Work

Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery

Year: 2025

Type: article

Abstract: This study aims on establishing and validate a deep learning signature based on magnetic resonance imaging (MRI) to predict postoperative anxiety in patients receiving lung cancer surgery. In the current study, 202 patients receiving lung cancer surgery were included. Preoperative MRI-T1WI images were collected to train the deep learning signature utilized the ResNet-152 algorithm. The relationships between clinical variables and postoperative anxiety were explored via Logistic regression and the predictive performances of the developed deep learning signature were evaluated via receiver operating characteristic analysis. Larger tumor size [odds ratio (OR), 2.044; 95% confidence interval (CI), 1.736–3.276; p = 0.002] and occurrence of lymph node metastasis (OR, 2.078; 95% CI, 1.023–3.221; p = 0.043) were revealed as independent predictors for postoperative anxiety. With the increase of deep learning scores, more patients experiencing postoperative anxiety were identified. Moreover, our deep learning signature yielded areas under the curve of 0.865 (95% CI, 0.800–0.930) and 0.822 (95% CI, 0.695–0.950) to predict postoperative anxiety. Therefore, our deep learning signature could help identify lung cancer patients with high risks of postoperative anxiety.

Source: Frontiers in Surgery

DOI: 10.3389/fsurg.2025.1573370

Authors: Qingqing Ji, Guohua Zhou, Xiangxiang Sun

Institutions: Shanghai University of Engineering Science, Ningbo First Hospital, University of Science and Technology of China

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801