Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative brain metastasis risk in non-small cell lung cancer
Abstract Background and objectives Brain metastasis (BM) significantly affects the prognosis of non-small cell lung cancer (NSCLC) patients. Increasing evidence suggests that adipose tissue influences cancer progression and metastasis. This study aimed to develop a predictive nomogram integrating me...
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BMC
2025-07-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-14466-5 |
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| author | Ye Niu Hao-Bo Jia Xue-Meng Li Wen-Juan Huang Ping-Ping Liu Le Liu Zeng-Yao Liu Qiu-Jun Wang Yuan-Zhou Li Shi-Di Miao Rui-Tao Wang Ze-Xun Duan |
| author_facet | Ye Niu Hao-Bo Jia Xue-Meng Li Wen-Juan Huang Ping-Ping Liu Le Liu Zeng-Yao Liu Qiu-Jun Wang Yuan-Zhou Li Shi-Di Miao Rui-Tao Wang Ze-Xun Duan |
| author_sort | Ye Niu |
| collection | DOAJ |
| description | Abstract Background and objectives Brain metastasis (BM) significantly affects the prognosis of non-small cell lung cancer (NSCLC) patients. Increasing evidence suggests that adipose tissue influences cancer progression and metastasis. This study aimed to develop a predictive nomogram integrating mediastinal fat area (MFA) and deep learning (DL)-derived tumor characteristics to stratify postoperative BM risk in NSCLC patients. Materials and methods A retrospective cohort of 585 surgically resected NSCLC patients was analyzed. Preoperative computed tomography (CT) scans were utilized to quantify MFA using ImageJ software (radiologist-validated measurements). Concurrently, a DL algorithm extracted tumor radiomic features, generating a deep learning brain metastasis score (DLBMS). Multivariate logistic regression identified independent BM predictors, which were incorporated into a nomogram. Model performance was assessed via area under the receiver operating characteristic curve (AUC), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results Multivariate analysis identified N stage, EGFR mutation status, MFA, and DLBMS as independent predictors of BM. The nomogram achieved superior discriminative capacity (AUC: 0.947 in the test set), significantly outperforming conventional models. MFA contributed substantially to predictive accuracy, with IDI and NRI values confirming its incremental utility (IDI: 0.123, P < 0.001; NRI: 0.386, P = 0.023). Calibration analysis demonstrated strong concordance between predicted and observed BM probabilities, while DCA confirmed clinical net benefit across risk thresholds. Conclusion This DL-enhanced nomogram, incorporating MFA and tumor radiomics, represents a robust and clinically useful tool for preoperative prediction of postoperative BM risk in NSCLC. The integration of adipose tissue metrics with advanced imaging analytics advances personalized prognostic assessment in NSCLC patients. |
| format | Article |
| id | doaj-art-db315b040d874ef5be18d9fd770da0a7 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-db315b040d874ef5be18d9fd770da0a72025-08-20T03:45:30ZengBMCBMC Cancer1471-24072025-07-012511910.1186/s12885-025-14466-5Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative brain metastasis risk in non-small cell lung cancerYe Niu0Hao-Bo Jia1Xue-Meng Li2Wen-Juan Huang3Ping-Ping Liu4Le Liu5Zeng-Yao Liu6Qiu-Jun Wang7Yuan-Zhou Li8Shi-Di Miao9Rui-Tao Wang10Ze-Xun Duan11Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical UniversityThe School of Computer Science and Engineering, University of Electronic Science and Technology of ChinaDepartment of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of General Practice, Second Affiliated Hospital of Harbin Medical University, Harbin Medical UniversityDepartment of Radiology, Harbin Medical University Cancer Hospital, Harbin Medical UniversitySchool of Computer Science and Technology, Harbin University of Science and TechnologyDepartment of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Emergency, Harbin Medical University Cancer Hospital, Harbin Medical UniversityAbstract Background and objectives Brain metastasis (BM) significantly affects the prognosis of non-small cell lung cancer (NSCLC) patients. Increasing evidence suggests that adipose tissue influences cancer progression and metastasis. This study aimed to develop a predictive nomogram integrating mediastinal fat area (MFA) and deep learning (DL)-derived tumor characteristics to stratify postoperative BM risk in NSCLC patients. Materials and methods A retrospective cohort of 585 surgically resected NSCLC patients was analyzed. Preoperative computed tomography (CT) scans were utilized to quantify MFA using ImageJ software (radiologist-validated measurements). Concurrently, a DL algorithm extracted tumor radiomic features, generating a deep learning brain metastasis score (DLBMS). Multivariate logistic regression identified independent BM predictors, which were incorporated into a nomogram. Model performance was assessed via area under the receiver operating characteristic curve (AUC), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results Multivariate analysis identified N stage, EGFR mutation status, MFA, and DLBMS as independent predictors of BM. The nomogram achieved superior discriminative capacity (AUC: 0.947 in the test set), significantly outperforming conventional models. MFA contributed substantially to predictive accuracy, with IDI and NRI values confirming its incremental utility (IDI: 0.123, P < 0.001; NRI: 0.386, P = 0.023). Calibration analysis demonstrated strong concordance between predicted and observed BM probabilities, while DCA confirmed clinical net benefit across risk thresholds. Conclusion This DL-enhanced nomogram, incorporating MFA and tumor radiomics, represents a robust and clinically useful tool for preoperative prediction of postoperative BM risk in NSCLC. The integration of adipose tissue metrics with advanced imaging analytics advances personalized prognostic assessment in NSCLC patients.https://doi.org/10.1186/s12885-025-14466-5Brain metastasesNon-small-cell lung cancerDeep learningMediastinal fat areaNomogram |
| spellingShingle | Ye Niu Hao-Bo Jia Xue-Meng Li Wen-Juan Huang Ping-Ping Liu Le Liu Zeng-Yao Liu Qiu-Jun Wang Yuan-Zhou Li Shi-Di Miao Rui-Tao Wang Ze-Xun Duan Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative brain metastasis risk in non-small cell lung cancer BMC Cancer Brain metastases Non-small-cell lung cancer Deep learning Mediastinal fat area Nomogram |
| title | Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative brain metastasis risk in non-small cell lung cancer |
| title_full | Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative brain metastasis risk in non-small cell lung cancer |
| title_fullStr | Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative brain metastasis risk in non-small cell lung cancer |
| title_full_unstemmed | Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative brain metastasis risk in non-small cell lung cancer |
| title_short | Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative brain metastasis risk in non-small cell lung cancer |
| title_sort | deep learning radiomics and mediastinal adipose tissue based nomogram for preoperative prediction of postoperative brain metastasis risk in non small cell lung cancer |
| topic | Brain metastases Non-small-cell lung cancer Deep learning Mediastinal fat area Nomogram |
| url | https://doi.org/10.1186/s12885-025-14466-5 |
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