Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatectomy or liver transplantation.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4079-4079
Author(s):  
Hidetoshi Nitta ◽  
Marc Antoine Allard ◽  
Mylene Sebagh ◽  
Gabriella Pittau ◽  
Oriana Ciacio ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Predictive Model ◽  
Validation Cohort ◽  
Microvascular Invasion ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

4079 Background: Microvascular invasion (MVI) is the strongest prognostic factor following surgery of hepatocellular carcinoma (HCC). However, it is usually not available on the preoperative setting. A predictive model of MVI in patients scheduled for hepatic resection (HR) or liver transplantation (LT) would thus help guiding treatment strategy. The aim of this study was to develop a predictive model for MVI of HCC before either HR or LT. Methods: HCC patients who consecutively performed HR or LT from January 1994 to June 2016 at a single institution were subdivided into a training and validation cohort. Risk factors for MVI in the training cohort were used to develop a predictive model for MVI, to be validated in the validation cohort. The outcomes of the HR and LT patients with high or low MVI probability based on the model, were compared using propensity score matching (PSM). Cut-off values for continuous factors were determined based on ROC curve analysis. Results: A total of 910 patients (425 HR, 485 LT) were included in the training (n = 637) and validation (n = 273) cohorts. In the training cohort, multivariate analysis demonstrated that alpha-fetoprotein ≥100ng/ml ( p < 0.0001), largest tumor size ≥40mm ( p = 0.0002), non-boundary HCC type on contrast-enhanced CT ( p = 0.001), neutrophils-to-lymphocytes ratio ≥3.2 ( p = 0.002), aspartate aminotransferase ≥62U/l ( p = 0.02) were independently associated with MVI. Combinations of these 5 factors varied the MVI probability from 15.5% to 91.1%. This predictive model achieved a good c-index of 0.76 in the validation cohort. In PSM (109 HR, 109 LT), there was no difference in survival between HR and LT patients among the high MVI probability (≥50%) patients, (5y-OS; 46.3% vs 42.2%, p = 0.77, 5y-RFS; 54.0% vs 28.8%, p = 0.21). Among the low probability ( < 50%), survival was significantly decreased following HR compared with LT (5y-OS; 54.1% vs 78.8%, p = 0.007, 5y-RFS; 17.3% vs 86.1%, p< 0.0001). Conclusions: This model developed from preoperative data allows reliable prediction of MVI, and may thus help with preoperative decisions about the suitability of HR or LT in patients with HCC.

scholarly journals A Nomogram Based on Combining Clinical Features and Contrast Enhanced Ultrasound LI-RADS Improves Prediction of Microvascular Invasion in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Hang Zhou ◽  
Jiawei Sun ◽  
Tao Jiang ◽  
Jiaqi Wu ◽  
Qunying Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Predictive Model ◽  
Clinical Features ◽  
Diagnostic Performance ◽  
Clinical Information ◽  
Microvascular Invasion ◽  
Liver Imaging ◽  
Contrast Enhanced Ultrasound ◽  
Training Cohort ◽  
Contrast Enhanced

PurposesTo establish a predictive model incorporating clinical features and contrast enhanced ultrasound liver imaging and reporting and data system (CEUS LI-RADS) for estimation of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.MethodsIn the retrospective study, 127 HCC patients from two hospitals were allocated as training cohort (n=98) and test cohorts (n=29) based on cutoff time-point, June 2020. Multivariate regression analysis was performed to identify independent indicators for developing predictive nomogram models. The area under receiver operating characteristic (AUC) curve was also determined to establish the diagnostic performance of different predictive models. Corresponding sensitivities and specificities of different models at the cutoff nomogram value were compared.ResultsIn the training cohort, clinical information (larger tumor size, higher AFP level) and CEUS LR-M were significantly correlated with the presence of MVI (all p&lt;0.05). By incorporating clinical information and CEUS LR-M, the predictive model (LR-M+Clin) achieved a desirable diagnostic performance (AUC=0.80 and 0.84) in both cohorts at nomogram cutoff score value of 89. The sensitivity of LR-M+Clin when predicting MVI in HCC patients was higher than that of the clinical model alone (86.7% vs. 46.7%, p=0.027), while specificities were 78.6% and 85.7% (p=0.06), respectively, in the test cohort. In addition, LR-M+Clin exhibited similar AUC and specificity, but a significantly higher sensitivity (86.7%) than those of LR-M alone and LR-5(No)+Clin (both sensitivities=73.3%, both p=0.048).ConclusionThe predictive model incorporating CEUS LR-M and clinical features was able to predict the MVI status of HCC and is a potential reliable preoperative tool for informing treatment.

scholarly journals Imaging Biomarkers of Contrast-enhanced Computed Tomography Predict Survival in Oesophageal Cancer After Definitive Concurrent Chemoradiotherapy

2020 ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography ◽  
Contrast Enhanced Computed Tomography

Abstract Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of oesophageal squamous cell carcinoma (OSCC) patients after definitive concurrent chemoradiotherapy (CCRT).Methods: A total of 151 ESCC patients who underwent definitive CCRT were included in this retrospective study. All patients were separated randomly to a training cohort (n=97) and the validation cohort (n=54). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score was constructed by using the least absolute shrinkage and selection operator with logistic regression analysis in training cohort and tested in the validation cohort. IBMsnomograms were built based on IBM score. The concordance index (C-index) was used to assess the performance of the nomograms. Finally, decision curve analysis was performed to estimate the clinical usefulness of the nomograms.Results: A total of 96 IBMs were extracted from each contrast-enhanced CT scan. The IBM score were consisted of 13 CT-based IBMs and were significantly correlated with 3-year overall survival (OS) and 3-year progression-free survival (PFS). Multivariate analysis revealed that IBM score was the independent prognostic factor. In the training cohort, the IBM score yielded an area under the curves (AUCs) of 0.802 (95% CI: 0.713–0.891, p<0.001) and 0.742 (95% CI: 0.620–0.889, p<0.001) in terms of 3-year OS and 3-year PFS, respectively. In validation cohort, the AUCs were 0.761(95% CI: 0. 639–0.900, p<0.001) and 0.761(95% CI: 0.629–0.893, p=0.001) for 3-year OS and 3-year PFS,respectively. Kaplan-Meier survival analysis showed significantly different between risk subgroups in training and validation cohort. The nomograms were built based on the IBM score showed good discrimination. In the training cohort, with the C-indices of IBMsnomograms were 0.732 (95%CI, 0.661–0.803) and 0.670(95%CI, 0.595–0.745) for OS and PFS, respectively. In the validation cohort C-indices were 0.677(95%CI, 0.583–0.771) and 0.678(95%CI, 0.591–0.765) for OS and PFS, respectively. The decision curve showed the clinical usefulness of nomograms.Conclusions: TheIBM score based on pre-treatment contrast-enhanced CT could predict the 3-year OS and 3-year PFS for OSCC patients after definitive CCRT. Further multicenter studies with larger sample sizes are warranted.

scholarly journals Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography

Abstract Background This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of patients with oesophageal squamous cell carcinoma (OSCC) after definitive concurrent chemoradiotherapy (CCRT). Methods Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were randomised to the training cohort (n = 99) or the validation cohort (n = 55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score, was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis, which was equal to the log-partial hazard of the Cox model in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualised survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms. Results Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. IBM scores were constructed from 11 CT-based IBMs for overall survival (OS) and 8 IBMs for progression-free survival (PFS), using the LASSO-Cox regression method in the training cohort. Multivariate analysis revealed that IBM score was an independent prognostic factor correlated with OS and PFS. In the training cohort, the C-indices of IBM scores were 0.734 (95% CI 0.664–0.804) and 0.658 (95% CI 0.587–0.729) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95% CI 0.578–0.766) and 0.666 (95% CI 0.574–0.758) for OS and PFS, respectively. Kaplan–Meier survival analysis showed a significant difference between risk subgroups in the training and validation cohorts. Decision curve analysis confirmed the clinical usefulness of the IBM score. Conclusions The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma

2019 ◽  
Vol 70 (6) ◽  
pp. 1133-1144 ◽  
Author(s):  
Xun Xu ◽  
Hai-Long Zhang ◽  
Qiu-Ping Liu ◽  
Shu-Wen Sun ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Microvascular Invasion ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT

2019 ◽  
Vol 29 (7) ◽  
pp. 3595-3605 ◽  
Author(s):  
Xiaohong Ma ◽  
Jingwei Wei ◽  
Dongsheng Gu ◽  
Yongjian Zhu ◽  
Bing Feng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Microvascular Invasion ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

scholarly journals Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy

2020 ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography

Abstract Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of patients with oesophageal squamous cell carcinoma (OSCC) after definitive concurrent chemoradiotherapy (CCRT).Methods: Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were randomised to the training cohort (n=99) or the validation cohort (n=55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score, was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis, which was equal to the log-partial hazard of the Cox model in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualised survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms.Results: Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. IBM scores were constructed from 11 CT-based IBMs for overall survival (OS) and 8 IBMs for progression-free survival (PFS), using the LASSO-Cox regression method in the training cohort. Multivariate analysis revealed that IBM score was an independent prognostic factor correlated with OS and PFS. In the training cohort, the C-indices of IBM scores were 0.734 (95%CI, 0.664–0.804) and 0.658 (95%CI, 0.587–0.729) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95%CI, 0.578–0.766) and 0.666 (95%CI, 0.574–0.758) for OS and PFS, respectively. Kaplan-Meier survival analysis showed a significant difference between risk subgroups in the training and validation cohorts. Decision curve analysis confirmed the clinical usefulness of the IBM score.Conclusions: The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

scholarly journals Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy

2020 ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography

Abstract Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of patients with oesophageal squamous cell carcinoma (OSCC) after definitive concurrent chemoradiotherapy (CCRT).Methods: Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were randomised to the training cohort (n=99) or the validation cohort (n=55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualised survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms.Results: Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. IBM scores were constructed from 11 CT-based IBMs for overall survival (OS) and 8 IBMs for progression-free survival (PFS), using the LASSO-Cox regression method in the training cohort. Multivariate analysis revealed that IBM score was an independent prognostic factor correlated with OS and PFS. In the training cohort, the C-indices of IBM scores were 0.734 (95%CI, 0.664–0.804) and 0.658 (95%CI, 0.587–0.729) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95%CI, 0.578–0.766) and 0.666 (95%CI, 0.574–0.758) for OS and PFS, respectively. Kaplan-Meier survival analysis showed a significant difference between risk subgroups in the training and validation cohorts. Decision curve analysis confirmed the clinical usefulness of the IBM score.Conclusions: The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

scholarly journals Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy

2020 ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography

Abstract Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of oesophageal squamous cell carcinoma (OSCC) patients after definitive concurrent chemoradiotherapy (CCRT). Methods: Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were separated randomly to a training cohort (n=99) and the validation cohort (n=55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualized survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms. Results: Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. The IBM score constructed by 11 CT-based IBMs, using LASSO-Cox regression method in training cohort. The multivariate analysis revealed that IBM score was the independent prognostic factor correlated with overall survival (OS) and progression-free survival (PFS). In the training cohort, the C-indices of IBM scores were 0.734 (95%CI, 0.664–0.804) and 0.678 (95%CI, 0.607–0.745) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95%CI, 0.578–0.766) and 0.662 (95%CI, 0.573–0.751) for OS and PFS, respectively. Kaplan-Meier survival analysis showed significantly different between risk subgroups in training and validation cohort. The decision curve showed the clinical usefulness of IBM score. Conclusions: The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

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