Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery
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 we...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Surgery |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsurg.2025.1573370/full |
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| author | Qingqing Ji Guohua Zhou Xiangxiang Sun |
| author_facet | Qingqing Ji Guohua Zhou Xiangxiang Sun |
| author_sort | Qingqing Ji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-977805145de343cda8cc4002b2ce0e39 |
| institution | Kabale University |
| issn | 2296-875X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Surgery |
| spelling | doaj-art-977805145de343cda8cc4002b2ce0e392025-08-20T03:39:57ZengFrontiers Media S.A.Frontiers in Surgery2296-875X2025-03-011210.3389/fsurg.2025.15733701573370Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgeryQingqing Ji0Guohua Zhou1Xiangxiang Sun2Shanghai University of Engineering Science, Shanghai, ChinaDepartment of Anesthesiology, Ningbo First Hospital, Ningbo, Zhejiang, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, ChinaThis 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.https://www.frontiersin.org/articles/10.3389/fsurg.2025.1573370/fulldeep learningbiomarkerpostoperative anxietylung cancersurgical resection |
| spellingShingle | Qingqing Ji Guohua Zhou Xiangxiang Sun Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery Frontiers in Surgery deep learning biomarker postoperative anxiety lung cancer surgical resection |
| title | Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery |
| title_full | Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery |
| title_fullStr | Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery |
| title_full_unstemmed | Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery |
| title_short | Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery |
| title_sort | deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery |
| topic | deep learning biomarker postoperative anxiety lung cancer surgical resection |
| url | https://www.frontiersin.org/articles/10.3389/fsurg.2025.1573370/full |
| work_keys_str_mv | AT qingqingji deeplearningsignaturetopredictpostoperativeanxietyinpatientsreceivinglungcancersurgery AT guohuazhou deeplearningsignaturetopredictpostoperativeanxietyinpatientsreceivinglungcancersurgery AT xiangxiangsun deeplearningsignaturetopredictpostoperativeanxietyinpatientsreceivinglungcancersurgery |