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A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors
Tumor vasculature is a key component of the tumor microenvironment that can influence tumor behavior and therapeutic resistance. We present a new imaging biomarker, quantitative vessel tortuosity (QVT), and evaluate its association with response and survival in patients with non–small cell lung canc...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699671/ https://www.ncbi.nlm.nih.gov/pubmed/36427313 http://dx.doi.org/10.1126/sciadv.abq4609 |
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author | Alilou, Mehdi Khorrami, Mohammadhadi Prasanna, Prateek Bera, Kaustav Gupta, Amit Viswanathan, Vidya Sankar Patil, Pradnya Velu, Priya Darsini Fu, Pingfu Velcheti, Vamsidhar Madabhushi, Anant |
author_facet | Alilou, Mehdi Khorrami, Mohammadhadi Prasanna, Prateek Bera, Kaustav Gupta, Amit Viswanathan, Vidya Sankar Patil, Pradnya Velu, Priya Darsini Fu, Pingfu Velcheti, Vamsidhar Madabhushi, Anant |
author_sort | Alilou, Mehdi |
collection | PubMed |
description | Tumor vasculature is a key component of the tumor microenvironment that can influence tumor behavior and therapeutic resistance. We present a new imaging biomarker, quantitative vessel tortuosity (QVT), and evaluate its association with response and survival in patients with non–small cell lung cancer (NSCLC) treated with immune checkpoint inhibitor (ICI) therapies. A total of 507 cases were used to evaluate different aspects of the QVT biomarkers. QVT features were extracted from computed tomography imaging of patients before and after ICI therapy to capture the tortuosity, curvature, density, and branching statistics of the nodule vasculature. Our results showed that QVT features were prognostic of OS (HR = 3.14, 0.95% CI = 1.2 to 9.68, P = 0.0006, C-index = 0.61) and could predict ICI response with AUCs of 0.66, 0.61, and 0.67 on three validation sets. Our study shows that QVT imaging biomarker could potentially aid in predicting and monitoring response to ICI in patients with NSCLC. |
format | Online Article Text |
id | pubmed-9699671 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-96996712022-12-05 A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors Alilou, Mehdi Khorrami, Mohammadhadi Prasanna, Prateek Bera, Kaustav Gupta, Amit Viswanathan, Vidya Sankar Patil, Pradnya Velu, Priya Darsini Fu, Pingfu Velcheti, Vamsidhar Madabhushi, Anant Sci Adv Physical and Materials Sciences Tumor vasculature is a key component of the tumor microenvironment that can influence tumor behavior and therapeutic resistance. We present a new imaging biomarker, quantitative vessel tortuosity (QVT), and evaluate its association with response and survival in patients with non–small cell lung cancer (NSCLC) treated with immune checkpoint inhibitor (ICI) therapies. A total of 507 cases were used to evaluate different aspects of the QVT biomarkers. QVT features were extracted from computed tomography imaging of patients before and after ICI therapy to capture the tortuosity, curvature, density, and branching statistics of the nodule vasculature. Our results showed that QVT features were prognostic of OS (HR = 3.14, 0.95% CI = 1.2 to 9.68, P = 0.0006, C-index = 0.61) and could predict ICI response with AUCs of 0.66, 0.61, and 0.67 on three validation sets. Our study shows that QVT imaging biomarker could potentially aid in predicting and monitoring response to ICI in patients with NSCLC. American Association for the Advancement of Science 2022-11-25 /pmc/articles/PMC9699671/ /pubmed/36427313 http://dx.doi.org/10.1126/sciadv.abq4609 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Physical and Materials Sciences Alilou, Mehdi Khorrami, Mohammadhadi Prasanna, Prateek Bera, Kaustav Gupta, Amit Viswanathan, Vidya Sankar Patil, Pradnya Velu, Priya Darsini Fu, Pingfu Velcheti, Vamsidhar Madabhushi, Anant A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors |
title | A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors |
title_full | A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors |
title_fullStr | A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors |
title_full_unstemmed | A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors |
title_short | A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors |
title_sort | tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors |
topic | Physical and Materials Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699671/ https://www.ncbi.nlm.nih.gov/pubmed/36427313 http://dx.doi.org/10.1126/sciadv.abq4609 |
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