Evidence map›Paper›PMID 35456265›Full record

ArticleJournal of clinical medicine2022

Oncological Outcomes of Robotic Lobectomy and Radical Lymphadenectomy for Early-Stage Non-Small Cell Lung Cancer.

Filippo Tommaso Gallina, Riccardo Tajè, Daniele Forcella, Felicita Corzani, Virna Cerasoli, Paolo Visca, Cecilia Coccia, Federico Pierconti, Isabella Sperduti, Fabiana Letizia Cecere and 3 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.3field-weighted citation impact, top 11% of its field
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

12 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Current state of evidence for RATS lobectomy for NSCLC.Journal of cardiothoracic surgery · 2026
    Review
  3. Article
  4. Article
  5. Application value of CT-guided localization using a coil in combination with medical adhesive in sublobar resection.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Applications of Robotic Surgery in Thoracic Diseases.Journal of clinical medicine · 2022
    Article
  11. Article
  12. Review
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

13 authors at 1 institution in 1 country.

Filippo Tommaso GallinaThoracic Surgery Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Riccardo TajèThoracic Surgery Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Daniele ForcellaThoracic Surgery Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Felicita CorzaniThoracic Surgery Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Virna CerasoliThoracic Surgery Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Paolo ViscaDepartment of Pathology, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Cecilia CocciaAnesthesiology and Intensive Care Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Federico PiercontiAnesthesiology and Intensive Care Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Isabella SperdutiBiostatistics, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Fabiana Letizia CecereMedical Oncology 1, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Federico CappuzzoMedical Oncology 2, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Enrico MelisThoracic Surgery Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Francesco FaccioloThoracic Surgery Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Istituti di Ricovero e Cura a Carattere Scientifico · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: While the thoracotomy approach was considered the gold standard until two decades ago, robotic surgery has increasingly strengthened its role in lung cancer treatment, improving patients’ peri-operative outcomes. In this study, we report our experience in robotic lobectomy for early-stage non-small cell lung cancer, with particular attention to oncological outcomes and nodal upstaging rate. Methods: We retrospectively reviewed patients who underwent lobectomy and radical lymphadenectomy at our Institute between 2016 and 2020. We selected 299 patients who met the inclusion criteria of the study. We analyzed the demographic features of the groups as well as their nodal upstaging rate after pathological examination. Then, we analyzed disease-free and overall survival of the entire enrolled patient population and we compared the same oncological outcomes in the upstaging and the non-upstaging group. Results: A total of 299 patients who underwent robotic lobectomy were enrolled. After surgery, 55 patients reported nodal hilar or mediastinal upstaging. The 3-year overall survival of the entire population was 82.8%. The upstaging group and the non-upstaging group were homogeneous for age, gender, smoking habits, clinical stage, tumor site, tumor histology. The non-upstaging group had better OS (p = 0.004) and DFS (p < 0.0001). Conclusion: Our results show that robotic surgery is a safe and feasible approach for the treatment of early-stage NSCLC, especially for its accuracy in mediastinal lymphadenectomy. The oncological outcomes were encouraging and consistent with previous findings.

Indexed as

early-stage NSCLClobectomynodal upstagingrobotic surgery

Identifiers

PMID35456265
PMCPMC9025272
OpenAlexW4223942988

What OpenQuestion holds

Textmetadata
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.