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Purpose: The purpose of this study is to compare the detection performance of the 3-dimensional convolutional neural network (3D CNN)-based computer-aided detection (CAD) models with radiologists of different levels of experience in detecting pulmonary nodules on thin-section computed tomography (CT). Patients and Methods: We retrospectively reviewed 1109 consecutive patients who underwent follow-up thin-section CT at our institution. The 3D CNN model for nodule detection was re-trained and complemented by expert augmentation. The annotations of a consensus panel consisting of two expert radiologists determined the ground truth. The detection performance of the re-trained CAD model and three other radiologists at different levels of experience were tested using a free-response receiver operating characteristic (FROC) analysis in the test group. Results: The detection performance of the re-trained CAD model was significantly better than that of the pre-trained network (sensitivity: 93.09% vs 38.44%). The re-trained CAD model had a significantly better detection performance than radiologists (average sensitivity: 93.09% vs 50.22%), without significantly increasing the number of false positives per scan (1.64 vs 0.68). In the training set, 922 nodules less than 3 mm in size in 211 patients at high risk were recommended for follow-up CT according to the Fleischner Society Guidelines. Fifteen of 101 solid nodules were confirmed to be lung cancer. Conclusion: The re-trained 3D CNN-based CAD model, complemented by expert augmentation, was an accurate and efficient tool in identifying incidental pulmonary nodules for subsequent management. [ABSTRACT FROM AUTHOR]

Background: The present study analyzed the relationship between clinical features and the T790M mutation in non-small cell lung cancer (NSCLC) patients resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) treatment. Methods: NSCLC patients with resistance to first-generation EGFR-TKIs in which the disease control time was more than 6 months after initial TKI treatment were enrolled. T790M mutation analysis was performed using one of the following methods according to each manufacturer's protocols: Cobas EGFR mutation test (41/105, 39.0%), digital PCR (42/105, 40.0%) or Scorpion amplification refractory mutation system (ARMS) (22/105, 21.0%). Sample type of T790M was from tissue only (53/105, 50.5%), plasma only (46/105, 43.8%), tissue and plasma (6/105, 5.7%). Results: Of 105 patients, 57 were T790M-positive and 48 were T790M-negative. T790M-positive patients had longer progression-free survival (PFS) after initial EGFR-TKI treatment (p = 0.019). T790M positivity was more frequent in patients treated with gefitinib than in those treated with icotinib (65% vs 40.54%, p = 0.018). The rate of T790M positivity was lower in patients with EGFR L858R (44.44%, 12/27) before TKI treatment than in those with EGFR 19del (72.0%, 36/50, p = 0.036). Patients who achieved PR after initial EGFR-TKI treatment had a higher rate of T790M positivity than those with SD (75.76% vs 50%, p = 0.023). There was no relationship between T790M status and age, gender, primary site, metastasis site, or treatment before TKI. Conclusion: Progression-free survival (PFS), drug type, response to initial EGFR-TKI treatment, and EGFR status before initial EGFR treatment were associated with the frequency of T790M mutation. [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]