Prospective randomized study on the efficacy of three-dimensional reconstructions of bronchovascular structures on preoperative chest CT scan in patients who are candidates for pulmonary segmentectomy surgery: the PATCHES (Prospective rAndomized sTudy efficaCy of tHree-dimensional rEconstructions Segmentecomy) study protocol.


Journal

Trials
ISSN: 1745-6215
Titre abrégé: Trials
Pays: England
ID NLM: 101263253

Informations de publication

Date de publication:
16 Sep 2023
Historique:
received: 13 04 2023
accepted: 22 08 2023
medline: 18 9 2023
pubmed: 17 9 2023
entrez: 16 9 2023
Statut: epublish

Résumé

Pulmonary segmentectomy, when combined with hilar and mediastinal lymphadenectomy, is currently considered the gold standard treatment for early-stage lung tumors (NSCLC) smaller than 2 cm in diameter. The preoperative planning for segmentectomies usually includes a contrast-enhanced CT with 2D reconstructions (axial, coronary, and sagittal). Recent technological advances allow 3D (volume rendering) reconstructions of preoperative CT scans, intended to improve the surgeon's understanding of the segmental anatomy. The study aims to investigate the added value of 3D reconstruction in enhancing the surgeon's understanding of anatomical structures, thus facilitating surgical planning and improving oncological outcomes. This is a prospective, randomized, controlled study. Patients will be randomized into two groups: 1. Group 2D: the preoperative workup for these patients will consist of a contrast-enhanced chest CT with two-dimensional (2D) reconstructions (axial, coronary, and sagittal); 2. Group 3D: the preoperative workup for these patients will consist of a contrast-enhanced chest CT with two-dimensional (2D) reconstructions (axial, coronary, and sagittal) and a 3D reconstruction (volume rendering) of the same chest CT employing dedicated software. The primary endpoints will be negative margin (R0) resection rate, resection margin (staple line-to-tumor distance), and thoracotomy conversions. We will use Fisher's exact test for binary outcomes and Mann-Whitney U test for continuous outcomes. For subgroup analyses, we will use regression. Multivariable analyses will be based on logistic regression for binary outcomes and linear regression for continuous outcomes. The protocol and the model informed consent forms have been reviewed and approved by the ethics committee (N.: 1-2023) concerning scientific content and compliance with applicable research and human subject regulations. A Subcommittee on Publications was established to review all publications and report its recommendations to the steering committee. The anonymized participant-level dataset and statistical code for generating the results will not be publicly available. The protocol was registered at ClinicalTrials.gov (ID: NCT05716815; Prospective rAndomized sTudy efficaCy tHree-dimensional rEconstructions Segmentectomy - Full-Text View - ClinicalTrials.gov). Jan 19, 2023.

Identifiants

pubmed: 37717001
doi: 10.1186/s13063-023-07600-w
pii: 10.1186/s13063-023-07600-w
pmc: PMC10504737
doi:

Banques de données

ClinicalTrials.gov
['NCT05716815']

Types de publication

Clinical Trial Protocol Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

594

Investigateurs

Firas Abu Akar (FA)
Giorgio Cannone (G)
Mahmoud Ismail (M)
Marcelo Jiménez (M)
Marko Kostic (M)
Calvin S H Ng (CSH)
Reinhold Perkmann (R)
Elena Priscindaro (E)
Lorenzo Spaggiari (L)
Paula Ugalde (P)

Commentaires et corrections

Type : ErratumIn

Informations de copyright

© 2023. BioMed Central Ltd., part of Springer Nature.

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Auteurs

Francesco Zaraca (F)

Department of Vascular and Thoracic Surgery, Regional Hospital, Bolzano, Italy.

Andreas Kirschbaum (A)

Department of Visceral, Thoracic and Vascular Surgery, University of Marburg, Marburg, Germany.

Marco Damiano Pipitone (MD)

Department of Vascular and Thoracic Surgery, Regional Hospital, Bolzano, Italy.

Luca Bertolaccini (L)

Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy. luca.bertolaccini@gmail.com.

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Classifications MeSH