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Humans can be exposed to multiple pollutants in the air and surface water. These environments are non-static, trans-boundary and correlated, creating a complex network, and significant challenges for research on environmental hazards, especially in real-world cancer research. This article reports on a large study (377 million people in 30 provinces of China) that evaluated the combined impact of air and surface water pollution on cancer. We formulate a spatial evaluation system and a common grading scale for co-pollution measurement, and validate assumptions that air and surface water environments are spatially connected and that cancers of different types tend to cluster in areas where these environments are poorer. We observe "dose–response" relationships in both the number of affected cancer types and the cancer incidence with an increase in degree of co-pollution. We estimate that 62,847 (7.4%) new cases of cancer registered in China in 2016 were attributable to air and surface water pollution, and the majority (69.7%) of these excess cases occurred in areas with the highest level of co-pollution. The findings clearly show that the environment cannot be considered as a set of separate entities. They also support the development of policies for cooperative environmental governance and disease prevention. Dissecting the associations between exposure to environmental pollution and cancer risk remains crucial. Here, the authors evaluate the impact of air and water pollution on cancer incidence in China using a spatial evaluation system and show that most excess cancer cases occurred in areas with the highest level of co-pollution. [ABSTRACT FROM AUTHOR]

Background: The current guidelines for managing screen‐detected pulmonary nodules offer rule‐based recommendations for immediate diagnostic work‐up or follow‐up at intervals of 3, 6, or 12 months. Customized visit plans are lacking. Purpose: To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL‐based policy models. Methods: Using a nested case–control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer‐free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL‐based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack‐years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL‐based policy models with rule‐based follow‐up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer). Results: We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack‐years, beyond those considered in guideline protocols) and the selection of follow‐up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL‐based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule‐based protocols, the three best‐performing RL‐based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes. Conclusions: This study highlights the potential of using an RL‐based approach that is both clinically interpretable and performance‐robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system. [ABSTRACT FROM AUTHOR]

Background: When they encounter various highly related postoperative complications, existing risk evaluation tools that focus on single or any complications are inadequate in clinical practice. This seriously hinders complication management because of the lack of a quantitative basis. An interpretable multilabel model framework that predicts multiple complications simultaneously is urgently needed., Materials and Methods: The authors included 50 325 inpatients from a large multicenter cohort (2014-2017). The authors separated patients from one hospital for external validation and randomly split the remaining patients into training and internal validation sets. A MARKov-EmbeDded (MARKED) multilabel model was proposed, and three models were trained for comparison: binary relevance, a fully connected network (FULLNET), and a deep neural network. Performance was mainly evaluated using the area under the receiver operating characteristic curve (AUC). The authors interpreted the model using Shapley Additive Explanations. Complication-specific risk and risk source inference were provided at the individual level., Results: There were 26 292, 6574, and 17 459 inpatients in the training, internal validation, and external validation sets, respectively. For the external validation set, MARKED achieved the highest average AUC (0.818, 95% CI: 0.771-0.864) across eight outcomes [compared with binary relevance, 0.799 (0.748-0.849), FULLNET, 0.806 (0.756-0.856), and deep neural network, 0.815 (0.765-0.866)]. Specifically, the AUCs of MARKED were above 0.9 for cardiac complications [0.927 (0.894-0.960)], neurological complications [0.905 (0.870-0.941)], and mortality [0.902 (0.867-0.937)]. Serum albumin, surgical specialties, emergency case, American Society of Anesthesiologists score, age, and sex were the six most important preoperative variables. The interaction between complications contributed more than the preoperative variables, and formed a hierarchical chain of risk factors, mild complications, and severe complications., Conclusion: The authors demonstrated the advantage of MARKED in terms of performance and interpretability. The authors expect that the identification of high-risk patients and the inference of the risk source for specific complications will be valuable for clinical decision-making., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)