"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To guide a deep learning (DL) model's attention toward brain lesion MRI characteristics by incorporating radiology report-derived textual features to achieve interpretable lesion detection. Materials and Methods In this retrospective study, 35282 brain MRI scans (January 2018-June 2023) and corresponding radiology reports from center 1 were used for training, validation, and internal testing. 2655 brain MRI scans (January 2022-December 2022) from centers 2-5 were reserved for external testing. Textual features were extracted from radiology reports to guide a DL model (ReportGuidedNet) focusing on lesion characteristics. Another DL model (PlainNet) without textual features was developed for comparative analysis. Both models diagnosed 15 conditions, including 14 diseases and normal brains. Performance of each model was assessed by calculating macro-and microaveraged area under the receiver operating characteristic curves (ma-AUC, mi-AUC). Attention maps, visualizing model attention, were assessed with a 5-point Likert scale. Results ReportGuidedNet outperformed PlainNet for all diagnoses on both internal (ma-AUC: 0.93 [95% CI: 0.91- 0.95] versus 0.85 [95% CI: 0.81-0.88]; mi-AUC: 0.93 [95% CI: 0.90-0.95] versus 0.89 [95% CI: 0.83-0.92]) and external (ma-AUC: 0.91 [95% CI: 0.88-0.93] versus 0.75 [95% CI: 0.72-0.79]; mi-AUC: 0.90 [95% CI: 0.87-0.92] versus 0.76 [95% CI: 0.72-0.80]) testing sets. The performance difference between internal and external testing sets was smaller for ReportGuidedNet than for PlainNet (Δma-AUC: 0.03 versus 0.10; Δmi-AUC: 0.02 versus 0.13). The Likert scale score of ReportGuidedNet was higher than that of PlainNet (mean ± SD: 2.50 ± 1.09 versus 1.32 ± 1.20; P < .001). Conclusion The integration of radiology report textual features improved the DL model's ability to detect brain lesions, enhancing interpretability and generalizability. Published under a CC BY 4.0 license.