A Nomogram Incorporating Tumor-Related Vessels for Differentiating Adenocarcinoma In Situ from Minimally Invasive and Invasive Adenocarcinoma Appearing as Subsolid Nodules.

OBJECTIVES: To develop a nomogram incorporating the quantity of tumor-related vessels (TRVs) and conventional CT features (CCTFs) for the preoperative differentiation of adenocarcinoma in situ (AIS) from minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) appearing as subsolid nodules. METHODS: High-resolution CT target scans of 274 subsolid nodules from 268 patients were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. A nomogram incorporating CCTFs with the category of TRVs (CTRVs, using TRVs as categorical variables) and a final nomogram combining the number of TRVs (QTRVs) and CCTFs were constructed using multivariable logistic regression analysis. The performance levels of the two nomograms were evaluated and validated on the training and validation datasets and then compared. RESULTS: The CCTF-QTRV nomogram incorporating abnormal air bronchogram, density, number of dilated and distorted vessels and number of adherent vessels showed more favorable predictive efficacy than the CCTF-CTRV nomogram (training cohort: area under the curve (AUC) = 0.893 vs. 0.844, validation cohort: AUC = 0.871 vs. 0.807). The net reclassification index (training cohort: 0.188, validation cohort: 0.326) and the integrated discrimination improvement values (training cohort: 0.091, validation cohort: 0.125) indicated that the CCTF-QTRV nomogram performed significantly better discriminative ability than the CCTF-CTRV nomogram (all p-value < 0.05). CONCLUSIONS: The nomogram incorporating the QTRVs and CCTFs showed favorable predictive efficacy for differentiating AIS from MIA-IAC appearing as subsolid nodules and may serve as a potential tool to provide individual care for these patients.

A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules.

OBJECTIVES: Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules. METHODS: A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve. RESULTS: The radiomics signature and nomogram yielded AUCs of 0.892 (95% confidence interval [CI]: 0.843-0.940) and 0.911 (95% CI: 0.867-0.955), respectively, in the primary group and 0.826 (95% CI: 0.727-0.926) and 0.843 (95% CI: 0.749-0.937), respectively, in the validation group. The clinical usefulness of the nomogram was demonstrated by decision curve analysis. CONCLUSIONS: A radiomics nomogram was developed by integrating the radiomics signature with clinical parameters and was easily used for the individualized prediction of two-year growth in case of CT-indeterminate small pulmonary nodules. KEY POINTS: • A radiomics nomogram was developed for predicting the two-year growth of CT-indeterminate small pulmonary nodules. • The nomogram integrated a CT-based radiomics signature with clinical parameters and was valuable in developing an individualized follow-up strategy for patients with indeterminate small pulmonary nodules.

Contrast-enhanced CT longitudinal tail sign as a marker of positive resection margins in adenoid cystic carcinoma of the central airway.

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<0.001). Moreover, positive margins in upper or/and lower directions were associated with the LTS in corresponding directions (P<0.001). CONCLUSIONS: Most central airway ACC patients exhibited positive margins following surgery. The appearance of the LTS on CECT was significantly associated with positive margins and could help preoperatively predict the submucosal invasion of ACC.

CT diagnosis and prognosis prediction of tracheal adenoid cystic carcinoma.

PURPOSE: To evaluate computed tomography (CT) features and establish a predictive model for the clinical diagnosis and prognosis of tracheal adenoid cystic carcinoma (ACC). METHOD: 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. RESULTS: Compared with tracheal non-ACC patients, univariate analysis showed that ACC patients were more likely to have extensive longitudinal length (p < 0.001) 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). CONCLUSIONS: 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.