Evidence map›Paper›PMID 39523273›Full record

ReviewAdvances in experimental medicine and biology2024

Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.

Cheng-Chia Lee, Huai-Che Yang, Hsiu-Mei Wu, Yen-Yu Lin, Chia-Feng Lu, Syu-Jyun Peng, Yu-Te Wu, Jason P Sheehan, Wan-Yuo Guo

Abstract readReview
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In one paragraph

Review in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Cheng-Chia LeeDepartment of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan. cclee12@vghtpe.gov.tw.
Huai-Che YangDepartment of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.
Hsiu-Mei WuSchool of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Yen-Yu LinDepartment of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.
Chia-Feng LuDepartment of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Syu-Jyun PengIn-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipai, Taiwan.
Yu-Te WuBrain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Jason P SheehanDepartment of Neurological Surgery, University of Virginia, Charlottesville, VA, USA.
Wan-Yuo GuoSchool of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in this field, particularly through the utilization of machine learning and deep learning models. The diverse implementation of AI algorithms was developed from various aspects of radiosurgery, including the successful detection of spontaneous tumors and the automated delineation or segmentation of lesions. These applications show potential for extension to longitudinal treatment follow-up. Additionally, the chapter highlights the established use of machine learning algorithms, particularly those incorporating radiomic-based analysis, in predicting treatment outcomes. The discussion encompasses current achievements, existing limitations, and the need for further investigation in the dynamic intersection of AI and radiosurgery.

Indexed as

Artificial IntelligenceRadiosurgeryAlgorithmsBrain NeoplasmsComputer SimulationDeep LearningHumansMachine LearningRadiation OncologyArtificial intelligenceDeep learningDetectionGamma KnifeMachine learningPredictionRadiomicsRadiosurgerySegmentationStereotactic

Identifiers

PMID39523273

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.