With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.
Objective To integrate multi-dimensional and multimodal data to develop a tool for predicting the risk of chronic kidney disease (CKD). Methods Data from the UK Biobank were utilized, involving 6561 participants recruited between 2006 and 2010, with a follow-up window from April 17, 2007 to November 30, 2022. In the development cohort (n=5248), a multimodal random survival forest (RSF) model was constructed, integrating conventional risk factors [variables from the CKD Prognosis Consortium (CKD-PC) equation], social determinants of health, Life’s Essential 8 data, and retinal optical coherence tomography imaging features. Comparative models included a base model (based solely on estimated glomerular filtration rate and urine albumin-to-creatinine ratio), the CKD-PC equation, a unimodal RSF model (conventional risk factors + social determinants of health + Life’s Essential 8 data), and an extended model (the multimodal RSF model plus a polygenic risk score). The performance of these models was compared in the validation cohort (n=1313). Results After a median follow-up of 12.7 years, 3.57% (234/6561) of the participants developed CKD. In the validation cohort, the 5-year concordance index (C-index) of the multimodal RSF model was 0.73 [95% confidence interval (CI) (0.68, 0.77)], which was significantly higher than that of the base model [C-index=0.67, 95%CI (0.65, 0.71)], the CKD-PC equation [C-index=0.64, 95%CI (0.58, 0.70)], and the unimodal RSF model [C-index=0.69, 95%CI (0.64, 0.73)], and the extended model with the inclusion of the polygenic risk score did not significantly improve predictive performance [C-index=0.72, 95%CI (0.67, 0.76)]. The results of the time-dependent area under the receiver operating characteristic curve analysis were consistent with these findings. Based on the predicted CKD risk derived from the multimodal RSF model for risk stratification, the actual proportions of individuals who developed CKD in the high-, medium-, and low-risk strata were 19.7%, 4.1%, and 1.5%, respectively. In addition to established risk factors, retinal imaging information, healthcare accessibility, and financial status were among the top-ranked predictors for CKD risk. Conclusion Integrating multi-dimensional and multimodal data—including conventional risk factors, social factors, lifestyle factors, and retinal imaging—can improve the performance of CKD risk prediction and stratification.