Wei Mu1, Ilke Tunali1, Jhanelle E Gray2, Jin Qi1, Matthew B Schabath2,3, Robert J Gillies4. 1. Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33612, USA. 2. Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 3. Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 4. Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33612, USA. Robert.Gillies@moffitt.org.
Abstract
INTRODUCTION: Immunotherapy has improved outcomes for patients with non-small cell lung cancer (NSCLC), yet durable clinical benefit (DCB) is experienced in only a fraction of patients. Here, we test the hypothesis that radiomics features from baseline pretreatment 18F-FDG PET/CT scans can predict clinical outcomes of NSCLC patients treated with checkpoint blockade immunotherapy. METHODS: This study included 194 patients with histologically confirmed stage IIIB-IV NSCLC with pretreatment PET/CT images. Radiomics features were extracted from PET, CT, and PET+CT fusion images based on minimum Kullback-Leibler divergence (KLD) criteria. The radiomics features from 99 retrospective patients were used to train a multiparametric radiomics signature (mpRS) to predict DCB using an improved least absolute shrinkage and selection operator (LASSO) method, which was subsequently validated in both retrospective (N = 47) and prospective test cohorts (N = 48). Using these cohorts, the mpRS was also used to predict progression-free survival (PFS) and overall survival (OS) by training nomogram models using multivariable Cox regression analyses with additional clinical characteristics incorporated. RESULTS: The mpRS could predict patients who will receive DCB, with areas under receiver operating characteristic curves (AUCs) of 0.86 (95%CI 0.79-0.94), 0.83 (95%CI 0.71-0.94), and 0.81 (95%CI 0.68-0.92) in the training, retrospective test, and prospective test cohorts, respectively. In the same three cohorts, respectively, nomogram models achieved C-indices of 0.74 (95%CI 0.68-0.80), 0.74 (95%CI 0.66-0.82), and 0.77 (95%CI 0.69-0.84) to predict PFS and C-indices of 0.83 (95%CI 0.77-0.88), 0.83 (95%CI 0.71-0.94), and 0.80 (95%CI 0.69-0.91) to predict OS. CONCLUSION: PET/CT-based signature can be used prior to initiation of immunotherapy to identify NSCLC patients most likely to benefit from immunotherapy. As such, these data may be leveraged to improve more precise and individualized decision support in the treatment of patients with advanced NSCLC.
INTRODUCTION: Immunotherapy has improved outcomes for patients with non-small cell lung cancer (NSCLC), yet durable clinical benefit (DCB) is experienced in only a fraction of patients. Here, we test the hypothesis that radiomics features from baseline pretreatment 18F-FDG PET/CT scans can predict clinical outcomes of NSCLC patients treated with checkpoint blockade immunotherapy. METHODS: This study included 194 patients with histologically confirmed stage IIIB-IV NSCLC with pretreatment PET/CT images. Radiomics features were extracted from PET, CT, and PET+CT fusion images based on minimum Kullback-Leibler divergence (KLD) criteria. The radiomics features from 99 retrospective patients were used to train a multiparametric radiomics signature (mpRS) to predict DCB using an improved least absolute shrinkage and selection operator (LASSO) method, which was subsequently validated in both retrospective (N = 47) and prospective test cohorts (N = 48). Using these cohorts, the mpRS was also used to predict progression-free survival (PFS) and overall survival (OS) by training nomogram models using multivariable Cox regression analyses with additional clinical characteristics incorporated. RESULTS: The mpRS could predict patients who will receive DCB, with areas under receiver operating characteristic curves (AUCs) of 0.86 (95%CI 0.79-0.94), 0.83 (95%CI 0.71-0.94), and 0.81 (95%CI 0.68-0.92) in the training, retrospective test, and prospective test cohorts, respectively. In the same three cohorts, respectively, nomogram models achieved C-indices of 0.74 (95%CI 0.68-0.80), 0.74 (95%CI 0.66-0.82), and 0.77 (95%CI 0.69-0.84) to predict PFS and C-indices of 0.83 (95%CI 0.77-0.88), 0.83 (95%CI 0.71-0.94), and 0.80 (95%CI 0.69-0.91) to predict OS. CONCLUSION: PET/CT-based signature can be used prior to initiation of immunotherapy to identify NSCLC patients most likely to benefit from immunotherapy. As such, these data may be leveraged to improve more precise and individualized decision support in the treatment of patients with advanced NSCLC.
Authors: Leena Gandhi; Delvys Rodríguez-Abreu; Shirish Gadgeel; Emilio Esteban; Enriqueta Felip; Flávia De Angelis; Manuel Domine; Philip Clingan; Maximilian J Hochmair; Steven F Powell; Susanna Y-S Cheng; Helge G Bischoff; Nir Peled; Francesco Grossi; Ross R Jennens; Martin Reck; Rina Hui; Edward B Garon; Michael Boyer; Belén Rubio-Viqueira; Silvia Novello; Takayasu Kurata; Jhanelle E Gray; John Vida; Ziwen Wei; Jing Yang; Harry Raftopoulos; M Catherine Pietanza; Marina C Garassino Journal: N Engl J Med Date: 2018-04-16 Impact factor: 91.245
Authors: Roelof J Beukinga; Jan Binne Hulshoff; Véronique E M Mul; Walter Noordzij; Gursah Kats-Ugurlu; Riemer H J A Slart; John T M Plukker Journal: Radiology Date: 2018-03-14 Impact factor: 11.105
Authors: Suzanne L Topalian; F Stephen Hodi; Julie R Brahmer; Scott N Gettinger; David C Smith; David F McDermott; John D Powderly; Richard D Carvajal; Jeffrey A Sosman; Michael B Atkins; Philip D Leming; David R Spigel; Scott J Antonia; Leora Horn; Charles G Drake; Drew M Pardoll; Lieping Chen; William H Sharfman; Robert A Anders; Janis M Taube; Tracee L McMiller; Haiying Xu; Alan J Korman; Maria Jure-Kunkel; Shruti Agrawal; Daniel McDonald; Georgia D Kollia; Ashok Gupta; Jon M Wigginton; Mario Sznol Journal: N Engl J Med Date: 2012-06-02 Impact factor: 91.245
Authors: Naiyer A Rizvi; Matthew D Hellmann; Alexandra Snyder; Pia Kvistborg; Vladimir Makarov; Jonathan J Havel; William Lee; Jianda Yuan; Phillip Wong; Teresa S Ho; Martin L Miller; Natasha Rekhtman; Andre L Moreira; Fawzia Ibrahim; Cameron Bruggeman; Billel Gasmi; Roberta Zappasodi; Yuka Maeda; Chris Sander; Edward B Garon; Taha Merghoub; Jedd D Wolchok; Ton N Schumacher; Timothy A Chan Journal: Science Date: 2015-03-12 Impact factor: 47.728
Authors: Jia Wu; Todd Aguilera; David Shultz; Madhu Gudur; Daniel L Rubin; Billy W Loo; Maximilian Diehn; Ruijiang Li Journal: Radiology Date: 2016-04-05 Impact factor: 11.105
Authors: Luís Felipe Campesato; Romualdo Barroso-Sousa; Leandro Jimenez; Bruna R Correa; Jorge Sabbaga; Paulo M Hoff; Luiz F L Reis; Pedro Alexandre F Galante; Anamaria A Camargo Journal: Oncotarget Date: 2015-10-27
Authors: Daniel T Huff; Peter Ferjancic; Mauro Namías; Hamid Emamekhoo; Scott B Perlman; Robert Jeraj Journal: Biomed Phys Eng Express Date: 2021-09-30
Authors: Diego de Miguel-Perez; Alessandro Russo; Oscar Arrieta; Murat Ak; Feliciano Barron; Muthukumar Gunasekaran; Priyadarshini Mamindla; Luis Lara-Mejia; Christine B Peterson; Mehmet E Er; Vishal Peddagangireddy; Francesco Buemi; Brandon Cooper; Paolo Manca; Rena G Lapidus; Ru-Ching Hsia; Andres F Cardona; Aung Naing; Sunjay Kaushal; Fred R Hirsch; Philip C Mack; Maria Jose Serrano; Vincenzo Adamo; Rivka R Colen; Christian Rolfo Journal: J Exp Clin Cancer Res Date: 2022-06-02
Authors: Wei Mu; Evangelia Katsoulakis; Christopher J Whelan; Kenneth L Gage; Matthew B Schabath; Robert J Gillies Journal: Br J Cancer Date: 2021-04-07 Impact factor: 7.640
Authors: Andrew Hope; Maikel Verduin; Thomas J Dilling; Ananya Choudhury; Rianne Fijten; Leonard Wee; Hugo Jwl Aerts; Issam El Naqa; Ross Mitchell; Marc Vooijs; Andre Dekker; Dirk de Ruysscher; Alberto Traverso Journal: Cancers (Basel) Date: 2021-05-14 Impact factor: 6.639