Evidence map›Paper›PMID 34201652›Full record

ReviewCancers2021

Technical Advances in Segmentectomy for Lung Cancer: A Minimally Invasive Strategy for Deep, Small, and Impalpable Tumors.

Takashi Eguchi, Toshihiko Sato, Kimihiro Shimizu

Abstract readReview
In one paragraph

Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Does Robotic Segmentectomy Improve Long-Term Outcomes in Non-Small Cell Lung Cancer?European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2026
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  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. National trends in the quality of segmentectomy for lung cancer.The Journal of thoracic and cardiovascular surgery · 2023
    Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article
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.

Takashi EguchiDivision of General Thoracic Surgery, Department of Surgery, School of Medicine, Shinshu University, Matsumoto 390-8621, Japan.ORCID 0000-0001-9460-0696
Toshihiko SatoDepartment of General Thoracic, Breast, Pediatric Surgery, Faculty of Medicine, Fukuoka University, Fukuoka 814-0180, Japan.
Kimihiro ShimizuDivision of General Thoracic Surgery, Department of Surgery, School of Medicine, Shinshu University, Matsumoto 390-8621, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the increased detection of early-stage lung cancer and the technical advancement of minimally invasive surgery (MIS) in the field of thoracic surgery, lung segmentectomy using MIS, including video- and robot-assisted thoracic surgery, has been widely adopted. However, lung segmentectomy can be technically challenging for thoracic surgeons due to (1) complex segmental and subsegmental anatomy with frequent anomalies, and (2) difficulty in localizing deep, small, and impalpable tumors, leading to difficulty in obtaining adequate margins. In this review, we summarize the published evidence and discuss key issues related to MIS segmentectomy, focusing on preoperative planning/simulation and intraoperative tumor localization. We also demonstrate two of our techniques: (1) three-dimensional computed tomography (3DCT)-based resection planning using a novel 3DCT processing software, and (2) tumor localization using a novel radiofrequency identification technology.

Indexed as

localizationlung segmentectomyradiofrequency identificationthree-dimensional computed tomography

Identifiers

PMID34201652
PMCPMC8268648

What OpenQuestion holds

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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.