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Quantitative vessel tortuosity radiomics on baseline non-contrast lung CT predict response to immunotherapy and are prognostic of overall survival
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Paper Abstract

Recently immune-checkpoint inhibitors have demonstrated promising clinical efficacy in patients with advanced non-small cell lung cancer (NSCLC). However, the response rates to immune checkpoint blockade drugs remain modest (45% in the front line setting and 20% in the second line setting). Consequently, there is an unmet need to develop accurate, validated biomarkers to predict which NSCLC patients will benefit from immunotherapy. While there has been recent interest in evaluating the role of texture and shape patterns of the nodule on CT scans to predict response to checkpoint inhibitors for NSCLC, our group has shown that nodule vessel morphology might also play a role in determining tumor aggressiveness and behavior. In this work we present a new approach using quantitative vessel tortuosity (QVT) radiomics, to predict response to checkpoint inhibitors and overall survival for patients with NSCLC treated with Nivolumab (a PD1 inhibitor) on a retrospective data set of 111 patients (D1) including 56 responders and 45 non-responders. Patients who did not receive Nivolumab after 2 cycles due to a lack of response or progression as per Response Evaluation Criteria in Solid Tumors (RECIST) were classified as non-responders, patients who had radiological response or stable disease as per RECIST were classified as responders. On D1, in conjunction with a linear discriminant analysis (LDA) classifier the QVT features were able to predict response to immunotherapy with an AUC of 0.73_0.04. Kaplan Meier analysis showed significant difference of overall survival between patients with low risk and high risk defined by the radiomics classifier (p-value = 0.004, HR= 2.29, 95% CI= 1.35 - 3.87).

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501F (13 March 2019); doi: 10.1117/12.2513648
Show Author Affiliations
Mehdi Alilou, Case Western Reserve Univ. (United States)
Pranjal Vaidya, Case Western Reserve Univ. (United States)
Mohammadhadi Khorrami, Case Western Reserve Univ. (United States)
Alexia Zagouras, Cleveland Clinic Foundation (United States)
Pradnya Patil, Cleveland Clinic Foundation (United States)
Kaustav Bera, Case Western Reserve Univ. (United States)
Pingfu Fu, Case Western Reserve Univ. (United States)
Vamsidhar Velcheti, NYU Langone (United States)
Anant Madabhushi, Case Western Reserve Univ. (United States)
Louis Stokes Cleveland Veterans Administration Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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