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Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC

INTRODUCTION: Radiological response assessment to immune checkpoint inhibitor is challenging due to atypical pattern of response and commonly used RECIST 1.1 criteria do not take into account the kinetics of tumor behavior. Our study aimed at evaluating the tumor growth rate (TGR) in addition to REC...

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Autores principales: Dall’Olio, Filippo G., Parisi, Claudia, Marcolin, Laura, Brocchi, Stefano, Caramella, Caroline, Conci, Nicole, Carpani, Giulia, Gelsomino, Francesco, Ardizzoni, Stefano, Marchese, Paola Valeria, Paccapelo, Alexandro, Grilli, Giada, Golfieri, Rita, Besse, Benjamin, Ardizzoni, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842375/
https://www.ncbi.nlm.nih.gov/pubmed/35173818
http://dx.doi.org/10.1177/17588359211058391
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author Dall’Olio, Filippo G.
Parisi, Claudia
Marcolin, Laura
Brocchi, Stefano
Caramella, Caroline
Conci, Nicole
Carpani, Giulia
Gelsomino, Francesco
Ardizzoni, Stefano
Marchese, Paola Valeria
Paccapelo, Alexandro
Grilli, Giada
Golfieri, Rita
Besse, Benjamin
Ardizzoni, Andrea
author_facet Dall’Olio, Filippo G.
Parisi, Claudia
Marcolin, Laura
Brocchi, Stefano
Caramella, Caroline
Conci, Nicole
Carpani, Giulia
Gelsomino, Francesco
Ardizzoni, Stefano
Marchese, Paola Valeria
Paccapelo, Alexandro
Grilli, Giada
Golfieri, Rita
Besse, Benjamin
Ardizzoni, Andrea
author_sort Dall’Olio, Filippo G.
collection PubMed
description INTRODUCTION: Radiological response assessment to immune checkpoint inhibitor is challenging due to atypical pattern of response and commonly used RECIST 1.1 criteria do not take into account the kinetics of tumor behavior. Our study aimed at evaluating the tumor growth rate (TGR) in addition to RECIST 1.1 criteria to assess the benefit of immune checkpoint inhibitors (ICIs). METHODS: Tumor real volume was calculated with a dedicated computed tomography (CT) software that semi-automatically assess tumor volume. Target lesions were identified according to RECIST 1.1. For each patient, we had 3 measurement of tumor volume. CT-1 was performed 8–12 weeks before ICI start, the CT at baseline for ICI was CT0, while CT + 1 was the first assessment after ICI. We calculated the percentage increase in tumor volume before (TGR1) and after immunotherapy (TGR2). Finally, we compared TGR1 and TGR2. If no progressive disease (PD), the group was disease control (DC). If PD but TGR2 < TGR1, it was called LvPD and if TGR2 ⩾ TGR1, HvPD. RESULTS: A total of 61 patients who received ICIs and 33 treated with chemotherapy (ChT) were included. In ICI group, 18 patients were HvPD, 22 LvPD, 21 DC. Median OS was 4.4 months (95% CI: 2.0–6.8, reference) for HvPD, 7.1 months (95% CI 5.4–8.8) for LvPD, p = 0.018, and 20.9 months (95% CI: 12.5–29.3) for DC, p < 0.001. In ChT group, 7 were categorized as HvPD, 17 as LvPD and 9 as DC. No difference in OS was observed in the ChT group (p = 0.786) CONCLUSION: In the presence of PD, a decrease in TGR may result in a clinical benefit in patients treated with ICI but not with chemotherapy. Monitoring TGR changes after ICIs administration can help physician in deciding to treat beyond PD.
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spelling pubmed-88423752022-02-15 Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC Dall’Olio, Filippo G. Parisi, Claudia Marcolin, Laura Brocchi, Stefano Caramella, Caroline Conci, Nicole Carpani, Giulia Gelsomino, Francesco Ardizzoni, Stefano Marchese, Paola Valeria Paccapelo, Alexandro Grilli, Giada Golfieri, Rita Besse, Benjamin Ardizzoni, Andrea Ther Adv Med Oncol Original Research INTRODUCTION: Radiological response assessment to immune checkpoint inhibitor is challenging due to atypical pattern of response and commonly used RECIST 1.1 criteria do not take into account the kinetics of tumor behavior. Our study aimed at evaluating the tumor growth rate (TGR) in addition to RECIST 1.1 criteria to assess the benefit of immune checkpoint inhibitors (ICIs). METHODS: Tumor real volume was calculated with a dedicated computed tomography (CT) software that semi-automatically assess tumor volume. Target lesions were identified according to RECIST 1.1. For each patient, we had 3 measurement of tumor volume. CT-1 was performed 8–12 weeks before ICI start, the CT at baseline for ICI was CT0, while CT + 1 was the first assessment after ICI. We calculated the percentage increase in tumor volume before (TGR1) and after immunotherapy (TGR2). Finally, we compared TGR1 and TGR2. If no progressive disease (PD), the group was disease control (DC). If PD but TGR2 < TGR1, it was called LvPD and if TGR2 ⩾ TGR1, HvPD. RESULTS: A total of 61 patients who received ICIs and 33 treated with chemotherapy (ChT) were included. In ICI group, 18 patients were HvPD, 22 LvPD, 21 DC. Median OS was 4.4 months (95% CI: 2.0–6.8, reference) for HvPD, 7.1 months (95% CI 5.4–8.8) for LvPD, p = 0.018, and 20.9 months (95% CI: 12.5–29.3) for DC, p < 0.001. In ChT group, 7 were categorized as HvPD, 17 as LvPD and 9 as DC. No difference in OS was observed in the ChT group (p = 0.786) CONCLUSION: In the presence of PD, a decrease in TGR may result in a clinical benefit in patients treated with ICI but not with chemotherapy. Monitoring TGR changes after ICIs administration can help physician in deciding to treat beyond PD. SAGE Publications 2022-02-12 /pmc/articles/PMC8842375/ /pubmed/35173818 http://dx.doi.org/10.1177/17588359211058391 Text en © The Author(s), 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage)
spellingShingle Original Research
Dall’Olio, Filippo G.
Parisi, Claudia
Marcolin, Laura
Brocchi, Stefano
Caramella, Caroline
Conci, Nicole
Carpani, Giulia
Gelsomino, Francesco
Ardizzoni, Stefano
Marchese, Paola Valeria
Paccapelo, Alexandro
Grilli, Giada
Golfieri, Rita
Besse, Benjamin
Ardizzoni, Andrea
Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
title Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
title_full Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
title_fullStr Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
title_full_unstemmed Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
title_short Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
title_sort monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced nsclc
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842375/
https://www.ncbi.nlm.nih.gov/pubmed/35173818
http://dx.doi.org/10.1177/17588359211058391
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