Development and validation of a risk prediction model for invasiveness of pure ground-glass nodules based on a systematic review and meta-analysis.

BACKGROUND: Assessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions. OBJECTIVE: Developing a predictive model to assess the aggressiveness of pure ground glass nodules in lung adenocarcinoma is the study's goal. METHODS: A comprehensive search for studies on the relationship between computed tomography(CT) characteristics and the aggressiveness of pure ground glass nodules was conducted using databases such as PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM, up to December 20, 2023. Two independent researchers were responsible for screening literature, extracting data, and assessing the quality of the studies. Meta-analysis was performed using Stata 16.0, with the training data derived from this analysis. To identify publication bias, Funnel plots and Egger tests and Begg test were employed. This meta-analysis facilitated the creation of a risk prediction model for invasive adenocarcinoma in pure ground glass nodules. Data on clinical presentation and CT imaging features of patients treated surgically for these nodules at the Third Affiliated Hospital of Kunming Medical University, from September 2020 to September 2023, were compiled and scrutinized using specific inclusion and exclusion criteria. The model's effectiveness for predicting invasive adenocarcinoma risk in pure ground glass nodules was validated using ROC curves, calibration curves, and decision analysis curves. RESULTS: In this analysis, 17 studies were incorporated. Key variables included in the model were the largest diameter of the lesion, average CT value, presence of pleural traction, and spiculation. The derived formula from the meta-analysis was: 1.16×the largest lesion diameter + 0.01 × the average CT value + 0.66 × pleural traction + 0.44 × spiculation. This model underwent validation using an external set of 512 pure ground glass nodules, demonstrating good diagnostic performance with an ROC curve area of 0.880 (95% CI: 0.852-0.909). The calibration curve indicated accurate predictions, and the decision analysis curve suggested high clinical applicability of the model. CONCLUSION: We established a predictive model for determining the invasiveness of pure ground-glass nodules, incorporating four key radiological indicators. This model is both straightforward and effective for identifying patients with a high likelihood of invasive adenocarcinoma.

A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma.

PURPOSE: Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients. MATERIALS AND METHODS: Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models. RESULTS: In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability. CONCLUSION: Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.

Extreme learning machines for regression based on V-matrix method.

This paper studies the joint effect of V-matrix, a recently proposed framework for statistical inferences, and extreme learning machine (ELM) on regression problems. First of all, a novel algorithm is proposed to efficiently evaluate the V-matrix. Secondly, a novel weighted ELM algorithm called V-ELM is proposed based on the explicit kernel mapping of ELM and the V-matrix method. Though V-matrix method could capture the geometrical structure of training data, it tends to assign a higher weight to instance with smaller input value. In order to avoid this bias, a novel method called VI-ELM is proposed by minimizing both the regression error and the V-matrix weighted error simultaneously. Finally, experiment results on 12 real world benchmark datasets show the effectiveness of our proposed methods.