Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis
Open accessAbstract
Significantly affecting patients’ clinical course and quality of life, a growing number of cancer cases are diagnosed with brain metastasis (BM) annually. Stereotactic radiotherapy is now a major treatment option for patients with BM. However, it may take months before the local response of BM to stereotactic radiation treatment is apparent on standard follow-up imaging. While machine learning in conjunction with radiomics has shown great promise in predicting the local response of BM before or early after radiotherapy, further development and widespread application of such techniques has been hindered by their dependency on manual tumour delineation. In this study, we explored the impact of using less-accurate automatically generated segmentation masks on the efficacy of radiomic features for radiotherapy outcome prediction in BM. The findings of this study demonstrate that while the effect of tumour delineation accuracy is substantial for segmentation models with lower dice scores (dice score ≤ 0.85), radiomic features and prediction models are rather resilient to imperfections in the produced tumour masks. Specifically, the selected radiomic features (six shared features out of seven) and performance of the prediction model (accuracy of 80% versus 80%, AUC of 0.81 versus 0.78) were fairly similar for the ground-truth and automatically generated segmentation masks, with dice scores close to 0.90. The positive outcome of this work paves the way for adopting high-throughput automatically generated tumour masks for discovering diagnostic and prognostic imaging biomarkers in BM without sacrificing accuracy.
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Under-segmentation, failing to fully capture lesion boundaries, can result in pathological regions being missed and thus left untreated.
“under-segmentation-failing to fully capture lesion boundaries-can result in pathological regions being missed and thus left untreated (Jalalifar et al. 2022; Zhao et al. 2025).”
From Conformal Lesion Segmentation for 3D Medical Images (2025) · cites this paper as Jalalifar et al. 2022
The Introduction (page unavailable) says the entire tumour volume should be segmented and accurate delineation is fundamental to radiomic analysis. It does not say that an incompletely segmented region would be left untreated; the study examines segmentation for imaging biomarkers and radiotherapy outcome prediction, not whether segmentation errors cause treatment omissions. (LLM, conf=0.83)
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- Authors
- Jalalifar, S. A., Soliman, H., Sahgal, A., & Sadeghi-Naini, A.
- Affiliations
- York University (CA)
- Sunnybrook Health Science Centre (CA)
- Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2022-10-20
- Volume
- 14
- Issue
- 20
- Pages
- 5133
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC9601104
- OpenAlex
- W4306938191
- Cited by
- 15
- References
- 34
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 1 · 2023: 3
- FWCI
- 1.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Natural Sciences and Engineering Research Council (NSERC) of Canada (CRDPJ507521-16, RGPIN-2016-06472, 1083)
- Lotte and John Hecht Memorial Foundation (CRDPJ507521-16, RGPIN-2016-06472, 1083)
- Terry Fox Foundation (CRDPJ507521-16, RGPIN-2016-06472, 1083)
- Ontario Ministry of Colleges and Universities (CRDPJ507521-16, RGPIN-2016-06472, 1083)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Brain Metastases and Treatment
- Keywords
- brain metastases, tumor segmentation, radiomics, stereotactic radiotherapy, radiotherapy outcomes, Dice score, quantitative MRI biomarkers, machine learning prediction models
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