Efficacy and Safety of Preoperative vs. Intraoperative Computed Tomography-Guided Lung Tumor Localization: A Randomized Controlled Trial

Background: Thoracoscopic removal of small pulmonary nodules is traditionally accomplished through a two-step approach—with lesion localization in a CT suite as the first step followed by lesion removal in an operating room as the second step. While the advent of hybrid operating rooms (HORs) has fostered our ability to offer a more patient-tailored approach that allows simultaneous localization and removal of small pulmonary nodules within a single-step, randomized controlled trials (RCTs) that compared the two techniques (two- vs. single-step) are still lacking.Methods: This is a RCT conducted in an academic hospital in Taiwan between October 2018 and December 2019. To compare the outcomes of traditional two-step preoperative CT-guided small pulmonary nodule localization followed by lesion removal vs. single-step intraoperative CT-guided lesion localization with simultaneous removal performed by a dedicated team of thoracic surgeons. The analysis was conducted in an intention-to-treat fashion. The primary study endpoint was the time required for lesion localization. Secondary endpoints included radiation doses, other procedural time indices, and complication rates.Results: A total of 24 and 25 patients who received the single- and two-step approach, respectively, were included in the final analysis. The time required for lesion localization was significantly shorter for patients who underwent the single-step procedure (median: 13 min) compared with the two step-procedure (median: 32 min, p < 0.001). Similarly, the radiation dose was significantly lower for the former than the latter (median: 5.64 vs. 10.65 mSv, respectively, p = 0.001).Conclusions: The single-step procedure performed in a hybrid operating room resulted in a simultaneous reduction of both localization procedural time and radiation exposure.

Simultaneous occurrence of accelerated nodulosis in lungs, liver, and kidneys, and acute exacerbation of interstitial pneumonia in a patient with rheumatoid arthritis: an autopsy case report

Background Accelerated nodulosis (ARN) is a rare variant of rheumatoid nodules (RNs) that is characterized by a rapid onset or the worsening of RNs. It generally develops at the fingers in patients with rheumatoid arthritis (RA) receiving methotrexate (MTX). Few case reports have described ARN at an extracutaneous location. Case presentation An elderly patient with long-standing RA was admitted to our hospital with acute respiratory failure. Computed tomography upon admission showed diffuse ground-glass opacities superimposed with subpleural reticular shadowing and honeycombing and multiple nodules in the lungs and liver. Despite the discontinuation of MTX and introduction of an immunosuppressive regimen with pulse methylprednisolone followed by a tapering dose of prednisolone and intravenous cyclophosphamide, the patient died due to the acute exacerbation (AE) of RA-related interstitial lung disease (ILD) following the parallel waxing and waning of a diffuse interstitial shadow and pulmonary and liver nodules. At autopsy, RNs were scattered throughout both lung fields in addition to extensive interstitial changes. RNs were also detected in the liver and kidneys. The foci of cryptococcosis were mainly identified in alveolar spaces. Based on the clinical and pathological findings, these nodules were most consistent with ARN because of acute increases in the size and number of previously detected pulmonary nodules. Conclusion The present case is noteworthy because ARN was concurrently detected in multiple internal organs and may be associated with the AE of RA-related ILD.

Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan

ObjectiveTo investigative the diagnostic performance of the morphological model, radiomics model, and combined model in differentiating invasive adenocarcinomas (IACs) from minimally invasive adenocarcinomas (MIAs).MethodsThis study retrospectively involved 307 patients who underwent chest computed tomography (CT) examination and presented as subsolid pulmonary nodules whose pathological findings were MIAs or IACs from January 2010 to May 2018. These patients were randomly assigned to training and validation groups in a ratio of 4:1 for 10 times. Eighteen categories of morphological features of pulmonary nodules including internal and surrounding structure were labeled. The following radiomics features are extracted: first-order features, shape-based features, gray-level co-occurrence matrix (GLCM) features, gray-level size zone matrix (GLSZM) features, gray-level run length matrix (GLRLM) features, and gray-level dependence matrix (GLDM) features. The chi-square test and F1 test selected morphology features, and LASSO selected radiomics features. Logistic regression was used to establish models. Receiver operating characteristic (ROC) curves evaluated the effectiveness, and Delong analysis compared ROC statistic difference among three models.ResultsIn validation cohorts, areas under the curve (AUC) of the morphological model, radiomics model, and combined model of distinguishing MIAs from IACs were 0.88, 0.87, and 0.89; the sensitivity (SE) was 0.68, 0.81, and 0.83; and the specificity (SP) was 0.93, 0.79, and 0.87. There was no statistically significant difference in AUC between three models (p > 0.05).ConclusionThe morphological model, radiomics model, and combined model all have a high efficiency in the differentiation between MIAs and IACs and have potential to provide non-invasive assistant information for clinical decision-making.

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions

BackgroundThe objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs).MethodsThis retrospective study enrolled 252 malignant pulmonary nodules with histopathologically confirmed SPLCs or PMs and randomly assigned them to a training or validation cohort. Clinical data were collected from the electronic medical records system. The imaging and radiomics features of each nodule were extracted from CT images.ResultsA rad-score was generated from the training cohort using the least absolute shrinkage and selection operator regression. A clinical and radiographic model was constructed using the clinical and imaging features selected by univariate and multivariate regression. A nomogram composed of clinical-radiographic factors and a rad-score were developed to validate the discriminative ability. The rad-scores differed significantly between the SPLC and PM groups. Sixteen radiomics features and four clinical-radiographic features were selected to build the final model to differentiate between SPLCs and PMs. The comprehensive clinical radiographic–radiomics model demonstrated good discriminative capacity with an area under the curve of the receiver operating characteristic curve of 0.9421 and 0.9041 in the respective training and validation cohorts. The decision curve analysis demonstrated that the comprehensive model showed a higher clinical value than the model without the rad-score.ConclusionThe proposed model based on clinical data, imaging features, and radiomics features could accurately discriminate SPLCs from PMs. The model thus has the potential to support clinicians in improving decision-making in a noninvasive manner.