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Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
by
Grilli, Giada
, Parisi, Claudia
, Marcolin, Laura
, Conci, Nicole
, Marchese, Paola Valeria
, Dall’Olio, Filippo G.
, Paccapelo, Alexandro
, Brocchi, Stefano
, Ardizzoni, Stefano
, Gelsomino, Francesco
, Carpani, Giulia
, Ardizzoni, Andrea
, Besse, Benjamin
, Caramella, Caroline
, Golfieri, Rita
in
Chemotherapy
/ Computed tomography
/ Disease control
/ Growth rate
/ Immune checkpoint inhibitors
/ Immunotherapy
/ Lung cancer
/ Non-small cell lung carcinoma
/ Original Research
/ Patients
/ Tumors
2022
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Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
by
Grilli, Giada
, Parisi, Claudia
, Marcolin, Laura
, Conci, Nicole
, Marchese, Paola Valeria
, Dall’Olio, Filippo G.
, Paccapelo, Alexandro
, Brocchi, Stefano
, Ardizzoni, Stefano
, Gelsomino, Francesco
, Carpani, Giulia
, Ardizzoni, Andrea
, Besse, Benjamin
, Caramella, Caroline
, Golfieri, Rita
in
Chemotherapy
/ Computed tomography
/ Disease control
/ Growth rate
/ Immune checkpoint inhibitors
/ Immunotherapy
/ Lung cancer
/ Non-small cell lung carcinoma
/ Original Research
/ Patients
/ Tumors
2022
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Do you wish to request the book?
Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
by
Grilli, Giada
, Parisi, Claudia
, Marcolin, Laura
, Conci, Nicole
, Marchese, Paola Valeria
, Dall’Olio, Filippo G.
, Paccapelo, Alexandro
, Brocchi, Stefano
, Ardizzoni, Stefano
, Gelsomino, Francesco
, Carpani, Giulia
, Ardizzoni, Andrea
, Besse, Benjamin
, Caramella, Caroline
, Golfieri, Rita
in
Chemotherapy
/ Computed tomography
/ Disease control
/ Growth rate
/ Immune checkpoint inhibitors
/ Immunotherapy
/ Lung cancer
/ Non-small cell lung carcinoma
/ Original Research
/ Patients
/ Tumors
2022
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Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
Journal Article
Monitoring tumor growth rate to predict immune checkpoint inhibitors’ treatment outcome in advanced NSCLC
2022
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Overview
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.
Publisher
SAGE Publications,Sage Publications Ltd,SAGE Publishing
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