ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.

In 2022, World Health Organization (WHO) launched the 5th edition of the classification of renal tumors. This classification continues to use the previous classification framework of renal tumors based on morphology and tissue structure, and proposes the concept of renal cell carcinoma defined by molecular features for the first time. This article interprets from the 3 aspects of historical changes of WHO classification and grading of renal tumors, comparison of 2022 and 2016 WHO classification of renal tumors, and the role of molecular characteristics in the new pathological types such as ELOC mutant renal cell carcinoma, ALK rearrangement renal cell carcinoma, eosinophilic solid and cystic renal cell carcinoma. The purpose is to better understand the WHO from the traditional classification system based on tissue morphology to a three-in-one integrated classification system covering morphology, immunophenotype and genetic characteristics, and to understand the important value of molecular pathology in guiding the work of pathologists and clinicians under the new classification system.

Background With the increasing exposure to hazardous chemicals in the workplace and frequency of occupational injuries and occupational safety accidents, the acquisition of occupational exposure limits of hazardous chemicals is imminent. Objective To obtain more unknown immediately dangerous to life or health (IDLH) concentrations of hazardous chemicals in the workplace by exploring the application of quantitative structure-activity relationship (QSAR) prediction method to IDLH concentrations, and to provide a theoretical basis and technical support for the assessment and prevention of occupational injuries. Methods QSAR was used to correlate the IDLH values of 50 benzene and its derivatives with the molecular structures of target compounds. Firstly, affinity propagation algorithm was applied to cluster sample sets. Secondly, Dragon 2.1 software was used to calculate and pre-screen 537 molecular descriptors. Thirdly, the genetic algorithm was used to select six characteristic molecular descriptors as dependent variables and to construct a multiple linear regression model (MLR) and two nonlinear models using support vector machine (SVM) and artificial neural network (ANN) respectively. Finally, model performance was evaluated by internal and external validation and Williams diagram was drawn to determine the scopes of selected models. Results The ANN model results showed that \begin{document}$ {R}_{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}}^{2} $\end{document}=0.8526 and \begin{document}$ {R}_{\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}}^{2} $\end{document}=0.8505 respectively, root mean square (RMSE) error=0.5243, mean absolute error (MAE)=0.4610, internal and external validation coefficients \begin{document}$ {Q}_{\mathrm{l}00}^{2} $\end{document}=0.8476 and \begin{document}$ {Q}_{\mathrm{e}\mathrm{x}\mathrm{t}}^{2} $\end{document}=0.8905 respectively. By comparison, the performance verification parameters of the ANN model were superior to the MLR and SVM models, and all substances were in the applicable domain. Conclusion At present, the ANN model has the best performance in fitting ability, stability, and prediction, and is suitable for predicting IDLH concentrations of benzene and its derivatives. Predicting the IDLH concentraitons of benzene and its derivatives by QSAR method is an effective method, and provides a theoretical basis and technical support for the development of occupational health and safety.