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Neuroendocrine Carcinoma of the Urinary Bladder: CT Findings and Radiomics Signature.

ABSTRACT: Background: We present a case series of Neuroendocrine Carcinoma of the Urinary Bladder (NECB) to analyse their radiologic appearance on CT, find a "Radiomic signature", and review the current literature. Methods: 14 CT cases of NECB were reviewed and compared with a control group of 42 patients with high-grade non-neuroendocrine bladder neoplasm for the following parameters: ring enhancement; implantation site; dimensions; density; margins; central necrosis; calcifications; number of lesions; wall thickness; depth of invasion in the soft tissue; invasion of fat tissue; invasion of adjacent organs; lymph-node involvement; abdominal organ metastasis. To extract radiomic features, volumes of interest of bladder lesions were manually delineated on the portal-venous phase. The radiomic features of the two groups were identified and compared. Results: Statistical differences among NECB and control group were found in the prevalence of male sex (100% vs. 69.0%), hydronephrosis (71.4% vs. 33.3%), mean density of the mass (51.01 ± 15.48 vs. 76.27 ± 22.26 HU); product of the maximum diameters on the axial plane (38.1 ± 59.3 vs. 14.44 ± 12.98 cm2) in the control group, trigonal region involvement (78.57% vs. 19.05%). About the radiomic features, Student's t-test showed significant correlation for the variables: "DependenceNonUniformity" (p: 0.048), "JointAverage" (p: 0.013), "LargeAreaLowGrayLevelEmphasis" (p: 0.014), "Maximum2DDiameterColumn" (p: 0.04), "Maximum 2DDiameterSlice" (p: 0.007), "MeanAbsoluteDeviation" (p: 0.021), "BoundingBoxA" (p: 0.022) and "CenterOfMassB" (p: 0.007). Conclusions: There is a typical pattern (male patient, large mass, trigonal area involvement) of NECB presentation on contrast-enhanced CT. Certain morphological characteristics and encouraging results about Radiomic features can help define the diagnosis.

SUBMITTER: Coppola A

PROVIDER: S-EPMC10607129 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Neuroendocrine Carcinoma of the Urinary Bladder: CT Findings and Radiomics Signature.

Coppola Andrea A Gatta Tonia T Pini Giacomo Maria GM Scordi Giorgia G Fontana Federico F Piacentino Filippo F Minici Roberto R Laganà Domenico D Basile Antonio A Dehò Federico F Carcano Giulio G Franzi Francesca F Uccella Silvia S Sessa Fausto F Venturini Massimo M

Journal of clinical medicine 20231013 20

<b>Background</b>: We present a case series of Neuroendocrine Carcinoma of the Urinary Bladder (NECB) to analyse their radiologic appearance on CT, find a "Radiomic signature", and review the current literature. <b>Methods</b>: 14 CT cases of NECB were reviewed and compared with a control group of 42 patients with high-grade non-neuroendocrine bladder neoplasm for the following parameters: ring enhancement; implantation site; dimensions; density; margins; central necrosis; calcifications; number ...[more]

PMID: 37892647

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Project description:Background: Radiomics has been widely used to non-invasively mine quantitative information from medical images and could potentially predict tumor phenotypes. Pathologic grade is considered a predictive prognostic factor for head and neck squamous cell carcinoma (HNSCC) patients. A preoperative histological assessment can be important in the clinical management of patients. We applied radiomics analysis to devise non-invasive biomarkers and accurately differentiate between well-differentiated (WD) and moderately differentiated (MD) and poorly differentiated (PD) HNSCC. Methods: This study involved 206 consecutive HNSCC patients (training cohort: n = 137; testing cohort: n = 69). In total, we extracted 670 radiomics features from contrast-enhanced computed tomography (CT) images. Radiomics signatures were constructed with a kernel principal component analysis (KPCA), random forest classifier and a variance-threshold (VT) selection. The associations between the radiomics signatures and HNSCC histological grades were investigated. A clinical model and combined model were also constructed. Areas under the receiver operating characteristic curves (AUCs) were applied to evaluate the performances of the three models. Results: In total, 670 features were selected by the KPCA and random forest methods from the CT images. The radiomics signatures had a good performance in discriminating between the two cohorts of HNSCC grades, with an AUC of 0.96 and an accuracy of 0.92. The specificity, accuracy, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of the abovementioned method with a VT selection for determining HNSCC grades were 0.83, 0.92, 0.96, 0.94, and 0.91, respectively; without VT, the corresponding results were 0.70, 0.83, 0.88, 0.80, and 0.84. The differences in accuracy, sensitivity and NPV were significant between these approaches (p < 0.05). The AUCs with VT and without VT were 0.96 and 0.89, respectively (p < 0.05). Compared to the combined model and the radiomics signatures, The clinical model had a worse performance, and the differences were significant (p < 0.05). The combined model had the best performance, but the difference between the combined model and the radiomics signature weren't significant (p > 0.05). Conclusions: The CT-based radiomics signature could discriminate between WD and MD and PD HNSCC and might serve as a biomarker for preoperative grading.

Project description:ObjectivesTo develop and validate a contrast-enhanced CT-based radiomics nomogram for the diagnosis of neuroendocrine carcinoma of the digestive system.MethodsThe clinical data and contrast-enhanced CT images of 60 patients with pathologically confirmed neuroendocrine carcinoma of the digestive system and 60 patients with non-neuroendocrine carcinoma of the digestive system were retrospectively collected from August 2015 to December 2021 at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and randomly divided into a training cohort (n=84) and a validation cohort (n=36). Clinical characteristics were analyzed by logistic regression and a clinical diagnosis model was developed. Radiomics signature were established by extracting radiomic features from contrast-enhanced CT images. Based on the radiomic signature and clinical characteristics, radiomic nomogram was developed. ROC curves and Delong's test were used to evaluate the diagnostic efficacy of the three models, calibration curves and application decision curves were used to analyze the accuracy and clinical application value of nomogram.ResultsLogistic regression results showed that TNM stage (stage IV) (OR 6.8, 95% CI 1.320-43.164, p=0. 028) was an independent factor affecting the diagnosis for NECs of the digestive system, and a clinical model was constructed based on TNM stage (stage IV). The AUCs of the clinical model, radiomics signature, and radiomics nomogram for the diagnosis of NECs of the digestive system in the training, validation cohorts and pooled patients were 0.643, 0.893, 0.913; 0.722, 0.867, 0.932 and 0.667, 0.887, 0.917 respectively. The AUCs of radiomics signature and radiomics nomogram were higher than clinical model, with statistically significant difference (Z=4.46, 6.85, both p < 0.001); the AUC difference between radiomics signature and radiomics nomogram was not statistically significant (Z=1.63, p = 0.104). The results of the calibration curve showed favorable agreement between the predicted values of the nomogram and the pathological results, and the decision curve analysis indicated that the nomogram had favorable application in clinical practice.ConclusionsThe nomogram constructed based on contrast-enhanced CT radiomics and clinical characteristics was able to effectively diagnose neuroendocrine carcinoma of the digestive system.

Dataset Information

Neuroendocrine Carcinoma of the Urinary Bladder: CT Findings and Radiomics Signature.

Publications

Neuroendocrine Carcinoma of the Urinary Bladder: CT Findings and Radiomics Signature.

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