Development and validation of an interpretable delta radiomics-based model for predicting invasive ground-glass nodules in lung adenocarcinoma: a retrospective cohort study.

Background: Radiomics models based on computed tomography (CT) can be used to differentiate invasive ground-glass nodules (GGNs) in lung adenocarcinoma to help determine the optimal timing of GGN resection, improve the accuracy of prognostic prediction, and reduce unnecessary surgeries. However, general radiomics does not fully utilize follow-up data and often lacks model interpretation. Therefore, this study aimed to build an interpretable model based on delta radiomics to predict GGN invasiveness. Methods: A retrospective analysis was conducted on a set of 303 GGNs that were surgically resected and confirmed as lung adenocarcinoma in Shanghai Chest Hospital between September 2017 and August 2022. Delta radiomics and general radiomics features were extracted from preoperative follow-up CT scans and combined with clinical features for modeling. The performance of the delta radiomics-clinical model was compared to that of the radiomics-clinical model. Additionally, Shapley additive explanations (SHAP) was employed to interpret and visualize the model. Results: Two models were constructed using a combination of 34 radiomic features and 10 delta radiomic features, along with 14 clinical features. The radiomics-clinical model and the delta radiomics-clinical model exhibited area under the curve (AUC) of 0.986 [95% confidence interval (CI): 0.977-0.995] and 0.974 (95% CI: 0.959-0.987) in the training set, respectively, and 0.949 (95% CI: 0.908-0.978) and 0.927 (95% CI: 0.879-0.966) in the test set, respectively. The DeLong test of the two models showed no statistical significance (P=0.10) in the test set. SHAP was used to output a summary plot for global interpretation, which showed that preoperative mass, three-dimensional (3D) length, mean diameter, volume, mean CT value, and delta radiomics feature original_firstorder_RootMeanSquared were the relatively more important features in the model. Waterfall plots for local interpretation showed how each feature contributed to the prediction output of a given GGN. Conclusions: The delta radiomics-based model proved to be a helpful tool for predicting the invasiveness of GGNs in lung adenocarcinoma. This approach offers a precise, noninvasive alternative in informing clinical decision-making. Additionally, SHAP provided insightful and user-friendly interpretations and visualizations of the model, enhancing its clinical applicability.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification.

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

Terahertz meta-biosensor based on high-Q electrical resonance enhanced by the interference of toroidal dipole.

Electrical dipole resonances typically have low Q factor and broad resonant linewidth caused by strong free-space coupling with high radiative loss. Here, we present a strategy for enhancing the Q factor of the electrical resonance via the interference of a toroidal dipole. To validate such a strategy, a metasurface consisting of two resonators is designed that responsible to the electric and toroidal dipoles. According to constructive and destructive hybridizations of the two dipole modes, enhanced and decreased Q factors are found respectively for the two hybrid modes, compared to the one for the conventional electric dipole resonance. As a practical application of such high Q resonance, we further experimentally investigate the sensing performance of the metasurface biosensor by detecting the cell concentration of lung cancer cells (type A549). Moreover, through monitoring both resonance frequency and amplitude variation of the metasurface biosensor, the dielectric permittivity of the lung cancer cells is delicately estimated by the conjoint analysis of both simulated and measured results. Our proposed metasurface paves a promising way for the study of multipole interference in the field of nanophotonics and validates its effectiveness in biomedical sensing.

Use of diffusion-weighted magnetic resonance imaging (DW-MRI) to predict early response to anti-tumor therapy in advanced non-small cell lung cancer (NSCLC): a comparison of intravoxel incoherent motion-derived parameters and apparent diffusion coefficient.

BACKGROUND: The intravoxel incoherent motion (IVIM) method of magnetic resonance imaging (MRI) analysis can provide information regarding many physiological and pathological processes. This study aimed to investigate whether IVIM-derived parameters and the apparent diffusion coefficient (ADC) can act as imaging biomarkers for predicting non-small cell lung cancer (NSCLC) response to anti-tumor therapy and compare their performances. METHODS: This prospective study included 45 patients with NSCLC treated with chemotherapy (29 men and 16 women, mean age 57.9±9.7 years). Diffusion-weighted imaging was performed with 13 b-values before and 2-4 weeks after treatment. The IVIM parameter pseudo-diffusion coefficient (D*), perfusion fraction (f), diffusion coefficient (D), and ADC from a mono-exponential model were obtained. Responses 2 months after chemotherapy were assessed. The diagnostic performance was evaluated, and optimal cut-off values were determined by receiver operating characteristic (ROC) curve analysis, and the differences of progression-free survival (PFS) in groups of responders and non-responders were tested by Cox regression and Kaplan-Meier survival analyses. RESULTS: Of 45 patients, 30 (66.7%) were categorized as responders, and 15 as non-responders. Differences in the diffusion coefficient D and ADC between responders and non-responders were statistically significant (all P<0.05). Conversely, differences in f and D* between responders and non-responders were both not statistically significance (all P>0.05). The ROC analyses showed the change in D value (ΔD) was the best predictor of early response to anti-tumor therapy [area under the ROC curve (AUC), 0.764]. The Cox-regression model showed that all ADC and D parameters were independent predictors of PFS, with a range of reduction in risk from 56.2% to 82.7%, and ΔD criteria responders had the highest reduction (82.7%). CONCLUSIONS: ADC and D derived from IVIM are potentially useful for the prediction of NSCLC treatment response to anti-tumor therapy. Although ΔD is best at predicting response to treatment, ΔADC measurement may simplify manual efforts and reduce the workload.