Evidence map›Paper›PMID 42794895›Full record

ReviewCancers2026

Single Anesthetic Approach to Diagnosis, Staging and Treatment of Lung Cancer.

Matthew Aizpuru, Jackson Wittenberg, Janani Reisenauer

Abstract readReview
In one paragraph

Review in Cancers, 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

3 authors.

Matthew AizpuruDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, 200 First St. SW, Rochester, MN 55906, USA.ORCID 0000-0002-6030-6226
Jackson WittenbergDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, 200 First St. SW, Rochester, MN 55906, USA.
Janani ReisenauerDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, 200 First St. SW, Rochester, MN 55906, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The conventional workup for suspected early-stage lung cancer requires multiple visits, anesthetics, and procedures. Single anesthetic event lung cancer surgery integrates shape-sensing robotic bronchoscopy, cone-beam CT, rapid on-site cytologic evaluation (ROSE), endobronchial ultrasound staging, lesion localization, and minimally invasive resection into one operative encounter. In this narrative review, we describe the technical components of this pathway, summarize the supporting literature, and offer expert, institution-based troubleshooting guidance for common intraoperative dilemmas. Reported diagnostic yields for shape-sensing robotic bronchoscopy range from 80 to 96%, with approximately 90% concordance between ROSE and final pathology. Published single-institution series report reductions in time from detection to resection of 15-51 days, cost savings of approximately $3000-$10,000, and perioperative outcomes (length of stay 1.8-3.6 days; complication rates comparable to traditional pathways) similar to staged care. The supporting evidence is retrospective and derived from small, single-institution, high-volume referral cohorts; no prospective comparative trials or long-term survival data yet exist. Single anesthetic event lung cancer surgery is therefore best regarded as an emerging, resource-intensive care pathway for carefully selected patients at experienced centers rather than an established standard of care, pending prospective, multicenter validation.

Indexed as

adenocarcinomalung cancerrobotic bronchoscopysingle anesthetic eventsingle stagess-RAB

Identifiers

PMID42794895
PMCPMC13604086

What OpenQuestion holds

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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.