi-phasic magnetic resonance imaging (MRI). MATERIALS AND METHODS A total of 472 HCC patients were included and divided into the training (n = 378) and validation (n = 94) cohorts in the retrospective study. We separately extracted radiomics features and deep features from eight phases of gadoxetic acid-enhanced MRI and utilized the least absolute shrinkage and selection operator logistic regression algorithm for feature selection and model construction. We integrated the selected two types of features into a combined model and established a radiomics model as well as a deep learning (DL) model for comparison. RESULTS In the training and validation cohorts, the combined model demonstrated better performance for stratifying patients at high risk of early recurrence (AUC of 0.911 and 0.840, accuracy of 0.779 and 0.777, sensitivity of 0.927 and 0.769, specificity 0.720 and 0.779) than the radiomics model (AUC of 0.740 and 0.780) and the DL model (AUC of 0.887 and 0.813). CONCLUSION The combined model integrating deep and radiomics features from multi-phasic MRI is efficient for noninvasively stratifying patients at high risk of early HCC recurrence after resection.
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Affiliation(s)
- Wenyu Gao
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China
| | - Wentao Wang
- Department of Radiology, Cancer Center, Shanghai Medical Imaging Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Rd., Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Danjun Song
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Interventional Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
| | - Chun Yang
- Department of Radiology, Cancer Center, Shanghai Medical Imaging Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Rd., Shanghai, 200032, China
| | - Kai Zhu
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mengsu Zeng
- Department of Radiology, Cancer Center, Shanghai Medical Imaging Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Rd., Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Sheng-Xiang Rao
- Department of Radiology, Cancer Center, Shanghai Medical Imaging Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Rd., Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, China.
| | - Manning Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China.
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Li XN, Hao DP, Qu MJ, Zhang M, Ma AB, Pan XD, Ma AJ. Development and Validation of a Plasma FAM19A5 and MRI-Based Radiomics Model for Prediction of Parkinson's Disease and Parkinson's Disease With Depression. Front Neurosci 2022; 15:795539. [PMID: 34975391 PMCID: PMC8718551 DOI: 10.3389/fnins.2021.795539] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 11/26/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Prediction and early diagnosis of Parkinson’s disease (PD) and Parkinson’s disease with depression (PDD) are essential for the clinical management of PD. Objectives: The present study aimed to develop a plasma Family with sequence similarity 19, member A5 (FAM19A5) and MRI-based radiomics nomogram to predict PD and PDD. Methods: The study involved 176 PD patients and 181 healthy controls (HC). Sandwich enzyme-linked immunosorbent assay (ELISA) was used to measure FAM19A5 concentration in the plasma samples collected from all participants. For enrolled subjects, MRI data were collected from 164 individuals (82 in the PD group and 82 in the HC group). The bilateral amygdala, head of the caudate nucleus, putamen, and substantia nigra, and red nucleus were manually labeled on the MR images. Radiomics features of the labeled regions were extracted. Further, machine learning methods were applied to shrink the feature size and build a predictive radiomics signature. The resulting radiomics signature was combined with plasma FAM19A5 concentration and other risk factors to establish logistic regression models for the prediction of PD and PDD. Results: The plasma FAM19A5 levels (2.456 ± 0.517) were recorded to be significantly higher in the PD group as compared to the HC group (2.23 ± 0.457) (P < 0.001). Importantly, the plasma FAM19A5 levels were also significantly higher in the PDD subgroup (2.577 ± 0.408) as compared to the non-depressive subgroup (2.406 ± 0.549) (P = 0.045 < 0.05). The model based on the combination of plasma FAM19A5 and radiomics signature showed excellent predictive validity for PD and PDD, with AUCs of 0.913 (95% CI: 0.861–0.955) and 0.937 (95% CI: 0.845–0.970), respectively. Conclusion: Altogether, the present study reported the development of nomograms incorporating radiomics signature, plasma FAM19A5, and clinical risk factors, which might serve as potential tools for early prediction of PD and PDD in clinical settings.
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Affiliation(s)
- Xue-Ning Li
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Da-Peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Mei-Jie Qu
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Meng Zhang
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - An-Bang Ma
- Shanghai Xunshi Technology Co., Ltd., Shanghai, China
| | - Xu-Dong Pan
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.,Institute of Cerebrovascular, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ai-Jun Ma
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.,Institute of Cerebrovascular, The Affiliated Hospital of Qingdao University, Qingdao, China
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Tang Y, Yang CM, Su S, Wang WJ, Fan LP, Shu J. Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma. BMC Cancer 2021; 21:1268. [PMID: 34819043 PMCID: PMC8611922 DOI: 10.1186/s12885-021-08947-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 11/01/2021] [Indexed: 12/15/2022] Open
Abstract
Background Radiomics may provide more objective and accurate predictions for extrahepatic cholangiocarcinoma (ECC). In this study, we developed radiomics models based on magnetic resonance imaging (MRI) and machine learning to preoperatively predict differentiation degree (DD) and lymph node metastasis (LNM) of ECC. Methods A group of 100 patients diagnosed with ECC was included. The ECC status of all patients was confirmed by pathology. A total of 1200 radiomics features were extracted from axial T1 weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and apparent diffusion coefficient (ADC) images. A systematical framework considering combinations of five feature selection methods and ten machine learning classification algorithms (classifiers) was developed and investigated. The predictive capabilities for DD and LNM were evaluated in terms of area under precision recall curve (AUPRC), area under the receiver operating characteristic (ROC) curve (AUC), negative predictive value (NPV), accuracy (ACC), sensitivity, and specificity. The prediction performance among models was statistically compared using DeLong test. Results For DD prediction, the feature selection method joint mutual information (JMI) and Bagging Classifier achieved the best performance (AUPRC = 0.65, AUC = 0.90 (95% CI 0.75–1.00), ACC = 0.85 (95% CI 0.69–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.88 (95% CI 0.64–0.97)), and the radiomics signature was composed of 5 selected features. For LNM prediction, the feature selection method minimum redundancy maximum relevance and classifier eXtreme Gradient Boosting achieved the best performance (AUPRC = 0.95, AUC = 0.98 (95% CI 0.94–1.00), ACC = 0.90 (95% CI 0.77–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.94 (95% CI 0.72–0.99)), and the radiomics signature was composed of 30 selected features. However, these two chosen models were not significantly different to other models of higher AUC values in DeLong test, though they were significantly different to most of all models. Conclusion MRI radiomics analysis based on machine learning demonstrated good predictive accuracies for DD and LNM of ECC. This shed new light on the noninvasive diagnosis of ECC.
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Affiliation(s)
- Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, Sichuan, China
| | - Chun Mei Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, and Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, 646000, Sichuan, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Luzhou, 646000, Sichuan, China
| | - Wei Jia Wang
- School of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, Sichuan, China
| | - Li Ping Fan
- Department of Ultrasound, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Luzhou, 646000, Sichuan, China.
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, and Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, 646000, Sichuan, China.
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Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13:1599-1615. [PMID: 34853638 PMCID: PMC8603458 DOI: 10.4251/wjgo.v13.i11.1599] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/02/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.
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Affiliation(s)
- Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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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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Li P, Wu L, Li Z, Li J, Ye W, Shi Z, Xu Z, Zhu C, Ye H, Liu Z, Liang C. Spleen Radiomics Signature: A Potential Biomarker for Prediction of Early and Late Recurrences of Hepatocellular Carcinoma After Resection. Front Oncol 2021; 11:716849. [PMID: 34485152 PMCID: PMC8414994 DOI: 10.3389/fonc.2021.716849] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 07/27/2021] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVES To explore the usefulness of spleen radiomics features based on contrast-enhanced computed tomography (CECT) in predicting early and late recurrences of hepatocellular carcinoma (HCC) patients after curative resection. METHODS This retrospective study included 237 HCC patients who underwent CECT and curative resection between January 2006 to January 2016. Radiomic features were extracted from CECT images, and then the spleen radiomics signatures and the tumor radiomics signatures were built. Cox regression analysis was performed to identify the independent risk factors of early and late recurrences. Then, multiple models were built to predict the recurrence-free survival of HCC after resection, and the incremental value of the radiomics signature to the clinicopathologic model was assessed and validated. Kaplan-Meier survival analysis was used to assess the association of the models with RFS. RESULTS The spleen radiomics signature was independent risk factor of early recurrence of HCC. The mixed model that integrated microvascular invasion, tumor radiomics signature and spleen radiomics signature for the prediction of early recurrence achieved the highest C-index of 0.780 (95% CI: 0.728,0.831) in the primary cohort and 0.776 (95% CI: 0.716,0.836) in the validation cohort, and presented better predictive performance than clinicopathological model and combined model. In the analysis of late recurrence, the spleen radiomics signature was the only prognostic factor associated with late recurrence of HCC. CONCLUSIONS The identified spleen radiomics signatures are prognostic factors of both early and late recurrences of HCC patients after surgery and improve the predictive performance of model for early recurrence.
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Affiliation(s)
- Pinxiong Li
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lei Wu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenhui Li
- Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiao Li
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Weitao Ye
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zeyan Xu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chao Zhu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Huifen Ye
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zaiyi Liu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Changhong Liang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Mao X, Guo Y, Wen F, Liang H, Sun W, Lu Z. Applying arterial enhancement fraction (AEF) texture features to predict the tumor response in hepatocellular carcinoma (HCC) treated with Transarterial chemoembolization (TACE). Cancer Imaging 2021; 21:49. [PMID: 34384496 PMCID: PMC8359085 DOI: 10.1186/s40644-021-00418-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/19/2021] [Indexed: 12/15/2022] Open
Abstract
Background To evaluate the application of Arterial Enhancement Fraction (AEF) texture features in predicting the tumor response in Hepatocellular Carcinoma (HCC) treated with Transarterial Chemoembolization (TACE) by means of texture analysis. Methods HCC patients treated with TACE in Shengjing Hospital of China Medical University from June 2018 to December 2019 were retrospectively enrolled in this study. Pre-TACE Contrast Enhanced Computed Tomography (CECT) and imaging follow-up within 6 months were both acquired. The tumor responses were categorized according to the modified RECIST (mRECIST) criteria. Based on the CECT images, Region of Interest (ROI) of HCC lesion was drawn, the AEF calculation and texture analysis upon AEF values in the ROI were performed using CT-Kinetics (C.K., GE Healthcare, China). A total of 32 AEF texture features were extracted and compared between different tumor response groups. Multi-variate logistic regression was performed using certain AEF features to build the differential models to predict the tumor response. The Receiver Operator Characteristic (ROC) analysis was implemented to assess the discriminative performance of these models. Results Forty-five patients were finally enrolled in the study. Eight AEF texture features showed significant distinction between Improved and Un-improved patients (p < 0.05). In multi-variate logistic regression, 9 AEF texture features were applied into modeling to predict “Improved” outcome, and 4 AEF texture features were applied into modeling to predict “Un-worsened” outcome. The Area Under Curve (AUC), diagnostic accuracy, sensitivity, and specificity of the two models were 0.941, 0.911, 1.000, 0.826, and 0.824, 0.711, 0.581, 1.000, respectively. Conclusions Certain AEF heterogeneous features of HCC could possibly be utilized to predict the tumor response to TACE treatment.
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Affiliation(s)
- Xiaonan Mao
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China
| | - Yan Guo
- GE Healthcare (China), Shanghai, China
| | - Feng Wen
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China
| | - Hongyuan Liang
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China
| | - Wei Sun
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China
| | - Zaiming Lu
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China.
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Öcal O, Rössler D, Ricke J, Seidensticker M. Advances in Diagnostic and Interventional Radiology in Hepatocellular Carcinoma. Dig Dis 2021; 40:458-467. [PMID: 34348282 DOI: 10.1159/000518101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/25/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths, and radiological imaging and locoregional therapies are essential for the management of patients with HCC. SUMMARY In cirrhotic patients, a characteristic imaging pattern establishes the noninvasive diagnosis of HCC with acceptable sensitivity and high specificity. In addition to diagnosis, imaging is used in the staging of patients and treatment allocation. Multiparametric MRI with hepatospecific contrast agents improves lesion detection, characterization, and treatment allocation; recently described imaging criteria allow identification of precursor lesions. Radiofrequency ablation (RFA) and transarterial chemoembolization (TACE) have been established in the treatment of patients with HCC at the early and intermediate stages, respectively. Microwave ablation has been described as an alternative to RFA in selected cases. Imaging-guided brachytherapy, a catheter-based radiotherapy technique, offers advantages to overcome some limitations of the aforementioned therapies, including the tumor location and size. Currently, no adjuvant therapy is recommended after RFA or TACE, but several new drugs are under evaluation. Furthermore, although the exact role of selective internal radiation therapy (SIRT) in HCC still needs to be defined, it is an alternative to systemic agents in patients with intolerance, and additional benefit has been shown in selected subgroups. Additionally, SIRT offers an alternate to TACE with higher objective response rates in patients who needs bridging before transplantation. KEY MESSAGES New imaging criteria improved lesion detection in patients at a risk for HCC, and advances in interventional therapies expanded the range of patients eligible for locoregional treatments.
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Affiliation(s)
- Osman Öcal
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Daniel Rössler
- Department of Medicine II, University Hospital, LMU Munich, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Max Seidensticker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
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Cannella R, Sartoris R, Grégory J, Garzelli L, Vilgrain V, Ronot M, Dioguardi Burgio M. Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice. Br J Radiol 2021; 94:20210220. [PMID: 33989042 PMCID: PMC8173689 DOI: 10.1259/bjr.20210220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Magnetic resonance imaging (MRI) is highly important for the detection, characterization, and follow-up of focal liver lesions. Several quantitative MRI-based methods have been proposed in addition to qualitative imaging interpretation to improve the diagnostic work-up and prognostics in patients with focal liver lesions. This includes DWI with apparent diffusion coefficient measurements, intravoxel incoherent motion, perfusion imaging, MR elastography, and radiomics. Multiple research studies have reported promising results with quantitative MRI methods in various clinical settings. Nevertheless, applications in everyday clinical practice are limited. This review describes the basic principles of quantitative MRI-based techniques and discusses the main current applications and limitations for the assessment of focal liver lesions.
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Affiliation(s)
- Roberto Cannella
- Service de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France.,Section of Radiology - BiND, University Hospital "Paolo Giaccone", Via del Vespro 129, 90127 Palermo, Italy.,Department of Health Promotion Sciences Maternal and Infant Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, 90127 Palermo, Italy
| | | | - Jules Grégory
- Service de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France.,Université de Paris, Paris, France
| | - Lorenzo Garzelli
- Service de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France.,Université de Paris, Paris, France
| | - Valérie Vilgrain
- Service de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France.,Université de Paris, Paris, France.,INSERM U1149, CRI, Paris, France
| | - Maxime Ronot
- Service de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France.,Université de Paris, Paris, France.,INSERM U1149, CRI, Paris, France
| | - Marco Dioguardi Burgio
- Service de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France.,INSERM U1149, CRI, Paris, France
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46
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Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27:1664-1690. [PMID: 33967550 PMCID: PMC8072192 DOI: 10.3748/wjg.v27.i16.1664] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/11/2021] [Accepted: 03/17/2021] [Indexed: 02/06/2023] Open
Abstract
Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.
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Affiliation(s)
- Jia-Sheng Cao
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Zi-Yi Lu
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Ming-Yu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Bin Zhang
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Sarun Juengpanich
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Jia-Hao Hu
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Shi-Jie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Win Topatana
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xue-Yin Zhou
- School of Medicine, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
| | - Xu Feng
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Ji-Liang Shen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Yu Liu
- College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xiu-Jun Cai
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
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