Objectives The aim of this single-centre, observational, retrospective study is to find a correlation using Radiomics between the analysis of CT texture features of primary lesion of neuroendocrine (NET) lung cancer subtypes (typical and atypical carcinoids, large and small cell neuroendocrine carcinoma), Ki-67 index and the presence of lymph nodal mediastinal metastases. Methods Twenty-seven patients (11 males and 16 females, aged between 48 and 81 years old—average age of 70,4 years) with histological diagnosis of pulmonary NET with known Ki-67 status and metastases who have performed pre-treatment CT in our department were included. All examinations were performed with the same CT scan (Sensation 16-slice, Siemens). The study protocol was a baseline scan followed by 70 s delay acquisition after administration of intravenous contrast medium. After segmentation of primary lesions, quantitative texture parameters of first and higher orders were extracted. Statistics nonparametric tests and linear correlation tests were conducted to evaluate the relationship between different textural characteristics and tumour subtypes. Results Statistically significant (p < 0.05) differences were seen in post-contrast enhanced CT in multiple first and higher-order extracted parameters regarding the correlation with classes of Ki-67 index values. Statistical analysis for direct acquisitions was not significant. Concerning the correlation with the presence of metastases, one histogram feature (Skewness) and one feature included in the Gray-Level Co-occurrence Matrix (ClusterShade) were significant on contrast-enhanced CT only. Conclusions CT texture analysis may be used as a valid tool for predicting the subtype of lung NET and its aggressiveness.
Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis can provide structural information not perceptible to human eyes. A systematic literature search was performed on 16 February 2022 for studies with a focus on texture analysis in oropharyngeal cancers. We conducted the research on PubMed, Scopus, and Web of Science platforms. Studies were screened for inclusion according to the preferred reporting items for systematic reviews. Twenty-six studies were included in our review. Nineteen articles related specifically to the oropharynx and seven articles analysed the head and neck area with sections dedicated to the oropharynx. Six, thirteen, and seven articles used MRI, CT, and PET, respectively, as the imaging techniques by which texture analysis was performed. Regarding oropharyngeal tumours, this review delineates the applications of texture analysis in (1) the diagnosis, prognosis, and assessment of disease recurrence or persistence after therapy, (2) early differentiation of HPV-positive versus HPV-negative cancers, (3) the detection of cancers not visualised by imaging alone, and (4) the assessment of lymph node metastases from unknown primary carcinomas.
Background and purpose Morphologic magnetic resonance imaging (MRI) for characterization of salivary gland tumors has limited utility, and the use of perfusion MRI data in the clinical setting is controversial. We examined the potential of tissue-normalized dynamic contrast-enhanced (DCE) MRI pharmacokinetic parameters of salivary gland tumors as imaging biomarkers for characterization and differentiation between benign and malignant lesions. Materials and methods DCE-MR images acquired from 60 patients with parotid and submandibular gland tumors were retrospectively reviewed. Pharmacokinetic parameters as transfer constant (Ktrans), rate constant (Kep), extracellular space volume (Ve), fractional plasma volume (Vp), and AEC (area of all times enhancement curve) were measured on both the lesion and the normal contralateral salivary gland parenchyma. Lesion/parenchyma ratio (L/P) for each parameter was calculated. Results Five groups of lesions were identified (reference: histopathology): pleomorphic adenomas(n = 20), Warthin tumors(n = 16), other benign entities(n = 4), non-Hodgkin lymphomas(n = 4), and malignancies(n = 16). Significant differences were seen for mean values of L/PKtrans (higher in malignancies), L/PKep (lower in adenomas than Warthin tumors), L/PVe (lower in Warthin tumors and lymphomas), L/PVp (higher in Warthin tumors and malignancies than adenomas), and L/PAEC (higher in malignancies). Significant differences were found between benign and malignant (non-lymphoproliferative) lesions in mean value of L/PKtrans (0.485 and 1.581), L/PVp (1.288 and 2.834), and L/PAEC (0.682 and 1.910). ROC analysis demonstrated the highest AUC (0.96) for L/PAEC, with sensitivity and specificity for malignancy of 93.8% and 97.5% (cutoff value = 1.038). Conclusion Lesion/parenchyma ratio of DCE-MRI pharmacokinetic data could be helpful for recognizing the principal types of salivary gland tumors; L/PAEC seems a valuable biomarker for differentiating benign from malignant tumors.
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