We are analyzing https://link.springer.com/article/10.1007/s00261-022-03646-7.

Title:
Non-contrast-enhanced CT texture analysis of primary and metastatic pancreatic ductal adenocarcinomas: value in assessment of histopathological grade and differences between primary and metastatic lesions | Abdominal Radiology
Description:
Purpose To evaluate the utility of non-contrast-enhanced CT texture analysis (CTTA) for predicting the histopathological differentiation of pancreatic ductal adenocarcinomas (PDAC) and to compare non-contrast-enhanced CTTA texture features between primary PDAC and hepatic metastases of PDAC. Methods This retrospective study included 120 patients with histopathologically confirmed PDAC. Sixty-five patients underwent CT-guided biopsy of primary PDAC, while 55 patients underwent CT-guided biopsy of hepatic PDAC metastasis. All lesions were segmented in non-contrast-enhanced CT scans for CTTA based on histogram analysis, co-occurrence matrix, and run-length matrix. Statistical analysis was conducted for 372 texture features using Mann–Whitney U test, Bonferroni–Holm correction, and receiver operating characteristic (ROC) analysis. A p value < 0.05 was considered statistically significant. Results Three features were identified that differed significantly between histopathological G2 and G3 primary tumors. Of these, “low gray-level zone emphasis” yielded the largest AUC (0.87 ± 0.04), reaching a sensitivity and specificity of 0.76 and 0.83, respectively, when a cut-off value of 0.482 was applied. Fifty-four features differed significantly between primary and hepatic metastatic PDAC. Conclusion Non-contrast-enhanced CTTA of PDAC identified differences in texture features between primary G2 and G3 tumors that could be used for non-invasive tumor assessment. Extensive differences between the features of primary and metastatic PDAC on CTTA suggest differences in tumor microenvironment. Graphical Abstract
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Keywords {🔍}

pdac, pubmed, article, texture, analysis, primary, features, patients, google, scholar, ctta, pancreatic, tumor, cancer, graylevel, histopathological, study, survival, differences, tumors, grade, size, metastatic, biopsy, matrix, low, adenocarcinoma, radiology, heterogeneity, nonenhanced, segmentation, central, cas, ductal, lesions, hepatic, ctguided, significant, auc, liver, lesion, log, statistically, zone, emphasis, computed, table, performed, graz, assessment,

Topics {✒️}

gray-level run-length matrix article download pdf gray-level intensity pairs gray-level dependence matrix unenhanced computed tomography underwent ct-guided biopsy small-cell lung cancer contrast medium-based variability single-multislice ct scanner apparently disease-free areas mann–whitney u-test contrast-enhanced ct scans contrast-enhanced ct scans obtaining false-positive results full access ct-guided biopsy procedure largest cross-sectional area gray-level intensity bonferroni–holm correction computational radiomics system higher-order parameters belonging pancreatic ductal adenocarcinoma gray level discretization quantitative ct texture image-guided biopsies related subjects funding agencies quantitative texture analysis tnmg staging system pancreatic ductal adenocarcinomas privacy choices/manage cookies colorectal cancer progression ct-guided biopsy original ct image image filtration purposes ajcc-tnm staging small lesion size run-length matrix ct-guided biopsies contrast-enhanced ct contrast enhanced ct initial significance level radiomic feature stability oesophageal cancer assessed enhanced ct scans tumor size data primary lung cancer receiver operating characteristic ct texture analysis 3d segmentation depicts

Questions {❓}

Dennie, Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?
Goh, Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis?

Schema {🗺️}

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         description:To evaluate the utility of non-contrast-enhanced CT texture analysis (CTTA) for predicting the histopathological differentiation of pancreatic ductal adenocarcinomas (PDAC) and to compare non-contrast-enhanced CTTA texture features between primary PDAC and hepatic metastases of PDAC. This retrospective study included 120 patients with histopathologically confirmed PDAC. Sixty-five patients underwent CT-guided biopsy of primary PDAC, while 55 patients underwent CT-guided biopsy of hepatic PDAC metastasis. All lesions were segmented in non-contrast-enhanced CT scans for CTTA based on histogram analysis, co-occurrence matrix, and run-length matrix. Statistical analysis was conducted for 372 texture features using Mann–Whitney U test, Bonferroni–Holm correction, and receiver operating characteristic (ROC) analysis. A p value < 0.05 was considered statistically significant. Three features were identified that differed significantly between histopathological G2 and G3 primary tumors. Of these, “low gray-level zone emphasis” yielded the largest AUC (0.87 ± 0.04), reaching a sensitivity and specificity of 0.76 and 0.83, respectively, when a cut-off value of 0.482 was applied. Fifty-four features differed significantly between primary and hepatic metastatic PDAC. Non-contrast-enhanced CTTA of PDAC identified differences in texture features between primary G2 and G3 tumors that could be used for non-invasive tumor assessment. Extensive differences between the features of primary and metastatic PDAC on CTTA suggest differences in tumor microenvironment.
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