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. 2025 Jul 1;25(1):1133.
doi: 10.1186/s12885-025-14466-5.

Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative‌ brain metastasis risk in non-small cell lung cancer

Ye Niu #  1 , Hao-Bo Jia #  2 , Xue-Meng Li #  1 , Wen-Juan Huang  1 , Ping-Ping Liu  1 , Le Liu  1 , Zeng-Yao Liu  1   3 , Qiu-Jun Wang  4 , Yuan-Zhou Li  5 , Shi-Di Miao  6 , Rui-Tao Wang  7 , Ze-Xun Duan  8
Affiliations

Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative‌ brain metastasis risk in non-small cell lung cancer

Ye Niu et al. BMC 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 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.

Supplementary Information: The online version contains supplementary material available at 10.1186/s12885-025-14466-5.

Keywords: Brain metastases; Deep learning; Mediastinal fat area; Nomogram; Non-small-cell lung cancer.

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Conflict of interest statement

Declarations. Ethics approval: This study was conducted in accordance with the Declaration of Helsinki. The Ethical Review Committee of Harbin Medical University Cancer Hospital approved this study (approval number: YD2024–06). Consent to participate: The need for informed consent was waived for this study by the Ethical Review Committee of Harbin Medical University Cancer Hospital, due to its retrospective design and irreversible anonymization of all data. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A model framework diagram
Fig. 2
Fig. 2
The nomogram predicts brain metastases
Fig. 3
Fig. 3
Receiver operating characteristic (ROC) curves for comparison among various prediction models
Fig. 4
Fig. 4
(A) Calibration curves comparing different models. (B) Decision curve analysis comparing different models
Fig. 5
Fig. 5
Kaplan-Meier survival analysis
See this image and copyright information in PMC

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