Preoperative Gadoxetic Acid-Enhanced MRI Based Nomogram Improves Prediction of Early HCC Recurrence After Ablation Therapy.
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ABSTRACT: Purpose
This study aimed to identify preoperative gadoxetic acid-enhanced MRI features and establish a nomogram for predicting early recurrence (≤ 2 years) of hepatocellular carcinoma (HCC) after ablation therapy.Methods
A total of 160 patients who underwent gadoxetic acid-enhanced MRI and ablation HCC therapy from January 2015 to June 2018, were included retrospectively and divided into a training cohort (n = 112) and a validation cohort (n = 48). Independent clinical risk factors and gadoxetic acid-enhanced MRI features associated with early recurrence were identified by univariate and multivariate logistic regression analysis and used for construction of a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.Results
Alpha-fetoprotein (AFP) level, tumor number, arterial peritumoral enhancement, satellite nodule and peritumoral hypointensity at hepatobiliary phases in the training cohort were identified as independent risk factors for early recurrence after ablation. A new nomogram that was constructed with these five features showed an area under the curve (AUC) of 0.843 (95%CI 0.771-0.916) and 0.835 (95%CI 0.713-0.956) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had good consistency and clinical utility.Conclusions
A new nomogram that was constructed using four preoperative gadoxetic acid-enhanced MRI features and serum AFP level can predict the risk of early HCC recurrence after ablation therapy with AUC up to 0.843. The strong performance of this nomogram may help hepatologists to categorize patients' recurrent risk to guide selecting treatment options and improve postoperative management.
SUBMITTER: Hu C
PROVIDER: S-EPMC8176857 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:This study was performed to prospectively develop and validate a radiomics nomogram for predicting postoperative early recurrence (≤1 year) of hepatocellular carcinoma (HCC) using whole-lesion radiomics features on preoperative gadoxetic acid-enhanced magnetic resonance (MR) images. METHODS:In total, 155 patients (training cohort: n = 108; validation cohort: n = 47) with surgically confirmed HCC were enrolled in this IRB-approved prospective study. Three-dimensional whole-lesion regions of interest were manually delineated along the tumour margins on multi-sequence MR images. Radiomics features were generated and selected to build a radiomics score using the least absolute shrinkage and selection operator (LASSO) method. Clinical characteristics and qualitative imaging features were identified by two independent radiologists and combined to establish a clinical-radiological nomogram. A radiomics nomogram comprising the radiomics score and clinical-radiological risk factors was constructed based on multivariable logistic regression analysis. Diagnostic performance and clinical usefulness were measured by receiver operation characteristic (ROC) and decision curves. RESULTS:In total, 14 radiomics features were selected to construct the radiomics score. For the clinical-radiological nomogram, the alpha-fetoprotein (AFP) level, gross vascular invasion and non-smooth tumour margin were included. The radiomics nomogram integrating the radiomics score with clinical-radiological risk factors showed better discriminative performance (AUC = 0.844, 95%CI, 0.769 to 0.919) than the clinical-radiological nomogram (AUC = 0.796, 95%CI, 0.712 to 0.881; P = 0.045), with increased clinical usefulness confirmed using a decision curve analysis. CONCLUSIONS:Incorporating multiple predictive factors, the radiomics nomogram demonstrated great potential in the preoperative prediction of early HCC recurrence after surgery.
| S-EPMC6518803 | biostudies-other
Project description:ObjectivesTo explore the role of preoperative gadoxetic acid-enhanced MRI in stratifying the risk of early recurrence in patients with LR-5 hepatocellular carcinoma (HCC) by LI-RADS v2018 after curative resection.MethodsBetween July 2015 and August 2018, this study evaluated consecutive treatment-naïve at-risk LR-5 HCC patients who underwent gadoxetic acid-enhanced MRI examination within 2 weeks before curative resection. The Cox regression analysis was performed to identify potential predictors of early recurrence. Disease-free survival (DFS) rates were analyzed and compared by using the Kaplan-Meier method and log-rank tests.ResultsFifty-three of 103 (51.5%) patients experienced early recurrence. Three MRI findings were significantly associated with early recurrence: corona enhancement (hazard ratio [HR]: 2.116; p = 0.013), peritumoral hypointensity on hepatobiliary phase (HBP) (HR: 2.262; p = 0.007), and satellite nodule (HR: 2.777; p = 0.005). An additional risk factor was AFP level > 400 ng/mL (HR: 1.975; p = 0.016). Based on the number of MRI predictors, LR-5 HCC patients were stratified into three subgroups: LR-5a (60/103; no predictor), LR-5b (26/103; one predictor), and LR-5c (17/103; two or three predictors), with low, medium, and high risk of early recurrence, respectively. The 2-year DFS rate of LR-5a, LR-5b, and LR-5c patients was 65.0%, 38.5%, and 5.9%, respectively, while the corresponding median DFS was undefined, 17.1 months, and 5.1 months, respectively (p < 0.001).ConclusionsIn at-risk LR-5 HCC patients, corona enhancement, peritumoral hypointensity on HBP, and satellite nodule could be used to preoperatively stratify the risk of early recurrence after hepatectomy.Key points• Corona enhancement, peritumoral hypointensity on HBP, satellite nodule, and serum AFP level > 400 ng/mL were significant predictors of early recurrence in patients with LR-5 HCC after hepatectomy. • Based on the number of predictive MRI findings, LR-5 HCC patients could be preoperatively stratified into three subgroups: LR-5a, LR-5b, and LR-5c, with significantly different risk of early recurrence and disease-free survival. • Preoperative risk stratification is essential for the identification of patients at increased risk of postoperative early recurrence, which may contribute to risk-based personalized management for LR-5 HCC patients.
| S-EPMC7979599 | biostudies-literature
Project description:Introduction: The emerging field of "radiomics" has considerable potential in disease diagnosis, pathologic grading, prognosis evaluation, and prediction of treatment response. We aimed to develop a novel radiomics nomogram based on radiomics features and clinical characteristics that could preoperatively predict early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) after partial hepatectomy. Methods: A predictive model was developed from a training cohort comprising 139 ICC patients diagnosed between January 2010 and June 2014. Radiomics features were extracted from arterial-phase image of contrast-enhanced magnetic resonance imaging. Feature selection and construction of a "radiomics signature" were through Spearman's rank correlation and least absolute shrinkage and selection operator (LASSO) logistic regression. Combined with clinical characteristics, a radiomics nomogram was developed with multivariable logistic regression. Performance of the nomogram was evaluated with regard to discrimination, calibration, and clinical utility. An independent validation cohort involving 70 patients recruited from July 2014 to March 2016 was used to evaluate the utility of the nomogram developed. Results: The radiomics signature, consisting of nine features, differed significantly between ER patients and non-ER patients in training and validation cohorts. The area under the curve (AUC) of the radiomics signature in training and validation cohorts was 0.82 (confidence interval [CI], 0.74-0.88) and 0.77 (95% CI, 0.65-0.86), respectively. The AUC of the radiomics nomogram combining the radiomics signature and clinical stage in the two cohorts was 0.90 (95%CI, 0.83-0.94) and 0.86 (95% CI, 0.76-0.93), respectively. Decision curve analysis confirmed the clinical usefulness of the radiomics nomogram. Conclusion: The non-invasive radiomics nomogram developed using the radiomics signature and clinical stage could be used to predict ER of ICC after partial hepatectomy.
| S-EPMC6131601 | biostudies-literature
Project description:BackgroundPredicting early recurrence (ER) after radical therapy for HCC patients is critical for the decision of subsequent follow-up and treatment. Radiomic features derived from the medical imaging show great potential to predict prognosis. Here we aim to develop and validate a radiomics nomogram that could predict ER after curative ablation.MethodsTotal 184 HCC patients treated from August 2007 to August 2014 were included in the study and were divided into the training (n = 129) and validation(n = 55) cohorts randomly. The endpoint was recurrence free survival (RFS). A set of 647 radiomics features were extracted from the 3 phases contrast enhanced computed tomography (CECT) images. The minimum redundancy maximum relevance algorithm (MRMRA) was used for feature selection. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to build a radiomics signature. Recurrence prediction models were built using clinicopathological factors and radiomics signature, and a prognostic nomogram was developed and validated by calibration.ResultsAmong the four radiomics models, the portal venous phase model obtained the best performance in the validation subgroup (C-index = 0.736 (95%CI:0.726-0.856)). When adding the clinicopathological factors to the models, the portal venous phase combined model also yielded the best predictive performance for training (C-index = 0.792(95%CI:0.727-0.857) and validation (C-index = 0.755(95%CI:0.651-0.860) subgroup. The combined model indicated a more distinct improvement of predictive power than the simple clinical model (ANOVA, P < 0.0001).ConclusionsThis study successfully built a radiomics nomogram that integrated clinicopathological and radiomics features, which can be potentially used to predict ER after curative ablation for HCC patients.
| S-EPMC6485136 | biostudies-literature
Project description:ObjectivesTo develop and validate a pre-transcatheter arterial chemoembolization (TACE) MRI-based radiomics model for predicting tumor response in intermediate-advanced hepatocellular carcinoma (HCC) patients.MaterialsNinety-nine intermediate-advanced HCC patients (69 for training, 30 for validation) treated with TACE were enrolled. MRI examinations were performed before TACE, and the efficacy was evaluated according to the mRECIST criterion 3 months after TACE. A total of 396 radiomics features were extracted from T2-weighted pre-TACE images, and least absolute shrinkage and selection operator (LASSO) regression was applied to feature selection and model construction. The performance of the model was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curves.ResultsThe AFP value, Child-Pugh score, and BCLC stage showed a significant difference between the TACE response (TR) and non-TACE response (nTR) patients. Six radiomics features were selected by LASSO and the radiomics score (Rad-score) was calculated as the sum of each feature multiplied by the non-zero coefficient from LASSO. The AUCs of the ROC curve based on Rad-score were 0.812 and 0.866 in the training and validation cohorts, respectively. To improve the diagnostic efficiency, the Rad-score was further integrated with the above clinical indicators to form a novel predictive nomogram. Results suggested that the AUC increased to 0.861 and 0.884 in the training and validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful.ConclusionThe radiomics and clinical indicator-based predictive nomogram can well predict TR in intermediate-advanced HCC and can further be applied for auxiliary diagnosis of clinical prognosis.Key points• The therapeutic outcome of TACE varies greatly even for patients with the same clinicopathologic features. • Radiomics showed excellent performance in predicting the TACE response. • Decision curves demonstrated that the novel predictive model based on the radiomics signature and clinical indicators has great clinical utility.
| S-EPMC8452577 | biostudies-literature
Project description:IntroductionThe use of multiparametric magnetic resonance imaging (mpMRI) to assess prostate cancer (PCa) has increased over the past decade. We aimed to assess if preoperative mpMRI lesion score, a variable routinely available for men undergoing pre-biopsy MRI, improves the performance of commonly used preoperative predictive models for PCa recurrence.Patients and methodsWe analyzed data from 372 patients with PCa treated with radical prostatectomy in 2012 to 2017 and assessed with pre-biopsy mpMRI within 6 months prior to surgery. Suspicious areas for cancer were scored on a standardized 5-point scale. Cox regression was used to assess the association between mpMRI score and the risk of postoperative biochemical recurrence. Two different models were tested accounting for factors included in the Kattan nomogram and in the D'Amico risk-classification.ResultsOverall, 53% and 30% of patients were found with a lesion scored 4 or 5 at pre-biopsy mpMRI, respectively. Risk varied widely by mpMRI (29% 2-year risk of biochemical recurrence for a score of 5 vs. 5% for a score of 1-2), and mpMRI score was associated with large hazard ratios after adjusting for stage, grade, and prostate-specific antigen: 1.66, 1.96, and 2.71 for scores 3, 4, and 5, respectively. However, 95% confidence intervals were very wide (0.19-14.20, 0.26-14.65, and 0.36-20.55, respectively) and included 1.ConclusionsOur data did not show that preoperative models, commonly used to assess PCa risk, were improved after including the pre-biopsy mpMRI score. However, the value of pre-biopsy mpMRI to improve preoperative risk models should be investigated in larger data sets.
| S-EPMC7524609 | biostudies-literature
Project description:BackgroundIn pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required.PurposeTo develop a radiomic nomogram based on MR radiomics to stratify patients preoperatively and potentially improve clinical practice.Study typeRetrospective.PopulationWe enrolled 303 patients from two medical centers. Patients with a disease-free survival ?12 months were assigned as the ER group (n =?130). Patients from the first medical center were divided into a training cohort (n =?123) and an internal validation cohort (n =?54). Patients from the second medical center were used as the external independent validation cohort (n =?126).Field strength/sequence3.0T axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w).AssessmentER was confirmed via imaging studies as MRI or CT. Risk factors, including clinical stage, CA19-9, and radiomic-related features of ER were assessed. In addition, to determine the intra- and interobserver reproducibility of radiomic features extraction, the intra- and interclass correlation coefficients (ICC) were calculated.Statistical testsThe area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and test groups. The results of decision curve analysis (DCA) indicated that the radiomic nomogram achieved the most net benefit.ResultsThe AUC values of ER evaluation for the radiomics signature were 0.80 (training cohort), 0.81 (internal validation cohort), and 0.78 (external validation cohort). Multivariate logistic analysis identified the radiomic signature, CA19-9 level, and clinical stage as independent parameters of ER. A radiomic nomogram was then developed incorporating the CA19-9 level and clinical stage. The AUC values for ER risk evaluation using the radiomic nomogram were 0.87 (training cohort), 0.88 (internal validation cohort), and 0.85 (external validation cohort).Data conclusionThe radiomic nomogram can effectively evaluate ER risks in patients with resectable pancreatic cancer preoperatively, which could potentially improve treatment strategies and facilitate personalized therapy in pancreatic cancer.Level of evidence4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2020;52:231-245.
| S-EPMC7317738 | biostudies-literature
Project description:ObjectiveTo evaluate the prognostic implications of preoperative magnetic resonance imaging (MRI) features of hepatocellular carcinoma (HCC) with a focus on those with targetoid appearance based on the Liver Imaging Reporting and Data System (LI-RADS), as well as known microvascular invasion (MVI) features.Materials and methodsThis retrospective study included 242 patients (190 male; mean age, 57.1 years) who underwent surgical resection of a single HCC (≤ 5 cm) as well as preoperative gadoxetic acid-enhanced MRI between January 2012 and March 2015. LI-RADS category was assigned, and the LR-M category was further classified into two groups according to rim arterial-phase hyperenhancement (APHE). The imaging features associated with MVI were also assessed. The overall survival (OS), recurrence-free survival (RFS), and their associated factors were evaluated.ResultsAmong the 242 HCCs, 190 (78.5%), 25 (10.3%), and 27 (11.2%) were classified as LR-4/5, LR-M with rim APHE, and LR-M without rim APHE, respectively. LR-M with rim APHE (vs. LR-4/5; hazard ratio [HR] for OS, 5.48 [p = 0.002]; HR for RFS, 2.09 [p = 0.042]) and tumor size (per cm increase; HR for OS, 6.04 [p = 0.009]; HR for RFS, 1.77 [p = 0.014]) but not MVI imaging features (p > 0.05) were independent factors associated with OS and RFS. Compared to the 5-year OS and RFS rates in the LR-4/5 group (93.9% and 66.8%, respectively), the LR-M with rim APHE group had significantly lower rates (68.0% and 45.8%, respectively, both p < 0.05), while the LR-M without rim APHE group did not significantly differ in the survival rates (91.3% and 80.2%, respectively, both p > 0.05).ConclusionFurther classification of LR-M according to the presence of rim APHE may help predict the postoperative prognosis of patients with a single HCC.
| S-EPMC8546127 | biostudies-literature
Project description:PurposeThe aim of this study was to develop and validate a radiomics nomogram based on radiomics features and clinical data for the non-invasive preoperative prediction of early recurrence (≤2 years) in patients with hepatocellular carcinoma (HCC).MethodsWe enrolled 262 HCC patients who underwent preoperative contrast-enhanced computed tomography and curative resection (training cohort, n=214; validation cohort, n=48). We applied propensity score matching (PSM) to eliminate redundancy between clinical characteristics and image features, and the least absolute shrinkage and selection operator (LASSO) was used to prevent overfitting. Next, a radiomics signature, clinical nomogram, and combined clinical-radiomics nomogram were built to predict early recurrence, and we compared the performance and generalization of these models.ResultsThe radiomics signature stratified patients into low-risk and high-risk, which show significantly difference in recurrence free survival and overall survival (P ≤ 0.01). Multivariable analysis identified dichotomised radiomics signature, alpha fetoprotein, and tumour number and size as key early recurrence indicators, which were incorporated into clinical and radiomics nomograms. The radiomics nomogram showed the highest area under the receiver operating characteristic curve (AUC), with significantly superior predictive performance over the clinical nomogram in the training cohort (0.800 vs 0.716, respectively; P = 0.001) and the validation cohort (0.785 vs 0.654, respectively; P = 0.039).ConclusionThe radiomics nomogram is a non-invasive preoperative biomarker for predicting early recurrence in patients with HCC. This model may be of clinical utility for guiding surveillance follow-ups and identifying optimal interventional strategies.
| S-EPMC7490026 | biostudies-literature
Project description:PURPOSE: The aim of this study was to assess the enhancement patterns of hepatic focal nodular hyperplasia (FNH) on gadoxetic acid-enhanced MRI and diffusion-weighted (DW) MRI. METHODS: This retrospective study had institutional review board approval. Gadoxetic acid-enhanced and DW MR images were evaluated in 23 patients with 30 FNHs (26 histologically proven and 4 radiologically diagnosed). The lesion enhancement patterns of the hepatobiliary phase images were classified as heterogeneous or homogeneous signal intensity (SI), and as dominantly high/iso or low SI compared with those of adjacent liver parenchyma. Heterogeneous (any) SI lesions and homogeneous low SI lesions were categorised into the fibrosis group, whereas homogeneous high/iso SI lesions were categorised into the non-fibrosis group. Additionally, lesion SI on T2 weighted images, DW images and apparent diffusion coefficient (ADC) values were compared between the two groups. RESULTS: The lesions showed heterogeneous high/iso SI (n=16), heterogeneous low SI (n=5), homogeneous high/iso SI (n=7) or homogeneous low SI (n=2) at the hepatobiliary phase MR images. The fibrosis group lesions were more likely to show high SI on DW images and T2 weighted images compared with those in the non-fibrosis group (p<0.05). ADC values tended to be lower in the fibrosis group than those in the non-fibrosis group without significance. CONCLUSION: FNH showed variable enhancement patterns on hepatobiliary phase images during gadoxetic acid-enhanced MRI. SI on DW and T2 weighted images differed according to the fibrosis component contained in the lesion. ADVANCES IN KNOWLEDGE: FNH shows a wide spectrum of imaging findings on gadoxetic acid-enhanced MRI and DW MRI.
| S-EPMC3745063 | biostudies-other