Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

Azoulay, Alain; Cros, Jérôme; Vullierme, Marie‐Pierre; Mestier, Louis de; Couvelard, Anne; Hentic, Olivia; Ruszniewski, P.; Sauvanet, Alain; Vilgrain, Valérie; Ronot, Maxime

doi:10.1016/j.diii.2020.06.006

Cited by 28 publications

(22 citation statements)

References 33 publications

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“…Mean value of contrast CT histogram often reflects the average enhancement degree of the whole lesion. Azoulay et al 32 indicated that high mean value was found to be related with high tumor grade in pancreatic neuroendocrine tumor and this was similar with our study that the mean value of G1 group were significantly lower than G2/G3/NECs. When the data are similar to a normal distribution, the median is capable to the mean.…”

Section: Discussionsupporting

confidence: 92%

Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis

Liang

Tan

et al. 2020

Cancer Medicine

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Objectives To investigate the diagnostic value of contrast‐enhanced computed tomography (CECT) histogram analysis in predicting the World Health Organization (WHO) grade of rectal neuroendocrine tumors (R‐NETs). Materials and Methods A total of 61 (35 G1, 12 G2, 10 G3, and 4 NECs) patients who underwent preoperative CECT and treated with surgery to be confirmed as R‐NETs were included in this study from January 2014 to May 2019. We depicted ROIs and measured the CECT texture parameters (mean, median, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis, and entropy) from arterial phase (AP) and venous phase (VP) images by two radiologists. We calculated intraclass correlation coefficient (ICC) and compared the histogram parameters between low‐grade (G1) and higher grade (HG) (G2/G3/NECs) by applying appropriate statistical method. We obtained the optimal parameters to identify G1 from HG using receiver operating characteristic (ROC) curves. Results The capability of AP and VP histogram parameters for differentiating G1 from HG was similar in several histogram parameters (mean, median, 10th, 25th, 75th, and 90th percentiles) (all p < 0.001). Skewness, kurtosis, and entropy on AP images showed no significant differences between G1 and HG ( p = 0.853, 0.512, 0.557, respectively). Entropy on VP images was significantly different ( p = 0.017) between G1 and HG, however, skewness and kurtosis showed no significant differences ( p = 0.654, 0.172, respectively). ROC analysis showed a good predictive performance between G1 and HG, and the 75th (AP) generated the highest area under the curve (AUC = 0.871), followed by the 25th (AP), mean (VP), and median (VP) (AUC = 0.864). Combined the size of tumor and the 75th (AP) generated the highest AUC. Conclusions CECT histogram parameters, including arterial and venous phases, can be used as excellent indicators for predicting G1 and HG of rectal neuroendocrine tumors, and the size of the tumor is also an important independent predictor.

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Section: Discussionsupporting

confidence: 92%

Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis

Liang

Tan

et al. 2020

Cancer Medicine

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“…When it comes to tumor diagnosis, researchers have found that several morphologic features such as ill-defined margins, large tumor size, heterogeneous and poor to moderate enhancement, vascular involvement, upstream Wirsung duct dilatation, and distant metastases are less frequently observed in G-1/G-2 NET by comparison with G-3 NEN [ 51 , 52 , 53 ]. One recent work looked into the specificities of morphological imaging in 13 patients with NET G-3 compared to 23 with NEC [ 54 ]. Patients with poorly differentiated lesions showed larger sized tumors, more necrosis and lower attenuation on pre-contrast and on portal venous phase CT images, with all results being significant [ 54 ].…”

Section: Imagingmentioning

confidence: 99%

“…One recent work looked into the specificities of morphological imaging in 13 patients with NET G-3 compared to 23 with NEC [ 54 ]. Patients with poorly differentiated lesions showed larger sized tumors, more necrosis and lower attenuation on pre-contrast and on portal venous phase CT images, with all results being significant [ 54 ]. Hemorrhagic content on MRI was only present in NEC ( p = 0.007) [ 54 ].…”

Section: Imagingmentioning

confidence: 99%

“…Patients with poorly differentiated lesions showed larger sized tumors, more necrosis and lower attenuation on pre-contrast and on portal venous phase CT images, with all results being significant [ 54 ]. Hemorrhagic content on MRI was only present in NEC ( p = 0.007) [ 54 ]. Due to the rarity of NEC and NET G-3, morphological criteria are mainly used in dedicated multidisciplinary meetings in centers with large output to predict invasiveness.…”

Section: Imagingmentioning

confidence: 99%

“…Radiomics refers to the high-throughput extraction and analysis of quantitative features from medical images [ 55 ]. Researchers have also shown that NET G-3 have a suggestive CT radiomics signature that helps differentiate them from NET G-1 and NET G-2 or from NEC [ 51 , 53 , 54 , 56 ].…”

Section: Imagingmentioning

confidence: 99%

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Digestive Well-Differentiated Grade 3 Neuroendocrine Tumors: Current Management and Future Directions

Pellat

Palmieri

Soyer

et al. 2021

Cancers

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Digestive well-differentiated grade 3 neuroendocrine tumors (NET G-3) have been clearly defined since the 2017 World Health Organization classification. They are still a rare category lacking specific data and standardized management. Their distinction from other types of neuroendocrine neoplasms (NEN) not only lies in morphology but also in genotype, aggressiveness, functional imaging uptake, and treatment response. Most of the available data comes from pancreatic series, which is the most frequent tumor site for this entity. In the non-metastatic setting, surgical resection is recommended, irrespective of grade and tumor site. For metastatic NET G-3, chemotherapy is the main first-line treatment with temozolomide-based regimen showing more efficacy than platinum-based regimen, especially when Ki-67 index <55%. Targeted therapies, such as sunitinib and everolimus, have also shown some positive therapeutic efficacy in small samples of patients. Functional imaging plays a key role for detection but also treatment selection. In the second or further-line setting, peptide receptor radionuclide therapy has shown promising response rates in high-grade NEN. Finally, immunotherapy is currently investigated as a new therapeutic approach with trials still ongoing. More data will come with future work now focusing on this specific subgroup. The aim of this review is to summarize the current data on digestive NET G-3 and explore future directions for their management.

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Automated analysis of computerized morphological features of cell clusters associated with malignancy on bile duct brushing whole slide images

et al. 2022

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Background: Bile duct brush specimens are difficult to interpret as they often present inflammatory and reactive backgrounds due to the local effects of stricture, atypical reactive changes, or previously installed stents, and often have low to intermediate cellularity. As a result, diagnosis of biliary adenocarcinomas is challenging and often results in large interobserver variability and low sensitivity Objective: In this work, we used computational image analysis to evaluate the role of nuclear morphological and texture features of epithelial cell clusters to predict the presence of pancreatic and biliary tract adenocarcinoma on digitized brush cytology specimens.Methods: Whole slide images from 124 patients, either diagnosed as benign or malignant based on clinicopathological correlation, were collected and randomly split into training (S T , N = 58) and testing (S v , N = 66) sets, with the exception of cases diagnosed as atypical on cytology were included in S v . Nuclear boundaries on cell clusters extracted from each image were segmented via a watershed algorithm. A total of 536 quantitative morphometric features pertaining to nuclear shape, size, and aggregate cluster texture were extracted from within the cell clusters. The most predictive features from patients in S T were selected via rank-sum, t-test, and minimum redundancy maximum relevance (mRMR) schemes. The selected features were then used to train three machine-learning classifiers.Results: Malignant clusters tended to exhibit lower textural homogeneity within the nucleus, greater textural entropy around the nuclear membrane, and longer minor axis lengths. The sensitivity of cytology alone was 74% (without atypicals) and 46% (with atypicals). With machine diagnosis, the sensitivity

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Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

Cited by 28 publications

References 33 publications

Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis

Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis

Digestive Well-Differentiated Grade 3 Neuroendocrine Tumors: Current Management and Future Directions

Automated analysis of computerized morphological features of cell clusters associated with malignancy on bile duct brushing whole slide images

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