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BACKGROUND: Due to submucosal infiltration’s biological nature along the airway, adenoid cystic carcinoma (ACC) frequently leaves positive surgical margins. This study evaluated the clinicopathologic, and computed tomography (CT) features for predicting surgical margin status in central airway ACC. METHODS: We retrospectively analyzed the files of 71 patients with ACC of the central airway proven by histopathology and surgery who had presented between January 2010 and December 2018. All patients were classified into positive and negative surgical margin groups according to margin status. Univariate analysis and multivariable logistic regression models were then performed to compare demography, histopathology, and CT characteristics between ACC patients with positive and negative margins. RESULTS: After surgical resection, 59 (83.1%) patients had positive margins, and 12 (16.9%) had negative margins. The contrast-enhanced CT (CECT) longitudinal tail sign (LTS) was identified in 55 of 59 (93.2%) patients with positive margins and was the only feature that had a significant association with positive margins (odds ratio 41.250, 95% CI: 7.886–215.767; P

BACKGROUND: Lung cancer is a major cause of death, and adenocarcinoma is the most common histologic subtype. Precise diagnosis and treatment of invasive adenocarcinoma (IAC) can substantially improve the survival of patients. However, early-stage adenocarcinomas frequently appear as subsolid nodules (SSN) on computed tomography (CT), and the optimal cut-off CT value for differentiating the invasiveness of SSNs in emphysematous patients is unclear. METHODS: High-resolution CT targeted scans of 187 pulmonary SSNs in 175 patients with emphysema as confirmed by surgery and histology were retrospectively reviewed. The mean CT value, the relative CT (rCT) values of 1 (nodule CT value − lung CT value), and 2 (nodule CT value/lung CT value), and the size of the SSNs were measured and calculated. The differentiating performance of the CT values between pre-invasive and invasive tumors was evaluated using a receiver operating characteristic (ROC) curve. RESULTS: Significant differences were found in the rCT values of 1 and 2 among pure ground-glass nodules (GGNs) with different levels of invasiveness, in the rCT values of 1 and 2 for the ground-glass component (GGC) and the mean CT value of the solid component (SC) of part-solid nodules (PSNs) between minimally invasive adenocarcinoma (MIA) and IAC (all P<

To evaluate computed tomography (CT) features and establish a predictive model for the clinical diagnosis and prognosis of tracheal adenoid cystic carcinoma (ACC).From January 2010 to December 2018, 82 patients with tracheal tumors, including 46 patients with ACC confirmed by surgery and histopathology, were enrolled in this study. These patients' clinicopathologic information, CT features and survival outcomes were recorded and analyzed. Independent predictors of diagnosis and prognosis of tracheal ACC were determined by both univariate and multivariate analyses.Compared with tracheal non-ACC patients, univariate analysis showed that ACC patients were more likely to have extensive longitudinal length (p0.001) and to appear as annular wall thickening (p = 0.001), transmural growth (p = 0.036), poorly defined border (p = 0.003) and mild enhancement (p = 0.001). Multivariate logistic analysis showed that longitudinal length and enhancement degree were independent predictors of tracheal ACC. The 3-year and 5-year disease-free survival (DFS) were 75.7 % and 64.5 %, respectively. Longitudinal length (≥ 34 mm), transverse length (≥ 20 mm) and transmural growth were associated with poor DFS in univariate analysis. After multivariate adjustment, only transverse length (≥ 20 mm) was an adverse prognostic factor for DFS (hazard ratio = 4.594, 95 % confidence interval = 1.240-17.017; p = 0.022).CT longitudinal length and enhancement degree of tumors showed satisfactory discrimination for tracheal ACC. Excessive CT transverse length might be an unfavorable indicator for ACC recurrence and could be helpful for predicting the survival outcomes of ACC at the initial diagnosis.

Accurate assessment of the invasion of intrathoracic structures by stage III thymic tumors assists their appropriate management. The present study aimed to evaluate the efficacy of computed tomography (CT) features for the prediction of stage III thymoma invasion. The pre-operative CT images of 66 patients with confirmed stage III thymic tumors were reviewed retrospectively. The CT features of invasion into the mediastinal pleura, lungs, pericardium and great vessels were analyzed, and their sensitivity, specificity, positive predictive value (PPV), negative predictive value and accuracy were calculated. For mediastinal pleural and pericardial invasion, an absence of space between the tumor and the mediastinal pleura/pericardium with mediastinal pleural/pericardial thickening and pleural/pericardial effusion exhibited a specificity and PPV of 100%, respectively. For lung invasion, a multi-lobular tumor convex to the lung with adjacent lung abnormalities exhibited a specificity and PPV of 91.2 and 81.3%, respectively. For vessel invasion, the specificity and PPV were each 100% for tumors abutting ≥50% of the vessel circumference, and for tumor oppression, deformation and occlusion of the vessel. In conclusion, recognition of the appropriate CT features can serve as a guide to invasion by stage III thymic tumors, and can facilitate the selection of appropriate pre-operative treatment.

Purpose: Idiopathic normal pressure hydrocephalus (iNPH) is known as a treatable form of dementia. Network analysis is emerging as a useful method to study neurological disorder diseases. No study has examined changes of structural brain networks of iNPH patients. We aimed to investigate alterations in the gray matter (GM) structural network of iNPH patients compared with normal elderly volunteers.Materials and Methods: Structural networks were reconstructed using covariance between regional GM volumes extracted from three-dimensional T1-weighted images of 29 possible iNPH patients and 30 demographically similar normal-control (NC) participants and compared with each other.Results: Global network modularity was significantly larger in the iNPH network (P < 0.05). Global network measures were not significantly different between the two networks (P > 0.05). Regional network analysis demonstrated eight nodes with significantly decreased betweenness located in the bilateral frontal, right temporal, right insula and right posterior cingulate regions, whereas only the left anterior cingulate was detected with significantly larger betweenness. The hubs of the iNPH network were mostly located in temporal areas and the limbic lobe, those of the NC network were mainly located in frontal areas.Conclusions: Network analysis was a promising method to study iNPH. Increased network modularity of the iNPH group was detected here, and modularity analysis should be paid much attention to explore the biomarker to select shunting-responsive patients.

Objective: To investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs. Methods: We retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.8mm, 0.4mm, 0.18 mm) were obtained by reconstructing the single HRCT scan using three combinations of field of view and matrix size. For each pixel size setting, radiomic features were extracted for all GGNs and radiomic signatures for the invasiveness of GGNs were built through two modeling pipelines for comparison. Results: The study finally extracted 788 radiomic features. 87% radiomic features demonstrated inter pixel size variation. By either modeling pipeline, the radiomic signature under small pixel size performed significantly better than those under middle or large pixel sizes in predicting the invasiveness of GGNs (p’s value Conclusions: Reducing pixel size could cause inconsistency in most radiomic features and improve the diagnostic performance of the downstream radiomic signatures. Particularly, super HRCTs with small pixel size resulted in more accurate radiomic signatures for the invasiveness of GGNs. Advances in knowledge: The dependence of radiomic features on pixel size will affect the performance of the downstream radiomic signatures. The future radiomic studies should consider this effect of pixel size.

Purpose To investigate the prevalence of multidetector CT (MDCT) air bronchograms and their value in predicting the invasiveness of lung adenocarcinomas. Methods MDCT scans of 606 nodules in 582 patients with a lung adenocarcinoma less than 2 cm in diameter confirmed by surgery and pathology were reviewed. Air bronchograms were classified into three patterns: type I, bronchus with intact lumen; type II, bronchus with dilated or tortuous lumen; and type III, bronchus with obstructed lumen. Results Air bronchograms were demonstrated on MDCT in 210 of 606 (34.7%) lung adenocarcinomas with 16.6% (35/211) preinvasive lesions (PL), 30.5% (50/164) minimally invasive adenocarcinoma (MIA), and 54.1% (125/231) invasive adenocarcinoma (IAC) (P = 0.000); 18.3% (44/240) pure ground-glass nodules (GGNs), 44.2% (137/310) mixed GGNs, and 51.8% (29/56) solid nodules (P = 0.000). Type I was slightly more common in MIA (36/164, 22.0%) than IAC (40/231, 17.3%) and PL (30/211, 14.2%) but without differences among them (P = 0.147). Type II (PL: 5/211, 2.4%; MIA: 13/164, 7.9%; IAC: 53/231, 22.9%) and type III (PL: 0/211; MIA: 1/164, 0.6%; IAC: 32/231, 13.9%) were observed more frequently with increasing lung adenocarcinoma invasiveness (both P = 0.000). Conclusions The prevalence and patterns of air bronchograms on MDCT can predict the invasiveness of small lung adenocarcinomas.

To investigate the high-resolution computed tomography (HRCT) features that distinguish lung adenocarcinomas in situ (AISs) and minimally invasive adenocarcinomas (MIAs) from invasive adenocarcinomas (IACs) appearing as ground-glass nodules (GGNs), and to select candidates for sublobar resection. Two hundred and twenty-nine patients with 237 GGNs of less than 2 cm (139 AIS-MIA nodules and 98 IAC nodules) confirmed by surgery and pathology were retrospectively reviewed. The HRCT features of the AIS-MIAs and IACs were analysed and compared. Receiver operating characteristic (ROC) analyses were conducted to determine the cutoff values for the qualitative variables and their diagnostic performances. Significant differences were found in the density, nodule and solid component diameters, CT values of the ground-glass and solid components, lobulated shape, spiculated margin, abnormal pulmonary vein and artery, air bronchogram, and pleural indentation of the GGNs between the two groups. Multivariate and ROC analyses revealed that larger diameter of nodules (≥12.2 mm) and solid components (≥6.7 mm), and higher CT values of the solid components (≥ -192 HU) in the GGNs with air bronchogram were significantly associated with IACs. HRCT can identify distinguishing morphological features between AIS-MIAs and IACs, and is helpful for selecting candidates for sublobar resection. • IACs appearing as GGNs were often ≥ 12.2 mm in diameter. • IACs were often ≥ 6.7 mm in solid component diameter. • The solid components of the IACs often exhibited ≥ -192 HU. • IACs exhibited air bronchogram more frequently than AIS-MIAs.