Dataset Information

A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis.

ABSTRACT: Liver cirrhosis poses a major risk for the development of hepatocellular carcinoma (HCC). This retrospective study investigated to what extent radiomic features allow the prediction of emerging HCC in patients with cirrhosis in contrast-enhanced computed tomography (CECT). A total of 51 patients with liver cirrhosis and newly detected HCC lesions (n = 82) during follow-up (FU-CT) after local tumor therapy were included. These lesions were not to have been detected by the radiologist in the chronologically prior CECT (PRE-CT). For training purposes, segmentations of 22 patients with liver cirrhosis but without HCC-recurrence were added. A total of 186 areas (82 HCCs and 104 cirrhotic liver areas without HCC) were analyzed. Using univariate analysis, four independent features were identified, and a multivariate logistic regression model was trained to classify the outlined regions as "HCC probable" or "HCC improbable". In total, 60/82 (73%) of segmentations with later detected HCC and 84/104 (81%) segmentations without HCC were classified correctly (AUC of 81%, 95% CI 74-87%), yielding a sensitivity of 72% (95% CI 57-83%) and a specificity of 86% (95% CI 76-96%). In conclusion, the model predicted the occurrence of new HCCs within segmented areas with an acceptable sensitivity and specificity in cirrhotic liver tissue in CECT.

SUBMITTER: Tietz E

PROVIDER: S-EPMC8471809 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis.

Tietz Eric E Truhn Daniel D Müller-Franzes Gustav G Berres Marie-Luise ML Hamesch Karim K Lang Sven Arke SA Kuhl Christiane Katharina CK Bruners Philipp P Schulze-Hagen Maximilian M

Diagnostics (Basel, Switzerland) 20210909 9

Liver cirrhosis poses a major risk for the development of hepatocellular carcinoma (HCC). This retrospective study investigated to what extent radiomic features allow the prediction of emerging HCC in patients with cirrhosis in contrast-enhanced computed tomography (CECT). A total of 51 patients with liver cirrhosis and newly detected HCC lesions (<i>n</i> = 82) during follow-up (FU-CT) after local tumor therapy were included. These lesions were not to have been detected by the radiologist in th ...[more]

PMID: 34573991

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Project description:BackgroundThere is a lack of data on computed tomography (CT) perfusion parameters in patients with cirrhosis and the vascular changes that occur with increasing severity of cirrhosis, as well as changes that can occur in the remote/background liver parenchyma when hepatocellular carcinoma (HCC) develops. This study aimed to evaluate the association between CT perfusion parameters in the background liver parenchyma in cirrhotic patients with and without HCC.MethodsThis prospective study comprised consecutive patients with cirrhosis with or without HCC. A CT perfusion scan of the whole liver was done on a 128-detector row CT scanner in the four-dimensional spiral mode. Arterial liver perfusion (ALP), portal venous perfusion (PVP), hepatic perfusion index (HPI), blood flow (BF), blood volume (BV), and time to peak (TTP) were assessed. The perfusion parameters of the background liver parenchyma (bALP, bPVP, bHPI, bBF, bBV, and bTTP) were compared between the patients with cirrhosis (group I) and cirrhosis with HCC (group II). Perfusion parameters were also compared between the background liver parenchyma and the HCC in group II.ResultsOf the 93 patients evaluated during the study period, 60 patients (30 in group I and 30 in group II, mean age, 54.5 years, 53 men) were included in the analysis. Among the perfusion parameters in the background parenchyma, bPVP was lower and bHPI was higher in group II, suggesting increased hepatic arterial perfusion of even the remote background liver parenchyma in patients with HCC (P = 0.001 and P = 0.01, respectively). Perfusion parameters were significantly altered with increasing severity of cirrhosis (based on Child-Pugh class) both within and between groups. Additionally, there were significant differences in all the perfusion parameters between HCC and the background cirrhotic liver.ConclusionHPI and PVP of background liver parenchyma were significantly different in cirrhosis with and without HCC and also showed a worsening trend with increasing grades of cirrhosis.

Project description:BackgroundDue to the high recurrence rate in hepatocellular carcinoma (HCC) after resection, preoperative prognostic prediction of HCC is important for appropriate patient management. Exploring and developing preoperative diagnostic methods has great clinical value in treating patients with HCC. This study sought to develop and evaluate a novel combined clinical predictive model based on standard triphasic computed tomography (CT) to discriminate microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsThe preoperative findings of 82 patients with HCC, including conventional clinical factors, CT imaging findings, and CT texture analysis (TA), were analyzed retrospectively. All included cases were divided into MVI-negative (n = 33; no MVI) and MVI-positive (n = 49; low or high risk of MVI) groups. TA parameters were extracted from non-enhanced, arterial, portal venous, and equilibrium phase images and subsequently calculated using the Artificial Intelligence Kit. After statistical analyses, a clinical model comprising conventional clinical and CT image risk factors, radiomics signature models, and a novel combined model (fused radiomic signature) was constructed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to assess the performance of the various models in discriminating MVI.ResultsWe found that tumor diameter and pathological grade were effective clinical predictors in clinical model and 12 radiomics features were effective for MVI prediction of each CT phase. The AUCs of the clinical, plain, artery, venous, and delay models were 0.77 (95% CI: 0.67-0.88), 0.75 (95% CI: 0.64-0.87), 0.79 (95% CI: 0.69-0.89), 0.73 (95% CI: 0.61-0.85), and 0.80 (95% CI: 0.70-0.91), respectively. The novel combined model exhibited the best performance, with an AUC of 0.83 (95% CI: 0.74-0.93).ConclusionsModels derived from triphasic CT can preoperatively predict MVI in patients with HCC. Of the models tested here, the novel combined model was most predictive and could become a useful tool to guide subsequent personalized treatment of HCC.

Project description:BackgroundFinding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance.MethodsWe trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73High vs. CD73Low) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set.ResultsTabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman's ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73High vs rad-CD73Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020).ConclusionsOur findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.

Project description:BackgroundRadiomics can be used to noninvasively predict molecular markers to address the clinical dilemma that some patients cannot accept invasive procedures. This research evaluated the prognostic significance of the expression level of ribonucleotide reductase regulatory subunit M2 (RRM2) in individuals with hepatocellular carcinoma (HCC) and established a radiomics model for predicting the RRM2 expression level.MethodsGenomic data for HCC patients and corresponding computed tomography (CT) images were accessed at The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which were utilized for prognosis analysis, radiomic feature extraction and model construction, respectively. The maximum relevance minimum redundancy algorithm (mRMR) and recursive feature elimination (RFE) were used for feature selection. Following feature extraction, a logistic regression algorithm was fitted to establish a dichotomous model that predicts RRM2 gene expression. Establishment of the radiomics nomogram was carried out using the Cox regression model. Receiver operating characteristic (ROC) curve analysis was employed to assess the model performance. Clinical utility was determined by decision curve analysis (DCA).ResultsHigh RRM2 expression acted as a risk factor for overall survival (OS) [hazard ratio (HR) =2.083, P<0.001] and was implicated in regulation of the immune response. Four optimal radiomics features were selected for prediction of RRM2 expression. A predictive nomogram was established using the clinical variables and radiomics score (RS), and the areas under the ROC curve (AUCs) of the time-dependent ROC curve of the model were 0.836, 0.757, and 0.729 for the 1-, 3-, and 5-year periods, respectively. DCA confirmed that the nomogram had good clinical usefulness.ConclusionsThe RRM2 expression level in HCC can considerably affect prognosis of these patients. Expression levels of RRM2 and prognosis of HCC individuals can be predicted through radiomics features by utilizing CT scan data.

Project description:ObjectiveThe purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases.MethodsWe retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set.ResultsFor TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone.ConclusionRadiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.MethodsPatients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.ResultsA total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765-0.9585) and 0.8088 (95% CI: 0.7064-0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353-0.8987) and 0.8017 (95% CI: 0.6878-0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646-0.9824) and an AUC of 0.9099 (95% CI: 0.8324-0.9873) for the training cohort and validation cohort, respectively.ConclusionWe developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

Dataset Information

A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis.

Publications

A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis.

Similar Datasets

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