Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

OBJECTIVES: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. METHODS: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. RESULTS: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. CONCLUSIONS: DLCT has added value for STAS prediction in lung adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. KEY POINTS: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

CT-based decision tree model for predicting EGFR mutation status in synchronous multiple primary lung cancers.

Background: The current study aimed to construct a computed tomography (CT)-based decision tree algorithm (DTA) model to predict the epidermal growth factor receptor (EGFR) mutation status in synchronous multiple primary lung cancers (SMPLCs). Methods: The demographic and CT findings of 85 patients with molecular profiling for surgically resected SMPLCs were reviewed retrospectively. Least absolute shrinkage and selection operator (LASSO) regression was used to select the potential predictors of EGFR mutation, and a CT-DTA model was developed. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to assess the performance of this CT-DTA model. Results: The CT-DTA model was applied to predict the EGFR mutant that had ten binary split, of which eight parameters to accurately categorize the lesions as follows: the presence of bubble-like vacuole sign (19.4% importance in the development of the model), presence of air bronchogram sign (17.4% importance), smoking status (15.7% importance), types of the lesions (14.8% importance), histology (12.6% importance), presence of pleural indentation sign (7.6% importance), gender (6.9% importance), and presence of lobulation sign (5.6% importance). The ROC analysis achieved an area under the curve (AUC) of 0.854. Multivariate logistic regression analysis demonstrated that this CT-DTA model was an independent predictor of EGFR mutation (P<0.001). Conclusions: CT-DTA model is a simple tool to predict the status of EGFR mutation in SMPLC patients and could be considered for treatment decision-making.

A Novel Prognostic Score Based on Multiple Quantitative Parameters of Chest CT for Patients with Synchronous Multiple Primary Lung Cancer: Is Solid Component Size a Better Prognostic Indicator?

BACKGROUND: There is no simple and definitive way to predict the prognosis of synchronous multiple primary lung cancer (SMPLC). In this study, we developed a clinical prognostic score for predicting the survival of patients with SMPLC. PATIENTS AND METHODS: This study included 206 patients with SMPLC between 2011 and 2020 at three hospitals. Kaplan-Meier analysis was used to determine the optimal cutoff values for the quantitative chest computed tomography (CT) parameters. Multivariable Cox proportional hazards regression was carried out to identify independent prognostic factors for predicting overall survival (OS) and disease-free survival (DFS). The time-dependent receiver operating characteristic curve was analyzed to evaluate the prognostic performance. RESULTS: A CT-based prognostic score (CTPS) comprising six chest CT parameters was developed. Compared with T stage, CTPS had a higher prediction accuracy for OS and DFS. All C-indices of the model reached a satisfactory level in both the development and validation cohorts. Significant differences in the OS and DFS curves were observed when the patients were stratified into different risk groups. The high-risk group (CTPS of 5-6) had poorer survival than the low-risk group (CTPS of 0-4). CONCLUSIONS: The developed CTPS and the corresponding risk stratification system are valid for predicting the survival of patients with SMPLC.

18F-FDG PET-Based Combined Baseline and End-Of-Treatment Radiomics Model Improves the Prognosis Prediction in Diffuse Large B Cell Lymphoma After First-Line Therapy.

RATIONALE AND OBJECTIVES: To develop a combined model incorporating the clinical and PET features for identifying patients with diffuse large B-cell lymphoma (DLBCL) at high risk of progression or relapse after first-line therapy, compared to International Prognostic Index (IPI) and Deauville score (DS) assessment. MATERIALS AND METHODS: 271 18F-FDG PET images with DLBCL were retrospectively collected and randomly divided into the training (n=190) and test dataset (n=81). All visible lesions were annotated. Baseline, end-of-treatment (EoT), and delta PET radiomics features were extracted. IPI model, the baseline clinical model group (MG), DS model, the combined clinical MG, the PET-based radiomics MG, and the combined MG were constructed to predict 2-year time to progression (2Y-TTP). For each MG, the cross-combination method was performed to generate 1680 candidate models based on three normalization methods, 20 features, 4 feature-selection methods, and 7 classifiers. The model achieving the highest AUC was selected as the best for each MG. Cox regression analysis was further performed. RESULTS: In the test set, the best combined model showed better discriminative power compared to IPI model, the best baseline clinical model, DS model, the best combined clinical model, and the best PET-based radiomics model (AUC 0.898 vs. 0.584, 0.695, 0.756, 0.824, 0.832; p < 0.001, 0.014, 0.018, 0.152, 0.042, respectively). The combined model was superior to other models for progression-free-survival prediction (C-index: 0.853 vs. 0.568, 0.666, 0.753, 0.808, 0.814, respectively). CONCLUSION: A combined model for identifying patients at high risk of progression or relapse after first-line therapy was constructed, superior to IPI and DS assessment.