Dataset Information

Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images.

ABSTRACT: Purpose:To evaluate interreader agreement in annotating semantic features on preoperative CT images to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Materials and Methods:Preoperative, contrast material-enhanced triphasic CT studies from 89 patients (median age, 64 years; age range, 36-85 years; 70 men) who underwent hepatic resection between 2008 and 2017 for a solitary HCC were reviewed. Three radiologists annotated CT images obtained during the arterial and portal venous phases, independently and in consensus, with features associated with MVI reported by other investigators. The assessed factors were the presence or absence of discrete internal arteries, hypoattenuating halo, tumor-liver difference, peritumoral enhancement, and tumor margin. Testing also included previously proposed MVI signatures: radiogenomic venous invasion (RVI) and two-trait predictor of venous invasion (TTPVI), using single-reader and consensus annotations. Cohen (two-reader) and Fleiss (three-reader) ? and the bootstrap method were used to analyze interreader agreement and differences in model performance, respectively. Results:Of HCCs assessed, 32.6% (29 of 89) had MVI at histopathologic findings. Two-reader agreement, as assessed by pairwise Cohen ? statistics, varied as a function of feature and imaging phase, ranging from 0.02 to 0.6; three-reader Fleiss ? varied from -0.17 to 0.56. For RVI and TTPVI, the best single-reader performance had sensitivity and specificity of 52% and 77% and 67% and 74%, respectively. In consensus, the sensitivity and specificity for the RVI and TTPVI signatures were 59% and 67% and 70% and 62%, respectively. Conclusion:Interreader variability in semantic feature annotation remains a challenge and affects the reproducibility of predictive models for preoperative detection of MVI in HCC.Supplemental material is available for this article.© RSNA, 2020.

SUBMITTER: Bakr S

PROVIDER: S-EPMC7263284 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images.

Bakr Shaimaa S Gevaert Olivier O Patel Bhavik B Kesselman Andrew A Shah Rajesh R Napel Sandy S Kothary Nishita N

Radiology. Imaging cancer 20200529 3

<h4>Purpose</h4>To evaluate interreader agreement in annotating semantic features on preoperative CT images to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).<h4>Materials and methods</h4>Preoperative, contrast material-enhanced triphasic CT studies from 89 patients (median age, 64 years; age range, 36-85 years; 70 men) who underwent hepatic resection between 2008 and 2017 for a solitary HCC were reviewed. Three radiologists annotated CT images obtained duri ...[more]

PMID: 32550600

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Project description:Purpose To evaluate factors contributing to interreader variation (IRV) in parameters measured at dynamic contrast material-enhanced (DCE) MRI in patients with glioblastoma who were participating in a multicenter trial. Materials and Methods A total of 18 patients (mean age, 57 years ± 13 [standard deviation]; 10 men) who volunteered for the advanced imaging arm of ACRIN 6677, a substudy of the RTOG 0625 clinical trial for recurrent glioblastoma treatment, underwent analyzable DCE MRI at one of four centers. The 78 imaging studies were analyzed centrally to derive the volume transfer constant (Ktrans) for gadolinium between blood plasma and tissue extravascular extracellular space, fractional volume of the extracellular extravascular space (ve), and initial area under the gadolinium concentration curve (IAUGC). Two independently trained teams consisting of a neuroradiologist and a technologist segmented the enhancing tumor on three-dimensional spoiled gradient-recalled acquisition in the steady-state images. Mean and median parameter values in the enhancing tumor were extracted after registering segmentations to parameter maps. The effect of imaging time relative to treatment, map quality, imager magnet and sequence, average tumor volume, and reader variability in tumor volume on IRV was studied by using intraclass correlation coefficients (ICCs) and linear mixed models. Results Mean interreader variations (± standard deviation) (difference as a percentage of the mean) for mean and median IAUGC, mean and median Ktrans, and median ve were 18% ± 24, 17% ± 23, 27% ± 34, 16% ± 27, and 27% ± 34, respectively. ICCs for these metrics ranged from 0.90 to 1.0 for baseline and from 0.48 to 0.76 for posttreatment examinations. Variability in reader-derived tumor volume was significantly related to IRV for all parameters. Conclusion Differences in reader tumor segmentations are a significant source of interreader variation for all dynamic contrast-enhanced MRI parameters. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Wolf in this issue.

Project description:Background and aimThe presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle related-variables for preoperative prediction of MVI in HCC.MethodsOne hundred fifty-eight HCC patients from institution A (training cohort) and 53 HCC patients from institution B (validation cohort) were included, all of whom underwent preoperative CT scan and curative resection with confirmed pathological diagnoses. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to data dimensionality reduction and screening. Nomogram was constructed based on the independent variables, and evaluated by external validation, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA).ResultsHistopathologically identified MVI was found in 101 of 211 patients (47.9%). The preoperative imaging and clinical variables associated with MVI were visceral adipose tissue (VAT) density, intramuscular adipose tissue index (IMATI), skeletal muscle (SM) area, age, tumor size and cirrhosis. Incorporating these 6 factors, the nomogram achieved good concordance index of 0.79 (95%CI: 0.72-0.86) and 0.75 (95%CI: 0.62-0.89) in training and validation cohorts, respectively. In addition, calibration curve exhibited good consistency between predicted and actual MVI probabilities. ROC curve and DCA of the nomogram showed superior performance than that of models only depended on clinical or imaging variables. Based on the nomogram score, patients were divided into high (> 273.8) and low (< = 273.8) risk of MVI presence groups. For patients with high MVI risk, wide-margin resection or anatomical resection could significantly improve the 2-year recurrence free survival.ConclusionBy combining 6 preoperative independently predictive factors of MVI, a nomogram was constructed. This model provides an optimal preoperative estimation of MVI risk in HCC patients, and may help to stratify high-risk individuals and optimize clinical decision making.

Project description:PurposeAccurate lesion segmentation is a prerequisite for radiomic feature extraction. It helps to reduce the features variability so as to improve the reporting quality of radiomics study. In this research, we aimed to conduct a radiomic feature reproducibility test of inter-/intra-observer delineation variability in hepatocellular carcinoma using 3D-CT images, 4D-CT images and multiple-parameter MR images.Materials and methodsFor this retrospective study, 19 HCC patients undergoing 3D-CT, 4D-CT and multiple-parameter MR scans were included in this study. The gross tumor volume (GTV) was independently delineated twice by two observers based on contrast-enhanced computed tomography (CECT), maximum intensity projection (MIP), LAVA-Flex, T2W FRFSE and DWI-EPI images. We also delineated the peritumoral region, which was defined as 0 to 5 mm radius surrounding the GTV. 107 radiomic features were automatically extracted from CECT images using 3D-Slicer software. Quartile coefficient of dispersion (QCD) and intraclass correlation coefficient (ICC) were applied to assess the variability of each radiomic feature. QCD<10% and ICC≥0.75 were considered small variations and excellent reliability. Finally, the principal component analysis (PCA) was used to test the feasibility of dimensionality reduction.ResultsFor tumor tissues, the numbers of radiomic features with QCD<10% indicated no obvious inter-/intra-observer differences or discrepancies in 3D-CT, 4D-CT and multiple-parameter MR delineation. However, the number of radiomic features (mean 89) with ICC≥0.75 was the highest in the multiple-parameter MR group, followed by the 3DCT group (mean 77) and the MIP group (mean 73). The peritumor tissues also showed similar results. A total of 15 and 7 radiomic features presented excellent reproducibility and small variation in tumor and peritumoral tissues, respectively. Two robust features showed excellent reproducibility and small variation in tumor and peritumoral tissues. In addition, the values of the two features both represented statistically significant differences among tumor and peritumoral tissues (P<0.05). The PCA results indicated that the first seven principal components could preserve at least 90% of the variance of the original set of features.ConclusionDelineation on multiple-parameter MR images could help to improve the reproducibility of the HCC CT radiomic features and weaken the inter-/intra-observer influence.

Dataset Information

Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images.

Publications

Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images.

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