1. Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.
- Author
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Wald T, Hamm B, Holzschuh JC, El Shafie R, Kudak A, Kovacs B, Pflüger I, von Nettelbladt B, Ulrich C, Baumgartner MA, Vollmuth P, Debus J, Maier-Hein KH, and Welzel T
- Subjects
- Humans, Retrospective Studies, Male, Female, Middle Aged, Contrast Media, Aged, Organometallic Compounds, Adult, Brain Neoplasms diagnostic imaging, Brain Neoplasms secondary, Deep Learning, Magnetic Resonance Imaging methods
- Abstract
Background: Gadolinium-enhanced "sampling perfection with application-optimized contrasts using different flip angle evolution" (SPACE) sequence allows better visualization of brain metastases (BMs) compared to "magnetization-prepared rapid acquisition gradient echo" (MPRAGE). We hypothesize that this better conspicuity leads to high-quality annotation (HAQ), enhancing deep learning (DL) algorithm detection of BMs on MPRAGE images., Methods: Retrospective contrast-enhanced (gadobutrol 0.1 mmol/kg) SPACE and MPRAGE data of 157 patients with BM were used, either annotated on MPRAGE resulting in normal annotation quality (NAQ) or on coregistered SPACE resulting in HAQ. Multiple DL methods were developed with NAQ or HAQ using either SPACE or MRPAGE images and evaluated on their detection performance using positive predictive value (PPV), sensitivity, and F1 score and on their delineation performance using volumetric Dice similarity coefficient, PPV, and sensitivity on one internal and four additional test datasets (660 patients)., Results: The SPACE-HAQ model reached 0.978 PPV, 0.882 sensitivity, and 0.916 F1-score. The MPRAGE-HAQ reached 0.867, 0.839, and 0.840, the MPRAGE NAQ 0.964, 0.667, and 0.798, respectively (p ≥ 0.157). Relative to MPRAGE-NAQ, the MPRAGE-HAQ F1-score detection increased on all additional test datasets by 2.5-9.6 points (p < 0.016) and sensitivity improved on three datasets by 4.6-8.5 points (p < 0.001). Moreover, volumetric instance sensitivity improved by 3.6-7.6 points (p < 0.001)., Conclusion: HAQ improves DL methods without specialized imaging during application time. HAQ alone achieves about 40% of the performance improvements seen with SPACE images as input, allowing for fast and accurate, fully automated detection of small (< 1 cm) BMs., Relevance Statement: Training with higher-quality annotations, created using the SPACE sequence, improves the detection and delineation sensitivity of DL methods for the detection of brain metastases (BMs)on MPRAGE images. This MRI cross-technique transfer learning is a promising way to increase diagnostic performance., Key Points: Delineating small BMs on SPACE MRI sequence results in higher quality annotations than on MPRAGE sequence due to enhanced conspicuity. Leveraging cross-technique ground truth annotations during training improved the accuracy of DL models in detecting and segmenting BMs. Cross-technique annotation may enhance DL models by integrating benefits from specialized, time-intensive MRI sequences while not relying on them. Further validation in prospective studies is needed., Competing Interests: Declarations. Ethics approval and consent to participate: The prospective study protocol, from which the data was used, was approved by the local ethics committee on 21 September 2017 (S-448/2017). This study complies with the Declaration of Helsinki, the American Medical Association’s professional code of conduct, the principles of Good Clinical Practice (GCP) guidelines, and the Federal Data Protection Act. The study is registered on clinicaltrials.gov under registry number NCT03303365, with a start date of February 1, 2018. All further details, including the study protocol, can be viewed at https://clinicaltrials.gov/ . Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests., (© 2025. The Author(s).)
- Published
- 2025
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