Evidence map›Paper›PMID 42026695›Full record

ArticleSystematic reviews2026

Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.

Sidharth Satish Menon, Umesh Velu, Lavanya Gurram, Ganesh Paramasivam, Manjunath Kn, Divya Susanna Patil, Vijay Shree Dhyani, Roshan David Jathanna, Shirley Lewis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Systematic reviews, 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

9 authors.

Sidharth Satish MenonDepartment of Radiation Oncology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
Umesh VeluDepartment of Radiation Oncology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
Lavanya GurramDepartment of Radiation Oncology, CancerCare Manitoba, Winnipeg, Manitoba, R3E0V9, Canada.
Ganesh ParamasivamDepartment of Cardiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
Manjunath KnManipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Divya Susanna PatilDepartment of Health Technology and Informatics, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India.
Vijay Shree DhyaniKasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
Roshan David JathannaManipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Shirley LewisDepartment of Radiation Oncology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India. shirley.salins@manipal.edu.ORCID 0000-0001-5306-1649

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDeep learning (DL)-based artificial intelligence (AI) models, the fourth generation in autosegmentation, have been adopted both for commercial and research applications worldwide and have shown great promise as reliable and comprehensive resource strategies for radiotherapy workflows.

methodsA comprehensive search will be conducted on Medline (PubMed), Scopus, the Cochrane Library, EMBASE, and Web of Science between January 2004 and December 2025. We will conduct a title, abstract, and full-text screening of all studies as per the eligibility criteria. Two reviewers will be involved in screening studies, quality appraisal, and data extraction, and a third reviewer will be consulted to resolve conflicts. Based on data availability, the data will be synthesised via meta-analysis and narrative synthesis. The reporting will be performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring the reliability and validity of the results. DISCUSSION: The model's performance will be assessed using various quantitative indices, qualitative tools, timesavings, and dosimetry. This review will enable us to determine the accuracy of autosegmentation for targets and various pelvic organs, boosting clinicians' confidence in facilitating the clinical implementation of such tools in routine clinical practice. SYSTEMATIC REVIEW REGISTRATION: The protocol is prospectively registered on PROSPERO. The registration ID is CRD42024491066.

Indexed as

Deep LearningPelvic NeoplasmsRadiation OncologyHumansMeta-Analysis as TopicSystematic Reviews as TopicAutosegmentationCancerConvolutional neural network (CNN)Deep learningDice coefficient scorePelvic malignancy

Identifiers

PMID42026695
PMCPMC13237914

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Registered trials

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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.