CT radiomics nomogram for the preoperative prediction of severe post-hepatectomy liver failure in patients with huge (≥ 10 cm) hepatocellular carcinoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Xiang F
PROVIDER: S-EPMC8667454 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectivesAppropriate patient selection is important to achieving good outcomes and obviating futile surgery in patients with huge (?10 cm) hepatocellular carcinoma (HCC). The aim of this study was to identify independent predictors of futile outcomes, defined as death within 3 months of surgery or within 1 year from early recurrence following hepatectomy for huge HCC.MethodsThe outcomes of 149 patients with huge HCCs who underwent resection during 1995-2012 were analysed. Multivariate logistic regression analysis was performed to identify preoperative independent predictors of futility.ResultsIndependent predictors of 3-month mortality (18.1%) were: total bilirubin level >34 ?mol/l [P = 0.0443; odds ratio (OR) 16.470]; platelet count of <150 000 cells/ml (P = 0.0098; OR 5.039), and the presence of portal vein tumour thrombosis (P = 0.0041; OR 5.138). The last of these was the sole independent predictor of 1-year recurrence-related mortality (17.2%). Rates of recurrence-related mortality at 3 months and 1 year were, respectively, 6.3% and 7.1% in patients with Barcelona Clinic Liver Cancer (BCLC) stage A disease, 12.5% and 14% in patients with BCLC stage B disease, and 37.8% (P = 0.0002) and 75% (P = 0.0002) in patients with BCLC stage C disease.ConclusionsAccording to the present data, among patients submitted to hepatectomy for huge HCC, those with a high bilirubin level, low platelet count and portal vein thrombosis are at higher risk for futile surgery. The presence of portal vein tumour thrombosis should be regarded as a relative contraindication to surgery.
| S-EPMC4474509 | 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:BackgroundRadiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsTwo hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsHigher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionsThe nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.
| S-EPMC6873064 | biostudies-literature
Project description:ObjectivePost-hepatectomy liver failure (PHLF) is a severe complication in patients with hepatocellular carcinoma (HCC) who underwent hepatectomy. This study aims to develop a nomogram of PHLF grade B-C in patients with huge HCC (diameter ≥ 10 cm).MethodsWe retrospectively collected clinical information of 514 and 97 patients who underwent hepatectomy for huge HCC at two medical centers between 2016 and 2021. Univariate and multivariate analysis were carried out to screen the independent risk factors of PHLF grade B-C, which were visualized as a nomogram.ResultsThree Hundred Forty Three Thousand One Hundred Seventy One and 97 HCC patients were included in the training cohort, internal validation cohort, and external validation cohort, with probabilities of PHLF grade B-C of 15.1%, 12.9%, and 22.7%, respectively. Pre-operative modified albumin-bilirubin (mALBI) grade (p < 0.001), Child-Pugh classification (p = 0.044), international normalized ratio (INR) (p = 0.005), cirrhosis (p = 0.019), and intraoperative blood loss (p = 0.004) were found to be independently associated with PHLF grade B-C in the training cohort. All the five independent factors were considered in the establishment of the nomogram model. In the internal validation cohort and external validation cohort, the area under receiver operating characteristic curve for the nomogram in PHLF grade B-C prediction reached 0.823 and 0.740, respectively. Divided into different risk groups according to the optimal cut-off value, patients in the high-risk group reported significantly higher frequency of PHLF grade B-C than those in the low-risk group, both in the training cohort and the validation cohort (p < 0.001).ConclusionsThe proposed noninvasive nomogram based on mALBI-Child-Pugh and three other indicators achieved optimal prediction performance of PHLF grade B-C in patients with huge HCC.
| S-EPMC9202189 | biostudies-literature
Project description:PURPOSE:To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS:A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS:Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell's Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION:The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.
| S-EPMC6154864 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature.MethodsIn all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram.ResultsThe radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram.ConclusionThe radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.
| S-EPMC7667801 | biostudies-literature
Project description:ObjectiveThis study aimed to develop a model to predict KRAS mutations in colorectal cancer according to radiomic signatures based on CT and clinical risk factors.MethodsThis retrospective study included 172 patients with colorectal cancer. All patients were randomized at a 7:3 ratio into a training cohort (n = 121, 38.8% positive for KRAS mutation) and a validation cohort (n = 51, 39.2% positive for KRAS mutation). Radiomics features were extracted from single-slice and full-volume regions of interest on the portal-venous CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was adopted to construct a radiomics signature, and logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA).Results1018 radiomics features were extracted from single-slice and full-volume ROIs. Eight features were retained to construct 2D (two-dimensional, 2D) radiomics model. Similarly, eight features were retained to construct 3D (three-dimensional, 3D) radiomics model. The area under the curve (AUC) values of the test cohort were 0.75 and 0.84, respectively. Delong test showed that the integrated nomogram (AUC = 0.92 in the test cohort) had better clinical predictive efficiency than 2D radiomics (p-value < 0.05) model and 3D radiomics model (p-value < 0.05).ConclusionThe 2D and 3D radiomics models can both predict KRAS mutations. And, the integrated nomogram can be better applied to predict KRAS mutation status in colorectal cancer.Advances in knowledgeCT-based radiomics showed satisfactory diagnostic significance for the KRAS status in colorectal cancer, the clinical-combined model may be applied in the individual pre-operative prediction of KRAS mutation.
| S-EPMC10996413 | biostudies-literature
Project description:PurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and methodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.ResultsThe radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.ConclusionOur CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.
| S-EPMC8358686 | biostudies-literature
Project description:ObjectivesTo develop and validate a PET/CT nomogram for preoperative estimation of lymph node (LN) staging in patients with non-small cell lung cancer (NSCLC).MethodsA total of 263 pathologically confirmed LNs from 124 patients with NCSLC were retrospectively analyzed. Positron-emission tomography/computed tomography (PET/CT) examination was performed before treatment according to the clinical schedule. In the training cohort (N = 185), malignancy-related features, such as SUVmax, short-axis diameter (SAD), and CT radiomics features, were extracted from the regions of LN based on the PET/CT scan. The Minimum-Redundancy Maximum-Relevance (mRMR) algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model were used for feature selection and radiomics score building. The radiomics score (Rad-Score) and SUVmax were incorporated in a PET/CT nomogram using the multivariable logistic regression analysis. The performance of the proposed model was evaluated with discrimination, calibration, and clinical application in an independent testing cohort (N = 78).ResultsThe radiomics scores consisting of 14 selected features were significantly associated with LN status for both training cohort with AUC of 0.849 (95% confidence interval (CI), 0.796-0.903) and testing cohort with AUC of 0.828 (95% CI, 0.782-0.919). The PET/CT nomogram incorporating radiomics score and SUVmax showed moderate improvement of the efficiency with AUC of 0.881 (95% CI, 0.834-0.928) in the training cohort and AUC of 0.872 (95% CI, 0.797-0.946) in the testing cohort. The decision curve analysis indicated that the PET/CT nomogram was clinically useful.ConclusionThe PET/CT nomogram, which incorporates Rad-Score and SUVmax, can improve the diagnostic performance of LN metastasis.Key points• The PET/CT nomogram (Int-Score) based on lymph node (LN) PET/CT images can reliably predict LN status in NSCLC. • Int-Score is a relatively objective diagnostic method, which can play an auxiliary role in the process of clinicians making treatment decisions.
| S-EPMC8270849 | biostudies-literature
Project description:BackgroundProliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment.MethodsPatients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis.ResultsThe areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P < 0.001) and progression-free survivals in the TACE outcome cohort (HR = 2.21; P = 0.03). Transcriptomics and pathomics analysis indicated that the radiomics nomogram was associated with carbon metabolism, immune cells infiltration, TP53 mutation, and heterogeneity of tumor cells.ConclusionThe CT-based radiomics nomogram could predict proliferative HCC, stratify clinical outcomes, and measure a pro-tumor microenvironment.
| S-EPMC11367757 | biostudies-literature