Studio comparativo con esame TC contrastografico e con PET-TC del tumore polmonare non a piccole cellule

Abstract

BackgroundPersonalized cancer therapy remains a challenge. In this context, we attempted to identify correlations between tumour angiogenesis, tumour metabolism and tumour cell type. To this aim, we used single=phase multidetector computed tomography (MDCT) and hybrid positron emission tomography-computed tomography (PET/CT) to determine whether net enhancement and standardized uptake value (SUVmax) were correlated with tumour size and cytology in patients affected by non-small cell lung cancer (NSCLC).Material/MethodsOur study included 38 patients (30 men, 8 women, mean age 70) with a NSCLC measuring between 3 cm and 7 cm, using a 16-slice multidetector CT (Brilliance Philips) and with PET-CT (Biograph 16 Siemens Medical Solutions). The following lesion parameters were evaluated: maximum diameter, medium density before contrast injection (CTpre), medium density after contrast injection (CTpost average), density in the most enhanced part of the lesion after contrast (CTpost max), net enhancement, SUVmax, age, and cytology. Correlation coefficient and p-value were computed for each pair of variables. In addition, correlations were computed for each pair of variables, and for all combinations of tumour types. We focused on subsets of data with more than 10 observations, and with correlation r>0.500 and p<0.05.ResultsA weak correlation (r=0.32; p=0.048) was found between SUVmax and tumour size; the correlation was stronger for masses larger than 31 mm (r=0.4515; p=0.0268). No other correlations were found among the variables examined.ConclusionsOur data may have prognostic significance, and could lead to more appropriate surgical treatment and better treatment outcome.

Looking for Lepidic Component inside Invasive Adenocarcinomas Appearing as CT Solid Solitary Pulmonary Nodules (SPNs): CT Morpho-Densitometric Features and 18-FDG PET Findings

“…However, in clinical practice, lung cancers that do not show ground-glass opacity at CT can also show false-negative 18-FDG PET findings, as in our study [12]. Furthermore, besides histopathological subtype, another primary factor influencing 18-FDG PET findings is the lesion size [12, 43]; this is in agreement with our results that showed a statistically significant (p=0.0155) correlation between lesion size and SUVmax (R=0.501).…”

PET/CT Radiomics in Lung Cancer: An Overview

“…In a retrospective study on a fairly large cohort of patients (n = 545), PET texture features were able to differentiate between primary and metastatic lung lesions, whereas CT features were not [48]. PET radiomic features were also found to correlate with histological subtype (specifically adenocarcinoma vs. squamous cell carcinoma) in [21,44], whereas, in [49,52], radiomics signatures based on CT texture features were significantly associated with tumor histology.…”

“…The objective of spatial filtering can be either to reduce noise or emphasize features at different scales. Common tools for this task are Butterworth smoothing [20], Gaussian filters [21], and Laplacian of Gaussian filters [22]. Windowing consists of applying a lower and upper threshold to the intensity values of the raw data, this way defining a range of acceptable values.…”

Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

“…In particular, body composition features measured by CT imaging are gaining increasing attention in clinical oncology, due to an identified prognostic adverse potential in patients with breast cancer, renal cell carcinoma, and lung and esophageal cancer [234,[237][238][239][240][241][242]. In this framework, the aim of the study by Shi et al [155] was to develop a radiomics model based on radiomic signature, clinic-pathologic factors, and body composition measures to estimate the overall survival (OS) in patients with PDAC.…”