Attention-Guided Deep Learning of Chemical Exchange Saturation Transfer Magnetic Resonance Imaging to Differentiate Between Tumor Progression and Radiation Necrosis in Brain Metastasis.
retrospective_cohort · Level III
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- Also identified by DOI 10.1016/j.ijrobp.2025.10.040.
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Abstract
Stereotactic radiosurgery (SRS) is a standard treatment for brain metastases; however, it may lead to radiation necrosis (RN). RN can be virtually indistinguishable from tumor progression (TP), which can have significant clinical implications on appropriate, time-sensitive treatment. This study investigated the effectiveness of multimodal chemical exchange saturation transfer magnetic resonance imaging (MRI), combined with T1/T2 mapping and/or conventional structural MRI, in addressing this diagnostic challenge, when analyzed through attention-guided deep learning. MRI data (3-dimensional amide proton transfer magnetization transfer ratio [Amide<sub>MTR</sub>], relayed nuclear Overhauser effect magnetization transfer ratio [rNOE<sub>MTR</sub>], T1 and T2 parametric maps, and postcontrast T1-weighted [T1c] and T2-weighted fluid-attenuated inversion recovery [T2-FLAIR] images) were acquired from 93 patients (230 brain metastases lesions) treated with SRS a few months prior. Lesion outcomes (TP/RN) were confirmed via histopathology and/or serial clinical imaging, including the use of perfusion imaging, over a follow-up period of at least 6 months. Data were split into training (47 patients; 184 lesions) and independent testing (46 patients; 46 lesions) sets. A 3-dimensional transformer model with 2 new attention mechanisms was developed to classify lesions using various combinations of multimodal MRI inputs. Among dual-channel models, T1c and T2-FLAIR yielded an area under the receiver operating characteristic curve (AUC) of 0.78 ± 0.01, whereas Amide<sub>MTR</sub> and rNOE<sub>MTR</sub> maps achieved 0.76 ± 0.01. Integrating Amide<sub>MTR</sub> and rNOE<sub>MTR</sub> with either T1/T2 maps or T1c/T2-FLAIR substantially improved performance (AUC = 0.84 ± 0.02 and 0.85 ± 0.02, respectively). The highest performance (AUC = 0.87 ± 0.01) was achieved using all 6 modalities. Attention-guided deep-learning analysis of chemical exchange saturation transfer MRI shows strong potential for accurately distinguishing RN from TP, underscoring the significance of multimodal MRI inputs for post-SRS lesion evaluation.