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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...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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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. |
format | Online Article Text |
id | pubmed-8842375 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
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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