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Background: The most effective method for improving the prognosis of lung cancer is the application of low-dose computed tomography (LDCT) for pulmonary nodule screening in populations at high risk. Timely diagnosis and treatment of early-stage lung cancer can contribute to higher long-term survival rates. However, it remains difficult to differentiate malignant from benign pulmonary nodules measuring 8–15 mm, and avoid overtreatment on the one hand and delayed diagnosis on the other hand. In this consensus paper, we aimed to clarify the definition of “high-risk indeterminate pulmonary nodules (IPNs)” and discuss appropriate evaluation and management to facilitate timely diagnosis of lung cancer to improve lung cancer outcome. Direction for future research was discussed. Methods: A multi-disciplinary panel of doctors and IT experts from Asia, Canada and the U.S. were invited to participate. Published evidence and consensus guidelines were used to develop this consensus was clarified. Their evaluation and management were discussed. Findings: The experts believed that the prevalence of pulmonary nodules was very high, and it that was difficult to diagnose early-stage lung cancer due to the small size of the nodules, often leading to delayed diagnosis or overtreatment. To address this issue and to improve long-term outcome, the panel considered important to revise the classification of high-risk IPNs, (1) as pulmonary nodules that cannot be clearly diagnosed with non-surgical biopsy procedures, but is highly suspicious for early-stage lung cancer. The panel also recommends the most responsible should arrange imaging evaluations and follow-ups, taking new technologies into account. Artificial intelligence (AI) assessment based on the Medical Internet of Things (MIoT) can be combined with expert opinion to form a human–computer multidisciplinary team (MDT) that can fully implement the three core procedures of the MIoT, namely, comprehensive perception, reliable transmission, and intelligent processing. This will help to upgrade the non-standard diagnosis and treatment, the so-called “handicraft workshop model”, to a modern assembly-line model that meets international standards. The MIoT technology, which has the potential to realize “simplification of complex problems, digitalization of simple problems, programming of digital problems, and systematization of programming problems”, can promote the homogeneous evaluation of pulmonary nodules by enhancing both the sensitivity and the specificity of detecting early-stage lung cancer, in order to avoid delayed diagnosis and overtreatment. Conclusion: To optimize the evaluation of early-stage lung cancer, and to avoid delayed diagnosis and overtreatment, it is necessary to propose and promote the concept of “high-risk IPNs”. The application of current technologies, AI, and a human–computer MDT, will facilitate improvement in nodule evaluation, transforming the current diagnosis and treatment model, which is akin to production in handicraft workshops, into a modern assembly-line model that meets international standards, and will ultimately result in better prognosis.

Background and aimsAsthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) is poorly recognized in China. Our study determined the distribution of ACO and its clinical characteristics among patients (aged ⩾40 years) with airflow limitation at Chinese tertiary hospitals.MethodsThis cross-sectional, non-interventional study (NCT02600221), conducted between December 2015 and October 2016 in 20 Tier-3 Chinese hospitals, included patients aged ⩾40 years with post-bronchodilator (BD) FEV1/FVC

To improve the operational efficiency of automated container terminals and the coordination between multiple operations, this article studies the integrated scheduling optimization problem of automated guided vehicles (AGVs) and yard cranes. The impact of charging constraints on AGV task allocation and scheduling is considered. With the goal of minimizing the maximum completion time of all tasks, a mixed integer programming model is proposed. A solution method based on a dynamic programming algorithm is designed, where a heuristic algorithm is used to assign tasks to the yard cranes, and the dynamic programming method is used to assign tasks to the AGVs based on the task assignment results of the yard cranes. Finally, the validity of the model and algorithm is tested by numerical experiments. Furthermore, the influence of the quantity of AGVs on the terminal operational efficiency and the impact of AGV charging strategies on AGV scheduling are analysed. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the study was to establish an ultrasonographic radiomics machine learning model based on endobronchial ultrasound (EBUS) to assist in diagnosing benign and malignant mediastinal and hilar lymph nodes (LNs). Methods: The clinical and ultrasonographic image data of 197 patients were retrospectively analyzed. The radiomics features extracted by EBUS-based radiomics were analyzed by the least absolute shrinkage and selection operator. Then, we used a support vector machine (SVM) algorithm to establish an EBUS-based radiomics model. A total of 205 lesions were randomly divided into training (n = 143) and validation (n = 62) groups. The diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis. Results: A total of 13 stable radiomics features with non-zero coefficients were selected. The SVM model exhibited promising performance in both groups. In the training group, the SVM model achieved an ROC area under the curve (AUC) of 0.892 (95% CI: 0.885–0.899), with an accuracy of 85.3%, sensitivity of 93.2%, and specificity of 79.8%. In the validation group, the SVM model had an ROC AUC of 0.906 (95% CI: 0.890–0.923), an accuracy of 74.2%, a sensitivity of 70.3%, and a specificity of 74.1%. Conclusion: The EBUS-based radiomics model can be used to differentiate mediastinal and hilar benign and malignant LNs. The SVM model demonstrated excellent potential as a diagnostic tool in clinical practice. [ABSTRACT FROM AUTHOR]

Background: With the increasing use of lung cancer screening, the detection of ground glass nodules (GGNs) has risen. However, the natural course of GGNs and their relationship to pathologic features remains unclear. Differentiating between invasive and pre‐invasive lesions based on GGN growth may improve clinical intervention timing. Ki‐67, a proliferation marker, holds value in assessing tumor malignancy. This study analyzes the association between GGN growth, pathology, and Ki‐67 expression to provide new insights into early‐stage lung cancer management. Methods: We retrospectively evaluated 183 GGNs with at least two preoperative CT scans. Nodule location, type, natural course, and volume doubling time (VDT) were compared between invasive adenocarcinoma (IAC) and pre‐IAC groups. We also assessed differences in Ki‐67 expression and correlated VDT with Ki‐67 levels. Results: A total of 183 nodules were finally included; gender, nodule location, smoking history, and duration of follow‐up did not differ between the IAC group and the pre‐IAC group, whereas age was statistically different between the two groups. Of the 183 nodules, 52 showed growth and the predominant pathologic type was IAC, these IACs showed more PSN in nodule type, while the IAC group showed more significant differences in nodule type, nodules growth, and VDT than the pre‐IAC group. There were also differences in pathologic type and VDT between different Ki‐67 expression groups, and Ki‐67 expression gradually increased as VDT decreased. Conclusion: Lung adenocarcinoma (LUAD) presenting as GGNs exhibit distinct natural courses among pathologic subtypes. VDT effectively distinguishes these growth characteristics, with IACs showing shorter VDT. The significant correlation between VDT and Ki‐67 expression suggests that combining these parameters may provide valuable insights into the biological behavior and invasiveness of LUAD. [ABSTRACT FROM AUTHOR]

Management of multiple primary lung cancer (MPLC) remains challenging, partly due to its increasing incidence, especially with the significant rise in cases of multiple lung nodules caused by low‐dose computed tomography screening. Moreover, the indefinite pathogenesis, diagnostic criteria, and treatment selection add to the complexity. In recent years, there have been continuous efforts to dissect the molecular characteristics of MPLC and explore new diagnostic approaches as well as treatment modalities, which will be reviewed here, with a focus on newly emerging evidence and future perspectives, hope to provide new insights into the management of MPLC and serve as inspiration for future research related to MPLC. [ABSTRACT FROM AUTHOR]