The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis

Guo, Chuangen; Zhuge, Xiaoling; Wang, Qidong; Xiao, Wenbo; Wang, Zhonglan; Wang, Zhongqiu; Zhan, Feng; Chen, Xiao

doi:10.1186/s40644-018-0170-8

Cited by 47 publications

(36 citation statements)

References 19 publications

(24 reference statements)

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“…The ROIs we selected for the resection margin included normal pancreatic tissue and tumor tissue for radiomics methods analysis. We analyzed the images of the resection margin at three layers in patients with pancreatic head adenocarcinoma, rather than the entire tumor at a single layer as in other studies, 38,39 as different resection margins could re ect different tumor biological characteristics present within the heterogenous primary tumor. This makes our study more detailed and representative compared to other studies.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma

Liu¹,

Liu²,

Qiu³

et al. 2020

Preprint

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Background: To identify preoperative computed tomography radiomics texture features which correlate with resection margin status and prognosis in resected pancreatic head adenocarcinoma. Methods: Improved prognostication methods utilizing novel non-invasive radiomic techniques may accurately predict resection margin status preoperatively. In an ongoing concerning pancreatic head adenocarcinoma, the venous enhanced CT images of 86 patients who underwent pancreaticoduodenectomy were selected, and the resection margin (>1 mm or ≤1 mm) was identified by pathological examination. Three regions of interests (ROIs) were then taken from superior to inferior facing the superior mesenteric vein and artery. Subsequent Laplacian-Dirichlet based texture analysis methods extracting algorithm flows of texture features within ROIs were analyzed and assessed in relation to patient prognosis.Results: Patients with >1 mm resection margin had an overall improved survival compared to ≤1 mm (P < 0.05). Distance 1 and 2 of Gray level co-occurrence matrix, high Gray-level run emphasis of run-length matrix and average filter of wavelet transform (all P < 0.05) were correlated with resection margin status (Area under the curve was 0.784, sensitivity was 75% and specificity was 79%). The energy of wavelet transform, the measure of smoothness of histogram and the variance in 2 direction of Gabor transform are independent predictors of overall survival prognosis, independent of resection margin.Conclusions: Resection margin status (>1 mm vs ≤1 mm) is a key prognostic factor in pancreatic adenocarcinoma and CT radiomic analysis have the potential to predict resection margin status preoperatively, and the radiomic labels may improve selection neoadjucant therapy. Trial registration: Clinicaltrials.gov/ct2/show/NCT02928081.

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Section: Discussionmentioning

confidence: 99%

Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma

Liu¹,

Liu²,

Qiu³

et al. 2020

Preprint

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“…Texture analysis is an emerging imaging-based post-processing method that allows for quantification of tissue heterogeneity (27). There has been a surge in recent years in the research application of CT texture analysis in tumor identification, staging, and therapy response assessment (19, 23, 27, 28). However, no studies have demonstrated the value of CT texture analysis in differentiating MFP from PDAC.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images

et al. 2019

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Purpose: To investigate the potential of computed tomography (CT) imaging features and texture analysis to differentiate between mass-forming pancreatitis (MFP) and pancreatic ductal adenocarcinoma (PDAC).Materials and Methods: Thirty patients with pathologically proved MFP and 79 patients with PDAC were included in this study. Clinical data and CT imaging features of the two lesions were evaluated. Texture features were extracted from arterial and portal phase CT images using commercially available software (AnalysisKit). Multivariate logistic regression analyses were used to identify relevant CT imaging and texture parameters to discriminate MFP from PDAC. Receiver operating characteristic curves were performed to determine the diagnostic performance of predictions.Results: MFP showed a larger size compared to PDAC (p = 0.009). Cystic degeneration, pancreatic ductal dilatation, vascular invasion, and pancreatic sinistral portal hypertension were more frequent and duct penetrating sign was less frequent in PDAC compared to MFP. Arterial CT attenuation, arterial, and portal enhancement ratios of MFP were higher than PDAC (p < 0.05). In multivariate analysis, arterial CT attenuation and pancreatic duct penetrating sign were independent predictors. Texture features in arterial phase including SurfaceArea, Percentile40, InverseDifferenceMoment_angle90_offset4, LongRunEmphasis_angle45_offset4, and uniformity were independent predictors. Texture features in portal phase including LongRunEmphasis_angle135_offset7, VoxelValueSum, LongRunEmphasis_angle135_offset4, and GLCMEntropy_angle45_offset1 were independent predictors. Areas under the curve of imaging feature-based, texture feature-based in arterial and portal phases, and the combined models were 0.84, 0.96, 0.93, and 0.98, respectively.Conclusions: CT texture analysis demonstrates great potential to differentiate MFP from PDAC.

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“…FDT factor = 0.114858*FDA + 0.167196*WavEnHL_s-4 + 0.971237*DifEntr/LngREmph + 0.976915*ShrtREmp (11) FDA-WDT factor = 0.813578*FDA + 0.784505*WavEnHL_s-4 + 0.190267*DifEntr/LngREmph + 0.152573*ShrtREmp (12) where the values of the variables in the equation are standardized by subtracting their means and dividing by their standard deviations. It also shows the estimated communalities, which can be interpreted as estimating the proportion of the variability in each variable attributable to the extracted factors.…”

Section: Estimation Of Recurrencementioning

confidence: 99%

“…Texture analysis may be divided into four methods: statistical, structural, model-based, and transform [9,10]. TA is commonly used in medicine to analyse X-ray photos, computed tomography, or magnetic resonance images [11][12][13][14] Irregular shape is a characteristic feature of leukoplakia. The measurement of a leukoplasm's area and shape is very difficult, especially comparing measurements before and after treatment.…”

Section: Introductionmentioning

confidence: 99%

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Application of Texture and Fractal Dimension Analysis to Estimate Effectiveness of Oral Leukoplakia Treatment Using an Er:YAG Laser—A Prospective Study

Jurczyszyn

Kozakiewicz

2020

Materials

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Background: Oral leukoplakia (OL) is a potential neoplasmic lesion. The aim of this study was to apply texture analysis (TA) and fractal dimension analysis (FDA) to estimate the effectiveness of OL treatment using an Er:YAG laser. Methods: Eighteen patients with 32 lesions were treated. Laser procedures were conducted using the LiteTouch™ Er:YAG Dental Laser. The diameter of the operational tip was 1.3 mm, the power was 50 mJ, the frequency was 50 Hz, and the wavelength was 2940 nm. TA was based on long and short-run emphasis inverse moments, difference entropy, inverse difference moment, and wavelet decomposition for two-dimensional photography. FDA was measured using the box-counting method. Results: Total response was achieved in 50% of lesions, partial response was observed in 47%, and 3% of lesions did not respond to treatment. Recurrence occurred in 34% of lesions. TA features indicated pathological images depicting leukoplakia and complete reconstruction of the correct mucosal image after laser ablation. The discrete wavelet transformation feature detects much larger structures than the properties derived from the run-length matrix and co-occurrence matrix. Conclusions: The Er:YAG laser is an effective treatment method in cases of oral leukoplakia. Leukoplakia treatment by Er:YAG laser is an effective modality, as revealed by the oral mucosa microstructure. TA and FDA are promising methods to estimate the effectiveness of OL treatment.

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The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis

Cited by 47 publications

References 19 publications

Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma

Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma

Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images

Application of Texture and Fractal Dimension Analysis to Estimate Effectiveness of Oral Leukoplakia Treatment Using an Er:YAG Laser—A Prospective Study

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