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Nong HY, Cen YY, Qin M, Qin WQ, Xie YX, Li L, Liu MR, Ding K. Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study. World J Gastrointest Oncol 2024; 16:1309-1318. [PMID: 38660663 PMCID: PMC11037072 DOI: 10.4251/wjgo.v16.i4.1309] [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: 10/30/2023] [Revised: 12/18/2023] [Accepted: 02/05/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC. AIM To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC. METHODS This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR). RESULTS Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%. CONCLUSION Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.
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Affiliation(s)
- Hai-Yang Nong
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
- Department of Radiology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Yong-Yi Cen
- Department of Radiology, The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Mi Qin
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Wen-Qi Qin
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - You-Xiang Xie
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Lin Li
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Man-Rong Liu
- Department of Ultrasound, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Ke Ding
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
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钟 伟, 梁 芳, 杨 蕊, 甄 鑫. [Prediction of microvascular invasion in hepatocellular carcinoma based on multi-phase dynamic enhanced CT radiomics feature and multi-classifier hierarchical fusion model]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2024; 44:260-269. [PMID: 38501411 PMCID: PMC10954521 DOI: 10.12122/j.issn.1673-4254.2024.02.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Indexed: 03/20/2024]
Abstract
OBJECTIVE To predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using a model based on multiphase dynamic-enhanced CT (DCE-CT) radiomics feature and hierarchical fusion of multiple classifiers. METHODS We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January, 2016 and April, 2020. The volume of interest was outlined in the early arterial phase, late arterial phase, portal venous phase and equilibrium phase, and radiomics features of these 4 phases were extracted. Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase. According to the hierarchical fusion strategy, a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model. The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve (AUC), accuracy, sensitivity, and specificity. The prediction model was also compared with the fusion models using a single phase or multiple phases, models based on a single phase with a single classifier, models with different base classifier diversities, and 8 classifier models based on other ensemble methods. RESULTS The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers, with AUC, accuracy, sensitivity, and specificity of 0.828, 0.766, 0.877, and 0.648, respectively. Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models. CONCLUSION The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.
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Affiliation(s)
- 伟雄 钟
- 南方医科大学生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 芳蓉 梁
- 华南理工大学医学院,广东 广州 510006School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - 蕊梦 杨
- 华南理工大学附属第二医院(广州市第一人民医院)放射科,广东 广州 510180Department of Radiology, Second Affiliated Hospital of South China University of Technology (Guangzhou First People's Hospital), Guangzhou 510180, China
- 华南理工大学医学院,广东 广州 510006School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - 鑫 甄
- 南方医科大学生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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Zhou Z, Xia T, Zhang T, Du M, Zhong J, Huang Y, Xuan K, Xu G, Wan Z, Ju S, Xu J. Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography. Abdom Radiol (NY) 2024; 49:611-624. [PMID: 38051358 DOI: 10.1007/s00261-023-04102-w] [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: 08/16/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 12/07/2023]
Abstract
PURPOSE Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.
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Affiliation(s)
- Zhenghao Zhou
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Teng Zhang
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Mingyang Du
- Cerebrovascular Disease Treatment Center, Nanjing Brain Hospital Affiliated to Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Jiarui Zhong
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Yunzhi Huang
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Kai Xuan
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Geyang Xu
- Information School, University of Washington, Seattle, WA, 98195, USA
| | - Zhuo Wan
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Shenghong Ju
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China.
| | - Jun Xu
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
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Huang Z, Xin JY, Wu LL, Luo HC, Li K. Dynamic contrast-enhanced ultrasonography with sonazoid predicts microvascular invasion in early-stage hepatocellular carcinoma. Br J Radiol 2023; 96:20230164. [PMID: 37750942 PMCID: PMC10607401 DOI: 10.1259/bjr.20230164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 07/24/2023] [Accepted: 08/17/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE Microvascular invasion (MVI) is an independent risk factor for the early recurrence and poor survival of hepatocellular carcinoma (HCC). This study aims to investigate the potential clinical value of dynamic contrast-enhanced ultrasound (DCE-ultrasound)-Sonazoid in pre-operatively assessing MVI in HCC. METHODS AND MATERIALS This single centre prospective study included 140 patients with histopathologically confirmed single HCC lesions. Patients were classified according to the post-operative pathological information presence of MVI: MVI+ group (n = 32) and MVI- group (n = 108). All patients underwent DCE-ultrasound within 1 week before surgery. The quantitative perfusion parameters of HCC lesions, margins of HCC lesions, and distal liver parenchyma were obtained and analyzed. RESULTS Clinicopathological (serum alpha-fetoprotein, Des-gamma-carboxyprothrombin, and pathological grade) and grayscale imaging features (tumor size) were significantly different between the MVI+ and MVI- groups (p < 0.05). Further quantitative analysis showed that when comparing the MVI+ and MVI- groups, half-decrease time and wash-out rate of HCC lesions and peak enhancement in the arterial phase of difference between the margin area of HCC and distal liver parenchyma were significantly different (p = 0.045, p = 0.035, and p = 0.023, respectively). Combining the above three quantitative parameters, the accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 69.3% (97/140), 37.8% (17/45), 84.3% (80/95), 53.1% (17/32), 74.1% (80/108), respectively. CONCLUSION DCE-ultrasound with quantitative perfusion analysis has the potential to predict MVI in HCC lesions. ADVANCES IN KNOWLEDGE DCE-ultrasound with quantitative perfusion analysis has the potential to predict MVI in HCC lesions.
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Affiliation(s)
- Zhe Huang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, Hubei Province, China
| | - Jun-Yi Xin
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, Hubei Province, China
| | - Ling-Ling Wu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, Hubei Province, China
| | - Hong-Chang Luo
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, Hubei Province, China
| | - Kaiyan Li
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, Hubei Province, China
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Da Fonseca LG, Yamamoto VJ, Trinconi Cunha M, Torre GS, Araujo RLC, Fonseca GM, Chen ATC, Chagas AL, Herman P, Alves VAF, Carrilho FJ. Treatment Outcomes in Patients with Advanced Fibrolamellar Hepatocellular Carcinoma Under Systemic Treatment: Analysis of Clinical Characteristics, Management, and Radiomics. J Hepatocell Carcinoma 2023; 10:1923-1933. [PMID: 37933267 PMCID: PMC10625783 DOI: 10.2147/jhc.s428741] [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: 07/31/2023] [Accepted: 10/24/2023] [Indexed: 11/08/2023] Open
Abstract
Purpose Fibrolamellar hepatocellular carcinoma (FLHCC) is a rare primary liver malignancy often diagnosed at advanced stages. While there are limited data on the efficacy of specific agents, we aim to report outcomes of patients treated with systemic therapies and explore prognostic factors. Patients and Methods Medical records of patients treated between 2010 and 2022 were reviewed. Treatments were defined after multidisciplinary assessment. Descriptive statistics were used for baseline demographics. Time-to-event outcomes were estimated using the Kaplan-Meier method, compared by log-rank and adjusted by a regression model. Radiomic features (including size, shape, and texture) of the primary lesion were extracted and dimensionality reduced. An unsupervised Gaussian Mixture Model (GMM) clustering was performed, and survival was compared between clusters. Results We identified 23 patients: 12 males, with a median age of 23.6 years. At diagnosis, 82.6% had metastases, most frequently to the lungs (39.1%), lymph nodes (39.1%), and peritoneum (21.7%). Patients received a median of three lines (1-8) of treatment, including different regimens. Sorafenib (39.1%), capecitabine (30.4%), and capecitabine/interferon (13%) were the most used first-line regimens. The median time-to-failure was 3.8 months (95% CI: 3.2-8.7). Capecitabine + interferon (42.1%) and platinum combinations (39.1%) were the most used second-line regimens, with a time-to-failure of 3.5 months (95% CI: 1.5-11.6). Median overall survival was 26.7 months (95% CI: 15.1-40.4). A high baseline neutrophil-to-lymphocyte ratio (NLR) was associated with worse survival (p=0.02). Radiomic features identified three clusters, with one cluster (n=6) having better survival (40.4 vs 22.6 months, p=0.039). Tumor sphericity in the arterial phase was the most relevant characteristic associated with a better prognosis (accuracy=0.93). Conclusion FLHCC has unique features compared to conventional HCC, including young onset, gender balance, and absence of hepatopathy. Systemic therapies can provide encouraging survival, but lack of uniformity precludes defining a preferable regimen. Radiomics and NLR were suggested to correlate with prognosis and warrant further validation.
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Affiliation(s)
- Leonardo G Da Fonseca
- Department of Medical Oncology, ICESP - Instituto do Cancer DO Estado de Sao Paulo, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Victor Junji Yamamoto
- Department of Medical Oncology, ICESP - Instituto do Cancer DO Estado de Sao Paulo, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Mateus Trinconi Cunha
- Department of Medical Oncology, ICESP - Instituto do Cancer DO Estado de Sao Paulo, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Giovanna Sawaya Torre
- Department of Radiology, ICESP - Instituto do Cancer do Estado de Sao Paulo, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Raphael L C Araujo
- Digestive Surgery Division, Department of Surgery, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Gilton Marques Fonseca
- Digestive Surgery Division, Department of Gastroenterology, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Andre Tsin Chih Chen
- Radiation Oncology Department - Instituto do Cancer do Estado de Sao Paulo, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Aline Lopes Chagas
- Division of Clinical Gastroenterology and Hepatology, Department of Gastroenterology, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Paulo Herman
- Digestive Surgery Division, Department of Gastroenterology, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | | | - Flair Jose Carrilho
- Division of Clinical Gastroenterology and Hepatology, Department of Gastroenterology, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
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Li Q, Luo Y, Liu D, Li B, Liu Y, Wang T. Construction and prognostic value of enhanced CT image omics model for noninvasive prediction of HRG in bladder cancer based on logistic regression and support vector machine algorithm. Front Oncol 2023; 12:966506. [PMID: 36727079 PMCID: PMC9884970 DOI: 10.3389/fonc.2022.966506] [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: 06/11/2022] [Accepted: 12/29/2022] [Indexed: 01/18/2023] Open
Abstract
Background Urothelial Carcinoma of the bladder (BLCA) is the most prevalent cancer of the urinary system. In cancer patients, HRG fusion is linked to a poor prognosis. The prediction of HRG expression by imaging omics in BLCA has not yet been fully investigated. Methods HRG expression in BLCA and healthy adjoining tissues was primarily identified utilizing data sourced from The Cancer Genome Atlas (TCGA). Using Kaplan-Meier survival curves and Landmark analysis, the relationship between HRG expression, clinicopathological parameters, and overall survival (OS) was investigated. Additionally, gene set variation analysis (GSVA) was conducted and CIBERSORTx was used to investigate the relationship between HRG expression and immune cell infiltration. The Cancer Imaging Archive (TCIA) provided CT images that were used for prognostic analysis, radiomic feature extraction, and construction of the model, respectively. The HRG expression levels were predicted using the constructed and evaluated LR and SMV models. Results HRG expression was shown to be substantially lower in BLCA tumors as opposed to that observed in normal tissues (p < 0.05). HRG expression had a close positive relationship with Eosinophils and a close negative relationship with B cells naive. The findings of the Landmark analysis illustrated that higher HRG was associated with improved patient survival at an early stage (P=0.048). The predictive performance of the two models, based on logistic regression analysis and support vector machine, was outstanding in the training and validation sets, yielding AUCs of 0.722 and 0.708, respectively, in the SVM model, and 0.727 and 0.662, respectively, in the LR.The models have good predictive efficiency. Conclusion HRG expression levels can have a significant impact on BLCA patients' prognoses. The radiomic characteristics can successfully predict the pre-surgical HRG expression levels, based on CT- Image omics.
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Wang X, Fu Y, Zhu C, Hu X, Zou H, Sun C. New insights into a microvascular invasion prediction model in hepatocellular carcinoma: A retrospective study from the SEER database and China. Front Surg 2023; 9:1046713. [PMID: 36684226 PMCID: PMC9853393 DOI: 10.3389/fsurg.2022.1046713] [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: 09/17/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
Background and Aims The prognosis of liver cancer is strongly influenced by microvascular infiltration (MVI). Accurate preoperative MVI prediction can aid clinicians in the selection of suitable treatment options. In this study, we constructed a novel, reliable, and adaptable nomogram for predicting MVI. Methods Using the Surveillance, Epidemiology, and End Results (SEER) database, we extracted the clinical data of 1,063 patients diagnosed with hepatocellular carcinoma (HCC) and divided it into either a training (n = 739) or an internal validation cohort (n = 326). Based on multivariate analysis, the training cohort data were analyzed and a nomogram was generated for MVI prediction. This was further verified using an internal validation cohort and an external validation cohort involving 293 Chinese patients. Furthermore, to evaluate the efficacy, accuracy, and clinical use of the nomogram, we used concordance index (C-index), calibration curve, and decision curve analysis (DCA) techniques. Results In accordance with the multivariate analysis, tumor size, tumor number, alpha-fetoprotein (AFP), and histological grade were independently associated with MVI. The established model exhibited satisfactory performance in predicting MVI. The C-indices were 0.719, 0.704, and 0.718 in the training, internal validation, and external validation cohorts, respectively. The calibration curves showed an excellent consistency between the predictions and actual observations. Finally, DCA demonstrated that the newly developed nomogram had favorable clinical utility. Conclusions We established and verified a novel preoperative MVI prediction model in HCC patients. This model can be a beneficial tool for clinicians in selecting an optimal treatment plan for HCC patients.
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Affiliation(s)
- Xingchang Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yiling Fu
- Department of Rehabilitation Medicine, Qilu Hospital of Shandong University (Qingdao), Qingdao, China
| | - Chengzhan Zhu
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiao Hu
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hao Zou
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China,Correspondence: Chuandong Sun Hao Zou
| | - Chuandong Sun
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China,Correspondence: Chuandong Sun Hao Zou
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Jiang Y, Wang K, Wang YR, Xiang YJ, Liu ZH, Feng JK, Cheng SQ. Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence. Technol Cancer Res Treat 2023; 22:15330338231212726. [PMID: 37933176 PMCID: PMC10631353 DOI: 10.1177/15330338231212726] [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: 07/26/2023] [Revised: 10/01/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023] Open
Abstract
Microvascular invasion of hepatocellular carcinoma is an important factor affecting tumor recurrence after liver resection and liver transplantation. There are many ways to classify microvascular invasion, however, an international consensus is urgently needed. Recently, artificial intelligence has emerged as an important tool for improving the clinical management of hepatocellular carcinoma. Many studies about microvascular invasion currently focus on preoperative and prognosis prediction of microvascular invasion using artificial intelligence. In this paper, we review the definition and staging of microvascular invasion, especially the diagnosis of it by using artificial intelligence. In preoperative prediction, deep learning based on multimodal data modeling of radiomics-screened features, clinical features, and medical images is currently the most effective means. In prognostic prediction, pathology is the gold standard, and the techniques used should more effectively utilize the global features of the pathology images.
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Affiliation(s)
- Yu Jiang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Kang Wang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Yu-Ran Wang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yan-Jun Xiang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Zong-Han Liu
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jin-Kai Feng
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Shu-Qun Cheng
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Yang D, Zhu M, Xiong X, Su Y, Zhao F, Hu Y, Zhang G, Pei J, Ding Y. Clinical features and prognostic factors in patients with microvascular infiltration of hepatocellular carcinoma: Development and validation of a nomogram and risk stratification based on the SEER database. Front Oncol 2022; 12:987603. [PMID: 36185206 PMCID: PMC9515492 DOI: 10.3389/fonc.2022.987603] [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: 08/11/2022] [Accepted: 08/29/2022] [Indexed: 11/29/2022] Open
Abstract
Background The goal is to establish and validate an innovative prognostic risk stratification and nomogram in patients of hepatocellular carcinoma (HCC) with microvascular invasion (MVI) for predicting the cancer-specific survival (CSS). Methods 1487 qualified patients were selected from the Surveillance, Epidemiology and End Results (SEER) database and randomly assigned to the training cohort and validation cohort in a ratio of 7:3. Concordance index (C-index), area under curve (AUC) and calibration plots were adopted to evaluate the discrimination and calibration of the nomogram. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities and compare it to the American Joint Committee on Cancer (AJCC) tumor staging system. C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement of the new model over the AJCC tumor staging system. The new risk stratifications based on the nomogram and the AJCC tumor staging system were compared. Results Eight prognostic factors were used to construct the nomogram for HCC patients with MVI. The C-index for the training and validation cohorts was 0.785 and 0.776 respectively. The AUC values were higher than 0.7 both in the training cohort and validation cohort. The calibration plots showed good consistency between the actual observation and the nomogram prediction. The IDI values of 1-, 3-, 5-year CSS in the training cohort were 0.17, 0.16, 0.15, and in the validation cohort were 0.17, 0.17, 0.17 (P<0.05). The NRI values of the training cohort were 0.75 at 1-year, 0.68 at 3-year and 0.67 at 5-year. The DCA curves indicated that the new model more accurately predicted 1-year, 3-year, and 5-year CSS in both training and validation cohort, because it added more net benefit than the AJCC staging system. Furthermore, the risk stratification system showed the CSS in different groups had a good regional division. Conclusions A comprehensive risk stratification system and nomogram were established to forecast CSS for patients of HCC with MVI.
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Affiliation(s)
- Dashuai Yang
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mingqiang Zhu
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiangyun Xiong
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yang Su
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College in Huazhong University of Science and Technology, Wuhan, China
| | - Fangrui Zhao
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yong Hu
- Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Youming Ding, ; Yong Hu,
| | - Guo Zhang
- Department of neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Junpeng Pei
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Youming Ding
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Youming Ding, ; Yong Hu,
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