CT based radiomic approach on first line pembrolizumab in lung cancer.


Journal

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

Informations de publication

Date de publication:
23 03 2021
Historique:
received: 09 07 2020
accepted: 24 02 2021
entrez: 24 3 2021
pubmed: 25 3 2021
medline: 25 2 2023
Statut: epublish

Résumé

Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P < 0.001). Kaplan-Meyer analysis showed that MPP < 56.2 were significantly associated with lower OS and PFS (P < 0.0035). Cox proportional analysis showed a significant correlation between MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment.

Identifiants

pubmed: 33758304
doi: 10.1038/s41598-021-86113-5
pii: 10.1038/s41598-021-86113-5
pmc: PMC7988058
doi:

Substances chimiques

Antibodies, Monoclonal, Humanized 0
Antineoplastic Agents, Immunological 0
Biomarkers, Tumor 0
pembrolizumab DPT0O3T46P

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

6633

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Auteurs

Marta Zerunian (M)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine-"Sapienza", University of Rome, Sant'Andrea University Hospital, via di Grottarossa 1035, 00189, Rome, Italy.

Damiano Caruso (D)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine-"Sapienza", University of Rome, Sant'Andrea University Hospital, via di Grottarossa 1035, 00189, Rome, Italy.

Alberto Zucchelli (A)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine-"Sapienza", University of Rome, Sant'Andrea University Hospital, via di Grottarossa 1035, 00189, Rome, Italy.

Michela Polici (M)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine-"Sapienza", University of Rome, Sant'Andrea University Hospital, via di Grottarossa 1035, 00189, Rome, Italy.

Carlo Capalbo (C)

Oncology Unit, Department of Clinical and Molecular Oncology-"Sapienza", University of Rome, Sant'Andrea University Hospital, via di Grottarossa 1035, 00189, Rome, Italy.

Marco Filetti (M)

Oncology Unit, Department of Clinical and Molecular Oncology-"Sapienza", University of Rome, Sant'Andrea University Hospital, via di Grottarossa 1035, 00189, Rome, Italy.

Federica Mazzuca (F)

Oncology Unit, Department of Clinical and Molecular Oncology-"Sapienza", University of Rome, Sant'Andrea University Hospital, via di Grottarossa 1035, 00189, Rome, Italy.

Paolo Marchetti (P)

Oncology Unit, Department of Clinical and Molecular Oncology-"Sapienza", University of Rome, Sant'Andrea University Hospital, via di Grottarossa 1035, 00189, Rome, Italy.
IDI-IRCCS, Via Monti di Creta 104, 00167, Rome, Italy.

Andrea Laghi (A)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine-"Sapienza", University of Rome, Sant'Andrea University Hospital, via di Grottarossa 1035, 00189, Rome, Italy. andrea.laghi@uniroma1.it.

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Classifications MeSH