Robotics in Interventional Oncology: A Call to Arms?

Jonathan Tefera, Youssef Ghosn, Francois H Cornelis, David C Madoff

Abstract readReview

In one paragraph

Review in Current oncology reports, 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

4 authors.

Jonathan TeferaDepartment of Radiology and Biomedical Imaging, Section of Interventional Radiology, Yale School of Medicine, 330 Cedar Street, TE-2, New Haven, CT, 06520, USA.

Youssef GhosnDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Francois H CornelisDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

David C MadoffDepartment of Radiology and Biomedical Imaging, Section of Interventional Radiology, Yale School of Medicine, 330 Cedar Street, TE-2, New Haven, CT, 06520, USA. david.madoff@yale.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewInterventional oncology (IO) is a core component of modern cancer care, providing precise, minimally invasive treatments that preserve quality of life. Despite advances in robotic surgery, adoption of robotics in IO has been limited. This review evaluates the potential benefits of robotics in IO and the factors constraining wider use. RECENT

findingsRobotic platforms have shown improved needle placement accuracy, greater workflow standardization, and significant reductions in operator radiation exposure, particularly through remote operation. Robotics may also shorten learning curves and enable technically complex tasks such as multi-needle tumor ablation and advanced endovascular navigation. However, high costs, workflow disruption, limited imaging compatibility, and a narrow range of high-value applications remain key barriers. Robotics in IO represents an evolutionary advance. Wider adoption will require evidence of improved outcomes, seamless integration into image-guided workflows, and sustainable business models that support real-world clinical use.

Indexed as

Medical OncologyNeoplasmsRoboticsRobotic Surgical ProceduresHumansArtificial intelligence integrationEndovascular roboticsImage-guided interventionsInterventional oncologyInterventional radiologyNeedle placement accuracyRadiation exposure reductionRobotic-assisted procedures

Identifiers

PMID41912756

DOI10.1007/s11912-026-01775-6

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.