The texture analysis as a predictive method in the assessment of the cytological specimen of CT-guided FNAC of the lung cancer

The lung cancer is the principle cause of the worldwide deaths and its prognosis is poor with a 5-year overall survival rate. Computed tomography (CT) gives many information about the prognosis, but the problem is the subject interpretation of the findings. Thanks to the computer-aided diagnosis/detection (CAD), it is possible to reduce the second opinion. "Radiomics" is an extension of CAD and overlaps the quantitative imaging data of the CT texture analysis (CTTA) with the clinical information, increasing the power and precision of the decision going through the personalized medicine. The aim of this study is to describe the role of the radiomics in the characterization of the pulmonary nodule. For this study, we retrospectively analyzed the images of the 87 NSCLC patients with a waiver of informed consent from the Institutional Review Board (IRB) at the Campania University "Luigi Vanvitelli" of Naples. All tumors were semiautomatically segmented by a radiologist with 10 years of experience using three diameters (AW Server 3.2). The examinations were acquired using 128 MDCT (GSI CT, GE) with a peak tube voltage of 120 kVp, tube current of 100 or 200 mA, and rotation times of 0.5 or 0.8 s. To confirm the imaging results, the FNAC was performed and for every nodule the following parameters were extracted: the presence of the solid component (named = 1), papillary component (named = 2), and mixed component (named = 3). Feature calculation was performed using the HealthMyne software and Integrated Platform That Enables Better Patient Management Decisions For Oncology. The radiologist uses the Rapid Precise Metrics (RPM)(TM) functionality to identify a lesion with the algorithm and these methods are put to work. The correlation between each feature and the tumor volume was calculated using a two-step cluster statistical analysis. In this retrospective study, in one year from 2018 to 2019 20 patients with lung adenocarcinoma confirmed with FNAC were enrolled. The pathologic results were subdivided into three categories: the solid architecture (group 1), papillary architecture (group 2), and mixed architecture (group 3). Nine lesions resulted with component 1, seven patients with component 2, and 3 patients with component 3. Eight females and 12 males with a median age 61 and 15 years (mean +/- SD = 67.4 +/- 9.7 years, range 39-73 years) were enrolled. The two results suggest, with p < 0.05, that the GGO variable is a good discriminating estimator of the kurtosis variable: GGO = "no" implies a high kurtosis value, while GGO = "yes" implies a low value. The numerous data obtained from the automatic analysis allow to have a fertile ground on which to develop a new concept of medicine which is precision medicine. The limit of this study is the poor sample. In the future, in order to have a more mature and consolidated discipline, it is necessary to increase the large scale of observations with further studies to establish the rigorous evaluation criteria. In order for radiomics to mature as a discipline in the future, it will be necessary to develop studies that consolidate its role to standardize the collected data.