Predicting Pathological Invasiveness of Lung Adenocarcinoma Manifesting as GGO-Predominant Nodules: A Combined Prediction Model Generated From DECT

Photon-counting detector CT provides superior subsolid nodule characterization compared to same-day energy-integrating detector CT

PURPOSE

To investigate the image quality and the performance of photon-counting detector (PCD) CT compared to conventional energy-integrating detector (EID) CT in identifying subsolid nodule (SSN) characteristics.

MATERIALS AND METHODS

Participants with SSNs who underwent same-day EID CT and PCD CT between October 2023 and April 2024 were prospectively included. The 1.0 mm EID CT images and, subsequently, 1.0 mm, 0.4 mm, and 0.2 mm PCD CT images were reviewed to assess image noise and subjective image quality on a 5-point Likert scale. SSN characteristics, including lobulation, spiculation, pleural retraction, air cavities, intra-nodular vessel signs, internal vascular changes, and heterogeneous solid components, were evaluated. Additionally, a step-by-step observation and comparison method was used to determine the presence of any additional characteristics.

RESULTS

Forty-eight participants (mean age: 56 ± 11 years; 16 males) with 89 SSNs were included. PCD CT significantly reduced radiation dose when using matched scans (1.79 ± 0.39 vs 2.17 ± 0.57 mSv, p < 0.001). Compared to 1.0 mm EID CT, 1.0 mm PCD CT images exhibited significantly lower objective image noise and higher subjective image quality (all p < 0.001). Compared to EID CT, PCD CT demonstrated enhanced visualization of subtle characteristics, except for lobulation, with a 0.4 mm section thickness offering a favorable balance between ultra-high resolution and perceived image quality for radiologists.

CONCLUSION

PCD CT facilitated radiation dose reduction and outperformed conventional EID CT in terms of image quality and visualization of SSN characteristics.

KEY POINTS

Question PCD CT, featuring ultra-high-resolution mode acquisition and a thinner reconstruction, has not been fully explored for characterizing SSNs. Findings Compared to EID CT, PCD CT was associated with lower objective image noise, higher subjective image quality, and superior SSN characterization. Clinical relevance PCD CT effectively reduced the radiation dose delivered to the patients and enabled more precise SSN characterization.

The clinical value of spectral CT combined with targeted scanning technology in the diagnosis of invasive ground glass nodules in pulmonary adenocarcinoma

BackgroundPulmonary adenocarcinoma, a predominant form of lung cancer, is characterized by diverse histopathological subtypes, including ground-glass nodules (GGNs), which may represent different degrees of malignancy. The accurate differentiation between invasive and non-invasive GGNs is paramount, as it significantly influences clinical management and treatment strategies.ObjectiveTo evaluate the clinical efficacy of spectral CT combined with targeted scanning in diagnosing invasive GGNs in pulmonary adenocarcinoma.MethodsA retrospective analysis of 120 patients with GGNs, who underwent spectral CT and targeted scanning at the Second Affiliated Hospital of Qiqihar Medical College (Nov 2021 - Dec 2022), was conducted. Patients were categorized based on postoperative pathology into non-invasive (66) and invasive (54) groups. Imaging features and values of various indicators including water concentration (WC), spectral curve slope (k value), and iodine concentration across different phases were analyzed and compared. Key independent factors for invasive GGN and the predictive value of the combined imaging technique were explored.ResultsThe invasive lesion group showed a higher incidence of fissure sign, spiculation sign, and bronchial inflation sign, along with increased values of WC, WCAP, and WCVP across phases (p < 0.05). These factors were identified as main influencers of invasive GGN, with OR values >1. The combined imaging parameters achieved an AUC of 0.914, sensitivity of 0.925, and specificity of 0.880, significantly outperforming individual indicators.ConclusionFissure sign, spiculation sign, bronchial inflation sign, WC, WCAP, and WCVP effectively predict invasive GGNs in pulmonary adenocarcinoma. Their combined application enhances predictive accuracy.

Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs).

MATERIALS AND METHODS

Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

RESULTS

Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05).

CONCLUSION

A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.

Correlation Between Intranodular Vessels and Tumor Invasiveness of Lung Adenocarcinoma Presenting as Ground-glass Nodules: A Deep Learning 3-Dimensional Reconstruction Algorithm-based Quantitative Analysis on Noncontrast Computed Tomography Images

PURPOSE

To evaluate the role of quantitative features of intranodular vessels based on deep learning in distinguishing pulmonary adenocarcinoma invasiveness.

MATERIALS AND METHODS

This retrospective study included 512 confirmed ground-glass nodules from 474 patients with 241 precursor glandular lesions (PGL), 126 minimally invasive adenocarcinomas (MIA), and 145 invasive adenocarcinomas (IAC). The pulmonary blood vessels were reconstructed on noncontrast computed tomography images using deep learning-based region-segmentation and region-growing techniques. The presence of intranodular vessels was evaluated based on the automatic calculation of vessel prevalence, vascular categories, and vessel volume percentage. Further comparisons were made between different invasive groups by the Mantel-Haenszel χ 2 test, χ 2 test, and analysis of variance.

RESULTS

The detection rate of intranodular vessels in PGL (33.2%) was significantly lower than that of MIA (46.8%, P = 0.011) and IAC (55.2%, P < 0.001), while the vascular categories were similar (all P > 0.05). Vascular changes were more common in IAC and MIA than in PGL, mainly in increased vessel volume percentage (12.4 ± 19.0% vs. 6.3 ± 13.1% vs. 3.9 ± 9.4%, P < 0.001). The average intranodular artery and vein volume percentage of IAC (7.5 ± 14.0% and 5.0 ± 10.1%) was higher than that of PGL (2.1 ± 6.9% and 1.7 ± 5.8%) and MIA (3.2 ± 9.1% and 3.1 ± 8.7%), with statistical significance (all P < 0.05).

CONCLUSIONS

The quantitative analysis of intranodular vessels on noncontrast computed tomography images demonstrated that the ground-glass nodules with increased internal vessel prevalence and volume percentages had higher possibility of tumor invasiveness.

Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules

Objectives

To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery.

Methods

The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA).

Results

The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model.

Conclusion

DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.

Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1

OBJECTIVES

Few studies have explored the use of spectral dual-layer detector-based computed tomography (SDCT) parameters, thymidine kinase-1 (TK1), and tumor abnormal protein (TAP) for the detection of ground-glass nodules (GGNs). Therefore, we aimed to evaluate the quantitative and qualitative parameters generated from SDCT for predicting the pathological subtypes of GGN-featured lung adenocarcinoma combined with TK1 and TAP.

MATERIAL AND METHODS

Between July 2021 and September 2022, 238 patients with GGNs were retrospectively enrolled in this study. SDCT and tests for TK1 and TAP were performed preoperatively, and the lesions were divided into glandular precursor lesions (PGL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), according to the pathological results. A receiver operating characteristic (ROC) curve was used to compare the diagnostic performance of these parameters. Multivariate logistic regression analysis was performed to construct a joint diagnostic model and create a nomogram.

RESULTS

This study included 238 GGNs, including 41 atypical adenomatous hyperplasias (AAH), 62 adenocarcinomas in situ (AIS), 49 MIA, and 86 IAC, with a high proportion of women, non-smokers, and pure ground-glass nodule (pGGN). CT100 keV (a/v), electronic density (EDW) (a/v), Daverage, Dsolid, TK1, and TAP of MIA and IAC were higher than those of PGL. The effective atomic number (Zeff (a/v)) was lower in MIA and IAC than in PGL (all p < 0.05). Logistic regression analysis showed that Zeff (a), EDW (a), TK1, Daverage, and internal bronchial morphology were crucial factors in predicting the aggressiveness of GGN. Zeff (a) had the highest diagnostic performance with an area under the ROC curve (AUC) = 0.896, followed by EDW (a) (AUC = 0.838) and CT100 keVa (AUC = 0.819). The diagnostic model and nomogram constructed using these five parameters (Zeff (a) + EDW (a) + CT100 keVa + Daverage + TK1) had an AUC = 0.933, which was higher than the individual parameters (p < 0.05).

CONCLUSIONS

Multiple quantitative and functional parameters can be selected based on SDCT, especially Zeff (a) and EDW (a), which have high sensitivity and specificity for predicting GGNs' invasiveness. Additionally, the combination of TK1 can further improve diagnostic performance, and using a nomogram is helpful for individualized predictions.