Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade

Guo, Chuangen; Zhuge, Xiaoling; Wang, Zhongqiu; Wang, Qidong; Sun, Ke; Zhan, Feng; Chen, Xiao

doi:10.1007/s00261-018-1763-1

Cited by 55 publications

(50 citation statements)

References 30 publications

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“…Texture parameters, such as entropy and kurtosis, show good performance in differentiating benign from malignant tumors (16, 17). Several studies also indicate that texture features are good predictors of tumor grades (18, 19). However, few studies have shown the role of texture features in PTs grading.…”

Section: Discussionmentioning

confidence: 99%

Differentiation Between G1 and G2/G3 Phyllodes Tumors of Breast Using Mammography and Mammographic Texture Analysis

et al. 2019

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Purpose: To determine the potential of mammography (MG) and mammographic texture analysis in differentiation between Grade 1 (G1) and Grade 2/ Grade 3 (G2/G3) phyllodes tumors (PTs) of breast. Materials and methods: A total of 80 female patients with histologically proven PTs were included in this study. 45 subjects who underwent pretreatment MG from 2010 to 2017 were retrospectively analyzed, including 14 PTs G1 and 31 PTs G2/G3. Tumor size, shape, margin, density, homogeneity, presence of fat, or calcifications, a halo-sign as well as some indirect manifestations were evaluated. Texture analysis features were performed using commercial software. Receiver operating characteristic curve (ROC) was used to determine the sensitivity and specificity of prediction. Results: G2/G3 PTs showed a larger size (>4.0 cm) compared to PTs G1 (64.52 vs. 28.57%, p = 0.025). A strong lobulation or multinodular confluent was more common in G2/G3 PTs compared to PTs G1 (64.52 vs. 14.29%, p = 0.004). Significant differences were also observed in tumors' growth speed and clinical manifestations ( p = 0.007, 0.022, respectively). Ten texture features showed significant differences between the two groups ( p < 0.05), Correlation_AllDirection_offset7_SD and ClusterProminence_AllDirection_offset7_SD were independent risk factors. The area under the curve (AUC) of imaging-based diagnosis, texture analysis-based diagnosis and the combination of the two approaches were 0.805, 0.730, and 0.843 (90.3% sensitivity and 85.7% specificity). Conclusions: Texture analysis has great potential to improve the diagnostic efficacy of MG in differentiating PTs G1 from PTs G2/G3.

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Section: Discussionmentioning

confidence: 99%

Differentiation Between G1 and G2/G3 Phyllodes Tumors of Breast Using Mammography and Mammographic Texture Analysis

et al. 2019

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“…Some CT and MR findings are effective in predicting the aggressiveness and grade of the tumor, for example as arterial phase hypovascularization, major vessel invasion, or size [ 25 , 26 , 27 ]. In particular, many studies in the literature have described the hypoenhancement of p-NETs tumors as a strong predictor of a high-grade tumor [ 28 , 29 , 30 , 31 ]. Our study reported analogous results on this latter parameter, with the hypovascularization of liver metastases being one of the best LTB descriptors in the prediction of high tumor grade.…”

Section: Discussionmentioning

confidence: 99%

“…The correlation between entropy values and different tumor grades of p-NETs has already been a matter of study [ 26 , 30 , 32 , 33 ]. In these studies, entropy was found to be an excellent predictor of the tumor grade when comparing G1 vs. G2–3 [ 26 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

Liver Tumor Burden in Pancreatic Neuroendocrine Tumors: CT Features and Texture Analysis in the Prediction of Tumor Grade and 18F-FDG Uptake

Beleù

Rizzo

Robertis³

et al. 2020

Cancers

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Pancreatic neuroendocrine tumors (p-NETs) are a rare group of neoplasms that often present with liver metastases. Histological characteristics, metabolic behavior, and liver tumor burden (LTB) are important prognostic factors. In this study, the usefulness of texture analysis of liver metastases in evaluating the biological aggressiveness of p-NETs was assessed. Fifty-six patients with liver metastases from p-NET were retrospectively enrolled. Qualitative and quantitative CT features of LTB were evaluated. Histogram-derived parameters of liver metastases were calculated and correlated with the tumor grade (G) and 18F-fluorodeoxyglucose (18F-FDG) standardized uptake value (SUV). Arterial relative enhancement was inversely related with G (−0.37, p = 0.006). Different metastatic spread patterns of LTB were not associated with histological grade. Arterialentropy was significantly correlated to G (−0.368, p = 0.038) and to Ki67 percentage (−0.421, p = 0.018). The ROC curve for the Arterialentropy reported an area under the curve (AUC) of 0.736 (95% confidence interval 0.545–0.928, p = 0.035) in the identification of G1–2 tumors. Arterialuniformity values were correlated to G (0.346, p = 0.005) and Ki67 levels (0.383, p = 0.033). Arterialentropy values were directly correlated with the SUV (0.449, p = 0.047) which was inversely correlated with Arterialuniformity (−0.499, p = 0.025). Skewness and kurtosis reported no significant correlations. In conclusion, histogram-derived parameters may predict adverse histological features and metabolic behavior of p-NET liver metastases.

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“…Considering the abdominal features of the pancreas and the variation in the volume and shape of the pancreas presented by the interpatient [31,32], this paper manually drew the ROI of the pancreas. First, the CT sequence images in DICOM format were converted into JPG format and then imported into LabelMe software where two clinicians (Wei Han has 21 years and Yilidan Reheman has 3 years of clinical experience in abdominal CT) manually drew ROIs along the edge of the pancreas [33]. To improve model generalization ability, we selected 3 cross-sectional images of plain CT scans with significant features from each CT sequence on average [27,34,35].…”

Section: Pancreas Segmentationmentioning

confidence: 99%

Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

Nie

Reheman

et al. 2020

IEEE Access

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Pancreatic cancer (PC) is a malignant tumor that seriously threatens the survival of patients. Artificial classification has practical difficulties, such as unstable classification accuracy, a heavy workload, and the classification results depend on the subjective judgment of the clinician during the diagnosis and staging of PC. In addition, accurate PC staging could better help clinicians deliver the optimal therapeutic schedule for PC patients of different stages. Therefore, this study proposes a comprehensive medical computer-aided method for preoperative diagnosis and staging of PC based on an ensemble learningsupport vector machine (EL-SVM) and computed tomography (CT) images. The least absolute shrinkage and selection operator (LASSO) algorithm was chosen for feature selection. In contrast to no feature selection, the model optimization time decreased by 19.94 seconds while maintaining precision. The EL-SVM learner was used to classify 168 CT images of normal pancreas and different stages of PC. The experimental results demonstrated that the normal pancreas (normal)-pancreatic cancer early stage (early stage) classification accuracy was 86.61%, the normal-pancreatic cancer stage III (stage III) classification accuracy was 87.04%, the normal-pancreatic cancer stage IV (stage IV) classification accuracy was 91.63%, the normal-PC classification accuracy was 87.89%, the early stage-stage III classification accuracy was 75.03%, and the early stage-stage IV classification accuracy was 81.22%, and the stage III-stage IV classification accuracy was 82.48%. Our experimental results prove that our proposed method is feasible and promising for clinical applications for the preoperative diagnosis and staging of PC via CT images.

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Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade

Abstract: Our data indicated that texture parameters have potential in grading PNENs, in particular in differentiating PNEC G3 from PNETs G1/G2.

Cited by 55 publications

References 30 publications

Differentiation Between G1 and G2/G3 Phyllodes Tumors of Breast Using Mammography and Mammographic Texture Analysis

Differentiation Between G1 and G2/G3 Phyllodes Tumors of Breast Using Mammography and Mammographic Texture Analysis

Liver Tumor Burden in Pancreatic Neuroendocrine Tumors: CT Features and Texture Analysis in the Prediction of Tumor Grade and 18F-FDG Uptake

Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

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