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Development and validation of a clinical factor-based nomogram for predicting imaging-defined cardiopulmonary abnormality risk in an asymptomatic screening population
Zhong-Yun He, Zhi-Wei Zhan, Le-Jiang Zhou, Jun Tang, Chen Chen, Qing-Jun Yang, Qiang Tian, Department of Radiology, Zhuzhou 331 Hospital, Zhuzhou 412002, Hunan Province, China
Xiao-Hong Wang, Department of Radiology, The Second Xiangya Hospital, Changsha 410011, Hunan Province, China
Co-first authors: Zhong-Yun He and Zhi-Wei Zhan.
Author contributions: Zhan ZW designed the research, analyzed the data, and wrote the paper; Yang QJ and Tian Q performed the research (epidemiological questionnaire data collection); Zhou LJ, Tang J, and Chen C performed the research (imaging data collection and organization); Wang XH analyzed the data; He ZY designed the research, wrote the paper, and obtained the funding. All authors have read and approved the final manuscript. He ZY and Zhan ZW contributed equally to this work as co-first authors.
AI contribution statement: The authors take full responsibility and accountability for all content of this manuscript, including any portions for which AI tools were used as assistive technologies. All AI-assisted outputs were carefully reviewed, validated, and approved by the authors. AI tools were not used to generate original scientific data, perform independent scientific analyses, or draw scientific conclusions.
Supported by Hunan Provincial Natural Science Foundation Regional Joint Fund Project, No. 2024JJ7652.
Institutional review board statement: This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Zhuzhou 331 Hospital (Approval No. 202503-K1-J1).
Informed consent statement: All study participants provided written informed consent prior to examination. This study was approved by the Ethics Committee of Zhuzhou 331 Hospital.
Conflict-of-interest statement: All authors declare that there are no conflicts of interest regarding the publication of this paper.
Data sharing statement: The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request.
Corresponding author: Zhong-Yun He, Department of Radiology, Zhuzhou 331 Hospital, No. 1398 Zhudong Road, Lusong District, Zhuzhou 412002, Hunan Province, China.
Received: June 2, 2026
Revised: June 23, 2026
Accepted: July 8, 2026
Published online: July 28, 2026
Processing time: 53 Days and 22.4 Hours
Revised: June 23, 2026
Accepted: July 8, 2026
Published online: July 28, 2026
Processing time: 53 Days and 22.4 Hours
Core Tip
Core Tip: This study developed and validated a clinical factor-based nomogram for predicting imaging-defined cardiopulmonary imaging abnormalities in 1035 asymptomatic screening individuals using artificial intelligence-assisted low-dose computed tomography quantitative analysis. Five independently associated factors were identified: Smoking history, abnormal metabolic status, body mass index, age, and gender. The nomogram achieved an area under the curve of 0.833 in the validation set. This model may assist early, non-invasive risk stratification for cardiopulmonary imaging abnormalities during routine lung cancer screening, potentially guiding timely preventive interventions in high-risk populations.