Role of visceral pleural invasion and tumor sizing at CT of resected NSCLC in clinical-radiological and pathological T agreement.

OBJECTIVE: To describe in non-small cell lung cancer (NSCLC) the impact of visceral pleural invasion (VPI) and of tumor sizing assessed at computed tomography (CT) on the agreement between clinical-radiological and pathological T staging and its prognostic value. METHODS: Patients affected by NSCLC treated by surgery in the period from January 2017 to September 2020 were retrospectively evaluated. Exclusion criteria were: (1) baseline CT not performed in our hospital; (2) failure of software segmentation at CT of the primary lesion. Clinical-radiological T (cT) was assessed at baseline CT, evaluating in particular T size by semi-automatic tool and VPI (cVPI) visually. Pathological T (pT) and VPI (pVPI) were recorded by pathological report and obtained after formalin-fixation and eventual elastic stain on surgical specimen. The agreement between cT and pT was evaluated by calculating the weighted kappa by Cohen (κw); the association between progression free survival (PFS) with both cT and pT was assessed by the Cox regression analysis. RESULTS: The study included 84 NSCLC in 82 patients (median age 71 years, IQR 63-76 years; females 22/82, 27%). The agreement between cT and pT was poor (κw 0.302, 95%CI 0.158-0.447). The main causes of disagreement were CT oversizing (21%) and false positive cVPI (29%). A significant association was found between PFS and pT2-T3 (HR 2.75, 95%CI 1.21-6.25, p=0.015) but not with cT2-T3 (not retained in the model). CONCLUSIONS: False positive cVPI and oversizing at CT are causes of disagreement between cT and pT in around one-third of resected NSCLC. PFS was significantly associated with pT but not with cT.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.

BACKGROUND: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. METHODS: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. RESULTS: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73-0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90-0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer "LungCTAnalyzer" and the median of the visual score (0.75 with a CI 0.67-82 and with a median value of 22% of disease extension for the software and 25% for the visual values). CONCLUSIONS: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.