Corrigendum: Convolutional neural network-based diagnostic model for a solid, indeterminate solitary pulmonary nodule or mass on computed tomography.

[This corrects the article DOI: 10.3389/fonc.2021.792062.].

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.

Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage I non-small cell lung cancer (NSCLC). Purpose To develop a deep learning signature for N2 metastasis prediction and prognosis stratification in clinical stage I NSCLC. Materials and Methods In this retrospective study conducted from May 2020 to October 2020 in a population with clinical stage I NSCLC, an internal cohort was adopted to establish a deep learning signature. Subsequently, the predictive efficacy and biologic basis of the proposed signature were investigated in an external cohort. A multicenter diagnostic trial (registration number: ChiCTR2000041310) was also performed to evaluate its clinical utility. Finally, on the basis of the N2 risk scores, the instructive significance of the signature in prognostic stratification was explored. The diagnostic efficiency was quantified with the area under the receiver operating characteristic curve (AUC), and the survival outcomes were assessed using the Cox proportional hazards model. Results A total of 3096 patients (mean age ± standard deviation, 60 years ± 9; 1703 men) were included in the study. The proposed signature achieved AUCs of 0.82, 0.81, and 0.81 in an internal test set (n = 266), external test cohort (n = 133), and prospective test cohort (n = 300), respectively. In addition, higher deep learning scores were associated with a lower frequency of EGFR mutation (P = .04), higher rate of ALK fusion (P = .02), and more activation of pathways of tumor proliferation (P < .001). Furthermore, in the internal test set and external cohort, higher deep learning scores were predictive of poorer overall survival (adjusted hazard ratio, 2.9; 95% CI: 1.2, 6.9; P = .02) and recurrence-free survival (adjusted hazard ratio, 3.2; 95% CI: 1.4, 7.4; P = .007). Conclusion The deep learning signature could accurately predict N2 disease and stratify prognosis in clinical stage I non-small cell lung cancer. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Park and Lee in this issue.

Radiomics for Survival Risk Stratification of Clinical and Pathologic Stage IA Pure-Solid Non-Small Cell Lung Cancer.

Background Radiomics-based biomarkers enable the prognostication of resected non-small cell lung cancer (NSCLC), but their effectiveness in clinical stage and pathologic stage IA pure-solid tumors requires further determination. Purpose To construct an efficient radiomics signature for survival risk stratification personalized for patients with clinical stage and pathologic stage IA pure-solid NSCLC. Materials and Methods In this retrospective study, six radiomics signatures were constructed for patients with stage IA pure-solid NSCLC who underwent resection between January 2011 and December 2013 at authors' institution and were tested in the radiogenomics data set. The radiomics features were extracted from the intratumoral two-dimensional region, three-dimensional volume, and peritumoral area using PyRadiomics. The discriminative abilities of the signatures were quantified using the area under the time-dependent receiver operating characteristic curve (AUC), and the optimal signature was selected for patient stratification. Results The study included 592 patients with stage IA pure-solid NSCLC (median age, 61 years; interquartile range, 55-66 years; 269 women) for radiomics analysis: 381 patients for training, 163 for internal validation, and 48 for external validation. The radiomics signature combining three-region features yielded the highest 3- and 5-year AUCs of 0.77 and 0.78, respectively, in the internal validation set and 0.76 and 0.75, respectively, in the external validation set. Multivariable analysis suggested that the radiomics signature remained an independent prognostic factor (hazard ratio, 6.2; 95% CI: 3.5, 11.0; P < .001) and improved the discriminative ability and clinical usefulness of conventional clinical predictors. Conclusion The radiomics signature with multiregional features helped stratify the survival risk of patients with clinical stage and pathologic stage IA pure-solid non-small cell lung cancer. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Hsu and Sohn in this issue.

Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography.

PURPOSE: To establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule (SPN) or mass (SPM) on computed tomography (CT). METHOD: A total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n=366), validation (n=46), and test (n=47) sets. Histopathologic analysis was available for each patient. An end-to-end CNN model was proposed to predict the natural history of solid indeterminate SPN/SPMs on CT. Receiver operating characteristic curves were plotted to evaluate the predictive performance of the proposed CNN model. The accuracy, sensitivity, and specificity of diagnoses by radiologists alone were compared with those of diagnoses by radiologists by using the CNN model to assess its clinical utility. RESULTS: For the CNN model, the AUC was 91% (95% confidence interval [CI]: 0.83-0.99) in the test set. The diagnostic accuracy of radiologists with the CNN model was significantly higher than that without the model (89 vs. 66%, P<0.01; 87 vs. 61%, P<0.01; 85 vs. 66%, P=0.03, in the train, validation, and test sets, respectively). In addition, while there was a slight increase in sensitivity, the specificity improved significantly by an average of 42% (the corresponding improvements in the three sets ranged from 43, 33, and 42% to 82, 78, and 84%, respectively; P<0.01 for all). CONCLUSION: The CNN model could be a valuable tool in non-invasively differentiating benign from malignant lesions manifesting as solid, indeterminate SPNs/SPMs on CT.

Fuzzy pricing for urban water resources: model construction and application.

A rational water price system plays a crucial role in the optimal allocation of water resources. In this paper, a fuzzy pricing model for urban water resources is presented, which consists of a multi-criteria fuzzy evaluation model and a water resources price (WRP) computation model. Various factors affecting WRP are comprehensively evaluated with multiple levels and objectives in the multi-criteria fuzzy evaluation model, while the price vectors of water resources are constructed in the WRP computation model according to the definition of the bearing water price index, and then WRP is calculated. With the incorporation of an operator's knowledge, it considers iterative weights and subjective preference of operators for weight-assessment. The weights determined are more rational and the evaluation results are more realistic. Particularly, dual water supply is considered in the study. Different prices being fixed for water resources with different qualities conforms to the law of water resources value (WRV) itself. A high-quality groundwater price computation model is also proposed to provide optimal water allocation and to meet higher living standards. The developed model is applied in Jinan for evaluating its validity. The method presented in this paper offers some new directions in the research of WRP.