Evidence map›Paper›PMID 41178916›Full record

ArticleWorld journal of clinical oncology2025

Robotic approach for lymphadenectomy of internal mammary lymph nodes in breast cancer: Five case reports.

Alessandro Pardolesi, Michele Ferrari, Giovanni Leuzzi, Alessia Stanzi, Matteo Calderoni, Clarissa Uslenghi, Marco Scarci, Federico Raveglia, Ugo Cioffi, Piergiorgio Solli

Abstract readCase Reports
In one paragraph

Article in World journal of clinical oncology, 2025. 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
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1 · What the graph read from it

What it found

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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

10 authors.

Alessandro PardolesiDepartment of Thoracic Surgery, Istituto Nazionale Tumori, Milan 20133, Lombardy, Italy.
Michele FerrariDepartment of Thoracic Surgery, Istituto Nazionale Tumori, Milan 20133, Lombardy, Italy. michele.ferrari@istitutotumori.mi.it.
Giovanni LeuzziDepartment of Thoracic Surgery, Istituto Nazionale Tumori, Milan 20133, Lombardy, Italy.
Alessia StanziDepartment of Thoracic Surgery, Istituto Nazionale Tumori, Milan 20133, Lombardy, Italy.
Matteo CalderoniDepartment of Thoracic Surgery, Istituto Nazionale Tumori, Milan 20133, Lombardy, Italy.
Clarissa UslenghiDepartment of Thoracic Surgery, Istituto Nazionale Tumori, Milan 20133, Lombardy, Italy.
Marco ScarciDepartment of Thoracic Surgery, University College London Hospitals, London W2 1NY, United Kingdom.
Federico RavegliaDepartment of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Lombardy, Italy.
Ugo CioffiDepartment of Surgery, University of Milan, Milan 20122, Lombardy, Italy.
Piergiorgio SolliDepartment of Thoracic Surgery, Istituto Nazionale Tumori, Milan 20133, Lombardy, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInternal mammary (IM) lymphadenopathies in breast cancer indicate a worse prognosis than axillary metastases, yet they are rarely assessed. Accurate staging is essential for treatment planning. Robotic biopsy offers a promising alternative to video-assisted thoracic surgery for precise histological sampling. This article outlines a systematic robot-assisted dissection approach to enhance staging accuracy and optimize breast cancer management. CASE SUMMARY: At our institution, robotic lymphadenectomy of the IM chain was performed in 5 patients between July 2020 and December 2024. Patients were positioned in a 30° semi-supine position with a roll under the shoulder to elevate the chest. The camera port was inserted in the fifth intercostal space along the mid-axillary line, allowing a 0°, 12 mm robotic camera to inspect the chest cavity; CO

conclusionRobotic biopsy of IM lymph nodes is safe, feasible, and provides key information on breast cancer management, with very rare contraindications.

Indexed as

Breast cancerCase reportInternal mammary chainLymph nodeRobotic surgery

Identifiers

PMID41178916
PMCPMC12576515

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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.