1
|
Chen HL, He RL, Gu MT, Zhao XY, Song KR, Zou WJ, Jia NY, Liu WM. Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma ≤ 3 cm based on enhanced magnetic resonance imaging. World J Gastrointest Oncol 2024; 16:1808-1820. [PMID: 38764811 PMCID: PMC11099422 DOI: 10.4251/wjgo.v16.i5.1808] [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: 11/10/2023] [Revised: 02/02/2024] [Accepted: 03/12/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND Vessels encapsulating tumor clusters (VETC) represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma (HCC). However, it seems that no one have focused on predicting VETC status in small HCC (sHCC). This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC (≤ 3 cm) patients. AIM To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients. METHODS A total of 309 patients with sHCC, who underwent segmental resection and had their VETC status confirmed, were included in the study. These patients were recruited from three different hospitals: Hospital 1 contributed 177 patients for the training set, Hospital 2 provided 78 patients for the test set, and Hospital 3 provided 54 patients for the validation set. Independent predictors of VETC were identified through univariate and multivariate logistic analyses. These independent predictors were then used to construct a VETC prediction model for sHCC. The model's performance was evaluated using the area under the curve (AUC), calibration curve, and clinical decision curve. Additionally, Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence, just as it is with the actual VETC status and early recurrence. RESULTS Alpha-fetoprotein_lg10, carbohydrate antigen 199, irregular shape, non-smooth margin, and arterial peritumoral enhancement were identified as independent predictors of VETC. The model incorporating these predictors demonstrated strong predictive performance. The AUC was 0.811 for the training set, 0.800 for the test set, and 0.791 for the validation set. The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets. Furthermore, the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC. Finally, early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group, regardless of whether considering the actual or predicted VETC status. CONCLUSION Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC (≤ 3 cm) patients, and it holds potential for predicting early recurrence. This model equips clinicians with valuable information to make informed clinical treatment decisions.
Collapse
Affiliation(s)
- Hui-Lin Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai 200438, China
| | - Rui-Lin He
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Meng-Ting Gu
- Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
| | - Xing-Yu Zhao
- Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
| | - Kai-Rong Song
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai 200438, China
| | - Wen-Jie Zou
- Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
| | - Ning-Yang Jia
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai 200438, China
| | - Wan-Min Liu
- Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
| |
Collapse
|
2
|
Chen H, Dong H, He R, Gu M, Zhao X, Song K, Zou W, Jia N, Liu W. Optimizing predictions: improved performance of preoperative gadobenate-enhanced MRI hepatobiliary phase features in predicting vessels encapsulating tumor clusters in hepatocellular carcinoma-a multicenter study. Abdom Radiol (NY) 2024:10.1007/s00261-024-04283-y. [PMID: 38713432 DOI: 10.1007/s00261-024-04283-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND Vessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI. METHODS This study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI). RESULTS CA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2. CONCLUSION HBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC.
Collapse
Affiliation(s)
- Huilin Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Hui Dong
- Department of Pathology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Ruilin He
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Mengting Gu
- Department of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China
| | - Xingyu Zhao
- Department of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China
| | - Kairong Song
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Wenjie Zou
- Department of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China
| | - Ningyang Jia
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China.
| | - Wanmin Liu
- Department of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China.
| |
Collapse
|
3
|
Yang J, Dong X, Wang F, Jin S, Zhang B, Zhang H, Pan W, Gan M, Duan S, Zhang L, Hu H, Ji W. A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:1074-1083. [PMID: 38175256 DOI: 10.1007/s00261-023-04141-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/14/2023] [Accepted: 11/22/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE This study aimed to build and evaluate a deep learning (DL) model to predict vessels encapsulating tumor clusters (VETC) and prognosis preoperatively in patients with hepatocellular carcinoma (HCC). METHODS 320 pathologically confirmed HCC patients (58 women and 262 men) from two hospitals were included in this retrospective study. Institution 1 (n = 219) and Institution 2 (n = 101) served as the training and external test cohorts, respectively. Tumors were evaluated three-dimensionally and regions of interest were segmented manually in the arterial, portal venous, and delayed phases (AP, PP, and DP). Three ResNet-34 DL models were developed, consisting of three models based on a single sequence. The fusion model was developed by inputting the prediction probability of the output from the three single-sequence models into logistic regression. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs. Early recurrence (ER) was defined as recurrence within two years of surgery and early recurrence-free survival (ERFS) rate was evaluated by Kaplan-Meier survival analysis. RESULTS Among the 320 HCC patients, 227 were VETC- and 93 were VETC+ . In the external test cohort, the fusion model showed an AUC of 0.772, a sensitivity of 0.80, and a specificity of 0.61. The fusion model-based prediction of VETC high-risk and low-risk categories exhibits a significant difference in ERFS rates, akin to the outcomes observed in VETC + and VETC- confirmed through pathological analyses (p < 0.05). CONCLUSIONS A DL framework based on ResNet-34 has demonstrated potential in facilitating non-invasive prediction of VETC as well as patient prognosis.
Collapse
Affiliation(s)
- Jiawen Yang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Xue Dong
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People's Republic of China
| | - Shengze Jin
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, 318000, Zhejiang, China
| | - Binhao Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Huangqi Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Wenting Pan
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Meifu Gan
- Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, 317000, Zhejiang, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Pudong New Town, No.1, Huatuo Road, Shanghai, 210000, China
| | - Limin Zhang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People's Republic of China
| | - Wenbin Ji
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China.
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, 318000, Zhejiang, China.
| |
Collapse
|
4
|
Zhang C, Zhong H, Zhao F, Ma ZY, Dai ZJ, Pang GD. Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography. World J Gastrointest Oncol 2024; 16:857-874. [PMID: 38577448 PMCID: PMC10989357 DOI: 10.4251/wjgo.v16.i3.857] [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: 12/26/2023] [Accepted: 01/29/2024] [Indexed: 03/12/2024] Open
Abstract
BACKGROUND Recently, vessels encapsulating tumor clusters (VETC) was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner, and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC). AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography (CECT) to predict the presence of VETC+ in HCC. METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers. Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase. Radiomics features, essential for identifying VETC+ HCC, were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set. The model's performance was validated on two separate test sets. Receiver operating characteristic (ROC) analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets. The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features. ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features, the radiomics features and the radiomics nomogram. RESULTS The study included 190 individuals from two independent centers, with the majority being male (81%) and a median age of 57 years (interquartile range: 51-66). The area under the curve (AUC) for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825, 0.788, and 0.680 in the training set and the two test sets. A total of 13 features were selected to construct the Rad-score. The nomogram, combining clinical-radiological and combined radiomics features could accurately predict VETC+ in all three sets, with AUC values of 0.859, 0.848 and 0.757. Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models. CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram, incorporating clinical-radiological features and combined radiomics features, in the identification of VETC+ HCC.
Collapse
Affiliation(s)
- Chao Zhang
- Department of Radiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China
| | - Hai Zhong
- Department of Radiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China
| | - Fang Zhao
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250014, Shandong Province, China
| | - Zhen-Yu Ma
- Department of Radiology, Linglong Yingcheng Hospital, Yantai 265499, Shandong Province, China
| | - Zheng-Jun Dai
- Department of Scientific Research, Huiying Medical Technology Co., Ltd, Beijing 100192, China
| | - Guo-Dong Pang
- Department of Radiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Chernyak V. Editorial for "Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma". J Magn Reson Imaging 2024; 59:120-121. [PMID: 37165916 DOI: 10.1002/jmri.28775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/12/2023] Open
Affiliation(s)
- Victoria Chernyak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| |
Collapse
|
7
|
Dong X, Yang J, Zhang B, Li Y, Wang G, Chen J, Wei Y, Zhang H, Chen Q, Jin S, Wang L, He H, Gan M, Ji W. Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma. J Magn Reson Imaging 2024; 59:108-119. [PMID: 37078470 DOI: 10.1002/jmri.28745] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Vessels encapsulating tumor cluster (VETC) is a critical prognostic factor and therapeutic predictor of hepatocellular carcinoma (HCC). However, noninvasive evaluation of VETC remains challenging. PURPOSE To develop and validate a deep learning radiomic (DLR) model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of VETC and prognosis of HCC. STUDY TYPE Retrospective. POPULATION A total of 221 patients with histologically confirmed HCC and stratified this cohort into training set (n = 154) and time-independent validation set (n = 67). FIELD STRENGTH/SEQUENCE A 1.5 T and 3.0 T; DCE imaging with T1-weighted three-dimensional fast spoiled gradient echo. ASSESSMENT Histological specimens were used to evaluate VETC status. VETC+ cases had a visible pattern (≥5% tumor area), while cases without any pattern were VETC-. The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI and reproducibility of segmentation was evaluated. Deep neural network and machine learning (ML) classifiers (logistic regression, decision tree, random forest, SVM, KNN, and Bayes) were used to develop nine DLR, 54 ML and clinical-radiological (CR) models based on AP, PP, and DP of DCE-MRI for evaluating VETC status and association with recurrence. STATISTICAL TESTS The Fleiss kappa, intraclass correlation coefficient, receiver operating characteristic curve, area under the curve (AUC), Delong test and Kaplan-Meier survival analysis. P value <0.05 was considered as statistical significance. RESULTS Pathological VETC+ were confirmed in 68 patients (training set: 46, validation set: 22). In the validation set, DLR model based on peritumor PP (peri-PP) phase had the best performance (AUC: 0.844) in comparison to CR (AUC: 0.591) and ML (AUC: 0.672) models. Significant differences in recurrence rates between peri-PP DLR model-predicted VETC+ and VETC- status were found. DATA CONCLUSIONS The DLR model provides a noninvasive method to discriminate VETC status and prognosis of HCC patients preoperatively. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 2.
Collapse
Affiliation(s)
- Xue Dong
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, Zhejiang, China
| | - Jiawen Yang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Binhao Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Yujing Li
- Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Guanliang Wang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Jinyao Chen
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Yuguo Wei
- Precision Health Institution, GE Healthcare, Xihu District, Hangzhou, China
| | - Huangqi Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Qingqing Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shengze Jin
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, Zhejiang, China
| | - Lingxia Wang
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, Zhejiang, China
| | - Haiqing He
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Meifu Gan
- Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Wenbin Ji
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, Zhejiang, China
| |
Collapse
|
8
|
Wu S, Zhan W, Liu L, Xie D, Yao L, Yao H, Liao G, Huang L, Zhou Y, You P, Huang Z, Li Q, Xu B, Wang S, Wang G, Zhang DK, Qiao G, Chan LWC, Lanuti M, Zhou H. Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB-IV NSCLC (LCDigital-IO Study): a multicenter retrospective study. J Immunother Cancer 2023; 11:e007369. [PMID: 37865396 PMCID: PMC10603353 DOI: 10.1136/jitc-2023-007369] [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] [Accepted: 09/18/2023] [Indexed: 10/23/2023] Open
Abstract
BACKGROUND The predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage IB-IV non-small cell lung cancer (NSCLC), including its biological context for ICIs treatment response prediction. METHODS CT images from 319 patients with pretreatment NSCLC receiving immunotherapy between January 2015 and November 2021 were retrospectively collected and composed a discovery (n=214), independent validation (n=54), and external test cohort (n=51). A set of 851 features was extracted from tumorous and peritumoral volumes of interest (VOIs). The reference standard is the durable clinical benefit (DCB, sustained disease control for more than 6 months assessed via radiological evaluation). The predictive value of combined radiomic signature (CRS) for pathological response was subsequently assessed in another cohort of 98 patients with resectable NSCLC receiving ICIs preoperatively. The association between radiomic features and tumor immune landscape on the online data set (n=60) was also examined. A model combining clinical predictor and radiomic signatures was constructed to improve performance further. RESULTS CRS discriminated DCB and non-DCB patients well in the training and validation cohorts with an area under the curve (AUC) of 0.82, 95% CI: 0.75 to 0.88, and 0.75, 95% CI: 0.64 to 0.87, respectively. In this study, the predictive value of CRS was better than programmed cell death ligand-1 (PD-L1) expression (AUC of PD-L1 subset: 0.59, 95% CI: 0.50 to 0.69) or clinical model (AUC: 0.66, 95% CI: 0.51 to 0.81). After combining the clinical signature with CRS, the predictive performance improved further with an AUC of 0.837, 0.790 and 0.781 in training, validation and D2 cohorts, respectively. When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95% CI: 0.67 to 0.81). Moreover, CRS showed a promising stratification ability on overall survival (HR: 0.49, 95% CI: 0.27 to 0.89; p=0.020) and progression-free survival (HR: 0.43, 95% CI: 0.26 to 0.74; p=0.002). CONCLUSION By analyzing both tumorous and peritumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker for distinguishing responders of ICIs therapy and stratifying their survival outcome efficiently, which may support the clinical decisions on the use of ICIs in advanced as well as patients with resectable NSCLC.
Collapse
Affiliation(s)
- Shaowei Wu
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Weijie Zhan
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, People's Republic of China
| | - Daipeng Xie
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Lintong Yao
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
- Shantou University Medical College, Shantou, China
| | - Henian Yao
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
- Guangdong Medical University, Zhanjiang, China
| | - Guoqing Liao
- Department of Thoracic Surgery, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Luyu Huang
- Department of Surgery, Competence Center of Thoracic Surgery, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Yubo Zhou
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Peimeng You
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, People's Republic of China
| | - Zekai Huang
- Guangdong Medical University, Zhanjiang, China
| | - Qiaxuan Li
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
- Shantou University Medical College, Shantou, China
| | - Bin Xu
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Siyun Wang
- Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Guangyi Wang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Dong-Kun Zhang
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Guibin Qiao
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Michael Lanuti
- Department of Thoracic Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Haiyu Zhou
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| |
Collapse
|
9
|
Liu K, Dennis C, Prince DS, Marsh-Wakefield F, Santhakumar C, Gamble JR, Strasser SI, McCaughan GW. Vessels that encapsulate tumour clusters vascular pattern in hepatocellular carcinoma. JHEP Rep 2023; 5:100792. [PMID: 37456680 PMCID: PMC10339254 DOI: 10.1016/j.jhepr.2023.100792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 04/13/2023] [Accepted: 04/28/2023] [Indexed: 07/18/2023] Open
Abstract
Vessels that encapsulate tumour clusters (VETC) is a distinct histologic vascular pattern associated with a novel mechanism of metastasis. First described in human cancers in 2004, its prevalence and prognostic significance in hepatocellular carcinoma (HCC) has only been appreciated in the past decade with a rapidly increasing body of literature. A robust biomarker of aggressive disease, the VETC pattern is easy to recognise but relies on histologic examination of tumour tissue for its diagnosis. Radiological recognition of the VETC pattern is an area of active research and is becoming increasingly accurate. As a prognostic marker, VETC has consistently proven to be an independent predictor of disease recurrence and overall survival in patients with HCC undergoing resection and liver transplantation. It can also guide treatment by predicting response to other therapies such as transarterial chemoembolisation and sorafenib. Without prospective randomised-controlled trials or routine evaluation of VETC in clinical practice, there are currently no firm treatment recommendations for VETC-positive tumours, although some perspectives are provided in this review based on the latest knowledge of their pathogenesis - a complex interplay between tumour angiogenesis and the immune microenvironment. Nevertheless, VETC has great potential as a future biomarker that could take us one step closer to precision medicine for HCC.
Collapse
Affiliation(s)
- Ken Liu
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- Liver Injury and Cancer Program, Centenary Institute, Sydney, NSW, Australia
| | - Claude Dennis
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
| | - David S. Prince
- Department of Gastroenterology, Liverpool Hospital, Sydney, NSW, Australia
| | - Felix Marsh-Wakefield
- Liver Injury and Cancer Program, Centenary Institute, Sydney, NSW, Australia
- Human Immunology Laboratory, The University of Sydney, Sydney, NSW, Australia
| | - Cositha Santhakumar
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
- Liver Injury and Cancer Program, Centenary Institute, Sydney, NSW, Australia
- Human Immunology Laboratory, The University of Sydney, Sydney, NSW, Australia
| | - Jennifer R. Gamble
- Centre for Endothelium, Vascular Biology Program, Centenary Institute, Sydney, NSW, Australia
| | - Simone I. Strasser
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Geoffrey W. McCaughan
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- Liver Injury and Cancer Program, Centenary Institute, Sydney, NSW, Australia
| |
Collapse
|
10
|
Zhang Y, He D, Liu J, Wei YG, Shi LL. Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma through dynamic contrast-enhanced magnetic resonance imaging-based radiomics. World J Gastroenterol 2023; 29:2001-2014. [PMID: 37155523 PMCID: PMC10122786 DOI: 10.3748/wjg.v29.i13.2001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/01/2023] [Accepted: 03/20/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is closely related to aggressive phenotype, gene mutation, carcinogenic pathway, and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis. With the development of imaging technology, successful applications of contrast-enhanced magnetic resonance imaging (MRI) have been reported in identifying the MTM-HCC subtype. Radiomics, as an objective and beneficial method for tumour evaluation, is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine.
AIM To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms.
METHODS This retrospective study enrolled 232 (training set, 162; test set, 70) hepatocellular carcinoma patients from April 2018 to September 2021. A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI, followed by dimension reduction of these features. Logistic regression (LR), K-nearest neighbour (KNN), Bayes, Tree, and support vector machine (SVM) algorithms were used to select the best radiomics signature. We used the relative standard deviation (RSD) and bootstrap methods to quantify the stability of these five algorithms. The algorithm with the lowest RSD represented the best stability, and it was used to construct the best radiomics model. Multivariable logistic analysis was used to select the useful clinical and radiological features, and different predictive models were established. Finally, the predictive performances of the different models were assessed by evaluating the area under the curve (AUC).
RESULTS The RSD values based on LR, KNN, Bayes, Tree, and SVM were 3.8%, 8.6%, 4.3%, 17.7%, and 17.4%, respectively. Therefore, the LR machine learning algorithm was selected to construct the best radiomics signature, which performed well with AUCs of 0.766 and 0.739 in the training and test sets, respectively. In the multivariable analysis, age [odds ratio (OR) = 0.956, P = 0.034], alpha-fetoprotein (OR = 10.066, P < 0.001), tumour size (OR = 3.316, P = 0.002), tumour-to-liver apparent diffusion coefficient (ADC) ratio (OR = 0.156, P = 0.037), and radiomics score (OR = 2.923, P < 0.001) were independent predictors of MTM-HCC. Among the different models, the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model (AUCs: 0.888 vs 0.836, P = 0.046) and radiological model (AUCs: 0.796 vs 0.688, P = 0.012), respectively, in the training set, highlighting the improved predictive performance of radiomics. The nomogram performed best, with AUCs of 0.896 and 0.805 in the training and test sets, respectively.
CONCLUSION The nomogram containing radiomics, age, alpha-fetoprotein, tumour size, and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype.
Collapse
Affiliation(s)
- Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang Province, China
| | - Dong He
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang Province, China
| | - Jing Liu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang Province, China
| | - Yu-Guo Wei
- Precision Health Institution, General Electric Healthcare, Hangzhou 310014, Zhejiang Province, China
| | - Lin-Lin Shi
- Department of Gastroenterology, Zhejiang Hospital of Integrated Traditional Chinese and Western Medicine, Hangzhou 310005, Zhejiang Province, China
| |
Collapse
|
11
|
Chu T, Zhao C, Zhang J, Duan K, Li M, Zhang T, Lv S, Liu H, Wei F. Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels that Encapsulate Tumor Clusters in Hepatocellular Carcinoma. Ann Surg Oncol 2022; 29:6774-6783. [PMID: 35754067 PMCID: PMC9492610 DOI: 10.1245/s10434-022-12000-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022]
Abstract
Background Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer death worldwide, and the prognosis remains dismal. In this study, two pivotal factors, microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC) were preoperatively predicted simultaneously to assess prognosis. Methods A total of 133 HCC patients who underwent surgical resection and preoperative gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) were included. The statuses of MVI and VETC were obtained from the pathological report and CD34 immunohistochemistry, respectively. A three-dimensional convolutional neural network (3D CNN) for single-task learning aimed at MVI prediction and for multitask learning aimed at simultaneous prediction of MVI and VETC was established by using multiphase Gd-EOB-DTPA-enhanced MRI. Results The 3D CNN for single-task learning achieved an area under receiver operating characteristics curve (AUC) of 0.896 (95% CI: 0.797–0.994). Multitask learning with simultaneous extraction of MVI and VETC features improved the performance of MVI prediction, with an AUC value of 0.917 (95% CI: 0.825–1.000), and achieved an AUC value of 0.860 (95% CI: 0.728–0.993) for the VETC prediction. The multitask learning framework could stratify high- and low-risk groups regarding overall survival (p < 0.0001) and recurrence-free survival (p < 0.0001), revealing that patients with MVI+/VETC+ were associated with poor prognosis. Conclusions A deep learning framework based on 3D CNN for multitask learning to predict MVI and VETC simultaneously could improve the performance of MVI prediction while assessing the VETC status. This combined prediction can stratify prognosis and enable individualized prognostication in HCC patients before curative resection. Supplementary Information The online version contains supplementary material available at 10.1245/s10434-022-12000-6.
Collapse
Affiliation(s)
- Tongjia Chu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Chen Zhao
- College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China
| | - Jian Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Kehang Duan
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Tianqi Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Shengnan Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Huan Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Feng Wei
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China.
| |
Collapse
|
12
|
Liao H, Jiang H, Chen Y, Duan T, Yang T, Han M, Xue Z, Shi F, Yuan K, Bashir MR, Shen D, Song B, Zeng Y. Predicting Genomic Alterations of Phosphatidylinositol-3 Kinase Signaling in Hepatocellular Carcinoma: A Radiogenomics Study Based on Next-Generation Sequencing and Contrast-Enhanced CT. Ann Surg Oncol 2022; 29:10.1245/s10434-022-11505-4. [PMID: 35286532 DOI: 10.1245/s10434-022-11505-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/07/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Exploring the genomic landscape of hepatocellular carcinoma (HCC) provides clues for therapeutic decision-making. Phosphatidylinositol-3 kinase (PI3K) signaling is one of the key pathways regulating HCC aggressiveness, and its genomic alterations have been correlated with sorafenib response. In this study, we aimed to predict somatic mutations of the PI3K signaling pathway in HCC samples through machine-learning-based radiomic analysis. METHODS HCC patients who underwent next-generation sequencing and preoperative contrast-enhanced CT were recruited from West China Hospital and The Cancer Genome Atlas for model training and validation, respectively. Radiomic features were extracted from volumes of interest (VOIs) covering the tumor (VOItumor) and peritumoral areas (5 mm [VOI5mm], 10 mm [VOI10mm], and 20 mm [VOI20mm] from tumor margin). Factor analysis, logistic regression analysis, least absolute shrinkage and selection operator, and random forest analysis were applied for feature selection and model construction. Model performance was characterized based on the area under the receiver operating characteristic curve (AUC). RESULTS A total of 132 HCC patients (mean age: 61.1 ± 14.7 years; 108 men) were enrolled. In the training set, the AUCs of radiomic signatures based on single CT phases were moderate (AUC 0.694-0.771). In the external validation set, the radiomic signature based on VOI10mm in arterial phase demonstrated the highest AUC (0.733) among all models. No improvement in model performance was achieved after adding the tumor radiomic features or manually assessed qualitative features. CONCLUSIONS Machine-learning-based radiomic analysis had potential for characterizing alterations of PI3K signaling in HCC and could help identify potential candidates for sorafenib treatment.
Collapse
Affiliation(s)
- Haotian Liao
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Duan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Miaofei Han
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Zhong Xue
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Kefei Yuan
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Mustafa R Bashir
- Department of Radiology and Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, USA
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
| | - Yong Zeng
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China.
| |
Collapse
|
13
|
Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics. Int J Mol Sci 2022; 23:ijms23031339. [PMID: 35163262 PMCID: PMC8835851 DOI: 10.3390/ijms23031339] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/22/2022] [Accepted: 01/22/2022] [Indexed: 02/08/2023] Open
Abstract
Humans are exposed to a complex mix of man-made electric and magnetic fields (MFs) at many different frequencies, at home and at work. Epidemiological studies indicate that there is a positive relationship between residential/domestic and occupational exposure to extremely low frequency electromagnetic fields and some types of cancer, although some other studies indicate no relationship. In this review, after an introduction on the MF definition and a description of natural/anthropogenic sources, the epidemiology of residential/domestic and occupational exposure to MFs and cancer is reviewed, with reference to leukemia, brain, and breast cancer. The in vivo and in vitro effects of MFs on cancer are reviewed considering both human and animal cells, with particular reference to the involvement of reactive oxygen species (ROS). MF application on cancer diagnostic and therapy (theranostic) are also reviewed by describing the use of different magnetic resonance imaging (MRI) applications for the detection of several cancers. Finally, the use of magnetic nanoparticles is described in terms of treatment of cancer by nanomedical applications for the precise delivery of anticancer drugs, nanosurgery by magnetomechanic methods, and selective killing of cancer cells by magnetic hyperthermia. The supplementary tables provide quantitative data and methodologies in epidemiological and cell biology studies. Although scientists do not generally agree that there is a cause-effect relationship between exposure to MF and cancer, MFs might not be the direct cause of cancer but may contribute to produce ROS and generate oxidative stress, which could trigger or enhance the expression of oncogenes.
Collapse
|
14
|
Guan R, Lin W, Zou J, Mei J, Wen Y, Lu L, Guo R. Development and Validation of a Novel Nomogram for Predicting Vessels that Encapsulate Tumor Cluster in Hepatocellular Carcinoma. Cancer Control 2022; 29:10732748221102820. [PMID: 35609265 PMCID: PMC9136459 DOI: 10.1177/10732748221102820] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/18/2022] [Accepted: 05/05/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Vessels that encapsulate tumor cluster (VETC) is associated with poor prognosis in hepatocellular carcinoma (HCC). Vessels that encapsulate tumor cluster estimation before initial treatment is helpful for clinical doctors. We aimed to construct a novel predictive model for VETC, using preoperatively accessible clinical parameters and imagine features. METHODS Totally, 365 HCC patients who received curative hepatectomy in the Sun Yat-Sen University Cancer Center from 2013 to 2014 were enrolled in this study. Vessels that encapsulate tumor cluster pattern was confirmed by immunochemistry staining. 243 were randomly assigned to the training cohort while the rest was assigned to the validation cohort. Independent predictive factors for VETC estimation were determined by univariate and multivariate logistic analysis. We further constructed a predictive nomogram for VETC in HCC. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curve, and calibration curve. Besides, the decision curve was plotted to evaluate the clinical usefulness. Ultimately, Kaplan-Meier survival curves were utilized to confirm the association between the nomogram and survival. RESULTS Immunochemistry staining revealed VETC in 87 patients (23.8%). lymphocyte to monocyte ratio (>7.75, OR = 4.06), neutrophil (>7, OR = 4.48), AST to ALT ratio (AAR > .86, OR = 2.16), ALT to lymphocyte ratio index (BLRI > 21.73, OR = 2.57), alpha-fetoprotein (OR = 1.1), and tumor diameter (OR = 2.65) were independent predictive factors. The nomogram incorporating these predictive factors performed well with an area under the curve (AUC) of .746 and .707 in training and validation cohorts, respectively. Calibration curves indicated the predicted probabilities closely corresponded with the actual VETC status. Moreover, the decision curve proved our nomogram could provide clinical benefits with patients. Finally, low probability of VETC group had significantly longer recurrence free survival (RFS) and overall survival (OS) than the high probability of the VETC group (all P < .001). CONCLUSION A novel predictive nomogram integrating clinical indicators and image characteristics shows strong predictive VETC performance and might provide standardized net clinical benefits.
Collapse
Affiliation(s)
- Renguo Guan
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wenping Lin
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jingwen Zou
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jie Mei
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuhua Wen
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Lianghe Lu
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Rongping Guo
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|