Meta-analysis of the correlation between CT-based features and invasive properties of pure ground-glass nodules

Predicting the Invasiveness of Mixed Ground-Glass Nodules Based on Spectral Computed Tomography-Derived Parameters and Tumor Abnormal Protein Levels: Development and Validation of a Model

RATIONALE AND OBJECTIVES

Mixed ground-glass nodules (mGGNs) are highly malignant and common nonspecific lung imaging findings. This study aimed to explore whether combining quantitative and qualitative spectral dual-layer detector-based computed tomography (SDCT)-derived parameters with serological tumor abnormal proteins (TAPs) and thymidine kinase 1 (TK1) expression enhances invasive mGGN diagnostic efficacy and to develop a joint diagnostic model.

MATERIALS AND METHODS

This prospective study included patients with mGGNs undergoing preoperative triple-phase contrast-enhanced SDCT with TAP and TK1 tests. Based on pathologic invasiveness, mGGNs were classified as noninvasive or invasive adenocarcinomas. To establish the predictive model, 397 patients were divided into training and internal validation cohorts. Another 144 patients comprised the external validation set. A nomogram predicting invasive mGGNs was generated and assessed using receiver operating characteristic curves.

RESULTS

CT100keV_a, Zeff_a, ED_a, TAP, Dsolid, and Internal_bronchial_morphology were identified as independent risk factors for mGGN invasiveness. The SDCT parameter-TAP nomogram combining these six predictors demonstrated satisfactory discrimination capabilities in all three datasets (areas under the curves 0.840-0.911). The optimal training set cutoff was 0.566, yielding an 88.2% sensitivity and 80.4% specificity. Decision curve analysis showed the highest net benefit across a breadth of threshold probabilities, and clinical impact curve analysis confirmed the model's clinical validity. The nomogram had significantly higher discriminative accuracy than any variable alone.

CONCLUSION

Multiple SDCT-derived parameters predict mGGN invasiveness, with Zeff_a playing a prominent role. The developed SDCT parameter-TAP nomogram has excellent diagnostic performance and high calibration accuracy, facilitating individual noninvasive risk prediction of malignant mGGNs.

CRITICAL RELEVANCE STATEMENT

Multiple quantitative and functional parameters derived from SDCT can predict the pathological invasiveness of mGGNs, with Zeff_a playing a prominent role. A SDCT parameters-TAP nomogram has excellent diagnostic performance and high calibration accuracy, facilitating noninvasive prediction of individual risks of malignant mGGNs.

Risk factors for malignant solid pulmonary nodules: a meta-analysis

BACKGROUND

Previous studies have indicated that clinical and imaging features may assist in distinguishing between benign and malignant solid lung nodules. Yet, the specific characteristics in question continue to be debated. This meta-analysis aims to identify risk factors for malignant solid lung nodules, thereby supporting informed clinical decision-making.

METHODS

A comprehensive search of databases including PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM was conducted up to October 6, 2024. Only publications in Chinese or English were considered. Data analysis was performed using Stata 16.0 software.

RESULTS

This analysis included 32 studies, comprising 7758 solid pulmonary nodules, of which 3359 were benign and 4399 were malignant. It was found that the incidence of spiculate signs in malignant solid pulmonary nodules (MSPN) was higher than in benign solid pulmonary nodules (BSPN) [OR = 3.06, 95% CI (2.35, 3.98), P < 0.05. Additionally, increases were observed in the incidences of vascular convergence[OR = 16.57, 95% CI (8.79, 31.24), P < 0.05], lobulated signs [OR = 5.17, 95% CI (3.83, 6.98)], air bronchogram sign[OR = 2.96, 95% CI (1.62, 5.41), P < 0.05], pleura traction sign [OR = 2.33, 95% CI (1.65, 3.29), P < 0.05], border blur [OR = 2.94, 95% CI (1.47, 5.85), P < 0.05], vacuole signs [OR = 5.25, 95% CI (2.66, 10.37), P < 0.05], and family history of cancer [OR = 3.85, 95% CI (2.43, 6.12), P < 0.05] compared to BSPN. Older age[OR = 1.06, 95% CI (1.04, 1.07), P < 0.05], higher prevalence in females [OR = 2.98, 95% CI (2.27, 3.92), P < 0.05], larger nodule diameters [OR = 1.25, 95% CI (1.13, 1.38), P < 0.05], and lower incidence of calcification [OR = 0.21, 95% CI (0.10, 0.48), P < 0.05] were also associated with MSPN. No significant differences were found between MSPN and BSPN regarding CEA and emphysema (all P > 0.05).

CONCLUSIONS

This meta-analysis highlights that spiculate sign, vascular convergence sign, lobulated sign, diameter, border blur, vacuole sign, age, gender, family history of cancer, pleura traction, air bronchogram sign, and calcification are significant markers for predicting malignancy in SPNs, potentially influencing clinical management. However, further well-designed, large-scale studies are needed to confirm these findings.

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.

Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions

PURPOSE

To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis.

MATERIALS AND METHODS

From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them.

RESULTS

Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness.

CONCLUSION

The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.