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Abstract Background The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false‐positive reduction, nodule classification, and prognosis. Methodology This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false‐positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. Results AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false‐positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. Conclusions AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false‐positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large‐scale validation of new deep learning‐based algorithms and multi‐center studies to improve the efficacy of AI in lung cancer screening.

The single event effect caused by space heavy ion radiation is one of the important factors affecting the safety and operation of spacecraft on orbit. In the research and evaluation of the frequency, spatial distribution and time characteristics of single event effects, linear energy transfer (LET) spectra of space radiation play an important role. On the Beidou navigation M15 and M16 satellites, a single event upset (SEU) and LET monitor was developed to obtain the upsets of the memory device and the LET spectra of space radiation which passes through the device. Through the measurement results from this monitor, the correlation between the device’s SEUs and the LET spectra could be studied.

This work introduces the instruments’ design of multi-directional medium-energy electron detector (energy range: 30–770 keV) and high-energy electron detector (energy range: 400 keV to 5.8 MeV) deployed on one of the BeiDou navigation satellites in medium earth orbit. Through the electromagnetic compatibility design, the problem of the detector signal being susceptible to electromagnetic interference is solved and the lower detection limit reaches a very low level of 20 keV. The proton contamination in the electron measurement can be effectively removed by designing the light-blocking layer and the logical working mode of the sensors. The detector circuits and sensor structure are optimized and the miniaturization of the instrument is realized. This study also introduces the calibration method of the detectors, and gives the ground test results, which can provide a basis for the on-orbit calibration and data processing of the instrument.

Based on orbit detection data acquired by a positive channel Metal Oxide Semiconductor (PMOS) dose detectors on FY4-A (GEO), BD3-M15 (MEO), and YH1-01A (LEO) between November 2018 and November 2022, investigations reveal variations in total dose and the mechanism of radiation dose increase within the geostationary earth orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) during the transition from the 24th to the 25th solar cycles. It provides the radiation dose parameters for the study of the space environment from different altitude orbits, and also provides an important basis for studying the solar minimum activity and dose generation The data indicate directional disparities in radiation doses among the orbital regions, with the hierarchy being FY4-A > YH1-01A > BD3-M15. Furthermore, the results show that the total doses of FY4-A and BD3-M15 were higher than that of YH1-01A by two orders of magnitude, with BD3-M15 > FY4-A > YH1-01A. The monthly radiation dose rates of FY4-A in GEO and BD3-M15 in MEO exhibited positive correlation with their corresponding APs during the solar minimum. Notably, for FY4-A, the monthly radiation dose rate during geomagnetic disturbed periods exceeded that of the dose rate during geomagnetic quiet periods by one order of magnitude. This analysis revealed the substantial impact of geomagnetic storms and space environment disturbances on radiation doses detected by MEO and GEO orbital satellites. These perturbations, attributable to medium- and small-scale high-energy electron storms induced by reproducible coronal holes, emerged as key driving factors of the increase in radiation doses in MEO and GEO environments. [ABSTRACT FROM AUTHOR]

Background: The prognosis of lung cancer varies widely, even in cases wherein the tumor stage, genetic mutation, and treatment regimens are the same. Thus, an effective means for risk stratification of patients with lung cancer is needed. Purpose: To develop and validate a combined model for predicting progression-free survival and risk stratification in patients with advanced anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) treated with ensartinib. Material and Methods: We analyzed 203 tumor lesions in 114 patients and evaluated average radiomic feature measures from all lesions at baseline and changes in these features after early treatment (Δradiomic features). Combined models were developed by integrating clinical with radiomic features. The prediction performance and clinical value of the proposed models were evaluated using receiver operating characteristic analysis, calibration curve, decision curve analysis (DCA), and Kaplan–Meier survival analysis. Results: Both the baseline and delta combined models achieved predictive efficacy with a high area under the curve. The calibration curve and DCA indicated the high accuracy and clinical usefulness of the combined models for tumor progression prediction. In the Kaplan–Meier analysis, the delta and baseline combined models, Δradiomic signature, and two selected clinical features could distinguish patients with a higher progression risk within 42 weeks. The delta combined model had the best performance. Conclusion: The combination of clinical and radiomic features provided a prognostic value for survival and progression in patients with NSCLC receiving ensartinib. Radiomic-signature changes after early treatment could be more valuable than those at baseline alone. [ABSTRACT FROM AUTHOR]