Evidence map›Paper›PMID 41892940›Full record

ReviewJournal of imaging2026

Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors-A Narrative Review.

Vivek Sanker, Suhrud Panchawgh, Anmol Kaur, Vinay Suresh, Dhanya Mahesh, Eeman Ahmad, Srinath Hariharan, Dhiraj Pangal, Maria Jose Cavgnaro, Mirabela Rusu and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of imaging, 2026. 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

12 authors.

Vivek SankerDepartment of Neurosurgery, Stanford University, Palo Alto, CA 94304, USA.ORCID 0000-0003-0615-8397
Suhrud PanchawghDepartment of Neurology, Mayo Clinic, Scottsdale, AZ 85259, USA.
Anmol KaurDepartment of Neurosurgery, Lady Hardinge Medical College and Associated Hospitals, New Delhi 110001, India.
Vinay SureshDepartment of Neurosurgery, King George Medical University, Lucknow 226003, India.ORCID 0000-0002-1401-9154
Dhanya MaheshDepartment of Neurosurgery, Dartmouth Geisel School of Medicine, Hanover, NH 03755, USA.
Eeman AhmadDepartment of Neurosurgery, Fatima Memorial Hospital College of Medicine and Dentistry, Lahore 54000, Pakistan.
Srinath HariharanDepartment of Radiology, Stanford University, Palo Alto, CA 94304, USA.
Dhiraj PangalDepartment of Neurosurgery, Stanford University, Palo Alto, CA 94304, USA.
Maria Jose CavgnaroDepartment of Neurosurgery, Stanford University, Palo Alto, CA 94304, USA.
Mirabela RusuDepartment of Radiology, Stanford University, Palo Alto, CA 94304, USA.ORCID 0000-0002-0372-0367
John RatliffDepartment of Neurosurgery, Stanford University, Palo Alto, CA 94304, USA.ORCID 0000-0003-3452-1907
Atman DesaiDepartment of Neurosurgery, Stanford University, Palo Alto, CA 94304, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The intersection between radiomics, the computational analysis of imaging data, and machine learning (ML) may lead to new developments in the diagnosis, prognosis, and management of diseases. For spinal tumors specifically, applications of these fields appear promising. In this educational narrative review, we provide a summary of the current advancements in radiomics and artificial intelligence (AI), as well as applications of both fields in the diagnosis and management of spinal tumors. We also provide a suggested workflow of radiomics and machine learning analysis of spinal tumors for researchers, including a list and description of commonly used radiomic features. Future directions in the field of radiomics and machine learning applications to spinal tumors may involve validating already proposed algorithms with larger datasets, ensuring that all computational applications to patient care maintain high ethical standards, and continuing work in developing novel and highly accurate computational techniques to enhance patient outcomes.

Indexed as

artificial intelligencedeep learningmachine learningradiomicsspinal cord tumors

Identifiers

PMID41892940
PMCPMC13028218

What OpenQuestion holds

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LicenceCC BY
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.