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Visual discrimination of screen-detected persistent from transient subsolid nodules: An observer study.

ABSTRACT: To evaluate whether, and to which extent, experienced radiologists are able to visually correctly differentiate transient from persistent subsolid nodules from a single CT examination alone and to determine CT morphological features to make this differentiation.We selected 86 transient and 135 persistent subsolid nodules from the National Lung Screening Trial (NLST) database. Four experienced radiologists visually assessed a predefined list of morphological features and gave a final judgment on a continuous scale (0-100). To assess observer performance, area under the receiver operating characteristic (ROC) curve was calculated. Statistical differences of morphological features between transient and persistent lesions were calculated using Chi-square. Inter-observer agreement of morphological features was evaluated by percentage agreement.Forty-nine lesions were excluded by at least 2 observers, leaving 172 lesions for analysis. On average observers were able to differentiate transient from persistent subsolid nodules ≥ 10 mm with an area under the curve of 0.75 (95% CI 0.67-0.82). Nodule type, lesion margin, presence of a well-defined border, and pleural retraction showed significant differences between transient and persistent lesions in two observers. Average pair-wise percentage agreement for these features was 81%, 64%, 47% and 89% respectively. Agreement for other morphological features varied from 53% to 95%.The visual capacity of experienced radiologists to differentiate persistent and transient subsolid nodules is moderate in subsolid nodules larger than 10 mm. Performance of the visual assessment of CT morphology alone is not sufficient to generally abandon a short-term follow-up for subsolid nodules.

SUBMITTER: Chung K

PROVIDER: S-EPMC5810988 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background The optimal management of patients with subsolid pulmonary nodules is of growing clinical concern. This study sought to develop and validate a more precise predictive model to evaluate the pathological invasiveness of patients with lung peripheral subsolid nodules (SSNs). Methods The data of 1,140 patients with peripheral SSNs who underwent surgical resection at Shanghai Renji Hospital from January 2014 to December 2018 were retrospectively analyzed. The patients were randomly assigned to a training and validation cohort (at a ratio of 2 to 1). Clinical parameters and imaging features were collected to estimate the independent predictors of pathological invasiveness of SSNs. A nomogram model was developed and applied to the validation cohort. The predictive performance of the nomogram model was evaluated by a calibration curve analysis, an area under the receiver operating characteristic curve (AUC) analysis, and a decision curve analysis (DCA), which was also compared with other diagnostic models. Results In the multivariate analysis, the nodule diameter (P<0.001), solid component size (P<0.001), mean CT attenuation (P=0.001), spiculation (P<0.001), and pleura indentation (P=0.011) were identified as independent predictors of the pathological invasiveness of SSNs. A nomogram model based on the results of the multivariate analysis was developed and showed a robust discrimination in the validation cohort, with an AUC of [0.890; 95% confidence interval (CI), 0873–0.907], which was higher than another two reported models. The calibration curve showed optimal agreement between the pathological invasive probability as predicted by the nomogram and the actual probability. Conclusions We developed and validated a nomogram model to evaluate the risk of the pathological invasiveness for patients with lung SSNs. The AUC of this nomogram model was higher than another two reported models. Our nomogram model may help clinicians to make individualized treatment more precisely.

Project description:BackgroundPatients with persistent pulmonary subsolid nodules have a relatively high incidence of lung adenocarcinoma. Preoperative early diagnosis of invasive pulmonary adenocarcinoma spectrum lesions could help avoid extensive advanced cancer management and overdiagnosis in lung cancer screening programs.MethodsIn total, 260 consecutive patients with persistent subsolid nodules ≤30 mm (n=260) confirmed by surgical pathology were retrospectively investigated from February 2016 to August 2020 at the Kaohsiung Veterans General Hospital. All patients underwent surgical resection within 3 months of the chest CT exam. The study subjects were divided into a training cohort (N=195) and a validation cohort (N=65) at a ratio of 3:1. The purpose of our study was to develop and validate a least absolute shrinkage and selection operator-derived nomogram integrating semantic-radiomic features in differentiating preinvasive and invasive pulmonary adenocarcinoma lesions, and compare its predictive value with clinical-semantic, semantic, and radiologist's performance.ResultsIn the training cohort of 195 subsolid nodules, 106 invasive lesions and 89 preinvasive lesions were identified. We developed a least absolute shrinkage and selection operator-derived combined nomogram prediction model based on six predictors (nodular size, CTR, roundness, GLCM_Entropy_log10, HISTO_Entropy_log10, and CONVENTIONAL_Humean) to predict the invasive pulmonary adenocarcinoma lesions. Compared with other predictive models, the least absolute shrinkage and selection operator-derived model showed better diagnostic performance with an area under the curve of 0.957 (95% CI: 0.918 to 0.981) for detecting invasive pulmonary adenocarcinoma lesions with balanced sensitivity (92.45%) and specificity (88.64%). The results of Hosmer-Lemeshow test showed P values of 0.394 and 0.787 in the training and validation cohorts, respectively, indicating good calibration power.ConclusionsWe developed a least absolute shrinkage and selection operator-derived model integrating semantic-radiomic features with good calibration. This nomogram may help physicians to identify invasive pulmonary adenocarcinoma lesions for guidance in personalized medicine and make more informed decisions on managing subsolid nodules.

Project description:ObjectivesTo validate the performances of two prediction models (Brock and Lee models) for the differentiation of minimally invasive adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions among subsolid nodules (SSNs).DesignA retrospective cohort study.SettingA tertiary university hospital in South Korea.Participants410 patients with 410 incidentally detected SSNs who underwent surgical resection for the pulmonary adenocarcinoma spectrum between 2011 and 2015.Primary and secondary outcome measuresUsing clinical and radiological variables, the predicted probability of MIA/IPA was calculated from pre-existing logistic models (Brock and Lee models). Areas under the receiver operating characteristic curve (AUCs) were calculated and compared between models. Performance metrics including sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were also obtained.ResultsFor pure ground-glass nodules (n=101), the AUC of the Brock model in differentiating MIA/IPA (59/101) from preinvasive lesions (42/101) was 0.671. Sensitivity, specificity, accuracy, PPV and NPV based on the optimal cut-off value were 64.4%, 64.3%, 64.4%, 71.7% and 56.3%, respectively. Sensitivity, specificity, accuracy, PPV and NPV according to the Lee criteria were 76.3%, 42.9%, 62.4%, 65.2% and 56.3%, respectively. AUC was not obtained for the Lee model as a single cut-off of nodule size (≥10 mm) was suggested by this model for the assessment of pure ground-glass nodules. For part-solid nodules (n=309; 26 preinvasive lesions and 283 MIA/IPAs), the AUC was 0.746 for the Brock model and 0.771 for the Lee model (p=0.574). Sensitivity, specificity, accuracy, PPV and NPV were 82.3%, 53.8%, 79.9%, 95.1% and 21.9%, respectively, for the Brock model and 77.0%, 69.2%, 76.4%, 96.5% and 21.7%, respectively, for the Lee model.ConclusionsThe performance of prediction models for the incidentally detected SSNs in differentiating MIA/IPA from preinvasive lesions might be suboptimal. Thus, an alternative risk calculation model is required for the incidentally detected SSNs.

Project description:Background Subsolid pulmonary nodules, comprising pure ground-glass nodules (GGNs) and part-solid nodules (PSNs), have a high risk of indolent malignancy. Lung Imaging Reporting and Data System (Lung-RADS) nodule management guidelines are based on expert opinion and lack independent validation. Purpose To evaluate Lung-RADS estimates of the malignancy rates of subsolid nodules, using nodules from the National Lung Screening Trial (NLST), and to compare Lung-RADS to the NELSON trial classification as well as the Brock University calculator. Materials and Methods Subsets of GGNs and PSNs were selected from the NLST for this retrospective study. A thoracic radiologist reviewed the baseline and follow-up CT images, confirmed that they were true subsolid nodules, and measured the nodules. The primary outcome for each nodule was the development of malignancy within the follow-up period (median, 6.5 years). Nodules were stratified according to Lung-RADS, NELSON trial criteria, and the Brock model. For analyses, nodule subsets were weighted on the basis of frequency in the NLST data set. Nodule stratification models were tested by using receiver operating characteristic curves. Results A total of 622 nodules were evaluated, of which 434 nodules were subsolid. At baseline, 304 nodules were classified as Lung-RADS category 2, with a malignancy rate of 3%, which is greater than the 1% in Lung-RADS (P = .004). The malignancy rate for GGNs smaller than 10 mm (two of 129, 1.3%) was smaller than that for GGNs measuring 10-19 mm (11 of 153, 6%) (P = .01). The malignancy rate for Lung-RADS category 3 was 14% (13 of 67), which is greater than the reported 2% in Lung-RADS (P < .001). The Brock model predicted malignancy better than Lung-RADS and the NELSON trial scheme (area under the receiver operating characteristic curve = 0.78, 0.70, and 0.67, respectively; P = .02 for Brock model vs NELSON trial scheme). Conclusion Subsolid nodules classified as Lung Imaging Reporting and Data System (Lung-RADS) categories 2 and 3 have a higher risk of malignancy than reported. The Brock risk calculator performed better than measurement-based classification schemes such as Lung-RADS. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Kauczor and von Stackelberg in this issue.

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Visual discrimination of screen-detected persistent from transient subsolid nodules: An observer study.

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