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Li H, Zhang D, Pei J, Hu J, Li X, Liu B, Wang L. Dual-energy computed tomography iodine quantification combined with laboratory data for predicting microvascular invasion in hepatocellular carcinoma: a two-centre study. Br J Radiol 2024; 97:1467-1475. [PMID: 38870535 PMCID: PMC11256957 DOI: 10.1093/bjr/tqae116] [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: 12/06/2023] [Revised: 05/16/2024] [Accepted: 06/05/2024] [Indexed: 06/15/2024] Open
Abstract
OBJECTIVES Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction. METHODS This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score. RESULTS Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively. CONCLUSIONS The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively. ADVANCES IN KNOWLEDGE Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours.
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Affiliation(s)
- Huan Li
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
| | - Dai Zhang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
| | - Jinxia Pei
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
| | - Jingmei Hu
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
| | - Bin Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
| | - Longsheng Wang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
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Yang C, Xu J, Wang S, Wang Y, Zhang Y, Piao C. Machine learning to predict the early recurrence of intrahepatic cholangiocarcinoma: A systematic review and meta‑analysis. Oncol Lett 2024; 28:385. [PMID: 38966582 PMCID: PMC11222917 DOI: 10.3892/ol.2024.14518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/12/2024] [Indexed: 07/06/2024] Open
Abstract
The prediction of early recurrent of intrahepatic cholangiocarcinoma (ICC) has been widely investigated; however, the predictive value is currently insufficient. To determine the effectiveness of machine learning (ML) for the diagnosis of early recurrent intrahepatic cholangiocarcinoma (ICC), particularly in comparison with clinical models, the present study aimed to determine which ML model had the best diagnostic performance for inpatients with recurrent ICC. In order to search for studies which could be included, three electronic databases were screened from inception to November 2023. A pairwise meta-analysis was performed to evaluate the diagnostic accuracy of the random effects model. A network meta-analysis was performed to identify the most effective ML-based diagnostic model based on the surface under the cumulative ranking curve score. A total of 5 studies of acceptable quality containing 1,247 patients with ICC were included in the present study. Following pairwise meta-analysis, it was found that the ML-based diagnostic accuracy was greater than that of clinical models (surface under the cumulative ranking curve score closer to 1, with significant differences), which initially proved that the ML-based diagnostic power was more optimal than that of clinical models. According to the network meta-analysis, the nomogram performed the best, indicating that this ML model achieved the best diagnostic accuracy for patients with recurrent ICC. In conclusion, the application of ML-based diagnostic models for patients with recurrent ICC was more optimal than the application of the clinical model. The nomogram model ranked first among the models and is therefore recommended for patients with recurrent ICC.
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Affiliation(s)
- Chao Yang
- Information Construction Department, Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, Liaoning 110032, P.R. China
| | - Jianhui Xu
- Information Construction Department, Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, Liaoning 110032, P.R. China
| | - Shuai Wang
- Department of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P.R. China
| | - Ying Wang
- Information Construction Department, Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, Liaoning 110032, P.R. China
| | - Yingshi Zhang
- Department of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P.R. China
| | - Chengzhe Piao
- Information Construction Department, Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, Liaoning 110032, P.R. China
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Geng Z, Wang S, Ma L, Zhang C, Guan Z, Zhang Y, Yin S, Lian S, Xie C. Prediction of microvascular invasion in hepatocellular carcinoma patients with MRI radiomics based on susceptibility weighted imaging and T2-weighted imaging. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01845-4. [PMID: 38997568 DOI: 10.1007/s11547-024-01845-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 07/01/2024] [Indexed: 07/14/2024]
Abstract
BACKGROUND The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance. PURPOSE To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients. MATERIALS AND METHODS A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation. RESULTS AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948). CONCLUSION SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits.
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Affiliation(s)
- Zhijun Geng
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, No. 651 Dongfeng East Road, Guangzhou, 510060, People's Republic of China
| | - Shutong Wang
- Department of Hepatic Surgery, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, People's Republic of China
| | - Lidi Ma
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, No. 651 Dongfeng East Road, Guangzhou, 510060, People's Republic of China
| | - Cheng Zhang
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, No. 651 Dongfeng East Road, Guangzhou, 510060, People's Republic of China
| | - Zeyu Guan
- Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
| | - Yunfei Zhang
- United Imaging Healthcare Co., Ltd, Shanghai, 201807, China
| | - Shaohan Yin
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, No. 651 Dongfeng East Road, Guangzhou, 510060, People's Republic of China
| | - Shanshan Lian
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, No. 651 Dongfeng East Road, Guangzhou, 510060, People's Republic of China
| | - Chuanmiao Xie
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, No. 651 Dongfeng East Road, Guangzhou, 510060, People's Republic of China.
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Wang C, Wu F, Wang F, Chong HH, Sun H, Huang P, Xiao Y, Yang C, Zeng M. The Association Between Tumor Radiomic Analysis and Peritumor Habitat-Derived Radiomic Analysis on Gadoxetate Disodium-Enhanced MRI With Microvascular Invasion in Hepatocellular Carcinoma. J Magn Reson Imaging 2024. [PMID: 38997242 DOI: 10.1002/jmri.29523] [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: 04/17/2024] [Revised: 06/14/2024] [Accepted: 06/17/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment. PURPOSE To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis. STUDY TYPE Retrospective. SUBJECTS Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94). FIELD STRENGTH/SEQUENCE 1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence. ASSESSMENT Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews. STATISTICAL TESTS Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance. RESULTS Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival. DATA CONCLUSION The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Cheng Wang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fei Wu
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Huan-Huan Chong
- Department of Radiology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China
| | - Haitao Sun
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Peng Huang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuyao Xiao
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
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Yang P, Cao Z, Hu X. Editorial for "Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma". J Magn Reson Imaging 2024; 60:243-244. [PMID: 37818933 DOI: 10.1002/jmri.29063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 09/14/2023] [Indexed: 10/13/2023] Open
Affiliation(s)
- Pengfei Yang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zuozhen Cao
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Xi Hu
- Department of Radiology, Affiliated Sir Run Run Shaw Hospital of Zhejiang University School of Medicine, Hangzhou, China
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Wang F, Liao HZ, Chen XL, Lei H, Luo GH, Chen GD, Zhao H. Preoperative prediction of microvascular invasion: new insights into personalized therapy for early-stage hepatocellular carcinoma. Quant Imaging Med Surg 2024; 14:5205-5223. [PMID: 39022260 PMCID: PMC11250313 DOI: 10.21037/qims-24-44] [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: 01/09/2024] [Accepted: 05/29/2024] [Indexed: 07/20/2024]
Abstract
Owing to advances in diagnosis and treatment methods over past decades, a growing number of early-stage hepatocellular carcinoma (HCC) diagnoses has enabled a greater of proportion of patients to receive curative treatment. However, a high risk of early recurrence and poor prognosis remain major challenges in HCC therapy. Microvascular invasion (MVI) has been demonstrated to be an essential independent predictor of early recurrence after curative therapy. Currently, biopsy is not generally recommended before treatment to evaluate MVI in HCC according clinical guidelines due to sampling error and the high risk of tumor cell seeding following biopsy. Therefore, the postoperative histopathological examination is recognized as the gold standard of MVI diagnosis, but this lagging indicator greatly impedes clinicians in selecting the optimal effective treatment for prognosis. As imaging can now noninvasively and completely assess the whole tumor and host situation, it is playing an increasingly important role in the preoperative assessment of MVI. Therefore, imaging criteria for MVI diagnosis would be highly desirable for optimizing individualized therapeutic decision-making and achieving a better prognosis. In this review, we summarize the emerging image characteristics of different imaging modalities for predicting MVI. We also discuss whether advances in imaging technique have generated evidence that could be practice-changing and whether advanced imaging techniques will revolutionize therapeutic decision-making of early-stage HCC.
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Affiliation(s)
- Fang Wang
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
- Departments of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Hua-Zhi Liao
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Xiao-Long Chen
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Hao Lei
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Guang-Hua Luo
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Guo-Dong Chen
- Department of Hepatobiliary Pancreatic Surgery, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Heng Zhao
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
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Li MG, Zhang YN, Hu YY, Li L, Lyu HL. Preoperative prediction of microvascular invasion classification in hepatocellular carcinoma based on clinical features and MRI parameters. Oncol Lett 2024; 28:310. [PMID: 38784602 PMCID: PMC11112147 DOI: 10.3892/ol.2024.14443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a critical pathological factor and the degree of MVI influences treatment decisions and patient prognosis. The present study aimed to predict the MVI classification based on preoperative MRI features and clinical parameters. The present retrospective cohort study included 150 patients (training cohort, n=108; validation cohort, n=42) with pathologically confirmed HCC. Clinical and imaging characteristics data were collected from Shengli Oilfield Central Hospital (Dongying, China). Univariate and multivariate logistic regression analyses were conducted to assess the association of clinical variables and MRI parameters with MVI (grade M1 and M2) and the M2 classification. Nomograms were developed based on the predictive factors of MVI and the M2 classification. The discrimination capability, calibration and clinical usefulness of the nomograms were evaluated. Multivariate analysis revealed an association between the Lens culinaris agglutinin-reactive fraction of α-fetoprotein, protein induced by vitamin K absence-II and tumor margin and MVI-positive status, while peritumoral enhancement and tumor size were demonstrated to be marginal predictors, but were also included in the nomogram. However, among MVI-positive patients, only peritumoral hypointensity and tumor size were demonstrated to be risk factors for the M2 classification. The nomograms, incorporating these variables, exhibited a strong ability to discriminate between MVI-positive and MVI-negative patients with HCC in both the training and validation cohort [area under the curve (AUC), 0.877 and 0.914, respectively] and good performance in predicting the M2 classification in the training and validation cohorts (AUC, 0.720 and 0.782, respectively). Nomograms incorporating clinical parameters and preoperative MRI features demonstrated promising potential as straightforward and effective tools for predicting MVI and the M2 classification in patients with HCC. Such predictive tools could aid in the judicious selection of optimal clinical treatments.
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Affiliation(s)
- Ming-Ge Li
- Department of Radiology, Tianjin Third Central Hospital, Tianjin 300170, P.R. China
| | - Ya-Nan Zhang
- Department of Radiology, Shengli Oilfield Central Hospital, Dongying, Shandong 257034, P.R. China
| | - Ying-Ying Hu
- Department of Pathology, Shengli Oilfield Central Hospital, Dongying, Shandong 257034, P.R. China
| | - Lei Li
- Department of Radiology, Shengli Oilfield Central Hospital, Dongying, Shandong 257034, P.R. China
| | - Hai-Lian Lyu
- Department of Radiology, Shengli Oilfield Central Hospital, Dongying, Shandong 257034, P.R. China
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Li SQ, Yang CX, Wu CM, Cui JJ, Wang JN, Yin XP. Prediction of glypican-3 expression in hepatocellular carcinoma using multisequence magnetic resonance imaging-based histology nomograms. Quant Imaging Med Surg 2024; 14:4436-4449. [PMID: 39022267 PMCID: PMC11250339 DOI: 10.21037/qims-24-111] [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: 01/17/2024] [Accepted: 05/11/2024] [Indexed: 07/20/2024]
Abstract
Background Hepatocellular carcinoma (HCC) is often associated with the overexpression of multiple proteins and genes. For instance, patients with HCC and a high expression of the glypican-3 (GPC3) gene have a poor prognosis, and noninvasive assessment of GPC3 expression before surgery is helpful for clinical decision-making. Therefore, our primary aim in this study was to develop and validate multisequence magnetic resonance imaging (MRI) radiomics nomograms for predicting the expression of GPC3 in individuals diagnosed with HCC. Methods We conducted a retrospective analysis of 143 patients with HCC, including 123 cases from our hospital and 20 cases from The Cancer Genome Atlas (TCGA) or The Cancer Imaging Archive (TCIA) public databases. We used preoperative multisequence MRI images of the patients for the radiomics analysis. We extracted and screened the imaging histologic features using fivefold cross-validation, Pearson correlation coefficient, and the least absolute shrinkage and selection operator (LASSO) analysis method. We used logistic regression (LR) to construct a radiomics model, developed nomograms based on the radiomics scores and clinical parameters, and evaluated the predictive performance of the nomograms using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results Our multivariate analysis results revealed that tumor morphology (P=0.015) and microvascular (P=0.007) infiltration could serve as independent predictors of GPC3 expression in patients with HCC. The nomograms integrating multisequence radiomics radiomics score, tumor morphology, and microvascular invasion had an area under the curve (AUC) value of 0.989. This approach was superior to both the radiomics model (AUC 0.979) and the clinical model (AUC 0.793). The sensitivity, specificity, and accuracy of 0.944, 0.800, and 0.913 for the test set, respectively, and the model's calibration curve demonstrated good consistency (Brier score =0.029). The decision curve analysis (DCA) indicated that the nomogram had a higher net clinical benefit for predicting the expression of GPC3. External validation of the model's prediction yielded an AUC value of 0.826. Conclusions Our study findings highlight the close association of multisequence MRI imaging and radiomic features with GPC3 expression. Incorporating clinical parameters into nomograms can offer valuable preoperative insights into tailoring personalized treatment plans for patients diagnosed with HCC.
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Affiliation(s)
- Si-Qi Li
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Cun-Xia Yang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Chun-Mei Wu
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Jing-Jing Cui
- United Imaging Intelligence (Beijing) Co., Ltd., Beijing, China
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Xiao-Ping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
- College of Nursing of Hebei University, Baoding, China
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Cao Y, Zhang W, Wang X, Lv X, Zhang Y, Guo K, Ren S, Li Y, Wang Z, Chen J. Multiparameter MRI-based radiomics analysis for preoperative prediction of type II endometrial cancer. Heliyon 2024; 10:e32940. [PMID: 38988546 PMCID: PMC11234004 DOI: 10.1016/j.heliyon.2024.e32940] [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: 10/09/2023] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/12/2024] Open
Abstract
Objectives This study aimed to develop and validate a radiomics nomogram based on multiparameter MRI for preoperative differentiation of type II and type I endometrial carcinoma (EC). Methods A total of 403 EC patients from two centers were retrospectively recruited (training cohort, 70 %; validation cohort, 30 %). Radiomics features were extracted from T2-weighted imaging, dynamic contrast-enhanced T1-weighted imaging at delayed phase(DCE4), and apparent diffusion coefficient (ADC) maps. Following dimensionality reduction, radiomics models were developed by logistic regression (LR), random forest (RF), bootstrap aggregating (Bagging), support vector machine (SVM), artificial neural network (ANN), and naive bayes (NB) algorithms. The diagnostic performance of each radiomics model was evaluated using the ROC curve. A nomogram was constructed by incorporating the optimal radiomics signatures with significant clinical-radiological features and immunohistochemistry (IHC) markers obtained from preoperative curettage specimens. The diagnostic performance and clinical value of the nomogram were evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Results Among the radiomics models, the NB model, developed from 12 radiomics features derived from ADC and DCE4 sequences, exhibited strong performance in both training and validation sets, with the AUC values of 0.927 and 0.869, respectively. The nomogram, incorporating the radiomics model with significant clinical-radiological features and IHC markers, demonstrated superior performance in both the training (AUC = 0.951) and the validation sets (AUC = 0.915). Additionally, it exhibited excellent calibration and clinical utility. Conclusions The radiomics nomogram has great potential to differentiate type II from type I EC, which may be an effective tool to guide clinical decision-making for EC patients.
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Affiliation(s)
- Yingying Cao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Wei Zhang
- Department of Radiology, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Xiaorong Wang
- Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
- Taixing People's Hospital, Jiangsu, China
| | - Xiaojing Lv
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Yaping Zhang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Kai Guo
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Shuai Ren
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Yuan Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Jingya Chen
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
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Sun S, Li L, Xu M, Wei Y, Shi F, Liu S. Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction. Abdom Radiol (NY) 2024; 49:1779-1791. [PMID: 38656367 DOI: 10.1007/s00261-024-04306-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
PURPOSE To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC. METHODS This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM). RESULTS T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively. CONCLUSION A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.
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Affiliation(s)
- Shuangshuang Sun
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Lin Li
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210008, China
| | - Mengying Xu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
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11
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Feng B, Wang L, Zhu Y, Ma X, Cong R, Cai W, Liu S, Hu J, Wang S, Zhao X. The Value of LI-RADS and Radiomic Features from MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma within 5 cm. Acad Radiol 2024; 31:2381-2390. [PMID: 38199902 DOI: 10.1016/j.acra.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 01/12/2024]
Abstract
RATIONALE AND OBJECTIVES To explore and compare the performance of LI-RADS® and radiomics from multiparametric MRI in predicting microvascular invasion (MVI) preoperatively in patients with solitary hepatocellular carcinoma (HCC)< 5 cm. METHODS We enrolled 143 patients with pathologically proven HCC and randomly stratified them into training (n = 100) and internal validation (n = 43) cohorts. Besides, 53 patients were enrolled to constitute an independent test cohort. Clinical factors and imaging features, including LI-RADS and three other features (non-smooth margin, incomplete capsule, and two-trait predictor of venous invasion), were reviewed and analyzed. Radiomic features from four MRI sequences were extracted. The independent clinic-imaging (clinical) and radiomics model for MVI-prediction were constructed by logistic regression and AdaBoost respectively. And the clinic-radiomics combined model was further constructed by logistic regression. We assessed the model discrimination, calibration, and clinical usefulness by using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis respectively. RESULTS Incomplete tumor capsule, corona enhancement, and radiomic features were related to MVI in solitary HCC<5 cm. The clinical model achieved AUC of 0.694/0.661 (training/internal validation). The single-sequence-based radiomic model's AUCs were 0.753-0.843/0.698-0.767 (training/internal validation). The combination model exhibited superior diagnostic performance to the clinical model (AUC: 0.895/0.848 [training/ internal validation]) and yielded an AUC of 0.858 in an independent test cohort. CONCLUSION Incomplete tumor capsule and corona enhancement on preoperative MRI were significantly related to MVI in solitary HCC<5 cm. Multiple-sequence radiomic features potentially improve MVI-prediction-model performance, which could potentially help determining HCC's appropriate therapy.
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Affiliation(s)
- Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China (B.F., L.W., Y.Z., X.M., R.C., W.C., X.Z.)
| | - Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China (B.F., L.W., Y.Z., X.M., R.C., W.C., X.Z.)
| | - Yongjian Zhu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China (B.F., L.W., Y.Z., X.M., R.C., W.C., X.Z.)
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China (B.F., L.W., Y.Z., X.M., R.C., W.C., X.Z.).
| | - Rong Cong
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China (B.F., L.W., Y.Z., X.M., R.C., W.C., X.Z.)
| | - Wei Cai
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China (B.F., L.W., Y.Z., X.M., R.C., W.C., X.Z.)
| | - Siyun Liu
- GE Healthcare (China), 1# Tongji South Road, Daxing District, Beijing, 100176, China (S.L., S.W.)
| | - Jiesi Hu
- Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, Shenzhen, 518055, China (J.H.)
| | - Sicong Wang
- GE Healthcare (China), 1# Tongji South Road, Daxing District, Beijing, 100176, China (S.L., S.W.)
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China (B.F., L.W., Y.Z., X.M., R.C., W.C., X.Z.)
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12
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Wang L, Feng B, Liang M, Li D, Cong R, Chen Z, Wang S, Ma X, Zhao X. Prognostic performance of MRI LI-RADS version 2018 features and clinical-pathological factors in alpha-fetoprotein-negative hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:1918-1928. [PMID: 38642093 DOI: 10.1007/s00261-024-04278-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 04/22/2024]
Abstract
PURPOSE To evaluate the role of the magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinical-pathological factors for predicting the prognosis of alpha-fetoprotein (AFP)-negative (≤ 20 ng/ml) hepatocellular carcinoma (HCC) patients, and to compare with other traditional staging systems. METHODS We retrospectively enrolled 169 patients with AFP-negative HCC who received preoperative MRI and hepatectomy between January 2015 and August 2020 (derivation dataset:validation dataset = 118:51). A prognostic model was constructed using the risk factors identified via Cox regression analysis. Predictive performance and discrimination capability were evaluated and compared with those of two traditional staging systems. RESULTS Six risk factors, namely the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade, were associated with recurrence-free survival. The prognostic model constructed using these factors achieved C-index of 0.705 and 0.674 in the derivation and validation datasets, respectively. Furthermore, the model performed better in predicting patient prognosis than traditional staging systems. The model effectively stratified patients with AFP-negative HCC into high- and low-risk groups with significantly different outcomes (p < 0.05). CONCLUSION A prognostic model integrating the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade may serve as a valuable tool for refining risk stratification in patients with AFP-negative HCC.
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Affiliation(s)
- Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Rong Cong
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Zhaowei Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing, 100176, China
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Baishya NK, Baishya K, Baishya K, Sarma R, Ray S. MRI Radiomics in Imaging of Focal Hepatic Lesions: A Narrative Review. Cureus 2024; 16:e62570. [PMID: 39027765 PMCID: PMC11255417 DOI: 10.7759/cureus.62570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
Magnetic resonance imaging (MRI) is generally used to identify, describe, and evaluate treatment responses for focal hepatic lesions. However, the diagnosis and differentiation of such lesions require considerable input from radiologists. In order to reduce these difficulties, radiomics is an artificial intelligence (AI)-based quantitative method that employs the extraction of image features to reliably detect and differentiate focal hepatic lesions. MRI radiomics is a novel technique for the characterization of focal hepatic lesions. It can aid in preoperative evaluation, treatment approach, and forecast microvascular invasion. Although many studies have illustrated its efficiency there are certain limitations such as the absence of a large diverse dataset, comparison with other AI models, integration with histopathological findings, clinical utility, and feasibility.
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Affiliation(s)
| | - Kangkana Baishya
- Electronics and Telecommunication, Assam Engineering College, Guwahati, IND
| | - Kakoli Baishya
- Radiodiagnosis, Fakhruddin Ali Ahmed Medical College and Hospital, Barpeta, IND
| | - Rahul Sarma
- Surgery, Guwahati Neurological Research Center (GNRC) Hospital, Guwahati, IND
| | - Sushmita Ray
- General Surgery, Fakhruddin Ali Ahmed Medical College and Hospital, Barpeta, IND
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14
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Ma X, Zhang L, Xiao Q, Huang Y, Lin L, Peng W, Gong J, Gu Y. Predicting Prognosis of Phyllodes Tumors Using a Mammography- and Magnetic Resonance Imaging-Based Radiomics Model: A Preliminary Study. Clin Breast Cancer 2024:S1526-8209(24)00119-8. [PMID: 38839461 DOI: 10.1016/j.clbc.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 06/07/2024]
Abstract
PURPOSE To investigate whether a radiomics model based on mammography (MG) and magnetic resonance imaging (MRI) can be used to predict disease-free survival (DFS) after phyllodes tumor (PT) surgery. METHOD About 131 PT patients who underwent MG and MRI before surgery between January 2010 and December 2020 were retrospectively enrolled, including 15 patients with recurrence and metastasis and 116 without recurrence. 884 and 3138 radiomic features were extracted from MG and MR images, respectively. Then, multiple radiomics models were established to predict the recurrence risk of the patients by applying a support vector machine classifier. The area under the ROC curve (AUC) was calculated to evaluate model performance. After dividing the patients into high- and low-risk groups based on the predicted radiomics scores, survival analysis was conducted to compare differences between the groups. RESULTS In total, 3 MG-related and 5 MRI-related radiomic models were established; the prediction performance of the T1WI feature fusion model was the best, with an AUC value of 0.93. After combining the features of MG and MRI, the AUC increased to 0.95. Furthermore, the MG, MRI and all-image radiomic models had statistically significant differences in survival between the high- and low-risk groups (P < .001). All-image radiomics model showed higher survival performance than the MG and MRI radiomics models alone. CONCLUSIONS Radiomics features based on preoperative MG and MR images can predict DFS after PT surgery, and the prediction score of the image radiomics model can be used as a potential indicator of recurrence risk.
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Affiliation(s)
- Xiaowen Ma
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Li Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qin Xiao
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Huang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Luyi Lin
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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15
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Haghshomar M, Rodrigues D, Kalyan A, Velichko Y, Borhani A. Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies. Front Oncol 2024; 14:1362737. [PMID: 38779098 PMCID: PMC11109422 DOI: 10.3389/fonc.2024.1362737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 04/12/2024] [Indexed: 05/25/2024] Open
Abstract
Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.
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Affiliation(s)
| | | | | | | | - Amir Borhani
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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16
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Lei Y, Feng B, Wan M, Xu K, Cui J, Ma C, Sun J, Yao C, Gan S, Shi J, Cui E. Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model. Abdom Radiol (NY) 2024; 49:1397-1410. [PMID: 38433144 DOI: 10.1007/s00261-024-04202-1] [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: 10/29/2023] [Revised: 01/04/2024] [Accepted: 01/12/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE To investigate the value of a multimodal deep learning (MDL) model based on computed tomography (CT) and magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS A total of 287 patients with HCC from our institution and 58 patients from another individual institution were included. Among these, 119 patients with only CT data and 116 patients with only MRI data were selected for single-modality deep learning model development, after which select parameters were migrated for MDL model development with transfer learning (TL). In addition, 110 patients with simultaneous CT and MRI data were divided into a training cohort (n = 66) and a validation cohort (n = 44). We input the features extracted from DenseNet121 into an extreme learning machine (ELM) classifier to construct a classification model. RESULTS The area under the curve (AUC) of the MDL model was 0.844, which was superior to that of the single-phase CT (AUC = 0.706-0.776, P < 0.05), single-sequence MRI (AUC = 0.706-0.717, P < 0.05), single-modality DL model (AUCall-phase CT = 0.722, AUCall-sequence MRI = 0.731; P < 0.05), clinical (AUC = 0.648, P < 0.05), but not to that of the delay phase (DP) and in-phase (IP) MRI and portal venous phase (PVP) CT models. The MDL model achieved better performance than models described above (P < 0.05). When combined with clinical features, the AUC of the MDL model increased from 0.844 to 0.871. A nomogram, combining deep learning signatures (DLS) and clinical indicators for MDL models, demonstrated a greater overall net gain than the MDL models (P < 0.05). CONCLUSION The MDL model is a valuable noninvasive technique for preoperatively predicting MVI in HCC.
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Affiliation(s)
- Yan Lei
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
- Zunyi Medical University, 1 Xiaoyuan Road, Zunyi, People's Republic of China
| | - Bao Feng
- Laboratory of Intelligent Detection and Information Processing, School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, People's Republic of China
| | - Meiqi Wan
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
- Zunyi Medical University, 1 Xiaoyuan Road, Zunyi, People's Republic of China
| | - Kuncai Xu
- Laboratory of Intelligent Detection and Information Processing, School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, People's Republic of China
| | - Jin Cui
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
| | - Changyi Ma
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
| | - Junqi Sun
- Department of Radiology, Yuebei People's Hospital, 133 Huimin Street, Shaoguan, People's Republic of China
| | - Changyin Yao
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
- Guangdong Medical University, 2 Wenming East Road, Zhanjiang, People's Republic of China
| | - Shiman Gan
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
- Guangdong Medical University, 2 Wenming East Road, Zhanjiang, People's Republic of China
| | - Jiangfeng Shi
- Laboratory of Intelligent Detection and Information Processing, School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, People's Republic of China
| | - Enming Cui
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China.
- Zunyi Medical University, 1 Xiaoyuan Road, Zunyi, People's Republic of China.
- Guangdong Medical University, 2 Wenming East Road, Zhanjiang, People's Republic of China.
- Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application, 23 Beijie Haibang Street, Jiangmen, People's Republic of China.
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Zhang ZH, Jiang C, Qiang ZY, Zhou YF, Ji J, Zeng Y, Huang JW. Role of microvascular invasion in early recurrence of hepatocellular carcinoma after liver resection: A literature review. Asian J Surg 2024; 47:2138-2143. [PMID: 38443255 DOI: 10.1016/j.asjsur.2024.02.115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/12/2023] [Accepted: 02/22/2024] [Indexed: 03/07/2024] Open
Abstract
Hepatectomy is widely considered a potential treatment for hepatocellular carcinoma (HCC). Unfortunately, one-third of HCC patients have tumor recurrence within 2 years after surgery (early recurrence), accounting for more than 60% of all recurrence patients. Early recurrence is associated with a worse prognosis. Previous studies have shown that microvascular invasion (MVI) is one of the key factors for early recurrence and poor prognosis in patients with HCC after surgery. This paper reviews the latest literature and summarizes the predictors of MVI, the correlation between MVI and early recurrence, the identification of suspicious nodules or subclinical lesions, and the treatment strategies for MVI-positive HCC. The aim is to explore the management of patients with MVI-positive HCC.
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Affiliation(s)
- Zhi-Hong Zhang
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Chuang Jiang
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ze-Yuan Qiang
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yi-Fan Zhou
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Jun Ji
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yong Zeng
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ji-Wei Huang
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China.
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18
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Wang Q, Qian X, Ma X, Qian B, Lu X, Shi Y. Response to the letter to the editor on the article: radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma. LA RADIOLOGIA MEDICA 2024; 129:818-821. [PMID: 38512621 PMCID: PMC11088552 DOI: 10.1007/s11547-024-01799-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/02/2024] [Indexed: 03/23/2024]
Affiliation(s)
- Qing Wang
- Graduate Department, Bengbu Medical College, Bengbu, 233000, Anhui, People's Republic of China
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No.199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China
| | - Xianling Qian
- Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, No.180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032, People's Republic of China
| | - Xijuan Ma
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No.199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China
| | - Baoxin Qian
- Huiying Medical Technology, Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, HaiDian District, Beijing, 100192, People's Republic of China
| | - Xin Lu
- Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Department of Radiology, Shanghai Geriatric Medical Center, No. 2560 Chunshen Rd, Shanghai, 201104, People's Republic of China
| | - Yibing Shi
- Graduate Department, Bengbu Medical College, Bengbu, 233000, Anhui, People's Republic of China.
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No.199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China.
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19
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Qian GX, Xu ZL, Li YH, Lu JL, Bu XY, Wei MT, Jia WD. Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis. World J Gastroenterol 2024; 30:2128-2142. [PMID: 38681988 PMCID: PMC11045480 DOI: 10.3748/wjg.v30.i15.2128] [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: 12/30/2023] [Revised: 02/08/2024] [Accepted: 03/12/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND The prognosis for hepatocellular carcinoma (HCC) in the presence of cirrhosis is unfavourable, primarily attributable to the high incidence of recurrence. AIM To develop a machine learning model for predicting early recurrence (ER) of post-hepatectomy HCC in patients with cirrhosis and to stratify patients' overall survival (OS) based on the predicted risk of recurrence. METHODS In this retrospective study, 214 HCC patients with cirrhosis who underwent curative hepatectomy were examined. Radiomics feature selection was conducted using the least absolute shrinkage and selection operator and recursive feature elimination methods. Clinical-radiologic features were selected through univariate and multivariate logistic regression analyses. Five machine learning methods were used for model comparison, aiming to identify the optimal model. The model's performance was evaluated using the receiver operating characteristic curve [area under the curve (AUC)], calibration, and decision curve analysis. Additionally, the Kaplan-Meier (K-M) curve was used to evaluate the stratification effect of the model on patient OS. RESULTS Within this study, the most effective predictive performance for ER of post-hepatectomy HCC in the background of cirrhosis was demonstrated by a model that integrated radiomics features and clinical-radiologic features. In the training cohort, this model attained an AUC of 0.844, while in the validation cohort, it achieved a value of 0.790. The K-M curves illustrated that the combined model not only facilitated risk stratification but also exhibited significant discriminatory ability concerning patients' OS. CONCLUSION The combined model, integrating both radiomics and clinical-radiologic characteristics, exhibited excellent performance in HCC with cirrhosis. The K-M curves assessing OS revealed statistically significant differences.
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Affiliation(s)
- Gui-Xiang Qian
- Department of Hepatic Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
| | - Zi-Ling Xu
- Department of Hepatic Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Anhui Medical University, Hefei 230001, Anhui Province, China
| | - Yong-Hai Li
- Department of Anorectal Surgery, the First People’s Hospital of Hefei, Hefei 230001, Anhui Province, China
| | - Jian-Lin Lu
- Department of Hepatic Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Anhui Medical University, Hefei 230001, Anhui Province, China
| | - Xiang-Yi Bu
- Department of Hepatic Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
| | - Ming-Tong Wei
- Department of Hepatic Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Anhui Medical University, Hefei 230001, Anhui Province, China
| | - Wei-Dong Jia
- Department of Hepatic Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
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Huang M, Zhang F, Li Z, Luo Y, Li J, Wang Z, Ma L, Chen G, Hu X. Fat fraction quantification with MRI estimates tumor proliferation of hepatocellular carcinoma. Front Oncol 2024; 14:1367907. [PMID: 38665944 PMCID: PMC11044697 DOI: 10.3389/fonc.2024.1367907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Purpose To assess the utility of fat fraction quantification using quantitative multi-echo Dixon for evaluating tumor proliferation and microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods A total of 66 patients with resection and histopathologic confirmed HCC were enrolled. Preoperative MRI with proton density fat fraction and R2* mapping was analyzed. Intratumoral and peritumoral regions were delineated with manually placed regions of interest at the maximum level of intratumoral fat. Correlation analysis explored the relationship between fat fraction and Ki67. The fat fraction and R2* were compared between high Ki67(>30%) and low Ki67 nodules, and between MVI negative and positive groups. Receiver operating characteristic (ROC) analysis was used for further analysis if statistically different. Results The median fat fraction of tumor (tFF) was higher than peritumor liver (5.24% vs 3.51%, P=0.012). The tFF was negatively correlated with Ki67 (r=-0.306, P=0.012), and tFF of high Ki67 nodules was lower than that of low Ki67 nodules (2.10% vs 4.90%, P=0.001). The tFF was a good estimator for low proliferation nodules (AUC 0.747, cut-off 3.39%, sensitivity 0.778, specificity 0.692). There was no significant difference in tFF and R2* between MVI positive and negative nodules (3.00% vs 2.90%, P=0.784; 55.80s-1 vs 49.15s-1, P=0.227). Conclusion We infer that intratumor fat can be identified in HCC and fat fraction quantification using quantitative multi-echo Dixon can distinguish low proliferative HCCs.
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Affiliation(s)
| | | | | | | | | | | | | | - Gen Chen
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xuemei Hu
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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21
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Zhang R, Wang Y, Li Z, Shi Y, Yu D, Huang Q, Chen F, Xiao W, Hong Y, Feng Z. Dynamic radiomics based on contrast-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma. BMC Med Imaging 2024; 24:80. [PMID: 38584254 PMCID: PMC11000376 DOI: 10.1186/s12880-024-01258-9] [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: 11/20/2023] [Accepted: 03/26/2024] [Indexed: 04/09/2024] Open
Abstract
OBJECTIVE To exploit the improved prediction performance based on dynamic contrast-enhanced (DCE) MRI by using dynamic radiomics for microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS We retrospectively included 175 and 75 HCC patients who underwent preoperative DCE-MRI from September 2019 to August 2022 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Static radiomics features were extracted from the mask, arterial, portal venous, and equilibrium phase images and used to construct dynamic features. The static, dynamic, and dynamic-static radiomics (SR, DR, and DSR) signatures were separately constructed based on the feature selection method of LASSO and classification algorithm of logistic regression. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each signature. RESULTS In the three radiomics signatures, the DSR signature performed the best. The AUCs of the SR, DR, and DSR signatures in the training set were 0.750, 0.751 and 0.805, respectively, while in the external validation set, the corresponding AUCs were 0.706, 0756 and 0.777. The DSR signature showed significant improvement over the SR signature in predicting MVI status (training cohort: P = 0.019; validation cohort: P = 0.044). After external validation, the AUC value of the SR signature decreased from 0.750 to 0.706, while the AUC value of the DR signature did not show a decline (AUCs: 0.756 vs. 0.751). CONCLUSIONS The dynamic radiomics had an improved effect on the MVI prediction in HCC, compared with the static DCE MRI-based radiomics models.
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Affiliation(s)
- Rui Zhang
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Wang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhi Li
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yushu Shi
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Danping Yu
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiang Huang
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Chen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenbo Xiao
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuan Hong
- College of Mathematical Medicine, Zhejiang Normal University School, Jinhua, China
| | - Zhan Feng
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Li Z, Wang F, Zhang H, Xie S, Peng L, Xu H, Wang Y. A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108052. [PMID: 38447320 DOI: 10.1016/j.ejso.2024.108052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/24/2024] [Accepted: 02/21/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVE Develop a method for selecting esophageal cancer patients achieving pathological complete response with pre-neoadjuvant therapy chest-enhanced CT scans. METHODS Two hundred and one patients from center 1 were enrolled, split into training and testing sets (7:3 ratio), with an external validation set of 30 patients from center 2. Radiomics features from intra-tumoral and peritumoral images were extracted and dimensionally reduced using Student's t-test and least absolute shrinkage and selection operator. Four machine learning classifiers were employed to build models, with the best-performing models selected based on accuracy and stability. ROC curves were utilized to determine the top prediction model, and its generalizability was evaluated on the external validation set. RESULTS Among 16 models, the integrated-XGBoost and integrated-random forest models performed the best, with average ROC AUCs of 0.906 and 0.918, respectively, and RSDs of 6.26 and 6.89 in the training set. In the testing set, AUCs were 0.845 and 0.871, showing no significant difference in ROC curves. External validation set AUCs for integrated-XGBoost and integrated-random forest models were 0.650 and 0.749. CONCLUSION Incorporating peritumoral radiomics features into the analysis enhances predictive performance for esophageal cancer patients undergoing neoadjuvant chemoradiotherapy, paving the way for improved treatment outcomes.
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Affiliation(s)
- Zhiyang Li
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China; West China School of Medicine, West China Hospital, Sichuan University, China
| | - Fuqiang Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China; West China School of Medicine, West China Hospital, Sichuan University, China
| | - Hanlu Zhang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China
| | - Shenglong Xie
- Department of Thoracic Surgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Peng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China
| | - Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, China.
| | - Yun Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China.
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Wang M, Cao L, Wang Y, Huang H, Cao S, Tian X, Lei J. Prediction of vessels encapsulating tumor clusters pattern and prognosis of hepatocellular carcinoma based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid magnetic resonance imaging. J Gastrointest Surg 2024; 28:442-450. [PMID: 38583894 DOI: 10.1016/j.gassur.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/26/2024] [Accepted: 02/03/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND Vessels encapsulating tumor clusters (VETC) is a novel vascular pattern distinct from microvascular invasion that is significantly associated with poor prognosis in patients with hepatocellular carcinoma (HCC). This study aimed to predict the VETC pattern and prognosis of patients with HCC based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) magnetic resonance imaging (MRI). METHODS Patients with HCC who underwent surgical resection and preoperative Gd-EOB-DTPA MRI between January 1, 2016 and August 31, 2022 were retrospectively included. The variables associated with VETC were evaluated using logistic regression. A nomogram model was constructed on the basis of independent risk factors. COX regression was used to determine the variables associated with recurrence-free survival (RFS). RESULTS A total of 98 patients with HCC were retrospectively included. Peritumoral hypointensity on the hepatobiliary phase (HBP) (odd ratio [OR], 2.58; 95% CI, 1.05-6.33; P = .04), tumor-to-liver signal intensity ratio on HBP of ≤0.75 (OR, 27.80; 95% CI, 1.53-502.91; P = .02), and tumor-to-liver apparent diffusion coefficient ratio of ≤1.23 (OR, 4.65; 95% CI, 1.01-21.38; P = .04) were independent predictors of VETC pattern. A nomogram was constructed by combining the aforementioned 3 significant variables. The accuracy, sensitivity, and specificity were 69.79%, 71.74%, and 68.00%, respectively, with an area under the receiver operating characteristic curve of 0.75 (95% CI, 0.65-0.83). The variables significantly associated with RFS of patients with HCC after surgery were Barcelona Clinic Liver Cancer stage (hazard ratio [HR], 2.15; 95% CI, 1.09-4.22; P = .03) and VETC pattern (HR, 2.28; 95% CI, 1.29-4.02; P = .004). CONCLUSION The preoperative imaging features based on Gd-EOB-DTPA MRI can be used to predict the VETC pattern, which has prognostic significance for postoperative RFS of patients with HCC.
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Affiliation(s)
- Miaomiao Wang
- The First Clinical Medical College of Lanzhou University, Lanzhou City, Gansu Province, China; Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
| | - Liang Cao
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
| | - Yinzhong Wang
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
| | - Hongliang Huang
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
| | - Shi Cao
- Department of Pathology, The First Hospital of Lanzhou University, Lanzhou City, Gansu Province, China
| | - Xiaoxue Tian
- Department of Nuclear Medicine, Second Hospital of LanZhou University, Lanzhou City, Gansu Province, China
| | - Junqiang Lei
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China.
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Huang D, Lin C, Jiang Y, Xin E, Xu F, Gan Y, Xu R, Wang F, Zhang H, Lou K, Shi L, Hu H. Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy. Front Oncol 2024; 14:1348678. [PMID: 38585004 PMCID: PMC10996281 DOI: 10.3389/fonc.2024.1348678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Objective To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy. Methods A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis. Results Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91). Conclusion We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.
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Affiliation(s)
- Dingpin Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chen Lin
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Yangyang Jiang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Enhui Xin
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rui Xu
- DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China
- DUT-RU Co-Research Center of Advanced Information Computing Technology (ICT) for Active Life, Dalian University of Technology, Dalian, Liaoning, China
| | - Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haiping Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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He X, Xu Y, Zhou C, Song R, Liu Y, Zhang H, Wang Y, Fan Q, Wang D, Chen W, Wang J, Guo D. Prediction of microvascular invasion and pathological differentiation of hepatocellular carcinoma based on a deep learning model. Eur J Radiol 2024; 172:111348. [PMID: 38325190 DOI: 10.1016/j.ejrad.2024.111348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/25/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE To develop a deep learning (DL) model based on preoperative contrast-enhanced computed tomography (CECT) images to predict microvascular invasion (MVI) and pathological differentiation of hepatocellular carcinoma (HCC). METHODS This retrospective study included 640 consecutive patients who underwent surgical resection and were pathologically diagnosed with HCC at two medical institutions from April 2017 to May 2022. CECT images and relevant clinical parameters were collected. All the data were divided into 368 training sets, 138 test sets and 134 validation sets. Through DL, a segmentation model was used to obtain a region of interest (ROI) of the liver, and a classification model was established to predict the pathological status of HCC. RESULTS The liver segmentation model based on the 3D U-Network had a mean intersection over union (mIoU) score of 0.9120 and a Dice score of 0.9473. Among all the classification prediction models based on the Swin transformer, the fusion models combining image information and clinical parameters exhibited the best performance. The area under the curve (AUC) of the fusion model for predicting the MVI status was 0.941, its accuracy was 0.917, and its specificity was 0.908. The AUC values of the fusion model for predicting poorly differentiated, moderately differentiated and highly differentiated HCC based on the test set were 0.962, 0.957 and 0.996, respectively. CONCLUSION The established DL models established can be used to noninvasively and effectively predict the MVI status and the degree of pathological differentiation of HCC, and aid in clinical diagnosis and treatment.
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Affiliation(s)
- Xiaojuan He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Yang Xu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Chaoyang Zhou
- Department of Radiology, The First Affiliated Hospital of Army Military Medical University, Chongqing 400038, PR China.
| | - Rao Song
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Yangyang Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Haiping Zhang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Yudong Wang
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, PR China.
| | - Qianrui Fan
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, PR China.
| | - Dawei Wang
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, PR China.
| | - Weidao Chen
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, PR China.
| | - Jian Wang
- Department of Radiology, The First Affiliated Hospital of Army Military Medical University, Chongqing 400038, PR China.
| | - Dajing Guo
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
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Ma J, Chen K, Li S, Zhu L, Yu Y, Li J, Ma J, Ouyang J, Wu Z, Tan Y, He Z, Liu H, Pan Z, Li H, Liu Q, Song E. MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study. Eur Radiol 2024; 34:1774-1789. [PMID: 37658888 DOI: 10.1007/s00330-023-10144-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/18/2023] [Accepted: 07/08/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES Accurate preoperative estimation of the risk of breast-conserving surgery (BCS) resection margin positivity would be beneficial to surgical planning. In this multicenter validation study, we developed an MRI-based radiomic model to predict the surgical margin status. METHODS We retrospectively collected preoperative breast MRI of patients undergoing BCS from three hospitals (SYMH, n = 296; SYSUCC, n = 131; TSPH, n = 143). Radiomic-based model for risk prediction of the margin positivity was trained on the SYMH patients (7:3 ratio split for the training and testing cohorts), and externally validated in the SYSUCC and TSPH cohorts. The model was able to stratify patients into different subgroups with varied risk of margin positivity. Moreover, we used the immune-radiomic models and epithelial-mesenchymal transition (EMT) signature to infer the distribution patterns of immune cells and tumor cell EMT status under different marginal status. RESULTS The AUCs of the radiomic-based model were 0.78 (0.66-0.90), 0.88 (0.79-0.96), and 0.76 (0.68-0.84) in the testing cohort and two external validation cohorts, respectively. The actual margin positivity rates ranged between 0-10% and 27.3-87.2% in low-risk and high-risk subgroups, respectively. Positive surgical margin was associated with higher levels of EMT and B cell infiltration in the tumor area, as well as the enrichment of B cells, immature dendritic cells, and neutrophil infiltration in the peritumoral area. CONCLUSIONS This MRI-based predictive model can be used as a reliable tool to predict the risk of margin positivity of BCS. Tumor immune-microenvironment alteration was associated with surgical margin status. CLINICAL RELEVANCE STATEMENT This study can assist the pre-operative planning of BCS. Further research on the tumor immune microenvironment of different resection margin states is expected to develop new margin evaluation indicators and decipher the internal mechanism. KEY POINTS • The MRI-based radiomic prediction model (CSS model) incorporating features extracted from multiple sequences and segments could estimate the margin positivity risk of breast-conserving surgery. • The radiomic score of the CSS model allows risk stratification of patients undergoing breast-conserving surgery, which could assist in surgical planning. • With the help of MRI-based radiomics to estimate the components of the immune microenvironment, for the first time, it is found that the margin status of breast-conserving surgery is associated with the infiltration of immune cells in the microenvironment and the EMT status of breast tumor cells.
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Affiliation(s)
- Jiafan Ma
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Kai Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Shunrong Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Liling Zhu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Yunfang Yu
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Jingwu Li
- Department of Breast Surgery, Tangshan People's Hospital, Tangshan, 063001, Hebei, China
| | - Jie Ma
- Department of Breast Surgery, Tangshan People's Hospital, Tangshan, 063001, Hebei, China
| | - Jie Ouyang
- Department of Breast Surgery, Tungwah Hospital, Sun Yat-sen University, Dongguan, 523413, China
| | - Zhuo Wu
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Yujie Tan
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Zifan He
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Haiqing Liu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Zhilong Pan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Haojiang Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, China.
| | - Qiang Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China.
| | - Erwei Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China.
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Zhang R, Li D, Chen Y, Xu W, Zhou W, Lin M, Xie X, Xu M. Development and Comparison of Prediction Models Based on Sonovue- and Sonazoid-Enhanced Ultrasound for Pathologic Grade and Microvascular Invasion in Hepatocellular Carcinoma. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:414-424. [PMID: 38155069 DOI: 10.1016/j.ultrasmedbio.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 10/31/2023] [Accepted: 12/01/2023] [Indexed: 12/30/2023]
Abstract
OBJECTIVE This study was aimed at developing and comparing prediction models based on Sonovue and Sonazoid contrast-enhanced ultrasound (CEUS) in predicting pathologic grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Also investigated was whether Kupffer phase images have additional predictive value for the above pathologic features. METHODS Ninety patients diagnosed with primary HCC who had undergone curative hepatectomy were prospectively enrolled. All patients underwent conventional ultrasound (CUS), Sonovue-CEUS and Sonazoid-CEUS examinations pre-operatively. Clinical, radiologic and pathologic features including pathologic grade, MVI and CD68 expression were collected. We developed prediction models comprising clinical, CUS and CEUS (Sonovue and Sonazoid, respectively) features for pathologic grade and MVI with both the logistic regression and machine learning (ML) methods. RESULTS Forty-one patients (45.6%) had poorly differentiated HCC (p-HCC) and 37 (41.1%) were MVI positive. For pathologic grade, the logistic model based on Sonazoid-CEUS had significantly better performance than that based on Sonovue-CEUS (area under the curve [AUC], 0.929 vs. 0.848, p = 0.035), whereas for MVI, these two models had similar accuracy (AUC, 0.810 vs. 0.786, p = 0.068). Meanwhile, we found that well-differentiated HCC tended to have a higher enhancement ratio in 6-12 min during the Kupffer phase of Sonazoid-CEUS, as well as higher CD68 expression compared with p-HCC. In addition, all of these models can effectively predict the risk of recurrence (p < 0.05). CONCLUSION Sonovue-CEUS and Sonazoid-CEUS were comparably excellent in predicting MVI, while Sonazoid-CEUS was superior to Sonovue-CEUS in predicting pathologic grade because of the Kupffer phase. The enhancement ratio in the Kupffer phase has additional predictive value for pathologic grade prediction.
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Affiliation(s)
- Rui Zhang
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Di Li
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yanlin Chen
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenxin Xu
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenwen Zhou
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Manxia Lin
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyan Xie
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ming Xu
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
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Shi R, Wang J, Zeng X, Luo H, Yang X, Guo Y, Yi L, Deng H, Yang P. Effect of anatomical liver resection on early postoperative recurrence in patients with hepatocellular carcinoma assessed based on a nomogram: a single-center study in China. Front Oncol 2024; 14:1365286. [PMID: 38476367 PMCID: PMC10929612 DOI: 10.3389/fonc.2024.1365286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 02/07/2024] [Indexed: 03/14/2024] Open
Abstract
Introduction We aimed to investigate risk factors for early postoperative recurrence in patients with hepatocellular carcinoma (HCC) and determine the effect of surgical methods on early recurrence to facilitate predicting the risk of early postoperative recurrence in such patients and the selection of appropriate treatment methods. Methods We retrospectively analyzed clinical data concerning 428 patients with HCC who had undergone radical surgery at Mianyang Central Hospital between January 2015 and August 2022. Relevant routine preoperative auxiliary examinations and regular postoperative telephone or outpatient follow-ups were performed to identify early postoperative recurrence. Risk factors were screened, and predictive models were constructed, including patients' preoperative ancillary tests, intra- and postoperative complications, and pathology tests in relation to early recurrence. The risk of recurrence was estimated for each patient based on a prediction model, and patients were categorized into low- and high-risk recurrence groups. The effect of anatomical liver resection (AR) on early postoperative recurrence in patients with HCC in the two groups was assessed using survival analysis. Results In total, 353 study patients were included. Multifactorial logistic regression analysis findings suggested that tumor diameter (≥5/<5 cm, odds ratio [OR] 2.357, 95% confidence interval [CI] 1.368-4.059; P = 0.002), alpha fetoprotein (≥400/<400 ng/L, OR 2.525, 95% CI 1.334-4.780; P = 0.004), tumor number (≥2/<2, OR 2.213, 95% CI 1.147-4.270; P = 0.018), microvascular invasion (positive/negative, OR 3.230, 95% CI 1.880-5.551; P < 0.001), vascular invasion (positive/negative, OR 4.472, 95% CI 1.395-14.332; P = 0.012), and alkaline phosphatase level (>125/≤125 U/L, OR 2.202, 95% CI 1.162-4.173; P = 0.016) were risk factors for early recurrence following radical HCC surgery. Model validation and evaluation showed that the area under the curve was 0.813. Hosmer-Lemeshow test results (X 2 = 1.225, P = 0.996 > 0.05), results from bootstrap self-replicated sampling of 1,000 samples, and decision curve analysis showed that the model also discriminated well, with potentially good clinical utility. Using this model, patients were stratified into low- and high-risk recurrence groups. One-year disease-free survival was compared between the two groups with different surgical approaches. Both groups benefited from AR in terms of prevention of early postoperative recurrence, with AR benefits being more pronounced and intraoperative bleeding less likely in the high-risk recurrence group. Discussion With appropriate surgical techniques and with tumors being realistically amenable to R0 resection, AR is a potentially useful surgical procedure for preventing early recurrence after radical surgery in patients with HCC.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Pei Yang
- Department of Hepatobiliary Surgery, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, China
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Fu L, Wang W, Lin L, Gao F, Yang J, Lv Y, Ge R, Wu M, Chen L, Liu A, Xin E, Yu J, Cheng J, Wang Y. Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics. Front Med (Lausanne) 2024; 11:1334062. [PMID: 38384418 PMCID: PMC10880444 DOI: 10.3389/fmed.2024.1334062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 01/11/2024] [Indexed: 02/23/2024] Open
Abstract
Objective High-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images. Methods This study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long's test. Results The radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long's test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model. Conclusion The identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation.
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Affiliation(s)
- Le Fu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wenjing Wang
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lingling Lin
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Feng Gao
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jiani Yang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yunyun Lv
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ruiqiu Ge
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Meixuan Wu
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Aie Liu
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Enhui Xin
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jianli Yu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jiejun Cheng
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yu Wang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
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Liu WM, Zhao XY, Gu MT, Song KR, Zheng W, Yu H, Chen HL, Xu XW, Zhou X, Liu AE, Jia NY, Wang PJ. Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma. World J Oncol 2024; 15:58-71. [PMID: 38274720 PMCID: PMC10807913 DOI: 10.14740/wjon1731] [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: 09/19/2023] [Accepted: 11/15/2023] [Indexed: 01/27/2024] Open
Abstract
Background The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models. Results The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively. Conclusions Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.
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Affiliation(s)
- Wan Min Liu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- These authors contributed equally to this work
| | - Xing Yu Zhao
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- These authors contributed equally to this work
| | - Meng Ting Gu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Kai Rong Song
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Wei Zheng
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Hui Yu
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Hui Lin Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiao Wen Xu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiang Zhou
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ai E Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Ning Yang Jia
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Pei Jun Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 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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Hou S, Wang H, Wang X, Chen H, Zhou B, Meng R, Sha X, Chang S, Wang H, Jiang W. Tumor-liver interface in MRI of liver metastasis enables prediction of EGFR mutation in patients with lung cancer: A proof-of-concept study. Med Phys 2024; 51:1083-1091. [PMID: 37408393 DOI: 10.1002/mp.16581] [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: 01/18/2023] [Revised: 04/19/2023] [Accepted: 06/05/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Preoperative prediction of the epidermal growth factor receptor (EGFR) status in non-small-cell lung cancer (NSCLC) patients with liver metastasis (LM) may have potential clinical values for assisting in treatment decision-making. PURPOSE To explore the value of tumor-liver interface (TLI)-based magnetic resonance imaging (MRI) radiomics for detecting the EGFR mutation in NSCLC patients with LM. METHODS This retrospective study included 123 and 44 patients from hospital 1 (between Feb. 2018 and Dec. 2021) and hospital 2 (between Nov. 2015 and Aug. 2022), respectively. The patients received contrast-enhanced T1-weighted (CET1) and T2-weighted (T2W) liver MRI scans before treatment. Radiomics features were extracted from MRI images of TLI and the whole tumor region, separately. The least absolute shrinkage and selection operator (LASSO) regression was used to screen the features and establish radiomics signatures (RSs) based on TLI (RS-TLI) and the whole tumor (RS-W). The RSs were evaluated by the receiver operating characteristic (ROC) curve analysis. RESULTS A total of 5 and 6 features were identified highly correlated with the EGFR mutation status from TLI and the whole tumor, respectively. The RS-TLI showed better prediction performance than RS-W in the training (AUCs, RS-TLI vs. RS-W, 0.842 vs. 0.797), internal validation (AUCs, RS-TLI vs. RS-W, 0.771 vs. 0.676) and external validation (AUCs, RS-TLI vs. RS-W, 0.733 vs. 0.679) cohort. CONCLUSION Our study demonstrated that TLI-based radiomics can improve prediction performance of the EGFR mutation in lung cancer patients with LM. The established multi-parametric MRI radiomics models may be used as new markers that can potentially assist in personalized treatment planning.
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Affiliation(s)
- Shaoping Hou
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, P.R. China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital, Shenyang, Liaoning, P.R. China
| | - Boyu Zhou
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Ruiqing Meng
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Xianzheng Sha
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Shijie Chang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P.R. China
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P.R. China
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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Berbís MÁ, Godino FP, Rodríguez-Comas J, Nava E, García-Figueiras R, Baleato-González S, Luna A. Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application. Abdom Radiol (NY) 2024; 49:322-340. [PMID: 37889265 DOI: 10.1007/s00261-023-04071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/28/2023]
Abstract
Radiomics allows the extraction of quantitative imaging features from clinical magnetic resonance imaging (MRI) and computerized tomography (CT) studies. The advantages of radiomics have primarily been exploited in oncological applications, including better characterization and staging of oncological lesions and prediction of patient outcomes and treatment response. The potential introduction of radiomics in the clinical setting requires the establishment of a standardized radiomics pipeline and a quality assurance program. Radiomics and texture analysis of the liver have improved the differentiation of hypervascular lesions such as adenomas, focal nodular hyperplasia, and hepatocellular carcinoma (HCC) during the arterial phase, and in the pretreatment determination of HCC prognostic factors (e.g., tumor grade, microvascular invasion, Ki-67 proliferation index). Radiomics of pancreatic CT and MR images has enhanced pancreatic ductal adenocarcinoma detection and its differentiation from pancreatic neuroendocrine tumors, mass-forming chronic pancreatitis, or autoimmune pancreatitis. Radiomics can further help to better characterize incidental pancreatic cystic lesions, accurately discriminating benign from malignant intrapancreatic mucinous neoplasms. Nonetheless, despite their encouraging results and exciting potential, these tools have yet to be implemented in the clinical setting. This non-systematic review will describe the essential steps in the implementation of the radiomics and feature extraction workflow from liver and pancreas CT and MRI studies for their potential clinical application. A succinct overview of reported radiomics applications in the liver and pancreas and the challenges and limitations of their implementation in the clinical setting is also discussed, concluding with a brief exploration of the future perspectives of radiomics in the gastroenterology field.
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Affiliation(s)
- M Álvaro Berbís
- Department of Radiology, HT Médica, San Juan de Dios Hospital, 14960, Córdoba, Spain.
- Department of Radiology, HT Médica, San Juan de Dios Hospital, Av. del Brillante, 106, 14012, Córdoba, Spain.
| | | | | | - Enrique Nava
- Department of Communications Engineering, University of Málaga, 29016, Málaga, Spain
| | - Roberto García-Figueiras
- Abdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain
| | - Sandra Baleato-González
- Abdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain
| | - Antonio Luna
- Department of Radiology, HT Médica, Clínica las Nieves, 23007, Jaén, Spain
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Qu Q, Liu Z, Lu M, Xu L, Zhang J, Liu M, Jiang J, Gu C, Ma Q, Huang A, Zhang X, Zhang T. Preoperative Gadoxetic Acid-Enhanced MRI Features for Evaluation of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma: Creating Nomograms for Risk Assessment. J Magn Reson Imaging 2023. [PMID: 38116997 DOI: 10.1002/jmri.29187] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/01/2023] [Accepted: 12/02/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Vessels encapsulating tumor cluster (VETC) and microvascular invasion (MVI) have a synergistic effect on prognosis assessment and treatment selection of hepatocellular carcinoma (HCC). Preoperative noninvasive evaluation of VETC and MVI is important. PURPOSE To explore the diagnosis value of preoperative gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI) features for MVI, VETC, and recurrence-free survival (RFS) in HCC. STUDY TYPE Retrospective. POPULATION 240 post-surgery patients with 274 pathologically confirmed HCC (allocated to training and validation cohorts with a 7:3 ratio) and available tumor marker data from August 2014 to December 2021. FIELD STRENGTH/SEQUENCE 3-T, T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT Three radiologists subjectively reviewed preoperative MRI, evaluated clinical and conventional imaging features associated with MVI+, VETC+, and MVI+/VETC+ HCC. Regression-based nomograms were developed for HCC in the training cohort. Based on the nomograms, the RFS prognostic stratification system was further. Follow-up occurred every 3-6 months. STATISTICAL TESTS Chi-squared test or Fisher's exact test, Mann-Whitney U-test or t-test, least absolute shrinkage and selection operator-penalized, multivariable logistic regression analyses, receiver operating characteristic analysis, Harrell's concordance index (C-index), Kaplan-Meier plots. Significance level: P < 0.05. RESULTS In the training group, 44 patients with MVI+ and 74 patients with VETC+ were histologically confirmed. Three nomograms showed good performance in the training (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.892 vs. 0.848 vs. 0.910) and validation (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.839 vs. 0.810 vs. 0.855) cohorts. The median follow-up duration for the training cohort was 43.6 (95% CI, 35.0-52.2) months and 25.8 (95% CI, 16.1-35.6) months for the validation cohort. Patients with either pathologically confirmed or nomogram-estimated MVI, VETC, and MVI+/VETC+ suffered higher risk of recurrence. DATA CONCLUSION GA-enhanced MRI and clinical variables might assist in preoperative estimation of MVI, VETC, and MVI+/VETC+ in HCC. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Qi Qu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Zixin Liu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Mengtian Lu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Lei Xu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Jiyun Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Maotong Liu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Jifeng Jiang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Chunyan Gu
- Department of Pathology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Qinrong Ma
- Department of Pathology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Aina Huang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Xueqin Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Tao Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
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Cao X, Yang H, Luo X, Zou L, Zhang Q, Li Q, Zhang J, Li X, Shi Y, Jin C. A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast-Enhanced MRI Radiomics. J Magn Reson Imaging 2023; 58:1930-1941. [PMID: 37177868 DOI: 10.1002/jmri.28725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC. PURPOSE To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast-enhanced MRI (DCE-MRI), along with clinical findings. STUDY TYPE Retrospective. SUBJECTS 249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected. FIELD STRENGTH/SEQUENCE Fat saturated T2-weighted, Fat saturated T1-weighted, and DCE-MRI performed at 1.5 T and 3.0 T. ASSESSMENT Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical-radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24-month survival for HCC. STATISTICAL TESTS The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan-Meier analysis. The discrimination performance of each model was quantified by the C-index. A P value <0.05 was considered statistically significant. RESULTS The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C-index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC. CONCLUSION The combined radiomic model provides superior ability to discern the possibility of recurrence-free survival in HCC over the total radiomic and the clinical-radiological models. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Xinshan Cao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Haoran Yang
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Xin Luo
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Linxuan Zou
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Qiang Zhang
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Qilin Li
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Juntao Zhang
- GE Healthcare Precision Health Institution, Shanghai, China
| | - Xiangfeng Li
- Department of Radiology, The Fourth People Hospital of Zibo, Zibo, China
| | - Yan Shi
- Department of Medical Ultrasonics, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Chenwang Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Kamal O, Horvat N, Arora S, Chaudhry H, Elmohr M, Khanna L, Nepal PS, Wungjramirun M, Nandwana SB, Shenoy-Bhangle AS, Lee J, Kielar A, Marks R, Elsayes K, Fung A. Understanding the role of radiologists in complex treatment decisions for patients with hepatocellular carcinoma. Abdom Radiol (NY) 2023; 48:3677-3687. [PMID: 37715846 PMCID: PMC11234513 DOI: 10.1007/s00261-023-04033-6] [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: 07/10/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/18/2023]
Abstract
Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver and represents a significant global health burden. Management of HCC can be challenging due to multiple factors, including variable expectations for treatment outcomes. Several treatment options are available, each with specific eligibility and ineligibility criteria, and are provided by a multidisciplinary team of specialists. Radiologists should be aware of the types of treatment options available, as well as the criteria guiding the development of individualized treatment plans. This awareness enables radiologists to contribute effectively to patient-centered multidisciplinary tumor boards for HCC and play a central role in reassessing care plans when the treatment response is deemed inadequate. This comprehensive review aims to equip radiologists with an overview of HCC staging systems, treatment options, and eligibility criteria. The review also discusses the significance of imaging in HCC diagnosis, treatment planning, and monitoring treatment response. Furthermore, we highlight the crucial branch points in the treatment decision-making process that depend on radiological interpretation.
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Affiliation(s)
- Omar Kamal
- Department of Diagnostic Radiology, Oregon Health & Science University, Mail Code: L340, 3181 SW Sam Jackson Park Road, Portland, OR, 97239, USA.
| | - Natally Horvat
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | | | | | - Manida Wungjramirun
- Department of Diagnostic Radiology, Oregon Health & Science University, Mail Code: L340, 3181 SW Sam Jackson Park Road, Portland, OR, 97239, USA
| | | | | | - James Lee
- University of Kentucky, Lexington, KY, USA
| | | | | | | | - Alice Fung
- Department of Diagnostic Radiology, Oregon Health & Science University, Mail Code: L340, 3181 SW Sam Jackson Park Road, Portland, OR, 97239, USA
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Liao ZJ, Lu L, Liu YP, Qin GG, Fan CG, Liu YP, Jia NY, Zhang L. Clinical and DCE-CT signs in predicting microvascular invasion in cHCC-ICC. Cancer Imaging 2023; 23:112. [PMID: 37978567 PMCID: PMC10655417 DOI: 10.1186/s40644-023-00621-3] [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: 06/12/2023] [Accepted: 10/16/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND To predict the microvascular invasion (MVI) in patients with cHCC-ICC. METHODS A retrospective analysis was conducted on 119 patients who underwent CT enhancement scanning (from September 2006 to August 2022). They were divided into MVI-positive and MVI-negative groups. RESULTS The proportion of patients with CEA elevation was higher in the MVI-positive group than in the MVI-negative group, with a statistically significant difference (P = 0.02). The MVI-positive group had a higher rate of peritumoral enhancement in the arterial phase (P = 0.01) whereas the MVI-negative group had more oval and lobulated masses (P = 0.04). According to the multivariate analysis, the increase in CEA (OR = 10.15, 95% CI: 1.11, 92.48, p = 0.04), hepatic capsular withdrawal (OR = 4.55, 95% CI: 1.44, 14.34, p = 0.01) and peritumoral enhancement (OR = 6.34, 95% CI: 2.18, 18.40, p < 0.01) are independent risk factors for predicting MVI. When these three imaging signs are combined, the specificity of MVI prediction was 70.59% (series connection), and the sensitivity was 100% (parallel connection). CONCLUSIONS Our multivariate analysis found that CEA elevation, liver capsule depression, and arterial phase peritumoral enhancement were independent risk factors for predicting MVI in cHCC-ICC.
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Affiliation(s)
- Zhong-Jian Liao
- Medical Imaging Department of Ganzhou People's Hospital, Ganzhou, 341000, China
| | - Lun Lu
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China
| | - Yi-Ping Liu
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China
| | - Geng-Geng Qin
- Medical Imaging Department of Ganzhou People's Hospital, Ganzhou, 341000, China
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Cun-Geng Fan
- Medical Imaging Department of Ganzhou People's Hospital, Ganzhou, 341000, China
| | - Yan-Ping Liu
- Medical Imaging Department of Ganzhou People's Hospital, Ganzhou, 341000, China
| | - Ning-Yang Jia
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China.
| | - Ling Zhang
- Medical Imaging Department of Ganzhou People's Hospital, Ganzhou, 341000, China.
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
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Ma X, Qian X, Wang Q, Zhang Y, Zong R, Zhang J, Qian B, Yang C, Lu X, Shi Y. Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma. LA RADIOLOGIA MEDICA 2023; 128:1296-1309. [PMID: 37679641 PMCID: PMC10620280 DOI: 10.1007/s11547-023-01704-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 08/11/2023] [Indexed: 09/09/2023]
Abstract
OBJECTIVE Microvascular invasion (MVI) is a significant adverse prognostic indicator of intrahepatic cholangiocarcinoma (ICC) and affects the selection of individualized treatment regimens. This study sought to establish a radiomics nomogram based on the optimal VOI of multi-sequence MRI for predicting MVI in ICC tumors. METHODS 160 single ICC lesions with MRI scanning confirmed by postoperative pathology were randomly separated into training and validation cohorts (TC and VC). Multivariate analysis identified independent clinical and imaging MVI predictors. Radiomics features were obtained from images of 6 MRI sequences at 4 different VOIs. The least absolute shrinkage and selection operator algorithm was performed to enable the derivation of robust and effective radiomics features. Then, the best three sequences and the optimal VOI were obtained through comparison. The MVI prediction nomogram combined the independent predictors and optimal radiomics features, and its performance was evaluated via the receiver operating characteristics, calibration, and decision curves. RESULTS Tumor size and intrahepatic ductal dilatation are independent MVI predictors. Radiomics features extracted from the best three sequences (T1WI-D, T1WI, DWI) with VOI10mm (including tumor and 10 mm peritumoral region) showed the best predictive performance, with AUCTC = 0.987 and AUCVC = 0.859. The MVI prediction nomogram obtained excellent prediction efficacy in both TC (AUC = 0.995, 95%CI 0.987-1.000) and VC (AUC = 0.867, 95%CI 0.798-0.921) and its clinical significance was further confirmed by the decision curves. CONCLUSION A nomogram combining tumor size, intrahepatic ductal dilatation, and the radiomics model of MRI multi-sequence fusion at VOI10mm may be a predictor of preoperative MVI status in ICC patients.
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Affiliation(s)
- Xijuan Ma
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No. 199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China
| | - Xianling Qian
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
| | - Qing Wang
- Graduate Department, Bengbu Medical College, Bengbu, 233000, Anhui, People's Republic of China
| | - Yunfei Zhang
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Central Research Institute, United Imaging Healthcare, No. 2258 Chengbei Rd, Shanghai, 201807, People's Republic of China
| | - Ruilong Zong
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No. 199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China
| | - Jia Zhang
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No. 199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China
| | - Baoxin Qian
- Huiying Medical Technology, Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing City, 100192, People's Republic of China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
| | - Xin Lu
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China.
- Department of Cancer Center, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China.
- Department of Radiology, Shanghai Geriatric Medical Center, No. 2560 Chunshen Rd, Shanghai, 201104, People's Republic of China.
| | - Yibing Shi
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No. 199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China.
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Cannella R, Santinha J, Bèaufrere A, Ronot M, Sartoris R, Cauchy F, Bouattour M, Matos C, Papanikolaou N, Vilgrain V, Dioguardi Burgio M. Performances and variability of CT radiomics for the prediction of microvascular invasion and survival in patients with HCC: a matter of chance or standardisation? Eur Radiol 2023; 33:7618-7628. [PMID: 37338558 DOI: 10.1007/s00330-023-09852-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/28/2023] [Accepted: 04/21/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVES To measure the performance and variability of a radiomics-based model for the prediction of microvascular invasion (MVI) and survival in patients with resected hepatocellular carcinoma (HCC), simulating its sequential development and application. METHODS This study included 230 patients with 242 surgically resected HCCs who underwent preoperative CT, of which 73/230 (31.7%) were scanned in external centres. The study cohort was split into training set (158 patients, 165 HCCs) and held-out test set (72 patients, 77 HCCs), stratified by random partitioning, which was repeated 100 times, and by a temporal partitioning to simulate the sequential development and clinical use of the radiomics model. A machine learning model for the prediction of MVI was developed with least absolute shrinkage and selection operator (LASSO). The concordance index (C-index) was used to assess the value to predict the recurrence-free (RFS) and overall survivals (OS). RESULTS In the 100-repetition random partitioning cohorts, the radiomics model demonstrated a mean AUC of 0.54 (range 0.44-0.68) for the prediction of MVI, mean C-index of 0.59 (range 0.44-0.73) for RFS, and 0.65 (range 0.46-0.86) for OS in the held-out test set. In the temporal partitioning cohort, the radiomics model yielded an AUC of 0.50 for the prediction of MVI, a C-index of 0.61 for RFS, and 0.61 for OS, in the held-out test set. CONCLUSIONS The radiomics models had a poor performance for the prediction of MVI with a large variability in the model performance depending on the random partitioning. Radiomics models demonstrated good performance in the prediction of patient outcomes. CLINICAL RELEVANCE STATEMENT Patient selection within the training set strongly influenced the performance of the radiomics models for predicting microvascular invasion; therefore, a random approach to partitioning a retrospective cohort into a training set and a held-out set seems inappropriate. KEY POINTS • The performance of the radiomics models for the prediction of microvascular invasion and survival widely ranged (AUC range 0.44-0.68) in the randomly partitioned cohorts. • The radiomics model for the prediction of microvascular invasion was unsatisfying when trying to simulate its sequential development and clinical use in a temporal partitioned cohort imaged with a variety of CT scanners. • The performance of the radiomics models for the prediction of survival was good with similar performances in the 100-repetition random partitioning and temporal partitioning cohorts.
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Affiliation(s)
- Roberto Cannella
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France
- Section of Radiology-BiND, University Hospital 'Paolo Giaccone', Palermo, Italy
- Department of Health Promotion Sciences Maternal and Infant Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, Palermo, Italy
| | - Joao Santinha
- Champalimaud Foundation-Centre for the Unknown, 1400-038, Lisbon, Portugal
| | | | - Maxime Ronot
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France
- Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
| | - Riccardo Sartoris
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France
- Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
| | - Francois Cauchy
- Department of HPB Surgery and Liver Transplantation, Hôpital Beaujon, Clichy, France
| | | | - Celso Matos
- Champalimaud Foundation-Centre for the Unknown, 1400-038, Lisbon, Portugal
| | | | - Valérie Vilgrain
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France
- Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
| | - Marco Dioguardi Burgio
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France.
- Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France.
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Wang L, Liang M, Feng B, Li D, Cong R, Chen Z, Wang S, Ma X, Zhao X. Microvascular invasion-negative hepatocellular carcinoma: Prognostic value of qualitative and quantitative Gd-EOB-DTPA MRI analysis. Eur J Radiol 2023; 168:111146. [PMID: 37832198 DOI: 10.1016/j.ejrad.2023.111146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/27/2023] [Accepted: 10/06/2023] [Indexed: 10/15/2023]
Abstract
OBJECTIVES The purpose of this study was to establish a model for predicting the prognosis of patients with microvascular invasion (MVI)-negative hepatocellular carcinoma (HCC) based on qualitative and quantitative analyses of Gd-EOB-DTPA magnetic resonance imaging (MRI). MATERIALS AND METHODS Consecutive patients with MVI-negative HCC who underwent preoperative Gd-EOB-DTPA MRI between January 2015 and December 2019 were retrospectively enrolled.In total, 122 patients were randomly assigned to the training and validation groups at a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed to identify significant clinical parameters and MRI features, including quantitative and qualitative parameters associated with prognosis, which were incorporated into a predictive nomogram. The end-point of this study was recurrence-free survival. Outcomes were compared between groups using the Kaplan-Meier method with the log-rank test. RESULTS During a median follow-up period of 58.86 months, 38 patients (31.15 %) experienced recurrence. Multivariate analysis revealed that lower relative enhancement ratio (RER), hepatobiliary phase hypointensity without arterial phase hyperenhancement, Liver Imaging Reporting and Data System category, mild-moderate T2 hyperintensity, and higher aspartate aminotransferase levels were risk factors associated with prognosis and then incorporated into the prognostic model. C-indices for training and validation groups were 0.732 and 0.692, respectively. The most appropriate cut-off value for RER was 1.197. Patients with RER ≤ 1.197 had significantly higher postoperative recurrence rates than those with RER > 1.197 (p = 0.004). CONCLUSION The model integrating qualitative and quantitative imaging parameters and clinical parameters satisfactorily predicted the prognosis of patients with MVI-negative HCC.
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Affiliation(s)
- Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Rong Cong
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Zhaowei Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Sicong Wang
- Sicong Wang, Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing 100176, China
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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Yang J, Liang S, Liu H, Hu C, Guan S, Kang H, Xu E, Yan R. Efficacy and Safety of Microwave Ablation Assisted by Ultrasound Fusion Imaging for Primary and Secondary Liver Cancers with a Diameter of 3-7 Cm. J Hepatocell Carcinoma 2023; 10:1839-1848. [PMID: 37873028 PMCID: PMC10590584 DOI: 10.2147/jhc.s424009] [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: 06/20/2023] [Accepted: 10/12/2023] [Indexed: 10/25/2023] Open
Abstract
Purpose To investigate the efficacy and safety of microwave ablation (MWA) assisted by ultrasound fusion imaging (FI) for primary and secondary liver cancers with a diameter of 3-7 cm. Patients and Methods A retrospective analysis was conducted on patients with primary and secondary liver cancers (3-7 cm) who underwent MWA with ultrasound FI assistance in our hospital from April 2020 to May 2022. Technical success, technique efficacy, local tumor progression (LTP), major complication, intrahepatic distant recurrence (IDR), and overall survival (OS) were assessed during the follow-up period. In addition, the ablation results of tumors between the medium-sized group (3.1-5.0 cm) and large-sized group (5.1-7.0 cm) were compared. Results 31 patients with 35 primary and secondary liver cancers were treated with MWA assisted by ultrasound FI. Complete ablation was achieved in 34 lesions with a technical success rate of 97.1%. Major complications occurred in 6.5% of patients (2/31), while no ablation-related deaths were reported. The median follow-up time of this study was 24 months (range:10 to 35 months). The technique efficacy rate was 97.1% (34/35), with LTP occurring in three lesions at a rate of 8.8% (3/34). The incidence of IDR was 38.7% (12/31) and the 2-year cumulative OS rate reached 96.7%. Moreover, there were no statistical differences in technique efficacy rate (p=0.286), LTP rate (p=0.328), major complication rate (p=0.503), IDR (p=0.857), and OS (p=0.118) between medium-sized group and large-sized group. Conclusion Ultrasound FI-assisted MWA has the potential to be an effective and safe therapeutic strategy for primary and secondary liver cancers ranging from 3-7 cm in size.
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Affiliation(s)
- Jing Yang
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong Province, People’s Republic of China
| | - Shuang Liang
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong Province, People’s Republic of China
| | - Huahui Liu
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong Province, People’s Republic of China
| | - Cai Hu
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong Province, People’s Republic of China
| | - Sainan Guan
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong Province, People’s Republic of China
| | - Haiyu Kang
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong Province, People’s Republic of China
| | - Erjiao Xu
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong Province, People’s Republic of China
| | - Ronghua Yan
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong Province, People’s Republic of China
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, Guangdong Province, People’s Republic of China
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Kang W, Cao X, Luo J. Effect of multiple peritumoral regions of interest ranges based on computed tomography radiomics for the prediction of early recurrence of hepatocellular carcinoma after resection. Quant Imaging Med Surg 2023; 13:6668-6682. [PMID: 37869280 PMCID: PMC10585524 DOI: 10.21037/qims-23-226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/07/2023] [Indexed: 10/24/2023]
Abstract
Background Early recurrence (ER) of hepatocellular carcinoma (HCC) is defined as recurrence that occurs within two years after resection. Our study aimed to determine the optimal peritumoral regions of interest (ROI) range by comparing the effect of multiple peritumoral radiomics ROIs on predicting ER of HCC, and to develop and validate a combined clinical-radiomics prediction model. Methods A total of 160 HCC patients were randomly divided into a training cohort (n=112) and a validation cohort (n=48). The intratumoral original ROI was outlined based on enhanced computed tomography images and then used as the base to sequentially extend outward 1-5 mm to form peritumoral ROI. We developed a logistic regression model to predict ER of HCC. The efficacy of different ROI prediction models was compared to determine the optimal ROI. The combined model divided the patients into a high-risk group and low-risk group. Results Ninety-seven (60.6%) of the patients were ER; the remaining 63 (39.4%) were not ER. The area under the curve values and 95% confidence intervals for ROI 3 were 0.867 (0.802-0.933) and 0.807 (0.682-0.931) in the training and validation cohorts, respectively, and ROI 3 was identified as the optimal ROI. Multivariate logistic regression analysis determined microvascular invasion (MVI) (P=0.037) and alpha-fetoprotein (AFP) (P=0.013) to be independent risk factors for ER. The combined clinical-radiomic model containing the radiomics score, MVI, and AFP had the optimal predictive efficacy, with area under the curve values and 95% confidence intervals of 0.903 (0.848-0.957) and 0.830 (0.709-0.952) in the training and validation cohort, respectively. Subgroup analysis showed significantly ER predicted in the high-risk group than the low-risk group (P<0.001). Conclusions Peritumoral radiomics 3 mm range was determined as the optimal ROI in this study. The clinical-radiomics combined models can effectively stratify high- and low-risk patients for timely clinical treatment and decision making.
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Affiliation(s)
- Wendi Kang
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Xiaomeng Cao
- Department of General Surgery, Gansu Provincial Hospital of TCM, Lanzhou, China
| | - Jianwei Luo
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
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Zhou J, Sun H, Wang Z, Cong W, Zeng M, Zhou W, Bie P, Liu L, Wen T, Kuang M, Han G, Yan Z, Wang M, Liu R, Lu L, Ren Z, Zeng Z, Liang P, Liang C, Chen M, Yan F, Wang W, Hou J, Ji Y, Yun J, Bai X, Cai D, Chen W, Chen Y, Cheng W, Cheng S, Dai C, Guo W, Guo Y, Hua B, Huang X, Jia W, Li Q, Li T, Li X, Li Y, Li Y, Liang J, Ling C, Liu T, Liu X, Lu S, Lv G, Mao Y, Meng Z, Peng T, Ren W, Shi H, Shi G, Shi M, Song T, Tao K, Wang J, Wang K, Wang L, Wang W, Wang X, Wang Z, Xiang B, Xing B, Xu J, Yang J, Yang J, Yang Y, Yang Y, Ye S, Yin Z, Zeng Y, Zhang B, Zhang B, Zhang L, Zhang S, Zhang T, Zhang Y, Zhao M, Zhao Y, Zheng H, Zhou L, Zhu J, Zhu K, Liu R, Shi Y, Xiao Y, Zhang L, Yang C, Wu Z, Dai Z, Chen M, Cai J, Wang W, Cai X, Li Q, Shen F, Qin S, Teng G, Dong J, Fan J. Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2022 Edition). Liver Cancer 2023; 12:405-444. [PMID: 37901768 PMCID: PMC10601883 DOI: 10.1159/000530495] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/24/2023] [Indexed: 10/31/2023] Open
Abstract
Background Primary liver cancer, of which around 75-85% is hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. Summary Since the publication of Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China in June 2017, which were updated by the National Health Commission in December 2019, additional high-quality evidence has emerged from researchers worldwide regarding the diagnosis, staging, and treatment of liver cancer, that requires the guidelines to be updated again. The new edition (2022 Edition) was written by more than 100 experts in the field of liver cancer in China, which not only reflects the real-world situation in China but also may reshape the nationwide diagnosis and treatment of liver cancer. Key Messages The new guideline aims to encourage the implementation of evidence-based practice and improve the national average 5-year survival rate for patients with liver cancer, as proposed in the "Health China 2030 Blueprint."
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Affiliation(s)
- Jian Zhou
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Huichuan Sun
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zheng Wang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wenming Cong
- Department of Pathology, The Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weiping Zhou
- The Third Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Ping Bie
- Institute of Hepatobiliary Surgery, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Lianxin Liu
- Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianfu Wen
- Department of Liver Surgery, West China Hospital of Sichuan University, Chengdu, China
| | - Ming Kuang
- Department of Hepatobiliary Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guohong Han
- Department of Liver Diseases and Digestive Interventional Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Zhiping Yan
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Maoqiang Wang
- Department of Interventional Radiology, Chinese PLA General Hospital, Beijing, China
| | - Ruibao Liu
- Department of Interventional Radiology, The Tumor Hospital of Harbin Medical University, Harbin, China
| | - Ligong Lu
- Department of Interventional Oncology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenggang Ren
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhaochong Zeng
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ping Liang
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Changhong Liang
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Min Chen
- Editorial Department of Chinese Journal of Digestive Surgery, Chongqing, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wenping Wang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jinlin Hou
- Department of Infectious Diseases, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yuan Ji
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jingping Yun
- Department of Pathology, Tumor Prevention and Treatment Center, Sun Yat-sen University, Guangzhou, China
| | - Xueli Bai
- Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Dingfang Cai
- Department of Integrative Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weixia Chen
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yongjun Chen
- Department of Hematology, Ruijin Hospital North, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenwu Cheng
- Department of Integrated Therapy, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Shuqun Cheng
- The Third Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Chaoliu Dai
- Department of Hepatobiliary and Spleenary Surgery, The Affiliated Shengjing Hospital, China Medical University, Shenyang, China
| | - Wengzhi Guo
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yabing Guo
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Baojin Hua
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Xiaowu Huang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weidong Jia
- Department of Hepatic Surgery, Affiliated Provincial Hospital, Anhui Medical University, Hefei, China
| | - Qiu Li
- Department of Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Tao Li
- Department of General Surgery, Qilu Hospital, Shandong University, Jinan, China
| | - Xun Li
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Yaming Li
- Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, China
| | - Yexiong Li
- Department of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Liang
- Department of Oncology, Peking University International Hospital, Beijing, China
| | - Changquan Ling
- Changhai Hospital of Traditional Chinese Medicine, Second Military Medical University, Shanghai, China
| | - Tianshu Liu
- Department of Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiufeng Liu
- Department of Medical Oncology, PLA Cancer Center, Nanjing Bayi Hospital, Nanjing, China
| | - Shichun Lu
- Institute and Hospital of Hepatobiliary Surgery of Chinese PLA, Chinese PLA Medical School, Chinese PLA General Hospital, Beijing, China
| | - Guoyue Lv
- Department of General Surgery, The First Hospital of Jilin University, Jilin, China
| | - Yilei Mao
- Department of Liver Surgery, Peking Union Medical College (PUMC) Hospital, PUMC and Chinese Academy of Medical Sciences, Beijing, China
| | - Zhiqiang Meng
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tao Peng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Weixin Ren
- Department of Interventional Radiology the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Hongcheng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Guoming Shi
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ming Shi
- Department of Hepatobiliary Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Tianqiang Song
- Department of Hepatobiliary Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kaishan Tao
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Jianhua Wang
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Kui Wang
- The Third Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Lu Wang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wentao Wang
- Department of Liver Surgery, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaoying Wang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhiming Wang
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, China
| | - Bangde Xiang
- Department of Hepatobiliary Surgery, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China
| | - Baocai Xing
- Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jianming Xu
- Department of Gastrointestinal Oncology, Affiliated Hospital Cancer Center, Academy of Military Medical Sciences, Beijing, China
| | - Jiamei Yang
- The Third Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jianyong Yang
- Department of Interventional Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yefa Yang
- Department of Hepatic Surgery and Interventional Radiology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Yunke Yang
- Department of Integrative Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Shenglong Ye
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhenyu Yin
- Department of Hepatobiliary Surgery, Zhongshan Hospital of Xiamen University, Xiamen, China
| | - Yong Zeng
- Department of Liver Surgery, West China Hospital of Sichuan University, Chengdu, China
| | - Bixiang Zhang
- Department of Surgery, Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Boheng Zhang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Leida Zhang
- Department of Hepatobiliary Surgery Institute, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Shuijun Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, ZhengZhou, China
| | - Ti Zhang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yanqiao Zhang
- Department of Gastrointestinal Medical Oncology, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, China
| | - Ming Zhao
- Minimally Invasive Interventional Division, Liver Cancer Group, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yongfu Zhao
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, ZhengZhou, China
| | - Honggang Zheng
- Department of Oncology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ledu Zhou
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Jiye Zhu
- Department of Hepatobiliary Surgery, Peking University People’s Hospital, Beijing, China
| | - Kangshun Zhu
- Department of Minimally Invasive Interventional Radiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Rong Liu
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yinghong Shi
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yongsheng Xiao
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lan Zhang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhifeng Wu
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhi Dai
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Minshan Chen
- Department of Hepatobiliary Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jianqiang Cai
- Department of Abdominal Surgical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weilin Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiujun Cai
- Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qiang Li
- Department of Hepatobiliary Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Feng Shen
- The Third Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Shukui Qin
- Department of Medical Oncology, PLA Cancer Center, Nanjing Bayi Hospital, Nanjing, China
| | - Gaojun Teng
- Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
| | - Jiahong Dong
- Department of Hepatobiliary and Pancreas Surgery, Beijing Tsinghua Changgung Hospital (BTCH), School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Jia Fan
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
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Ansari G, Mirza-Aghazadeh-Attari M, Mohseni A, Madani SP, Shahbazian H, Pawlik TM, Kamel IR. Response Assessment of Primary Liver Tumors to Novel Therapies: an Imaging Perspective. J Gastrointest Surg 2023; 27:2245-2259. [PMID: 37464140 DOI: 10.1007/s11605-023-05762-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/11/2023] [Indexed: 07/20/2023]
Abstract
The latest developments in cancer immunotherapy, namely the introduction of immune checkpoint inhibitors, have led to a fundamental change in advanced cancer treatments. Imaging is crucial to identify tumor response accurately and delineate prognosis in immunotherapy-treated patients. Simultaneously, advances in image acquisition techniques, notably functional and molecular imaging, have facilitated more accurate pretreatment evaluation, assessment of response to therapy, and monitoring for tumor recurrence. Traditional approaches to assessing tumor progression, such as RECIST, rely on changes in tumor size, while new strategies for evaluating tumor response to therapy, such as the mRECIST and the EASL, rely on tumor enhancement. Moreover, the assessment of tumor volume, enhancement, cellularity, and perfusion are some novel techniques that have been investigated. Validation of these novel approaches should rely on comparing their results with those of standard evaluation methods (EASL, mRECIST) while considering the ultimate outcome, which is patient survival. More recently, immunotherapy has been used in the management of primary liver tumors. However, little is known about its efficacy. This article reviews imaging modalities and techniques for assessing tumor response and survival in immunotherapy-treated patients with primary hepatic malignancies.
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Affiliation(s)
- Golnoosh Ansari
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Mohammad Mirza-Aghazadeh-Attari
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Alireza Mohseni
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Seyedeh Panid Madani
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Haneyeh Shahbazian
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Timothy M Pawlik
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center, James Comprehensive Cancer Center, Columbus, OH, USA
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
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Yao J, Li K, Yang H, Lu S, Ding H, Luo Y, Li K, Xie X, Wu W, Jing X, Liu F, Yu J, Cheng Z, Tan S, Dou J, Dong X, Wang S, Zhang Y, Li Y, Qi E, Han Z, Liang P, Yu X. Analysis of Sonazoid contrast-enhanced ultrasound for predicting the risk of microvascular invasion in hepatocellular carcinoma: a prospective multicenter study. Eur Radiol 2023; 33:7066-7076. [PMID: 37115213 DOI: 10.1007/s00330-023-09656-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/23/2023] [Accepted: 03/07/2023] [Indexed: 04/29/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the potential of Sonazoid contrast-enhanced ultrasound (SNZ-CEUS) as an imaging biomarker for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS From August 2020 to March 2021, we conducted a prospective multicenter study on the clinical application of Sonazoid in liver tumor; a MVI prediction model was developed and validated by integrating clinical and imaging variables. Multivariate logistic regression analysis was used to establish the MVI prediction model; three models were developed: a clinical model, a SNZ-CEUS model, and a combined model and conduct external validation. We conducted subgroup analysis to investigate the performance of the SNZ-CEUS model in non-invasive prediction of MVI. RESULTS Overall, 211 patients were evaluated. All patients were split into derivation (n = 170) and external validation (n = 41) cohorts. Patients who had MVI accounted for 89 of 211 (42.2%) patients. Multivariate analysis revealed that tumor size (> 49.2 mm), pathology differentiation, arterial phase heterogeneous enhancement pattern, non-single nodular gross morphology, washout time (< 90 s), and gray value ratio (≤ 0.50) were significantly associated with MVI. Combining these factors, the area under the receiver operating characteristic (AUROC) of the combined model in the derivation and external validation cohorts was 0.859 (95% confidence interval (CI): 0.803-0.914) and 0.812 (95% CI: 0.691-0.915), respectively. In subgroup analysis, the AUROC of the SNZ-CEUS model in diameter ≤ 30 mm and ˃ 30 mm cohorts were 0.819 (95% CI: 0.698-0.941) and 0.747 (95% CI: 0.670-0.824). CONCLUSIONS Our model predicted the risk of MVI in HCC patients with high accuracy preoperatively. CLINICAL RELEVANCE STATEMENT Sonazoid, a novel second-generation ultrasound contrast agent, can accumulate in the endothelial network and form a unique Kupffer phase in liver imaging. The preoperative non-invasive prediction model based on Sonazoid for MVI is helpful for clinicians to make individualized treatment decisions. KEY POINTS • This is the first prospective multicenter study to analyze the possibility of SNZ-CEUS preoperatively predicting MVI. • The model established by combining SNZ-CEUS image features and clinical features has high predictive performance in both derivation cohort and external validation cohort. • The findings can help clinicians predict MVI in HCC patients before surgery and provide a basis for optimizing surgical management and monitoring strategies for HCC patients.
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Affiliation(s)
- Jundong Yao
- Department of Interventional Ultrasound, First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
- Chinese PLA Medical School, Beijing, 100853, China
| | - Kaiyan Li
- Department of Ultrasound Imaging, Affiliated Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Hong Yang
- Department of Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Shichun Lu
- Department of Hepatobiliary Surgery, First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China
| | - Hong Ding
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Yan Luo
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Kai Li
- Department of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China
| | - Xiaoyan Xie
- Department of Ultrasound, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Wei Wu
- Department of Ultrasound, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Xiang Jing
- Department of Ultrasound, the Third Central Hospital of Tianjin, Tianjin, 300170, China
| | - Fangyi Liu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - Jie Yu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - Zhigang Cheng
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - Shuilian Tan
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - Jianping Dou
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - XueJuan Dong
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - Shuo Wang
- Department of Interventional Ultrasound, First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - Yiqiong Zhang
- Department of Interventional Ultrasound, First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - Yunlin Li
- Department of Interventional Ultrasound, First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - Erpeng Qi
- Department of Interventional Ultrasound, First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China
| | - Zhiyu Han
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China.
| | - Ping Liang
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China.
| | - XiaoLing Yu
- Department of Interventional Ultrasound, First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, 100853, China.
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Zheng R, Zhang X, Liu B, Zhang Y, Shen H, Xie X, Li S, Huang G. Comparison of non-radiomics imaging features and radiomics models based on contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma within 5 cm. Eur Radiol 2023; 33:6462-6472. [PMID: 37338553 DOI: 10.1007/s00330-023-09789-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/15/2023] [Accepted: 03/30/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVES The purpose of this study is to establish microvascular invasion (MVI) prediction models based on preoperative contrast-enhanced ultrasound (CEUS) and ethoxybenzyl-enhanced magnetic resonance imaging (EOB-MRI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm. METHODS Patients with a single HCC ≤ 5 cm and accepting CEUS and EOB-MRI before surgery were enrolled in this study. Totally, 85 patients were randomly divided into the training and validation cohorts in a ratio of 7:3. Non-radiomics imaging features, the CEUS and EOB-MRI radiomics scores were extracted from the arterial phase, portal phase and delayed phase images of CEUS and the hepatobiliary phase images of EOB-MRI. Different MVI predicting models based on CEUS and EOB-MRI were constructed and their predictive values were evaluated. RESULTS Since univariate analysis revealed that arterial peritumoral enhancement on the CEUS image, CEUS radiomics score, and EOB-MRI radiomics score were significantly associated with MVI, three prediction models, namely the CEUS model, the EOB-MRI model, and the CEUS-EOB model, were developed. In the validation cohort, the areas under the receiver operating characteristic curve of the CEUS model, the EOB-MRI model, and the CEUS-EOB model were 0.73, 0.79, and 0.86, respectively. CONCLUSIONS Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm. CLINICAL RELEVANCE STATEMENT Radiomics models based on CEUS and EOB-MRI are effective for MVI predicting and conducive to pretreatment decision-making in patients with a single HCC within 5 cm. KEY POINTS • Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. • There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm.
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Affiliation(s)
- Ruiying Zheng
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaoer Zhang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Baoxian Liu
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yi Zhang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hui Shen
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaoyan Xie
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Shurong Li
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
| | - Guangliang Huang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
- Department of Medical Ultrasonics, Guangxi Hospital Division of the First Affiliated Hospital, Sun Yat-Sen University, Guangxi, China.
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Wang Z, Zhang H, Lin F, Zhang R, Ma H, Shi Y, Yang P, Zhang K, Zhao F, Mao N, Xie H. Intra- and Peritumoral Radiomics of Contrast-Enhanced Mammography Predicts Axillary Lymph Node Metastasis in Patients With Breast Cancer: A Multicenter Study. Acad Radiol 2023; 30 Suppl 2:S133-S142. [PMID: 37088646 DOI: 10.1016/j.acra.2023.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 04/25/2023]
Abstract
RATIONALE AND OBJECTIVES This multicenter study aimed to explore the feasibility of radiomics based on intra- and peritumoral regions on preoperative breast cancer contrast-enhanced mammography (CEM) to predict axillary lymph node (ALN) metastasis. MATERIALS AND METHODS A total of 809 patients with preoperative breast cancer CEM images from two centers were retrospectively recruited. Least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features extracted from CEM images in regions of the tumor and peritumoral area of five and ten mm as well as construct radiomics signature. A nomogram, including the optimal radiomics signature and clinicopathological factors, was then constructed. Nomogram performance was evaluated using AUC and compared with breast radiologists directly. RESULTS In the internal testing set, AUCs of peritumoral signatures decreased when the peritumoral area increased and signaturetumor + 10mm demonstrated the best performance with an AUC of 0.712. The nomogram incorporating signaturetumor + 10mm, tumor diameter, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and CEM-reported lymph node status yielded maximum AUCs of 0.753 and 0.732 in internal and external testing sets, respectively. Moreover, the nomogram outperformed radiologists and improved diagnostic performance of radiologists. CONCLUSION The nomogram based on CEM intra- and peritumoral regions may provide a noninvasive auxiliary tool to guide treatment strategy of ALN metastasis in breast cancer.
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Affiliation(s)
- Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000; Institute of medical imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
| | - Ran Zhang
- Artificial Intelligence and Clinical Innovation Institute, Huiying Medical Technology Co., Ltd, P. R. China, 100192
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Ping Yang
- Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Kun Zhang
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Feng Zhao
- School of Compute Science and Technology, Shandong Technology and Business University, Yantai, Shandong, People's Republic of China, 264000
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000.
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Liao L, Wang H, Zhang Y. Radiomics under 2D regions, 3D regions, and peritumoral regions reveal tumor heterogeneity in non-small cell lung cancer: a multicenter study. LA RADIOLOGIA MEDICA 2023; 128:1079-1092. [PMID: 37486526 DOI: 10.1007/s11547-023-01676-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/29/2023] [Indexed: 07/25/2023]
Abstract
PURPOSE Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor prognosis. Radiomic features have emerged as promising predictors of the tumor phenotype. However, the role of underlying information surrounding the cancer remains unclear. MATERIALS AND METHODS We conducted a retrospective study of 508 patients with NSCLC from three institutions. Radiomics models were built using features from six tumor regions and seven classifiers to predict three prognostically significant tumor phenotypes. The models were evaluated and interpreted by the mean area under the receiver operating characteristic curve (AUC) under nested cross-validation and Shapley values. The best-performing predictive models corresponding to six tumor regions and three tumor phenotypes were identified for further comparative analysis. In addition, we designed five experiments with different voxel spacing to assess the sensitivity of the experimental results to the spatial resolution of the voxels. RESULTS Our results demonstrated that models based on 2D, 3D, and peritumoral region features yielded mean AUCs and 95% confidence intervals of 0.759 and [0.747-0.771] for lymphovascular invasion, 0.889 and [0.882-0.896] for pleural invasion, and 0.839 and [0.829-0.849] for T-staging in the testing cohort, which was significantly higher than all other models. Similar results were obtained for the model combining the three regional features at five voxel spacings. CONCLUSION Our study revealed the predictive role of the developed methods with multi-regional features for the preoperative assessment of prognostic factors in NSCLC. The analysis of different voxel spacing and model interpretability strengthens the experimental findings and contributes to understanding the biological significance of the radiological phenotype.
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Affiliation(s)
- Xingping Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000, China
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China
| | - Liefa Liao
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia.
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia.
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000, China.
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50
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Chen C, Liu J, Gu Z, Sun Y, Lu W, Liu X, Chen K, Ma T, Zhao S, Zhao H. Integration of Multimodal Computed Tomography Radiomic Features of Primary Tumors and the Spleen to Predict Early Recurrence in Patients with Postoperative Adjuvant Transarterial Chemoembolization. J Hepatocell Carcinoma 2023; 10:1295-1308. [PMID: 37576612 PMCID: PMC10422964 DOI: 10.2147/jhc.s423129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 07/28/2023] [Indexed: 08/15/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is one of the most lethal malignancies in the world. Patients with HCC choose postoperative adjuvant transarterial chemoembolization (PA-TACE) after surgical resection to reduce the risk of recurrence. However, many of them have recurrence within a short period. Methods In this retrospective analysis, a total of 173 patients who underwent PA-TACE between September 2016 and March 2020 were recruited. Radiomic features were derived from the arterial and venous phases of each patient. Early recurrence (ER)-related radiomics features of HCC and the spleen were selected to build two rad-scores using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Logistic regression was applied to establish the Radiation (Rad)_score by combining the two regions. We constructed a nomogram containing clinical information and dual-region rad-scores, which was evaluated in terms of discrimination, calibration, and clinical usefulness. Results All three radiological scores showed good performance for ER prediction. The combined Rad_score performed the best, with an area under the curve (AUC) of 0.853 (95% confidence interval [CI], 0.783-0.908) in the training set and 0.929 (95% CI, 0.789-0.988) in the validation set. Multivariate analysis identified total bilirubin (TBIL) and the combined Rad_score as independent prognostic factors for ER. The nomogram was found to be clinically valuable, as determined by the decision curves (DCA) and clinical impact curves (CIC). Conclusion A multimodal dual-region radiomics model combining HCC and the spleen is an independent prognostic tool for ER. The combination of dual-region radiomics features and clinicopathological factors has a good clinical application value.
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Affiliation(s)
- Cong Chen
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Jian Liu
- Dalian Medical University and Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Zhuxin Gu
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Yanjun Sun
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Wenwu Lu
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Xiaokan Liu
- Department of Interventional Radiology, Affiliated Hospital 2 of Nantong University, Nantong, 226001, People’s Republic of China
| | - Kang Chen
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Tianzhi Ma
- Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, People’s Republic of China
| | - Suming Zhao
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Hui Zhao
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
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