MRI-based intratumoral and peritumoral heterogeneity for predicting intracranial progression in EGFR-mutant lung adenocarcinoma patients with treatment-naive brain metastases receiving first-line third-generation EGFR-TKIs.
6 affiliations
- 1Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Tianwen Avenue No. 288, Nan'an District, Chongqing, 400010, China.
- 2Department of Radiology, The Second Qilu Hospital of Shandong University, Jinan, 250033, China.
- 3Institute of Research and Clinical Innovations, Neusoft Medical Systems Co. Ltd, Beijing, 100094, China.
- 4Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan City, 250117, China.
- 5Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan City, 250117, China. [email protected].
- 6Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China. [email protected].
Abstract
Purpose
To develop and externally test a multiparametric MRI habitat-based framework integrating intratumoral heterogeneity (ITH), peritumoral heterogeneity (PTH), and clinical characteristics for predicting intracranial progression in patients with EGFR-mutant lung adenocarcinoma and de novo brain metastases receiving first-line third-generation (3rd-gen) EGFR-TKI monotherapy.Materials and methods
This retrospective multicenter study included patients with EGFR-mutant lung adenocarcinoma and de novo brain metastases who received first-line 3rd-gen EGFR-TKI monotherapy. Patients were assigned to an internal training cohort (n = 90) and an external testing cohort (n = 51). The binary prediction outcome was intracranial progression within 20 months after treatment initiation. Pretreatment contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging were analyzed. The index brain metastasis and a 6-mm peritumoral region were segmented, followed by voxel-wise habitat clustering. Habitat-specific radiomic features were extracted and selected to construct ITH, PTH, and combined intratumoral-peritumoral habitat (PITH) signatures. Clinical predictors were evaluated using univariable and multivariable logistic regression. Support vector machine models were evaluated using receiver operating characteristic, decision curve, calibration, and Kaplan-Meier analyses.Results
A total of 141 patients were analyzed, including 75 with progressive disease (PD) and 66 without PD (non-PD). Multivariable logistic regression identified EGFR mutation subtype (odds ratio [OR] = 2.390, 95% confidence interval [CI]: 1.187-4.811, P = 0.015) and the maximum diameter of the index brain metastasis as independent predictors (OR = 2.048, 95% CI: 1.204-3.483, P = 0.008). Models based on imaging heterogeneity features outperformed the clinical model overall. The clinical model achieved an AUC of 0.662, while the ITH, PTH, and PITH models achieved AUCs of 0.715, 0.724, and 0.758, respectively. The combined model showed favorable performance (AUC 0.795, accuracy 76.5%, sensitivity 70.4%, and specificity 83.3%). Patients classified by the combined model as high risk had significantly shorter intracranial progression-free survival (iPFS), than those classified as low risk (log-rank P = 0.019).Conclusion
The proposed framework integrating ITH, PTH, and clinical features may provide a noninvasive approach for predicting intracranial progression risk and stratifying prognosis in patients with EGFR-mutant lung adenocarcinoma with newly diagnosed BMs receiving first-line 3rd-gen EGFR-TKI monotherapy.
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BibTeX
@article{deng2026mribased,
title = {{MRI-based intratumoral and peritumoral heterogeneity for predicting intracranial progression in EGFR-mutant lung adenocarcinoma patients with treatment-naive brain metastases receiving first-line third-generation EGFR-TKIs.}},
author = {Guangchuan Deng and Houying Li and Zhengjun Dai and Jing Fan and Xiaopeng Song and Guodong Pang and Zhenxiang Li and Fang Zhao},
journal = {Journal of translational medicine},
year = {2026},
month = oct,
doi = {10.1186/s12967-026-09109-7},
url = {https://europepmc.org/article/MED/42958288},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - MRI-based intratumoral and peritumoral heterogeneity for predicting intracranial progression in EGFR-mutant lung adenocarcinoma patients with treatment-naive brain metastases receiving first-line third-generation EGFR-TKIs.
AU - Guangchuan Deng
AU - Houying Li
AU - Zhengjun Dai
AU - Jing Fan
AU - Xiaopeng Song
AU - Guodong Pang
AU - Zhenxiang Li
AU - Fang Zhao
PY - 2026
DA - 2026/10/17
JO - Journal of translational medicine
DO - 10.1186/s12967-026-09109-7
UR - https://europepmc.org/article/MED/42958288
AB - Purpose To develop and externally test a multiparametric MRI habitat-based framework integrating intratumoral heterogeneity (ITH), peritumoral heterogeneity (PTH), and clinical characteristics for predicting intracranial progression in patients with EGFR-mutant lung adenocarcinoma and de novo brain metastases receiving first-line third-generation (3rd-gen) EGFR-TKI monotherapy.Materials and methods This retrospective multicenter study included patients with EGFR-mutant lung adenocarcinoma and de novo brain metastases who received first-line 3rd-gen EGFR-TKI monotherapy. Patients were assigned to an internal training cohort (n = 90) and an external testing cohort (n = 51). The binary prediction outcome was intracranial progression within 20 months after treatment initiation. Pretreatment contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging were analyzed. The index brain metastasis and a 6-mm peritumoral region were segmented, followed by voxel-wise habitat clustering. Habitat-specific radiomic features were extracted and selected to construct ITH, PTH, and combined intratumoral-peritumoral habitat (PITH) signatures. Clinical predictors were evaluated using univariable and multivariable logistic regression. Support vector machine models were evaluated using receiver operating characteristic, decision curve, calibration, and Kaplan-Meier analyses.Results A total of 141 patients were analyzed, including 75 with progressive disease (PD) and 66 without PD (non-PD). Multivariable logistic regression identified EGFR mutation subtype (odds ratio [OR] = 2.390, 95% confidence interval [CI]: 1.187-4.811, P = 0.015) and the maximum diameter of the index brain metastasis as independent predictors (OR = 2.048, 95% CI: 1.204-3.483, P = 0.008). Models based on imaging heterogeneity features outperformed the clinical model overall. The clinical model achieved an AUC of 0.662, while the ITH, PTH, and PITH models achieved AUCs of 0.715, 0.724, and 0.758, respectively. The combined model showed favorable performance (AUC 0.795, accuracy 76.5%, sensitivity 70.4%, and specificity 83.3%). Patients classified by the combined model as high risk had significantly shorter intracranial progression-free survival (iPFS), than those classified as low risk (log-rank P = 0.019).Conclusion The proposed framework integrating ITH, PTH, and clinical features may provide a noninvasive approach for predicting intracranial progression risk and stratifying prognosis in patients with EGFR-mutant lung adenocarcinoma with newly diagnosed BMs receiving first-line 3rd-gen EGFR-TKI monotherapy.
SN - 1479-5876
ER -