Search

Simple Summary Interstitial lung disease is a group of diseases characterized by chronic lung inflammation that can be related to oncological treatments, such as traditional chemotherapy drugs and the newest targeted therapies and immunotherapies. Awareness about this potentially fatal adverse event is paramount in patient management and to make a conscious therapeutic choice. It represents a differential diagnostic challenge, especially in the context of the COVID-19 pandemic. Our aim is to describe the incidence and characteristics of this adverse event across oncological treatment groups and to promote greater knowledge about this important toxicity. Abstract Interstitial lung disease is recognized as a group of diseases with a different etiopathogenesis characterized by chronic lung inflammation with the accumulation of inflammatory cells, lymphocytes and macrophages, and the consequent release of proinflammatory cytokines. Various degrees of pulmonary fibrosis can be associated with this inflammatory condition. Interstitial lung disease related to oncological drugs is a relevant problem in clinical practice. The etiopathogenetic mechanisms underlying this adverse event are not completely known but can be partly explained by the mechanism of action of the drug involved. Therefore, knowledge of the relevance of this potentially fatal adverse event supported by the reported safety data of pivotal studies becomes fundamental in the management of patients. The prompt diagnosis of drug-related pneumonia and the consequent differential diagnosis with other forms of pneumonia allow a rapid suspension of treatment and the establishment of an immunosuppressive treatment if necessary. In the context of the health emergency related to SARS CoV2 infection and COVID-19-related interstitial lung disease, such knowledge holds decisive relevance in the conscious choice of cancer treatments. Our intent was to describe the oncological drugs most correlated with this adverse event by reporting, where possible, the percentages of insurgency in pivotal studies to provide an overview and therefore promote greater awareness of this important toxicity related to oncological treatment.

Persistent part-solid nodules (PSNs) with a solid component &lt, 6 mm usually represent minimally invasive adenocarcinomas and are significantly less aggressive than PSNs with a solid component &ge, 6 mm. However, not all PSNs with a small solid component behave in the same way: some nodules exhibit an indolent course, whereas others exhibit more aggressive behavior. Thus, predicting the future behavior of this subtype of PSN remains a complex and fascinating diagnostic challenge. The main purpose of this study was to apply open-source software to investigate which quantitative computed tomography (CT) features may be useful for predicting the behavior of a select group of PSNs. We retrospectively selected 50 patients with a single PSN with a solid component &lt, 6 mm and diameter &lt, 15 mm. Computerized analysis was performed using ImageJ software for each PSN and various quantitative features were calculated from the baseline CT images. The area, perimeter, mean Feret diameter, linear mass density, circularity and solidity were significantly related to nodule growth (p &le, 0.031). Therefore, quantitative CT analysis was helpful for predicting the future behavior of a select group of PSNs with a solid component &lt, 15 mm.

Purpose To demonstrate the accuracy of an unsupervised (fully automated) software for fat segmentation in magnetic resonance imaging. The proposed software is a freeware solution developed in ImageJ that enables the quantification of metabolically different adipose tissues in large cohort studies. Methods The lumbar part of the abdomen (19 cm in craniocaudal direction, centered in L3) of eleven healthy volunteers (age range: 21–46 years, BMI range: 21.7–31.6 kg/m 2 ) was examined in a breath hold on expiration with a GE T1 Dixon sequence. Single-slice and volumetric data were considered for each subject. The results of the visceral and subcutaneous adipose tissue assessments obtained by the unsupervised software were compared to supervised segmentations of reference. The associated statistical analysis included Pearson correlations, Bland-Altman plots and volumetric differences (VD % ). Results Values calculated by the unsupervised software significantly correlated with corresponding supervised segmentations of reference for both subcutaneous adipose tissue – SAT (R = 0.9996, p % were (1.6 ± 2.9)% for SAT and (4.9 ± 6.9)% for VAT. In the volumetric analysis, VD % were (1.3 ± 0.9)% for SAT and (2.9 ± 2.7)% for VAT. Conclusions The developed software is capable of segmenting the metabolically different adipose tissues with a high degree of accuracy. This free add-on software for ImageJ can easily have a widespread and enable large-scale population studies regarding the adipose tissue and its related diseases.