Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Pet Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature

Laura Evangelista
* ORCID logo ,
Francesco Fiz
ORCID logo ,
Riccardo Laudicella
ORCID logo ,
Francesco Bianconi
,
Angelo Castello
,
Priscilla Guglielmo
,
Virginia Liberini
ORCID logo ,
Luigi Manco
ORCID logo ,
Viviana Frantellizi
ORCID logo ,
Alessia Giordano
,
Luca Urso
,
Stefano Panareo
ORCID logo ,
Barbara Palumbo
,
Luca Filippi
ORCID logo
Version 1 : Received: 9 May 2023 / Approved: 11 May 2023 / Online: 11 May 2023 (05:32:06 CEST)

A peer-reviewed article of this Preprint also exists.

Evangelista, L.; Fiz, F.; Laudicella, R.; Bianconi, F.; Castello, A.; Guglielmo, P.; Liberini, V.; Manco, L.; Frantellizzi, V.; Giordano, A.; Urso, L.; Panareo, S.; Palumbo, B.; Filippi, L. PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature. Cancers 2023, 15, 3258. Evangelista, L.; Fiz, F.; Laudicella, R.; Bianconi, F.; Castello, A.; Guglielmo, P.; Liberini, V.; Manco, L.; Frantellizzi, V.; Giordano, A.; Urso, L.; Panareo, S.; Palumbo, B.; Filippi, L. PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature. Cancers 2023, 15, 3258.

Abstract

Aim: to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET)-radiomics in lung cancer patients candidates or undergoing immunotherapy. Materials and Methods: A systematic review was conducted on databases and web sources. English-language original articles were considered. The title and abstract were in-dependently reviewed to evaluate study inclusion. Papers duplicate, out-of-topic, review or edi-torials articles and letters to editors were excluded. For each study, the radiomics analysis was assessed based on the relies on radiomics quality score (RQS 2.0). The review was registered on the PROSPERO database with the number CRD42023402302. Results: 15 papers were included, 13 were qualified as conventional radiomics approaches, and two were as Deep Learning radiomics. The content of each study was different, indeed, 7 papers investigated the potential role of radiomics to predict PD-L1 expression and tumor microenvironment before starting immunotherapy. Moreo-ver, 2 were relative to the prediction of response and 4 investigated the utility of radiomics to predict the response to immunotherapy. Finally, 2 papers were relative to the prediction of adverse events due to the immunotherapy. Conclusions: radiomics is promising in the evaluation of TME and for the prediction of response to immunotherapy, but some limitations should be overpassed.

Keywords

immunotherapy; lung cancer; PET; response to therapy; PD-L1

Subject

Medicine and Pharmacology, Oncology and Oncogenics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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