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[(18)F]FDG PET Immunotherapy Radiomics Signature (iRADIOMICS) Predicts Response of Non-small-cell Lung Cancer Patients Treated with Pembrolizumab
BACKGROUND: Immune checkpoint inhibitors have changed the paradigm of cancer treatment; however, non-invasive biomarkers of response are still needed to identify candidates for non-responders. We aimed to investigate whether immunotherapy [(18)F]FDG PET radiomics signature (iRADIOMICS) predicts resp...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Sciendo
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409607/ https://www.ncbi.nlm.nih.gov/pubmed/32726293 http://dx.doi.org/10.2478/raon-2020-0042 |
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author | Valentinuzzi, Damijan Vrankar, Martina Boc, Nina Ahac, Valentina Zupancic, Ziga Unk, Mojca Skalic, Katja Zagar, Ivana Studen, Andrej Simoncic, Urban Eickhoff, Jens Jeraj, Robert |
author_facet | Valentinuzzi, Damijan Vrankar, Martina Boc, Nina Ahac, Valentina Zupancic, Ziga Unk, Mojca Skalic, Katja Zagar, Ivana Studen, Andrej Simoncic, Urban Eickhoff, Jens Jeraj, Robert |
author_sort | Valentinuzzi, Damijan |
collection | PubMed |
description | BACKGROUND: Immune checkpoint inhibitors have changed the paradigm of cancer treatment; however, non-invasive biomarkers of response are still needed to identify candidates for non-responders. We aimed to investigate whether immunotherapy [(18)F]FDG PET radiomics signature (iRADIOMICS) predicts response of metastatic non-small-cell lung cancer (NSCLC) patients to pembrolizumab better than the current clinical standards. PATIENTS AND METHODS: Thirty patients receiving pembrolizumab were scanned with [(18)F]FDG PET/CT at baseline, month 1 and 4. Associations of six robust primary tumour radiomics features with overall survival were analysed with Mann-Whitney U-test (MWU), Cox proportional hazards regression analysis, and ROC curve analysis. iRADIOMICS was constructed using univariate and multivariate logistic models of the most promising feature(s). Its predictive power was compared to PD-L1 tumour proportion score (TPS) and iRECIST using ROC curve analysis. Prediction accuracies were assessed with 5-fold cross validation. RESULTS: The most predictive were baseline radiomics features, e.g. Small Run Emphasis (MWU, p = 0.001; hazard ratio = 0.46, p = 0.007; AUC = 0.85 (95% CI 0.69–1.00)). Multivariate iRADIOMICS was found superior to the current standards in terms of predictive power and timewise with the following AUC (95% CI) and accuracy (standard deviation): iRADIOMICS (baseline), 0.90 (0.78–1.00), 78% (18%); PD-L1 TPS (baseline), 0.60 (0.37–0.83), 53% (18%); iRECIST (month 1), 0.79 (0.62–0.95), 76% (16%); iRECIST (month 4), 0.86 (0.72–1.00), 76% (17%). CONCLUSIONS: Multivariate iRADIOMICS was identified as a promising imaging biomarker, which could improve management of metastatic NSCLC patients treated with pembrolizumab. The predicted non-responders could be offered other treatment options to improve their overall survival. |
format | Online Article Text |
id | pubmed-7409607 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Sciendo |
record_format | MEDLINE/PubMed |
spelling | pubmed-74096072020-09-01 [(18)F]FDG PET Immunotherapy Radiomics Signature (iRADIOMICS) Predicts Response of Non-small-cell Lung Cancer Patients Treated with Pembrolizumab Valentinuzzi, Damijan Vrankar, Martina Boc, Nina Ahac, Valentina Zupancic, Ziga Unk, Mojca Skalic, Katja Zagar, Ivana Studen, Andrej Simoncic, Urban Eickhoff, Jens Jeraj, Robert Radiol Oncol Research Article BACKGROUND: Immune checkpoint inhibitors have changed the paradigm of cancer treatment; however, non-invasive biomarkers of response are still needed to identify candidates for non-responders. We aimed to investigate whether immunotherapy [(18)F]FDG PET radiomics signature (iRADIOMICS) predicts response of metastatic non-small-cell lung cancer (NSCLC) patients to pembrolizumab better than the current clinical standards. PATIENTS AND METHODS: Thirty patients receiving pembrolizumab were scanned with [(18)F]FDG PET/CT at baseline, month 1 and 4. Associations of six robust primary tumour radiomics features with overall survival were analysed with Mann-Whitney U-test (MWU), Cox proportional hazards regression analysis, and ROC curve analysis. iRADIOMICS was constructed using univariate and multivariate logistic models of the most promising feature(s). Its predictive power was compared to PD-L1 tumour proportion score (TPS) and iRECIST using ROC curve analysis. Prediction accuracies were assessed with 5-fold cross validation. RESULTS: The most predictive were baseline radiomics features, e.g. Small Run Emphasis (MWU, p = 0.001; hazard ratio = 0.46, p = 0.007; AUC = 0.85 (95% CI 0.69–1.00)). Multivariate iRADIOMICS was found superior to the current standards in terms of predictive power and timewise with the following AUC (95% CI) and accuracy (standard deviation): iRADIOMICS (baseline), 0.90 (0.78–1.00), 78% (18%); PD-L1 TPS (baseline), 0.60 (0.37–0.83), 53% (18%); iRECIST (month 1), 0.79 (0.62–0.95), 76% (16%); iRECIST (month 4), 0.86 (0.72–1.00), 76% (17%). CONCLUSIONS: Multivariate iRADIOMICS was identified as a promising imaging biomarker, which could improve management of metastatic NSCLC patients treated with pembrolizumab. The predicted non-responders could be offered other treatment options to improve their overall survival. Sciendo 2020-07-29 /pmc/articles/PMC7409607/ /pubmed/32726293 http://dx.doi.org/10.2478/raon-2020-0042 Text en © 2020 Damijan Valentinuzzi, Martina Vrankar, Nina Boc, Valentina Ahac, Ziga Zupancic, Mojca Unk, Katja Skalic, Ivana Zagar, Andrej Studen, Urban Simoncic, Jens Eickhoff, Robert Jeraj, published by Sciendo http://creativecommons.org/licenses/by-nc-nd/3.0 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. |
spellingShingle | Research Article Valentinuzzi, Damijan Vrankar, Martina Boc, Nina Ahac, Valentina Zupancic, Ziga Unk, Mojca Skalic, Katja Zagar, Ivana Studen, Andrej Simoncic, Urban Eickhoff, Jens Jeraj, Robert [(18)F]FDG PET Immunotherapy Radiomics Signature (iRADIOMICS) Predicts Response of Non-small-cell Lung Cancer Patients Treated with Pembrolizumab |
title | [(18)F]FDG PET Immunotherapy Radiomics Signature (iRADIOMICS) Predicts Response of Non-small-cell Lung Cancer Patients Treated with Pembrolizumab |
title_full | [(18)F]FDG PET Immunotherapy Radiomics Signature (iRADIOMICS) Predicts Response of Non-small-cell Lung Cancer Patients Treated with Pembrolizumab |
title_fullStr | [(18)F]FDG PET Immunotherapy Radiomics Signature (iRADIOMICS) Predicts Response of Non-small-cell Lung Cancer Patients Treated with Pembrolizumab |
title_full_unstemmed | [(18)F]FDG PET Immunotherapy Radiomics Signature (iRADIOMICS) Predicts Response of Non-small-cell Lung Cancer Patients Treated with Pembrolizumab |
title_short | [(18)F]FDG PET Immunotherapy Radiomics Signature (iRADIOMICS) Predicts Response of Non-small-cell Lung Cancer Patients Treated with Pembrolizumab |
title_sort | [(18)f]fdg pet immunotherapy radiomics signature (iradiomics) predicts response of non-small-cell lung cancer patients treated with pembrolizumab |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409607/ https://www.ncbi.nlm.nih.gov/pubmed/32726293 http://dx.doi.org/10.2478/raon-2020-0042 |
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