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Deng K, Chen T, Leng Z, Yang F, Lu T, Cao J, Pan W, Zheng Y. Radiomics as a tool for prognostic prediction in transarterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:1099-1117. [PMID: 39060885 PMCID: PMC11322429 DOI: 10.1007/s11547-024-01840-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024]
Abstract
INTRODUCTION Transarterial chemoembolization (TACE) is one of the predominant locoregional therapeutic modalities for addressing hepatocellular carcinoma (HCC). However, achieving precise prognostic predictions and effective patient selection remains a challenging pursuit. The primary objective of this systematic review and meta-analysis is to evaluate the efficacy of radiomics in forecasting the prognosis associated with TACE treatment. METHODS A comprehensive exploration of pertinent original studies was undertaken, encompassing databases of PubMed, Web of Science and Embase. The studies' quality was meticulously evaluated employing the quality assessment of diagnostic accuracy studies 2 (QUADAS-2), the radiomics quality score (RQS) and the METhodological RadiomICs Score (METRICS). Pooled statistics, along with 95% confidence intervals (95% CI), were computed for sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Additionally, a summary receiver operating characteristic curve (sROC) was generated. To discern potential sources of heterogeneity, meta-regression and subgroup analyses were performed. RESULTS The systematic review incorporated 29 studies, comprising a total of 5483 patients, with 14 studies involving 2691 patients qualifying for inclusion in the meta-analysis. The assessed studies exhibited commendable quality with regard to bias risk, with mean RQS of 12.90 ± 5.13 (35.82% ± 14.25%) and mean METRICS of 62.98% ± 14.58%. The pooled sensitivity was 0.83 (95% CI: 0.78-0.87), specificity was 0.86 (95% CI: 0.79-0.92), PLR was 6.13 (95% CI: 3.79-9.90), and NLR was 0.20 (95% CI: 0.15-0.27). The area under the sROC was 0.90 (95% CI: 0.87-0.93). Significant heterogeneity within all the included studies was observed, while meta-regression and subgroup analyses revealed homogeneous and promising findings in subgroups where principal methodological variables such as modeling algorithms, imaging modalities, and imaging phases were specified. CONCLUSION Radiomics models have exhibited robust predictive capabilities concerning prognosis subsequent to TACE, thereby presenting promising prospects for clinical translation.
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Affiliation(s)
- Kaige Deng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Tong Chen
- Department of Medical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 100021, China
| | - Zijian Leng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Fan Yang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Tao Lu
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jingying Cao
- Zunyi Medical University, Zunyi, Guizhou, 563000, China
| | - Weixuan Pan
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.
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Hoffmann E, Masthoff M, Kunz WG, Seidensticker M, Bobe S, Gerwing M, Berdel WE, Schliemann C, Faber C, Wildgruber M. Multiparametric MRI for characterization of the tumour microenvironment. Nat Rev Clin Oncol 2024; 21:428-448. [PMID: 38641651 DOI: 10.1038/s41571-024-00891-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 04/21/2024]
Abstract
Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.
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Affiliation(s)
- Emily Hoffmann
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Max Masthoff
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Wolfgang G Kunz
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Max Seidensticker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Bobe
- Gerhard Domagk Institute of Pathology, University Hospital Münster, Münster, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, Münster, Germany
| | | | | | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Moritz Wildgruber
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
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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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Liu K, Zheng X, Lu D, Tan Y, Hou C, Dai J, Shi W, Jiang B, Yao Y, Lu Y, Cao Q, Chen R, Zhang W, Xie J, Chen L, Jiang M, Zhang Z, Liu L, Liu J, Li J, Lv W, Wu X. A multi-institutional study to predict the benefits of DEB-TACE and molecular targeted agent sequential therapy in unresectable hepatocellular carcinoma using a radiological-clinical nomogram. LA RADIOLOGIA MEDICA 2024; 129:14-28. [PMID: 37863847 DOI: 10.1007/s11547-023-01736-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 09/28/2023] [Indexed: 10/22/2023]
Abstract
OBJECTIVE Exploring the efficacy of a Radiological-Clinical (Rad-Clinical) model in predicting prognosis of unresectable hepatocellular carcinoma (HCC) patients after drug eluting beads transcatheter arterial chemoembolization (DEB-TACE) to optimize the targeted sequential treatment. METHODS In this retrospective analysis, we included 202 patients with unresectable HCC who received DEB-TACE treatment in 17 institutions from June 2018 to December 2022. Progression-free survival (PFS)-related radiomics features were computationally extracted from HCC patients to build a radiological signature (Rad-signature) model with least absolute shrinkage and selection operator regression. A Rad-Clinical model for postoperative PFS was further constructed according to the Rad-signature and clinical variables by Cox regression analysis. It was presented as a nomogram and evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. And further evaluate the application value of Rad-Clinical model in clinical stages and targeted sequential therapy of HCC. RESULTS Tumor size, Barcelona Clinic Liver Cancer (BCLC) stage, and radiomics score (Rad-score) were found to be independent risk factors for PFS after DEB-TACE treatment for unresectable HCC, with the Rad-Clinical model being the greatest predictor of PFS in these patients (hazard ratio: 2.08; 95% confidence interval: 1.56-2.78; P < 0.001) along with high 6 months, 12 months, 18 months, and 24 months area under the curves of 0.857, 0.810, 0.843, and 0.838, respectively. In addition, compared to the radiomics and clinical nomograms, the Radiological-Clinical nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (45.2%, 95% CI 0.260-0.632, p < 0.05) and integrated discrimination improvement (14.9%, 95% CI 0.064-0.281, p < 0.05). Based on this model, low-risk patients had higher PFS than high-risk patients in BCLC-B and C stages (P = 0.021). Targeted sequential therapy for patients with high and low-risk HCC in BCLC-B stage exhibited significant benefits (P = 0.018, P = 0.012), but patients with high-risk HCC in BCLC-C stage did not benefit much (P = 0.052). CONCLUSION The Rad-Clinical model may be favorable for predicting PFS in patients with unresectable HCC treated with DEB-TACE and for identifying patients who may benefit from targeted sequential therapy.
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Affiliation(s)
- Kaicai Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Xiaomin Zheng
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Dong Lu
- Department of Interventional Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China
| | - Yulin Tan
- Department of Interventional Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, China
| | - Changlong Hou
- Department of Interventional Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China
| | - Jiaying Dai
- Department of Interventional Radiology, Anqing Municipal Hospital, Anqing, 246000, Anhui, China
| | - Wanyin Shi
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Bo Jiang
- Department of Interventional Ultrasound, The Second Affiliated Hospital, Anhui Medical University, Hefei, 230022, Anhui, China
| | - Yibin Yao
- Department of Radiology, Tongling People's Hospital, Tongling, 244300, Anhui, China
| | - Yuhe Lu
- Department of Interventional Radiology, Chuzhou First People's Hospital, Chuzhou, 233290, Anhui, China
| | - Qisheng Cao
- Department of Interventional Radiology, Maanshan City People's Hospital, Maanshan, 243000, Anhui, China
| | - Ruiwen Chen
- Department of Interventional Radiology, Huainan First People's Hospital, Huainan, 232000, Anhui, China
| | - Wangao Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, 230022, Anhui, China
| | - Jun Xie
- Department of Radiology, Fuyang People's Hospital, Fuyang, 236600, Anhui, China
| | - Lei Chen
- Department of Radiology, Fuyang Second People's Hospital, Fuyang, 236600, Anhui, China
| | - Mouying Jiang
- Department of Radiology, Anqing First People's Hospital, Anqing, 246000, Anhui, China
| | - Zhang Zhang
- Department of Radiology, Wuhu Second People's Hospital, Wuhu, 241000, Anhui, China
| | - Lu Liu
- Department of Radiology, Funan Third Hospital, Fuyang, 236600, Anhui, China
| | - Jie Liu
- Department of Radiology, Yingshang County People's Hospital, Fuyang, 236600, Anhui, China
| | - Jianying Li
- CT Advanced Application, GE HealthCare China, Beijing, 100186, China
| | - Weifu Lv
- Department of Interventional Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
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Gómez FM, Van der Reijd DJ, Panfilov IA, Baetens T, Wiese K, Haverkamp-Begemann N, Lam SW, Runge JH, Rice SL, Klompenhouwer EG, Maas M, Helmberger T, Beets-Tan RG. Imaging in interventional oncology, the better you see, the better you treat. J Med Imaging Radiat Oncol 2023; 67:895-902. [PMID: 38062853 DOI: 10.1111/1754-9485.13610] [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: 04/06/2023] [Accepted: 11/22/2023] [Indexed: 01/14/2024]
Abstract
Imaging and image processing is the fundamental pillar of interventional oncology in which diagnostic, procedure planning, treatment and follow-up are sustained. Knowing all the possibilities that the different image modalities can offer is capital to select the most appropriate and accurate guidance for interventional procedures. Despite there is a wide variability in physicians preferences and availability of the different image modalities to guide interventional procedures, it is important to recognize the advantages and limitations for each of them. In this review, we aim to provide an overview of the most frequently used image guidance modalities for interventional procedures and its typical and future applications including angiography, computed tomography (CT) and spectral CT, magnetic resonance imaging, Ultrasound and the use of hybrid systems. Finally, we resume the possible role of artificial intelligence related to image in patient selection, treatment and follow-up.
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Affiliation(s)
- Fernando M Gómez
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Valencia, Spain
- Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Ilia A Panfilov
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Tarik Baetens
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Kevin Wiese
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Siu W Lam
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jurgen H Runge
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Samuel L Rice
- Radiology, Interventional Radiology Section, UT Southwestern Medical Center, Dallas, TX, USA
| | | | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Thomas Helmberger
- Institut für Radiologie, Neuroradiologie und Minimal-Invasive Therapie, München Klinik Bogenhausen, Munich, Germany
| | - Regina Gh Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands
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Feng L, Chen Q, Huang L, Long L. Radiomics features of computed tomography and magnetic resonance imaging for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis. Front Oncol 2023; 13:1194200. [PMID: 37519801 PMCID: PMC10374837 DOI: 10.3389/fonc.2023.1194200] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 06/26/2023] [Indexed: 08/01/2023] Open
Abstract
Purpose To examine the methodological quality of radiomics-related studies and evaluate the ability of radiomics to predict treatment response to transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC). Methods A systematic review was performed on radiomics-related studies published until October 15, 2022, predicting the effectiveness of TACE for HCC. Methodological quality and risk of bias were assessed using the Radiomics Quality Score (RQS) and Quality Assessment of Diagnostic Accuracy Studies-2 tools, respectively. Pooled sensitivity, pooled specificity, and area under the curve (AUC) were determined to evaluate the utility of radiomics in predicting the response to TACE for HCC. Results In this systematic review, ten studies were eligible, and six of these studies were used in our meta-analysis. The RQS ranged from 7-21 (maximum possible score: 36). The pooled sensitivity and specificity were 0.89 (95% confidence interval (CI) = 0.79-0.95) and 0.82 (95% CI = 0.64-0.92), respectively. The overall AUC was 0.93 (95% CI = 0.90-0.95). Conclusion Radiomics-related studies evaluating the efficacy of TACE in patients with HCC revealed promising results. However, prospective and multicenter trials are warranted to make radiomics more feasible and acceptable.
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Affiliation(s)
- Lijuan Feng
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qianjuan Chen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Linjie Huang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Wang Y, Liu Z, Xu H, Yang D, Jiang J, Asayo H, Yang Z. MRI-based radiomics model and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma. BMC Med Imaging 2023; 23:67. [PMID: 37254089 DOI: 10.1186/s12880-023-01030-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 05/23/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Prediction of locoregional treatment response is important for further therapeutic strategy in patients with hepatocellular carcinoma. This study aimed to investigate the role of MRI-based radiomics and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma. METHODS The initial postoperative MRI after locoregional treatment in 100 patients with hepatocellular carcinoma was retrospectively analysed. The outcome was evaluated according to mRECIST at 6 months. We delineated the tumour volume of interest on arterial phase, portal venous phase and T2WI. The radiomics features were selected by using the independent sample t test or nonparametric Mann‒Whitney U test and the least absolute shrinkage and selection operator. The clinical variables were selected by using univariate analysis and multivariate analysis. The radiomics model and combined model were constructed via multivariate logistic regression analysis. A nomogram was constructed that incorporated the Rad score and selected clinical variables. RESULTS Fifty patients had an objective response, and fifty patients had a nonresponse. Nine radiomics features in the arterial phase were selected, but none of the portal venous phase or T2WI radiomics features were predictive of the treatment response. The best radiomics model showed an AUC of 0.833. Two clinical variables (hCRP and therapy method) were selected. The AUC of the combined model was 0.867. There was no significant difference in the AUC between the combined model and the best radiomics model (P = 0.573). Decision curve analysis demonstrated the nomogram has satisfactory predictive value. CONCLUSIONS MRI-based radiomics analysis may serve as a promising and noninvasive tool to predict outcome of locoregional treatment in HCC patients, which will facilitate the individualized follow-up and further therapeutic strategies guidance.
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Affiliation(s)
- Yuxin Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Zhenhao Liu
- Department of Radiology, Affiliated Hospital of Changzhi Institute of Traditional Chinese Medicine, Changzhi, 046099, China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Dawei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Jiahui Jiang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Himeko Asayo
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
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8
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Chen TY, Yang ZG, Li Y, Li MQ. Radiomic advances in the transarterial chemoembolization related therapy for hepatocellular carcinoma. World J Radiol 2023; 15:89-97. [PMID: 37181821 PMCID: PMC10167813 DOI: 10.4329/wjr.v15.i4.89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/03/2023] [Accepted: 03/30/2023] [Indexed: 04/26/2023] Open
Abstract
Radiomics is a hot topic in the research on customized oncology treatment, efficacy evaluation, and tumor prognosis prediction. To achieve the goal of mining the heterogeneity information within the tumor tissue, the image features concealed within the tumoral images are turned into quantifiable data features. This article primarily describes the research progress of radiomics and clinical-radiomics combined model in the prediction of efficacy, the choice of treatment modality, and survival in transarterial chemoembolization (TACE) and TACE combination therapy for hepatocellular carcinoma.
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Affiliation(s)
- Tian-You Chen
- Department of Interventional Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Zong-Guo Yang
- Department of Integrative Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Ying Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China
| | - Mao-Quan Li
- Department of Interventional & Vascular Surgery, Tenth People's Hospital of Tongji University, Tongji University, Shanghai 200433, China
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Criss C, Nagar AM, Makary MS. Hepatocellular carcinoma: State of the art diagnostic imaging. World J Radiol 2023; 15:56-68. [PMID: 37035828 PMCID: PMC10080581 DOI: 10.4329/wjr.v15.i3.56] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/12/2023] [Accepted: 03/22/2023] [Indexed: 03/27/2023] Open
Abstract
Primary liver cancer is the fourth most common malignancy worldwide, with hepatocellular carcinoma (HCC) comprising up to 90% of cases. Imaging is a staple for surveillance and diagnostic criteria for HCC in current guidelines. Because early diagnosis can impact treatment approaches, utilizing new imaging methods and protocols to aid in differentiation and tumor grading provides a unique opportunity to drastically impact patient prognosis. Within this review manuscript, we provide an overview of imaging modalities used to screen and evaluate HCC. We also briefly discuss emerging uses of new imaging techniques that offer the potential for improving current paradigms for HCC characterization, management, and treatment monitoring.
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Affiliation(s)
- Cody Criss
- Heritage College of Osteopathic Medicine, Ohio University, Athens, OH 45701, United States
| | - Arpit M Nagar
- Department of Radiology, The Ohio State University Medical Center, Columbus, OH 43210, United States
| | - Mina S Makary
- Department of Radiology, The Ohio State University Medical Center, Columbus, OH 43210, United States
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Chen M, Kong C, Qiao E, Chen Y, Chen W, Jiang X, Fang S, Zhang D, Chen M, Chen W, Ji J. Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI. Insights Imaging 2023; 14:38. [PMID: 36854872 PMCID: PMC9975141 DOI: 10.1186/s13244-023-01380-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/29/2023] [Indexed: 03/02/2023] Open
Abstract
OBJECTIVES This study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance. METHODS The subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN). RESULTS DNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951-0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735). CONCLUSIONS DNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.
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Affiliation(s)
- Mingzhen Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Chunli Kong
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Enqi Qiao
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Yaning Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Weiyue Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Xiaole Jiang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Shiji Fang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Dengke Zhang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Minjiang Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Weiqian Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. .,Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. .,Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. .,Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. .,Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
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11
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Fang C, Luo R, Zhang Y, Wang J, Feng K, Liu S, Chen C, Yao R, Shi H, Zhong C. Hepatectomy versus transcatheter arterial chemoembolization for resectable BCLC stage A/B hepatocellular carcinoma beyond Milan criteria: A randomized clinical trial. Front Oncol 2023; 13:1101162. [PMID: 36923427 PMCID: PMC10010190 DOI: 10.3389/fonc.2023.1101162] [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: 11/17/2022] [Accepted: 02/03/2023] [Indexed: 03/02/2023] Open
Abstract
Background Hepatectomy is the recommended option for radical treatment of BCLC stage A/B hepatocellular carcinoma (HCC) that has progressed beyond the Milan criteria. This study evaluated the efficacy and safety of preoperative neoadjuvant transcatheter arterial chemoembolization (TACE) for these patients. Methods In this prospective, randomized, open-label clinical study, BCLC stage A/B HCC patients beyond the Milan criteria were randomly assigned (1:1) to receive either neoadjuvant TACE prior to hepatectomy (NT group) or hepatectomy alone (OP group). The primary outcome was overall survival (OS), while the secondary outcomes were progression-free survival (PFS) and adverse events (AEs). Results Of 249 patients screened, 164 meeting the inclusion criteria were randomly assigned to either the NT group (n = 82) or OP group (n = 82) and completed follow-up requirements. Overall survival was significantly greater in the NT group compared to the OP group at 1 year (97.2% vs. 82.4%), two years (88.4% vs. 60.4%), and three years (71.6% vs. 45.7%) (p = 0.0011) post-treatment. Similarly, PFS was significantly longer in the NT group than the OP group at 1 year (60.1% vs. 39.9%), 2 years (53.4% vs. 24.5%), and 3 years (42.2% vs. 24.5%) (p = 0.0003). No patients reported adverse events of grade 3 or above in either group. Conclusions Neoadjuvant TACE prolongs the survival of BCLC stage A/B HCC patients beyond the Milan criteria without increasing severe adverse events frequency. Clinical trial registration https://www.chictr.org.cn/, identifier ChiCTR2200055618.
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Affiliation(s)
- Chongkai Fang
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Rui Luo
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ying Zhang
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jinan Wang
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Kunliang Feng
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Silin Liu
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chuyao Chen
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ruiwei Yao
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Hanqian Shi
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chong Zhong
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.,The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lingnan Medical Research Center of Guangzhou University of Chinese Medicine, Guangzhou, China
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12
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Qin S, Lin Z, Liu N, Zheng Y, Jia Q, Huang X. Prediction of postoperative reintervention risk for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound ablation. Int J Hyperthermia 2023; 40:2226847. [PMID: 37394476 DOI: 10.1080/02656736.2023.2226847] [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: 03/31/2023] [Revised: 06/07/2023] [Accepted: 06/13/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVE To predict the risk of postoperative reintervention for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound (HIFU) ablation. METHODS Among patients with uterine fibroids treated with HIFU from 2019 to 2021, 180 were selected per the inclusion and exclusion criteria (42 reintervention and 138 non-reintervention). All patients were randomly assigned to either the training (n = 125) or validation (n = 55) cohorts. Multivariate analysis was used to determine independent clinical-imaging features of reintervention risk. The Relief and LASSO algorithm were used to select optimal radiomics features. Random forest was used to construct the clinical-imaging model based on independent clinical-imaging features, the radiomics model based on optimal radiomics features, and the combined model incorporating the above features. An independent test cohort of 45 patients with uterine fibroids tested these models. The integrated discrimination index (IDI) was used to compare the discrimination performance of these models. RESULTS Age (p < .001), fibroid volume (p = .001) and fibroid enhancement degree (p = .001) were identified as independent clinical-imaging features. The combined model had AUCs of 0.821 (95% CI: 0.712-0.931) and 0.818 (95% CI: 0.694-0.943) in the validation and independent test cohorts, respectively. The predictive performance of the combined model was 27.8% (independent test cohort, p < .001) and 29.5% (independent test cohort, p = .001) better than the clinical-imaging and radiomics models, respectively. CONCLUSION The combined model can effectively predict the risk of postoperative reintervention for uterine fibroids before HIFU ablation. It is expected to help clinicians to develop accurate, personalized treatment and management plans. Future studies will need to be prospectively validated.
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Affiliation(s)
- Shize Qin
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Zhenyang Lin
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Nian Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yulin Zheng
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Qing Jia
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiaohua Huang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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13
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Qin S, Jiang Y, Wang F, Tang L, Huang X. Development and validation of a combined model based on dual-sequence MRI radiomics for predicting the efficacy of high-intensity focused ultrasound ablation for hysteromyoma. Int J Hyperthermia 2022; 40:2149862. [PMID: 36535929 DOI: 10.1080/02656736.2022.2149862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES To determine the value of dual-sequence magnetic resonance imaging (MRI)-based radiomics in predicting the efficacy of high-intensity focused ultrasound (HIFU) ablation for hysteromyoma. METHODS A total of 142 patients with 172 hysteromyomas (95 hysteromyomas from the sufficient ablation group, and 77 hysteromyomas from the insufficient ablation group) were enrolled in the study. The clinical-radiological model was constructed with independent clinical-radiological risk factors, the radiomics model was constructed based on the optimal radiomics features of hysteromyoma from dual sequences, and the two groups of features were incorporated to construct the combined model. A fivefold cross validation procedure was adopted to validate these models. A nomogram was constructed, applying the combined model in the training cohort. The models were assessed with receiver operating characteristic (ROC) curves and integrated discrimination improvement (IDI). An independent test cohort comprising 40 patients was used to evaluate the performance of the optimal model. RESULTS Among the three models, the average areas under the ROC curves (AUC) of the radiomics model and combined model were 0.803 (95% confidence interval (CI): 0.726-0.881) and 0.841 (95% CI: 0.772-0.909), which were better than the clinical-radiological model in the training cohort. The IDI showed that the combined model had the best prediction accuracy. The combined model also showed good discrimination in both the validation cohort (AUC = 0.834) and the independent test cohort (AUC = 0.801). CONCLUSION The combined model based on the dual-sequence MRI radiomics is the most promising tool from our study to assist clinicians in predicting HIFU ablation efficacy.
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Affiliation(s)
- Shize Qin
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yu Jiang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Lingling Tang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiaohua Huang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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14
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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15
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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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16
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Cannella R, Cammà C, Matteini F, Celsa C, Giuffrida P, Enea M, Comelli A, Stefano A, Cammà C, Midiri M, Lagalla R, Brancatelli G, Vernuccio F. Radiomics Analysis on Gadoxetate Disodium-Enhanced MRI Predicts Response to Transarterial Embolization in Patients with HCC. Diagnostics (Basel) 2022; 12:diagnostics12061308. [PMID: 35741118 PMCID: PMC9221802 DOI: 10.3390/diagnostics12061308] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/17/2022] [Accepted: 05/20/2022] [Indexed: 02/04/2023] Open
Abstract
Objectives: To explore the potential of radiomics on gadoxetate disodium-enhanced MRI for predicting hepatocellular carcinoma (HCC) response after transarterial embolization (TAE). Methods: This retrospective study included cirrhotic patients treated with TAE for unifocal HCC naïve to treatments. Each patient underwent gadoxetate disodium-enhanced MRI. Radiomics analysis was performed by segmenting the lesions on portal venous (PVP), 3-min transitional, and 20-min hepatobiliary (HBP) phases. Clinical data, laboratory variables, and qualitative features based on LI-RADSv2018 were assessed. Reference standard was based on mRECIST response criteria. Two different radiomics models were constructed, a statistical model based on logistic regression with elastic net penalty (model 1) and a computational model based on a hybrid descriptive-inferential feature extraction method (model 2). Areas under the ROC curves (AUC) were calculated. Results: The final population included 51 patients with HCC (median size 20 mm). Complete and objective responses were obtained in 14 (27.4%) and 29 (56.9%) patients, respectively. Model 1 showed the highest performance on PVP for predicting objective response with an AUC of 0.733, sensitivity of 100%, and specificity of 40.0% in the test set. Model 2 demonstrated similar performances on PVP and HBP for predicting objective response, with an AUC of 0.791, sensitivity of 71.3%, specificity of 61.7% on PVP, and AUC of 0.790, sensitivity of 58.8%, and specificity of 90.1% on HBP. Conclusions: Radiomics models based on gadoxetate disodium-enhanced MRI can achieve good performance for predicting response of HCCs treated with TAE.
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Affiliation(s)
- Roberto Cannella
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127 Palermo, Italy; (F.M.); (M.M.); (R.L.); (G.B.)
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, 90127 Palermo, Italy; (C.C.); (P.G.); (M.E.); (C.C.)
- Correspondence: (R.C.); (F.V.)
| | - Carla Cammà
- University of Palermo, Piazza Marina, 61, 90133 Palermo, Italy;
| | - Francesco Matteini
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127 Palermo, Italy; (F.M.); (M.M.); (R.L.); (G.B.)
| | - Ciro Celsa
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, 90127 Palermo, Italy; (C.C.); (P.G.); (M.E.); (C.C.)
- Department of Surgical, Oncological and Oral Sciences (D.C.O.S.), University of Palermo, 90133 Palermo, Italy
| | - Paolo Giuffrida
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, 90127 Palermo, Italy; (C.C.); (P.G.); (M.E.); (C.C.)
| | - Marco Enea
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, 90127 Palermo, Italy; (C.C.); (P.G.); (M.E.); (C.C.)
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy;
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada Pietrapollastra-Pisciotto, 90015 Cefalù, Italy;
| | - Calogero Cammà
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, 90127 Palermo, Italy; (C.C.); (P.G.); (M.E.); (C.C.)
| | - Massimo Midiri
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127 Palermo, Italy; (F.M.); (M.M.); (R.L.); (G.B.)
| | - Roberto Lagalla
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127 Palermo, Italy; (F.M.); (M.M.); (R.L.); (G.B.)
| | - Giuseppe Brancatelli
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127 Palermo, Italy; (F.M.); (M.M.); (R.L.); (G.B.)
| | - Federica Vernuccio
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127 Palermo, Italy; (F.M.); (M.M.); (R.L.); (G.B.)
- Department of Radiology, University Hospital of Padova, Via Nicolò Giustiniani, 2, 35128 Padua, Italy
- Correspondence: (R.C.); (F.V.)
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17
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Li Y, Xu Z, An C, Chen H, Li X. Multi-Task Deep Learning Approach for Simultaneous Objective Response Prediction and Tumor Segmentation in HCC Patients with Transarterial Chemoembolization. J Pers Med 2022; 12:jpm12020248. [PMID: 35207736 PMCID: PMC8875107 DOI: 10.3390/jpm12020248] [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: 12/22/2021] [Revised: 02/01/2022] [Accepted: 02/08/2022] [Indexed: 12/24/2022] Open
Abstract
This study aimed to develop a deep learning-based model to simultaneously perform the objective response (OR) and tumor segmentation for hepatocellular carcinoma (HCC) patients who underwent transarterial chemoembolization (TACE) treatment. A total of 248 patients from two hospitals were retrospectively included and divided into the training, internal validation, and external testing cohort. A network consisting of an encoder pathway, a prediction pathway, and a segmentation pathway was developed, and named multi-DL (multi-task deep learning), using contrast-enhanced CT images as input. We compared multi-DL with other deep learning-based OR prediction and tumor segmentation methods to explore the incremental value of introducing the interconnected task into a unified network. Additionally, the clinical model was developed using multivariate logistic regression to predict OR. Results showed that multi-DL could achieve the highest AUC of 0.871 in OR prediction and the highest dice coefficient of 73.6% in tumor segmentation. Furthermore, multi-DL can successfully perform the risk stratification that the low-risk and high-risk patients showed a significant difference in survival (p = 0.006). In conclusion, the proposed method may provide a useful tool for therapeutic regime selection in clinical practice.
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Affiliation(s)
- Yuze Li
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing 100084, China; (Y.L.); (Z.X.)
| | - Ziming Xu
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing 100084, China; (Y.L.); (Z.X.)
| | - Chao An
- Department of Minimal Invasive Intervention, Sun Yat-sen University Cancer Center, Guangzhou 510060, China;
| | - Huijun Chen
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing 100084, China; (Y.L.); (Z.X.)
- Correspondence: (H.C.); (X.L.)
| | - Xiao Li
- Department of Interventional Therapy, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- Correspondence: (H.C.); (X.L.)
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18
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Dong Z, Lin Y, Lin F, Luo X, Lin Z, Zhang Y, Li L, Li ZP, Feng ST, Cai H, Peng Z. Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography. J Hepatocell Carcinoma 2021; 8:1473-1484. [PMID: 34877267 PMCID: PMC8643205 DOI: 10.2147/jhc.s334674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/05/2021] [Indexed: 12/20/2022] Open
Abstract
Purpose The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment. Patients and Methods Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model. Results Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively. Conclusion Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.
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Affiliation(s)
- Zhi Dong
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Fangzeng Lin
- Department of Interventional Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Xuyi Luo
- Department of Emergency, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Zhi Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Yinhong Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Lujie Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Zi-Ping Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Huasong Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Zhenpeng Peng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
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Zou ZM, An TZ, Li JX, Zhang ZS, Xiao YD, Liu J. Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study. J Cancer 2021; 12:7079-7087. [PMID: 34729109 PMCID: PMC8558659 DOI: 10.7150/jca.63370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 10/03/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose: To develop and validate a random forest (RF) based predictive model of early refractoriness to transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (HCC). Methods: A total of 227 patients with unresectable HCC who initially treated with TACE from three independent institutions were retrospectively included. Following a random split, 158 patients (70%) were assigned to a training cohort and the remaining 69 patients (30%) were assigned to a validation cohort. The process of variables selection was based on the importance variable scores generated by RF algorithm. A RF predictive model incorporating the selected variables was developed, and five-fold cross-validation was performed. The discrimination and calibration of the RF model were measured by a receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test. Results: The potential variables selected by RF algorithm for developing predictive model of early TACE refractoriness included patients' age, number of tumors, tumor distribution, platelet count (PLT), and neutrophil-to-lymphocyte ratio (NLR). The results showed that the RF predictive model had good discrimination ability, with an area under curve (AUC) of 0.863 in the training cohort and 0.767 in the validation cohort, respectively. In Hosmer-Lemeshow test, the RF model had a satisfactory calibration with P values of 0.538 and 0.068 in training cohort and validation cohort, respectively. Conclusion: The RF algorithm-based model has a good predictive performance in the prediction of early TACE refractoriness, which may easily be deployed in clinical routine and help to determine the optimal patient of care.
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Affiliation(s)
- Zhi-Min Zou
- Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, 410011, China.,Department of Radiology, Hunan Children's Hospital, Changsha, 410007, China
| | - Tian-Zhi An
- Department of Interventional Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550002, China
| | - Jun-Xiang Li
- Department of Interventional Radiology, Guizhou Medical University Affiliated Cancer Hospital, Guiyang, 550004, China
| | - Zi-Shu Zhang
- Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, 410011, China
| | - Yu-Dong Xiao
- Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, 410011, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, 410011, China.,Department of Radiology Quality Control Center, Changsha, 410011, China
| | - Jun Liu
- Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, 410011, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, 410011, China.,Department of Radiology Quality Control Center, Changsha, 410011, China
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20
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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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21
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Jin ZC, Zhong BY. Application of radiomics in hepatocellular carcinoma: A review. Artif Intell Med Imaging 2021; 2:64-72. [DOI: 10.35711/aimi.v2.i3.64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/19/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with low 5-year survival rate. The high molecular heterogeneity in HCC poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of HCC. However, current clinical practice based on single bioptic or archived tumor tissue has been deficient in identifying useful biomarkers. The concept of radiomics was first proposed in 2012 and is different from the traditional imaging analysis based on the qualitative or semi-quantitative analysis by radiologists. Radiomics refers to high-throughput extraction of large amounts number of high-dimensional quantitative features from medical images through machine learning or deep learning algorithms. Using the radiomics method could quantify tumoral phenotypes and heterogeneity, which may provide benefits in clinical decision-making at a lower cost. Here, we review the workflow and application of radiomics in HCC.
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Affiliation(s)
- Zhi-Cheng Jin
- Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Bin-Yan Zhong
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China
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