ArticleFrontiers in oncology2021
Deep Learning-Based Computer-Aided Detection System for Automated Treatment Response Assessment of Brain Metastases on 3D MRI.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated longitudinal treatment response assessment of brain tumors: A systematic review.Neuro-oncology · 2025Pooled it
- Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study.Neuro-oncology · 2024Trial
- Central Nervous System Involvement in Acute Myeloid Leukemia: From Pathophysiology to Neuroradiologic Features and the Emerging Role of Artificial Intelligence.Journal of clinical medicine · 2026Review
- Challenges of Applying the RANO-BM Criteria for Characterization of Brain Metastases Treatment Response.Current oncology (Toronto, Ont.) · 2026Review
- Deep learning [Clinical and translational radiation oncology · 2026Article
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- A federated learning architecture for secure and private neuroimaging analysis.Patterns (New York, N.Y.) · 2024Article
- Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.Scientific reports · 2024Article
- Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging.Mayo Clinic proceedings. Digital health · 2024Article
- Construction and evaluation of a gated high-resolution neural network for automatic brain metastasis detection and segmentation.European radiology · 2023Article
- Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response.International journal of molecular sciences · 2023Review
- Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis.Cancers · 2023Review
- Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment.Frontiers in oncology · 2023Article
- 2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data.Frontiers in neuroinformatics · 2022Article
- Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics.Frontiers in oncology · 2022Article
- The value of AI for assessing longitudinal brain metastases treatment response.Neuro-oncology advancesArticle
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15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough accurate treatment response assessment for brain metastases (BMs) is crucial, it is highly labor intensive. This retrospective study aimed to develop a computer-aided detection (CAD) system for automated BM detection and treatment response evaluation using deep learning.
methodsWe included 214 consecutive MRI examinations of 147 patients with BM obtained between January 2015 and August 2016. These were divided into the training (174 MR images from 127 patients) and test datasets according to temporal separation (temporal test set #1; 40 MR images from 20 patients). For external validation, 24 patients with BM and 11 patients without BM from other institutions were included (geographic test set). In addition, we included 12 MRIs from BM patients obtained between August 2017 and March 2020 (temporal test set #2). Detection sensitivity, dice similarity coefficient (DSC) for segmentation, and agreements in one-dimensional and volumetric Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria between CAD and radiologists were assessed.
resultsIn the temporal test set #1, the sensitivity was 75.1% (95% confidence interval [CI]: 69.6%, 79.9%), mean DSC was 0.69 ± 0.22, and false-positive (FP) rate per scan was 0.8 for BM ≥ 5 mm. Agreements in the RANO-BM criteria were moderate (
conclusionsOur CAD showed potential for automated treatment response assessment of BM ≥ 5 mm.
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