Histology profiling of lung tumors: tru-cut versus full-core system for CT-guided biopsies.

PURPOSE: We aimed to compare the diagnostic yield and procedure-related complications of two different types of systems for percutaneous CT-guided lung biopsy. MATERIAL AND METHODS: All patients with a lung lesion who underwent a CT-guided lung biopsy at our institution, between January 2019 and 2021, were retrospectively analyzed. The inclusion criteria were: (a) Procedures performed using either a fully automated tru-cut or a semi-automated full-core biopsy needle, (b) CT images demonstrating the position of the needles within the lesion, (c) histopathological result of the biopsy and (d) clinical follow-up for at least 12 months and\or surgical histopathological results. A total of 400 biopsy fulfilling the inclusion criteria were selected and enrolled in the study. RESULTS: Overall technical success was 100% and diagnostic accuracy was 84%. Tru-cut needles showed a significantly higher diagnostic accuracy when compared to full-core needles (91% vs. 77%, p = 0.0004) and a lower rate of pneumothorax (31% vs. 41%, p = 0.047). Due to the statistically significant different of nodules size between the two groups, we reiterated the statistical analysis splitting our population around the 20 mm cut-off for nodule size. We still observed a significant difference in diagnostic accuracy between tru-cut and full-core needles favoring the former for both smaller and larger lesions (81% vs. 71%, p = 0.025; and 92% vs. 81%; p = 0.01, respectively). CONCLUSION: Our results demonstrated that the use of automated tru-cut needles is associated with higher histopathological diagnostic accuracy compared to semi-automated full-core needles for CTLB.

Iterative Reconstruction: State-of-the-Art and Future Perspectives.

ABSTRACT: Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.