Cylindrical TGR as early radiological predictor of RLT progression in GEPNETs: a proof of concept.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
09 Jul 2024
Historique:
received: 04 01 2024
accepted: 03 07 2024
medline: 10 7 2024
pubmed: 10 7 2024
entrez: 9 7 2024
Statut: epublish

Résumé

This study aims to assess the predictive capability of cylindrical Tumor Growth Rate (cTGR) in the prediction of early progression of well-differentiated gastro-entero-pancreatic tumours after Radio Ligand Therapy (RLT), compared to the conventional TGR. Fifty-eight patients were included and three CT scans per patient were collected at baseline, during RLT, and follow-up. RLT response, evaluated at follow-up according to RECIST 1.1, was calculated as a percentage variation of lesion diameters over time (continuous values) and as four different RECIST classes. TGR between baseline and interim CT was computed using both conventional (approximating lesion volume to a sphere) and cylindrical (called cTGR, approximating lesion volume to an elliptical cylinder) formulations. Receiver Operating Characteristic (ROC) curves were employed for Progressive Disease class prediction, revealing that cTGR outperformed conventional TGR (area under the ROC equal to 1.00 and 0.92, respectively). Multivariate analysis confirmed the superiority of cTGR in predicting continuous RLT response, with a higher coefficient for cTGR (1.56) compared to the conventional one (1.45). This study serves as a proof of concept, paving the way for future clinical trials to incorporate cTGR as a valuable tool for assessing RLT response.

Identifiants

pubmed: 38982134
doi: 10.1038/s41598-024-66668-9
pii: 10.1038/s41598-024-66668-9
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

15782

Informations de copyright

© 2024. The Author(s).

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Auteurs

Federica Scalorbi (F)

Nuclear Medicine Department, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy.

Enrico Matteo Garanzini (EM)

Department of Radiodiagnostics and Radiotherapy, IRCCS Fondazione Istituto Nazionale Tumori, Milan, Italy.

Giuseppina Calareso (G)

Department of Radiodiagnostics and Radiotherapy, IRCCS Fondazione Istituto Nazionale Tumori, Milan, Italy.

Chiara Marzi (C)

Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Viale Morgagni 59, 50134, Florence, Italy. chiara.marzi@unifi.it.

Gabriella Di Rocco (G)

Department of Radiodiagnostics and Radiotherapy, IRCCS Fondazione Istituto Nazionale Tumori, Milan, Italy.
Post-Graduation School of Radiology, Department of Health Sciences, University of Milan, Milan, Italy.

Giovanni Argiroffi (G)

Nuclear Medicine Department, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy.

Michela Baccini (M)

Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Viale Morgagni 59, 50134, Florence, Italy.

Sara Pusceddu (S)

Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

Alfonso Marchianò (A)

Department of Radiodiagnostics and Radiotherapy, IRCCS Fondazione Istituto Nazionale Tumori, Milan, Italy.

Marco Maccauro (M)

Nuclear Medicine Department, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy.

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