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Everything you need to know about Computed Tomography (CT) & CT Scanning

Pancreas: Texture Analysis Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Pancreas ❯ Texture Analysis

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  • Purpose: Grades of pancreatic neuroendocrine neoplasms (PNENs) are associated with the choice of treatment strategies. Texture analysis has been used in tumor diagnosis and staging evaluation. In this study, we aim to evaluate the potential ability of texture parameters in differentiation of PNENs grades.
    Results: There were significant differences in tumor margin, pancreatic duct dilatation, lymph nodes invasion, size, portal enhancement ratio (PER), arterial enhancement ratio (AER), mean grey-level intensity, kurtosis, entropy, and uniformity among G1, G2, and pancreatic neuroendocrine carcinoma (PNEC) G3 (p < 0.01).
    Conclusions: Our data indicated that texture parameters have potential in grading PNENs, in particular in differentiating PNEC G3 from PNETs G1/G2.
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • “The texture parameters included mean grey-level inten- sity, kurtosis, skewness, entropy, and uniformity which had been used in previous reports. The mean of each parameter was calculated automatically. The mathematical expression and means of texture parame- ters were reported in the previous study. The inter- observer agreements in ROIs were assessed with Conger’s kappa test. For those cases with interobserver disagreement, texture analysis was not performed until the two readers reach a consensus.”
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585

  • Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • “The higher standard deviations in texture parameters were present in PNET G2 and PNEC. PNET G2 and PNEC showed more heterogeneous than PNET G1 on contrast-enhanced imaging [12]. PNET G2 usually showed marked enhancement. Mild enhancement was also found in PNETs G2. PNEC usually showed mild enhancement. However, marked enhancement also can be found in PNEC. Therefore, the standard deviation is higher in PNET G2/PNEC than PNET G1.”
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • In conclusion, our data indicate that tumor size, pancreatic duct dilation, local invasion/metastases, AER, and PER have potential for differentiating PNEC G3 from PNET G1/G2. Moreover, our data indicate that texture analysis on contrast-enhanced CT images may represent as promising, non-invasive biomarkers to evaluate the pathologic grade of PNENs.”
    Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade
    Chuangen Guo et al.
    Abdom Radiol (2019) 44:576–585
  • Objectives: This study was designed to estimate the performance of textural features derived from contrast- enhanced CT in the differential diagnosis of pancreatic serous cystadenomas and pancreatic mucinous cystadenomas.
    Conclusions: In conclusion, our study provided preliminary evidence that textural features derived from CT images were useful in differential diagnosis of pancreatic mucinous cystadenomas and serous cystadenomas, which may provide a non-invasive approach to determine whether surgery is needed in clinical practice. However, multicentre studies with larger sample size are needed to confirm these results.
    Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494
  • Methods: Fifty-three patients with pancreatic serous cystadenoma and 25 patients with pancreatic mucinous cystadenoma were included. Textural parameters of the pancreatic neoplasms were extracted using the LIFEx software, and were analyzed using random forest and Least Absolute Shrinkage and Selection Operator (LASSO) methods. Patients were randomly divided into training and validation sets with a ratio of 4:1; random forest method was adopted to constructed a diagnostic prediction model. Scoring metrics included sensitivity, specificity, accuracy, and AUC.
    Results: Radiomics features extracted from contrast-enhanced CT were able to discriminate pancreatic mucinous cystadenomas from serous cystadenomas in both the training group (slice thickness of 2 mm, AUC 0.77, sensitivity 0.95, specificity 0.83, accuracy 0.85; slice thickness of 5 mm, AUC 0.72, sensitivity 0.90, specificity 0.84, accuracy 0.86) and the validation group (slice thickness of 2 mm, AUC 0.66, sensitivity 0.86, specificity 0.71, accuracy 0.74; slice thickness of 5 mm, AUC 0.75, sensitivity 0.85, specificity 0.83, accuracy 0.83).
    Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494

  • Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494
  • “The textural parameters were obtained using Local Image features Extraction (LIFEx) software in the portal vein phase CT images. A two-dimensional region of interest (ROI) was delineated around the boundary of tumor lesion in each layer of transaxial CT images to form a three-dimensional ROI. ROIs were drawn independently by two radiologists, who were unaware of the diagnosis of patients. Minimum, maximum, mean, and standard deviation of the density values inside the ROI were calculated. From these primary calculations, geometry based and histogram based features, the Gray-level co- occurrence matrix (GLCM), the Neighborhood gray-level different matrix (NGLDM), the Gray level run length matrix (GLRLM) and the Gray level zone length matrix (GLZLM) were obtained.”
    Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning
    Jing Yang et al.
    Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494
  • Purpose The aim of this study was to investigate the relationship between CT imaging phenotypes and genetic and biological characteristics in pancreatic ductal adenocarcinoma (PDAC).
    Conclusions In this study, we demonstrate that in PDAC SMAD4 status and tumor stromal content can be predicted using radiomic analysis of preoperative CT imaging. These data show an association between resectable PDAC imaging features and underlying tumor biology and their potential for future precision medicine.
    CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma
    Marc A. Attiyeh et al.
    Abdominal Radiology (in press) https://doi.org/10.1007/s00261-019-02112-1
  • “In this study, we identified radiomic features associated with PDAC genetic alterations and stromal content. These asso- ciations show the potential of using noninvasive imaging on pre-surgical pancreas cancer patients for precision medicine. Linking radiomic features to underlying tumor biology is an area of great interest, given the ubiquity of diagnostic imaging and the challenges and costs in performing molec- ular analyses. Recent DNA/RNA sequencing studies have provided in-depth insights into individual tumor’s genetic makeup and have demonstrated some prognostic power for survival that may guide personalized treatment.”
    CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma
    Marc A. Attiyeh et al.
    Abdominal Radiology (in press) https://doi.org/10.1007/s00261-019-02112-1
  • “In conclusion, we demonstrate that radiomic features extracted from clinical CT images are associated with genotype, the number of altered genes, and stromal content in PDAC. These associations may underlie the observation that PDAC imaging features are associated with survival. Further studies will be needed to increase sample size and perform external validation."
    CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma
    Marc A. Attiyeh et al.
    Abdominal Radiology (in press) https://doi.org/10.1007/s00261-019-02112-1 
  • OBJECTIVE. The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas.
    RESULTS. Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%.
    CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Fishman EK et al
    AJR 2019; 213:1–9
  • RESULTS. Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%.
    CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S et al.
    AJR 2019; 213:1–9
  • “CT features of early PDAC can be subtle and missed by even experienced radiologists. Early signs of PDAC such as pancreatic parenchyma inhomogeneity and loss of normal fatty marbling of the pancreas have been described on retrospective CT review up to 34 months before the diagnosis of PDAC. Quantitative analysis of these imaging features offers the potential for computer-aided diagnosis of PDAC.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • “This study aimed to tackle the second goal—differentiation of abnormal from normal pancreatic tissue using segmentation of the entire pancreas (i.e., without relying on separate segmentation of the tumor region). Our results showed that, after manual segmentation of pancreas boundaries, radiomics features and the random forest classifier were highly accurate in differentiating PDAC cases from normal control cases (sensitivity, 100%; specificity, 98.5%; accuracy, 99.2%). The radiomics features most relevant to differentiate PDAC from normal pancreas were based on shape and textural heterogeneity.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • “Given the high accuracy of automatic pan- creas segmentation by existing algorithms, these algorithms could be used to generate the boundaries for pancreas segmentation, and then the radiomics feature analysis algorithm could be performed to differentiate PDAC from normal pancreas. Some technical hurdles need to be overcome before these complex algorithms can be combined, but we anticipate that will be possible in the near future.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • ”All of the scans in the current study were obtained at a single institution on units manufactured by a single vendor using matched protocols and the same reconstruction algorithm. Differences in image acquisition, reconstruction, segmentation, and feature extraction can affect radiomics features and results. There is currently no standardization in the optimal protocol for imaging acquisition and postprocessing for radiomics analysis.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • “This preliminary study showed that the radiomics features extracted from the whole pancreas can be used to differentiate between CT images of patients with PDAC and CT images of healthy control subjects. There is the potential to combine this algorithm with automatic organ segmentation algorithms for automatic detection of PDAC.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • Results: Only 31 of 102 serous cystic neoplasm cases in this study were recognized correctly by clinicians before the surgery. Twenty-two features were selected from the radiomics system after 100 bootstrapping repetitions of the least absolute shrinkage selection operator regression. The diagnostic scheme performed accurately and robustly, showing the area under the receiver operating characteristic curve 1⁄4 0.767, sensitivity 1⁄4 0.686, and specificity 1⁄4 0.709. In the independent validation cohort, we acquired similar results with receiver operating characteristic curve 1⁄4 0.837, sensitivity 1⁄4 0.667, and specificity 1⁄4 0.818.
    Conclusion: The proposed radiomics-based computer-aided diagnosis scheme could increase preoperative diagnostic accuracy and assist clinicians in making accurate management decisions.
    Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images
    Ran Wei et al.
    Technology in Cancer Research & Treatment
    Volume 18: 1-9; 2019
  • “A total of 17 intensity and texture features were selected, showing difference between SCNs and non-SCNs. Typically, the intensity T-range, wavelet intensity T-median, and wavelet neighborhood gray-tone difference matrix (NGTDM) busyness were the most distinguishable.”
    Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images
    Ran Wei et al.
    Technology in Cancer Research & Treatment
    Volume 18: 1-9; 2019
  • “In our retrospective study of 260 patients with PCN, we were surprised to find that the overall preoperative diagnostic accuracy by clinicians was 37.3% (97 of 260), and only 30.4% (31 of 102) of SCN cases were correctly diagnosed. This meant that more than two-thirds of patients with SCN suffered unnecessary pancreatic resection.”
    Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images
    Ran Wei et al.
    Technology in Cancer Research & Treatment
    Volume 18: 1-9; 2019
  • “Furthermore, radiomics high-throughput features containing intensity features, texture features, and their wavelet decomposition forms fully utilized image information and obtained more image details that were hard to discover with the naked human eyes.”
    Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images
    Ran Wei et al.
    Technology in Cancer Research & Treatment
    Volume 18: 1-9; 2019
  • “In conclusion, our study proposed a radiomics-based CAD scheme and stressed the role of radiomics analysis as a novel noninvasive method for improving the preoperative diagnostic accuracy of SCNs. In all, 409 quantitative features were auto- matically extracted, and a feature subset containing the 22 most statistically significant features was selected after 100 boot- strapping repetitions. Our proposed method improved the diag- nostic accuracy and performed well in all metrics, with AUC of 0.767 in the cross-validation cohort and 0.837 in the independent validation cohort. This demonstrated that our CAD scheme could provide a powerful reference for the diagnosis of clinicians to reduce misjudgment and avoid overtreatment.”
    Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images
    Ran Wei et al.
    Technology in Cancer Research & Treatment
    Volume 18: 1-9; 2019
  • “In conclusion, our study proposed a radiomics-based CAD scheme and stressed the role of radiomics analysis as a novel noninvasive method for improving the preoperative diagnostic accuracy of SCNs. In all, 409 quantitative features were auto- matically extracted, and a feature subset containing the 22 most statistically significant features was selected after 100 boot- strapping repetitions. Our proposed method improved the diag- nostic accuracy and performed well in all metrics, with AUC of 0.767 in the cross-validation cohort and 0.837 in the independent validation cohort. This demonstrated that our CAD scheme could provide a powerful reference for the diagnosis of clinicians to reduce misjudgment and avoid overtreatment.”
    Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images
    Ran Wei et al.
    Technology in Cancer Research & Treatment
    Volume 18: 1-9; 2019
  • “Generally, accepted chemotherapy combinations for pancreatic adenocarcinoma include FOLFIRINOX (leucovorin, fluorouracil, irinotecan, and oxaliplatin) and gemcitabine-based chemotherapy. Since FOLFIRINOX has found to improve overall survival of patients with metastatic pancreatic adenocarcinoma when com- pared with gemcitabine, the same multidrug chemotherapy regimen became a rational choice to treat borderline and locally advanced pancreatic adenocarci- noma to render patients with locally advanced cancer resectable. Gemcitabine have been the most widely used agents along with 5-fluorouracil for patients with pancreatic adenocarcinoma. Gemcitabine with nabpaclitaxal chemotherapy including nab-paclitaxal-pacrotaxine and other regimen are being tested, and their efficacies are being investigated.”


    Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual- energy CT 
Satomi Kawamoto,1 Matthew. K. Fuld, Daniel Laheru, Peng Huang, Elliot K. Fishman
Abdom Radiol (2018) (in press)
  • “Conventional anticancer chemotherapy may affect tumor vascularization. Previous studies have shown reduction in CT perfusion parameters after conventional chemotherapy in various types of tumors including rectal cancer and non-small cell lung cancer . Many of the conventional chemotherapeutic agents are cytotoxins that are capable of damaging the vascular endothelium. These observations might be based on the loss of angiogenic cytokine support after cell death.”


    Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual- energy CT 
Satomi Kawamoto,1 Matthew. K. Fuld, Daniel Laheru, Peng Huang, Elliot K. Fishman
Abdom Radiol (2018) (in press)
  • “In conclusion, iodine uptake by pancreatic adenocarcinoma using DECT may add supplemental information for assessment of treatment response, although tumor iodine uptake by pancreatic adenocarcinoma is small, and it may be difficult to apply to each case. Normalized tumor iodine uptake might be more sensitive than iodine concentration to measure treatment response. More data are necessary to confirm these observations.”


    Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual- energy CT 
Satomi Kawamoto,1 Matthew. K. Fuld, Daniel Laheru, Peng Huang, Elliot K. Fishman
Abdom Radiol (2018) (in press)
  • OBJECTIVE. The purposes of this study were to assess whether CT texture analysis and CT features are predictive of pancreatic neuroendocrine tumor (PNET) grade based on the World Health Organization (WHO) classification and to identify features related to disease progression after surgery. 

    CONCLUSION. CT texture analysis and CT features are predictive of PNET aggressiveness and can be used to identify patients at risk of early disease progression after surgical resection. 


    Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
  • “Although PNETs can be grouped by cell type of origin and the presence of symptoms (functional and nonfunctional), the most important differentiation is tumor grade. According to the World Health Organization (WHO) classification system, PNETs can be classified as low grade (less aggressive with the highest 5-year survival rate, 85%), intermediate grade, and high grade (most aggressive with the lowest 5-year survival rate,9%).”

    

Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
  • “The results of our study showed that CT texture analysis and CT features can be used to predict PNET grade. The odds of having an intermediate- or high-grade tumor in a tumor larger than 2.0 cm were 3.3 times as high as those in smaller tumors; in tumors associated with vascular involvement, 25.2 times as high as those in tumors without vascular involvement; in tumors associated with pancreatic duct dilatation, 6 times as high as those in tumors without pancreatic duct dilatation; in tumors associated with lymphadenopathy, 6.8 times as high as those in tumors not associated with lymphadenopathy; and in tumors with high entropy values, 3.7 times as high as those in tumors with low entropy values.”


    Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
  • “Our results are in accordance with findings in the literature. The texture parameter entropy, which is thought to reflect tissue heterogeneity, has previously been reported as one of the best quantitative parameters for differentiating benign from neoplastic thrombi, assessing glioma grade, and assessing patient outcome in colorectal cancer.”


    Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
  • “CT texture analysis and CT features can be used to predict PNET grade according to the WHO classification. They also can be used to identify patients at risk of early recurrence or progression after surgical resection.”


    Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
  • “Therefore, the texture parameter entropy and the CT features (vascular involvement, pancreatic duct dilatation, lymphadenopathy, and size greater than 2.0 cm, which has already been adopted by some institutions) can be used as imaging biomarkers to identify patients who would bene t from surgery, from the watch-and-wait approach, or from postoperative adjuvant therapy. More-over, there has been much interest in creating Radiomics models to predict tumor aggressiveness and patient outcomes for multiple tumors. The results of our study may contribute to the development of a robust predictive model that combines quantitative and qualita- tive imaging parameters (e.g., texture parameters) and clinical predictors.”

    
Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
  • “CT texture analysis and CT features can be used to predict PNET grade according to the WHO classification. They also can be used to identify patients at risk of early recurrence or progression after surgical resection.”


    Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
  • “The images were evaluated for tumor location, tumor size, tumor pattern, predominantly solid or cystic composition, presence of calcification, presence of heterogeneous enhancement on contrast-enhanced images, presence of pancreatic duct dilatation, presence of pancreatic atrophy, presence of vascular involvement by the tumor, and presence of lymphadenopathy. Texture features were also extracted from CT images. Surgically verified tumors were graded according to the WHO classification, and patients underwent CT or MRI follow-up after surgical resection.”


    Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
  • “The CT features predictive of a more aggressive tumor (grades 2 and 3) were size larger than 2.0 cm (odds ratio [OR], 3.3; p = 0.014), presence of vascular involvement (OR, 25.2; p = 0.003), presence of pancreatic ductal dilatation (OR, 6.0; p = 0.002), and presence of lymphadenopathy (OR, 6.8; p = 0.002). The texture parameter entropy (OR, 3.7; p = 0.008) was also predictive of more aggressive tumors.”


    Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
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