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Background: Increased use of thin-section computed tomography (CT) scans has revealed that small lung nodules, termed ground-glass nodules, are frequent in primary breast cancer patients and are associated with pre-invasive or invasive pulmonary adenocarcinomas. However, little is known of the incidence and fate of ground-glass nodules. Purpose: To elucidate the incidence and natural course of CT-detected pulmonary ground-glass nodules in Chinese women with breast cancer. Material and Methods: We retrospectively reviewed data from female breast cancer patients who underwent lung CT scans and who were followed for ≥3 months after the initial scan to identify the incidence of ground-glass nodules and any changes in them during the follow-up period. Results: Between January 2008 and April 2018, 693 out of 4682 breast cancer patients (14.8%) had persistent lung ground-glass nodules as detected by CT scan. The median age was 52 years (interquartile range [IQR] = 45–62 years). Median nodule size was 4.9 mm in diameter on initial CT scan. Frequency of growth was 7.5% (52/693 patients). Median volume doubling time was 1092 days (IQR = 719–1808 days) for 39 growing in size nodules. Initial nodule size, nodule type, and follow-up period were independent predictors of nodule growth. Conclusion: Most pulmonary ground-glass nodules in breast cancer patients were stable during long-term follow-up; most growing nodules had an indolent clinical course, suggesting that nodules should be monitored until growth is detected. This information is clinically relevant for accurate diagnosis of cancer stage and for appropriate treatment plans for patients with lung ground-glass nodules. [ABSTRACT FROM AUTHOR]

Background Differentiating between malignant and benign solitary pulmonary lesions (SPLs) is challenging. Purpose To determine diagnostic performance of intravoxel incoherent motion-based diffusion-weighted imaging (DW-IVIM) in distinguishing malignant from benign SPLs, using histogram analysis derived whole-tumor and single-section region of interest (ROI). Material and Methods This retrospective study received institutional review board approval. A total of 129 patients with diagnosed SPLs underwent DW-IVIM and apparent diffusion coefficient (ADC). ADC, slow diffusion coefficient (D), fast diffusion coefficient (D*), and perfusion fraction (f) were calculated separately by outlining whole-tumor and single-section ROI. Inter-observer reliability was assessed by inter-class correlation coefficient (ICC). ADC and DW-IVIM parameters were analyzed using independent-sample T-test. Receiver operating characteristic (ROC) analysis was constructed to determine diagnostic performance. Multiple logistic regression was performed to identify independent factors associated with malignant SPLs. Results There were 48 benign SPLs found in 35 patients and 94 patients with lung cancer (LC). ICC for whole-tumor ROI (range, 0.89-0.95) was higher than that for single-section ROI (range, 0.61-0.71). Mean ADC and D were significantly lower in the malignant group. ADC and D 10th showed significantly higher AUC values than did mean ADC and D. D showed significantly higher diagnostic accuracy in mean, 10th, and 25th percentiles than ADC values (all Ps < 0.05). D 10th was found to be an independent factor in discriminating LCs with an odds ratio of -1.217. Conclusion Volumetric analysis had higher reproducibility and diagnostic accuracy than did single-section. Further, compared to ADC, D value differentiated benign SPLs from LCs more accurately. [ABSTRACT FROM AUTHOR]

Objectives: To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans.Methods: This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified. Volume and percentage solid volume were measured by using a computer-assisted segmentation method. Radiomic features quantifying intensity, texture and wavelet were extracted from the segmented volume of interest (VOI). Twenty best features were chosen by using the Relief method and subsequently fed to a support vector machine (SVM) for discriminating adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC). Performance of the radiomic signatures was compared with volumetric analysis via receiver-operating curve (ROC) analysis and logistic regression analysis.Results: The accuracy of proposed radiomic signatures for predicting AIS/MIA from IAC achieved 80.5% with ROC analysis (Az value, 0.829; sensitivity, 72.1%; specificity, 80.9%), which showed significantly higher accuracy than volumetric analysis (69.5%, P = 0.049). Regression analysis showed that radiomic signatures had superior prognostic performance to volumetric analysis, with AIC values of 81.2% versus 70.8%, respectively.Conclusions: The radiomic tumour-phenotypes biomarker exhibited better diagnostic accuracy than traditional volumetric analysis in discriminating lung adenocarcinoma with different disease-specific survival.Key Points: • Radiomic biomarker on CT was designed to identify phenotypes of lung adenocarcinoma • Built up radiomic signature for lung adenocarcinoma manifested as subsolid nodules • Retrospective study showed radiomic signature had greater diagnostic accuracy than volumetric analysis • Radiomics help to evaluate intratumour heterogeneity within lung adenocarcinoma • Medical decision can be given with more confidence. [ABSTRACT FROM AUTHOR]