Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk.

Background: Radiomics based on computed tomography (CT) images is potential in promoting individualized treatment of non-small cell lung cancer (NSCLC), however, its role in immunotherapy needs further exploration. The aim of this study was to develop a CT-based radiomics score to predict the efficacy of immune checkpoint inhibitor (ICI) monotherapy in patients with advanced NSCLC. Methods: Two hundred and thirty-six ICI-treated patients were retrospectively included and divided into a training cohort (n=188) and testing cohort (n=48) at a ratio of 8 to 2. The efficacy outcomes of ICI were evaluated based on overall survival (OS) and progression-free survival (PFS). We designed a survival network and combined it with a Cox regression model to obtain patients' OS risk score (OSRS) and PFS risk score (PFSRS). Results: Based on OSRS and PFSRS, patients were divided into high- and low-risk groups in the training cohort and the test cohort with distinctly different [training cohort, log-rank P<0.001, hazard ratio (HR): 4.14; test cohort, log-rank P=0.014, HR: 4.54] and PFS (training cohort, log-rank P<0.001, HR: 4.52; test cohort, log-rank P<0.001, HR: 6.64). Further joint evaluation of OSRS and PFSRS showed that both were significant in the Cox regression model (P<0.001), and multi-overall survival risk score (MOSRS) displayed more outstanding stratification capabilities than OSRS in both the training (P<0.001) and test cohorts (P=0.002). None of the clinical characteristics were significant in the Cox regression model, and the score that predicted the best immune response was not as good as the risk score from follow-up information in the performance of prognostic stratification. Conclusions: We developed a CT imaging-based score with the potential to become an independent prognostic factor to screen patients who would benefit from ICI treatment, which suggested that CT radiomics could be applied for individualized immunotherapy of NSCLC. Our findings should be further validated by future larger multicenter study.

Non-Dissociative Activation of Chemisorbed Dinitrogen on One or Two Vanadium Atoms Supported by a Mo6 S8 Cluster.

Adsorption of N2 on Mo6 S8 q _Vx clusters (x=0, 1, 2; q=0, ±1) were systematically studied by density functional theory calculations with dispersion corrections. It was found that the N2 can be chemisorbed and undergo non-dissociative activation on single or double metal atoms. The adsorption and activation are influenced by metal types (V or Mo), N2 coordination modes and charge states of the clusters. Particularly, anionic Mo6 S8 - _V2 clusters have remarkable ability to fix and activate N2 . In Mo6 S8 - _V2 , two V atoms prefer to adsorb on two adjacent S-Mo-S hollow sites, leading to the formation of a supported V…V unit. The N2 is bridged side-on coordinated with these two V atoms with high adsorption energy and significant charge transfer. The bond order, bond length and vibration frequency of the adsorbed N2 are close to those of a N-N single bond.

Non-stoichiometric molybdenum sulfide clusters and their reactions with the hydrogen molecule.

Structures of non-stoichiometric MoxSy clusters (x = 2-4; y = 2-10) were studied by density functional calculations with global optimization. Besides 1T phase like structures, a novel regular grid structure in which Mo atoms are well separated by S atoms was found, which might be used as a building-block to construct a new type of two-dimensional molybdenum sulfide monolayer. The hydrogen molecule prefers to be adsorbed onto Mo atoms rather than S atoms, and Mo atoms with less S coordination have a higher ability to adsorb H2. In addition, the reaction pathways for H2 dissociation were studied on two clusters with the highest H2 adsorption energy (Mo2S4 and Mo3S3). The vacant bridge site of Mo-Mo in S-deficient clusters, which corresponds to the sulfur vacancy in the bulk phase MoS2, is favored by H atom adsorption and plays an important role in the H atom transfer on MoxSy clusters. Our results provide a new aspect to understand the reason why S defect in MoS2 and MoS2 with an Mo-edge could enhance the catalytic performance in the hydrogen evolution reaction.

Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma.

BACKGROUND: Robust imaging biomarkers are needed for risk stratification in stage I lung adenocarcinoma patients in order to select optimal treatment regimen. We aimed to construct and validate a radiomics nomogram for predicting the disease-free survival (DFS) of patients with resected stage I lung adenocarcinoma, and further identifying candidates benefit from adjuvant chemotherapy (ACT). METHODS: Using radiomics approach, we analyzed 554 patients' computed tomography (CT) images from three multicenter cohorts. Prognostic radiomics features were extracted from computed tomography (CT) images and selected using least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for DFS stratification. The biological basis of radiomics was explored in the Radiogenomics dataset (n=79) by gene set enrichment analysis (GSEA). Then a nomogram that integrated the signature with these significant clinicopathologic factors in the multivariate analysis were constructed in the training cohort (n=238), and its prognostic accuracy was evaluated in the validation cohort (n=237). Finally, the predictive value of nomogram for ACT benefits was assessed. RESULTS: The radiomics signature with higher score was significantly associated with worse DFS in both the training and validation cohorts (P<0.001). The GSEA presented that the signature was highly correlated to characteristic metabolic process and immune system during cancer progression. Multivariable analysis revealed that age (P=0.031), pathologic TNM stage (P=0.043), histologic subtype (P=0.010) and the signature (P<0.001) were independently associated with patients' DFS. The integrated radiomics nomogram showed good discrimination performance, as well as good calibration and clinical utility, for DFS prediction in the validation cohort. We further found that the patients with high points (point ≥8.788) defined by the radiomics nomogram obtained a significant favorable response to ACT (P=0.04) while patients with low points (point <8.788) showed no survival difference (P=0.7). CONCLUSIONS: The radiomics nomogram could be used for prognostic prediction and ACT benefits identification for patient with resected stage I lung adenocarcinoma.

Small stoichiometric (MoS2)n clusters with the 1T phase.

Stoichiometric (MoS2)n clusters (n = 1-6) were systematically studied by density functional theory calculations with hybrid B3LYP and pure GGA PW91 functionals. The most stable structures of these clusters were obtained by global optimizations with a genetic algorithm. A triangle of Mo3 capped with a S atom was found to be a favourite building block, which can construct the most stable structures (or at least candidates) for n = 3-6 clusters. These types of structures can be viewed as fragments of MoS2 monolayer with the 1T phase, which is an important phase that has unique reactivity and conductivity properties. Electronic structures were analyzed by means of density of states and frontier molecular orbitals (FMOs), which suggest that both Mo and S contribute to the FMOs below the Fermi energy while Mo atoms contribute more toward the unoccupied FMOs.