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Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection.

ABSTRACT: To investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840-0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637-0.828) and 0.844 (95% CI 0.751-0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.

SUBMITTER: Zhu Y

PROVIDER: S-EPMC8443588 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Early relapse after hepatectomy for intrahepatic cholangiocarcinoma (ICC) has a tremendous influence on the long-term survival outcomes of ICC patients. The purpose of our study was to investigate risk factors for early tumor relapse and confirm whether early relapse was correlated with ICC patients' long-term survival outcomes. Three hundred and twenty-two consecutive ICC patients undergoing partial hepatectomy at Liver Surgery Department of Zhongshan Hospital (Fudan University, Shanghai, China) between January 2005 and December 2011 were included in this retrospectively study. The definition of early relapse had been described as tumor relapse within 24 months after hepatectomy in ICC patients. We identified a total of 168 ICC patients with early relapse and 23 ICC patients with late relapse after hepatectomy. From the time of relapse, the long-term survival outcomes were worse among patients who had early vs. late relapse (median OS 16.5 vs. 44.7 months, respectively; P < 0.0001). The overall survival of the early relapse group was lower than that of the late relapse group (P < 0.0001). Multivariate Cox regression analysis indicated that multiple tumors (hazard ratio [HR], 1.951; 95% CI, 1.382-2.755; P < 0.001), lymphonodus metastasis (HR, 1.517; 95% CI, 1.061-2.168; P = 0.022), and higher serum CA19-9 levels (HR, 1.495; 95% CI, 1.095-2.039; P = 0.011) were independent risk factors of early relapse. Moreover, multiple tumors (HR, 1.641; 95% CI, 1.120-2.406; P = 0.011), lymphonodus metastasis (HR, 2.008; 95% CI, 1.367-2.949; P < 0.001), elevated NLR (HR, 1.921; 95% CI, 1.331-2.774; P < 0.001) and higher serum CA19-9 levels (HR, 1.990; 95% CI, 1.409-2.812; P < 0.001) were independent predictors of overall survival for ICC patients with early relapse. Collectively, our findings demonstrated that multiple tumors, lymphonodus metastasis, and higher serum CA19-9 levels were associated with the increased risks of early relapse and worse prognoses of ICC after curative-intent resection.

Project description:BackgroundPreoperative diagnosis of microvascular invasion (MVI) and tumor grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) using imaging findings can facilitate patient treatment decision-making. This study was conducted to establish and validate nomograms based on magnetic resonance imaging (MRI) radiomics and morphological features for predicting the MVI and tumor grading of IMCC before radical hepatectomy.MethodsA total of 235 patients with resected IMCC at the Chinese Academy of Medical Sciences and Peking Union Medical College were divided into a training set (n=167) and a validation set (n=68), retrospectively. Clinical data and MRI morphological features were recorded. Univariate and multivariate analyses were conducted to identify the significant features for the prediction of MVI and tumor grading. Radiomics features were extracted from T2-weighted imaging fat-suppressed and diffusion-weighted imaging (DWI). Radiomics signatures (rad_scores) were built based on the least absolute shrinkage and selection operator (LASSO) method. Then, the nomograms were constructed by combining the rad_scores and the significant clinical or MRI morphologic features. The predictive performances for MVI and tumor grading were evaluated by the area under the receiver operating characteristic curve (AUC), calibration, and clinical utility.ResultsTotals of 16 and 9 radiomics features were selected to build the rad_scores for the prediction of MVI and tumor grading for the training and validation set, respectively. The nomogram for the prediction of MVI comprised the morphologic features including number of tumors, tumor margin, and rad_score. For the prediction of tumor grading, the nomogram comprised the number of tumors, tumor necrosis, and rad_score. The best discriminations were observed in the training and validation sets for the MVI nomogram [AUCs of 0.874, 95% confidence interval (CI): (0.822-0.926) and 0.869 (0.783-0955)] and tumor grading nomogram [AUCs of 0.827 (0.763-0.891) and 0.848 (0.759-0.937)]. Decision curve analysis (DCA) further confirmed the clinical utilities of the nomograms.ConclusionsNomograms based on MRI radiomics and morphological features can effectively predict the individualized risks of MVI and tumor grading for IMCC.

Project description:ImportanceAlthough surgery offers the best chance of a potential cure for patients with localized, resectable intrahepatic cholangiocarcinoma (ICC), prognosis of patients remains dismal largely because of a high incidence of recurrence.ObjectiveTo predict very early recurrence (VER) (ie, recurrence within 6 months after surgery) following resection for ICC in the pre- and postoperative setting.Design, setting, and participantsPatients who underwent curative-intent resection for ICC between May 1990 and July 2016 were identified from an international multi-institutional database. The study was conducted at The Ohio State University in collaboration with all other participating institutions. The data were analyzed in December 2019.Main outcomes and measuresTwo logistic regression models were constructed to predict VER based on pre- and postoperative variables. The final models were used to develop an online calculator to predict VER and the tool was internally and externally validated.ResultsAmong 880 patients (median age, 59 years [interquartile range, 51-68 years]; 388 women [44.1%]; 428 [50.2%] white; 377 [44.3%] Asian; 27 [3.2%] black]), 196 (22.3%) developed VER. The 5-year overall survival among patients with and without VER was 8.9% vs 49.8%, respectively (P < .001). A preoperative model was able to stratify patients relative to the risk for VER: low risk (6-month recurrence-free survival [RFS], 87.7%), intermediate risk (6-month RFS, 72.3%), and high risk (6-month RFS, 49.5%) (log-rank P < .001). The postoperative model similarly identified discrete cohorts of patients based on probability for VER: low risk (6-month RFS, 90.0%), intermediate risk (6-month RFS, 73.1%), and high risk (6-month RFS, 48.5%) (log-rank, P < .001). The calibration and predictive accuracy of the pre- and postoperative models were good in the training (C index: preoperative, 0.710; postoperative, 0.722) as well as the internal (C index: preoperative, 0.715; postoperative, 0.728; bootstrapping resamples, n = 5000) and external (C index: postoperative, 0.672) validation data sets.Conclusion and relevanceAn easy-to-use online calculator was developed to help clinicians predict the chance of VER after curative-intent resection for ICC. The tool performed well on internal and external validation. This tool may help clinicians in the preoperative selection of patients for neoadjuvant therapy as well as during the postoperative period to inform surveillance strategies.

Project description:OBJECTIVE:To construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after curative treatment. MATERIALS AND METHODS:In total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI). RESULTS:By comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E (P = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 (P = 0.01) compared to PT-E. CONCLUSION:The CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E.

Project description:Inflammation has a critical role in the development and progression of cancers. We developed a novel systemic inflammation score (SIS) based on lymphocyte, monocyte, and CA19-9 and explored its prognostic value in intrahepatic cholangiocarcinoma (ICC). From January 2005 to December 2011, 322 consecutive ICC patients who underwent curative resection in our center were included in this study, and validated in a retrospective study of 126 patients enrolled from 2012 to 2014. Clinicopathological variables including preoperative serum CA19-9 and LMR were analyzed. The cutoff values of CA19-9 and LMR were determined based on receiver operating characteristics curve analysis in the primary cohort. Kaplan-Meier curves and multivariate Cox-regression analyses were calculated for time to recurrence (TTR) and overall survival (OS). In univariate analysis of all patients, all three inflammatory and tumor marker including NLR ? 2.49 (P<0.001), LMR ? 4.45 (P=0.002), and CA19-9?89 (P<0.001) were associated with poor prognoses. When omitting SIS in multivariate analysis, preoperative LMR (P =0.006) and serum CA19-9 (P<0.001) were independent predictors of OS. In addition, elevated CA19-9 (P=0.001), multiple tumors (P<0.001), and lymph node metastasis (P<0.001) were significant predictors of worse recurrence free survival. Moreover, high SIS was significantly associated with aggressive tumor behaviours including large tumor size (P<0.001), multiple tumors (P=0.033), lymphonodus node metastasis (P=0.001), and high TNM stage (P<0.0001). Finally, univariate and multivariate analyses revealed the SIS was an independent predictor for TTR (HR=2.077, 95% CI, 1.365-3.162, P=0.001) and OS (HR=3.133 95% CI, 2.058-4.769, P<0.001). These results were further confirmed in the validation cohort. In conclusions, our findings demonstrate that the SIS as a potentially powerful prognostic biomarker in ICC that predicts poor clinical outcomes and is a promising tool for ICC treatment strategy decisions.

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Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection.

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