Literature DB >> 33959972

Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers.

Kyle J Lafata1,2,3,4, Yushi Chang1,4, Chunhao Wang1,4, Yvonne M Mowery1,5, Irina Vergalasova6, Donna Niedzwiecki7, David S Yoo1, Jian-Guo Liu8,9, David M Brizel1,5, Fang-Fang Yin1,4.   

Abstract

PURPOSE: This study investigated the prognostic potential of intra-treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20 Gy is associated with recurrence-free survival (RFS).
MATERIALS AND METHODS: Sixty-four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB-approved study. Investigational 18 F-FDG-PET/CT images were acquired prior to treatment and 2 weeks (20 Gy) into a seven-week course of therapy. Fifty-five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were naïvely compared to residual disease and/or subsequent recurrence and used to derive Kaplan-Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume.
RESULTS: While pre-treatment radiomics were not prognostic, intra-treatment radiomic expression was intrinsically associated with both residual/recurrent disease (P = 0.0256, χ 2 test) and RFS (HR = 7.53, 95% CI = 2.54-22.3; P = 0.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR = 2.70; 95% CI = 1.26-5.76; P = 0.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20 Gy (adjusted HR = 2.69; 95% CI = 1.03-7.04; P = 0.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20 Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches.
CONCLUSIONS: Our data illustrate the prognostic value of intra-treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study was strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow-up times, and unsupervised clustering formalism that is less prone to hyper-parameter tuning and over-fitting compared to supervised learning.
© 2021 American Association of Physicists in Medicine.

Entities:  

Keywords:  imaging biomarker; machine learning; oropharyngeal cancer; radiomics

Mesh:

Substances:

Year:  2021        PMID: 33959972      PMCID: PMC9113754          DOI: 10.1002/mp.14926

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.506


  59 in total

1.  18F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia.

Authors:  Lisanne V van Dijk; Walter Noordzij; Charlotte L Brouwer; Ronald Boellaard; Johannes G M Burgerhof; Johannes A Langendijk; Nanna M Sijtsema; Roel J H M Steenbakkers
Journal:  Radiother Oncol       Date:  2017-09-23       Impact factor: 6.280

2.  Development and validation of a staging system for HPV-related oropharyngeal cancer by the International Collaboration on Oropharyngeal cancer Network for Staging (ICON-S): a multicentre cohort study.

Authors:  Brian O'Sullivan; Shao Hui Huang; Jie Su; Adam S Garden; Erich M Sturgis; Kristina Dahlstrom; Nancy Lee; Nadeem Riaz; Xin Pei; Shlomo A Koyfman; David Adelstein; Brian B Burkey; Jeppe Friborg; Claus A Kristensen; Anita B Gothelf; Frank Hoebers; Bernd Kremer; Ernst-Jan Speel; Daniel W Bowles; David Raben; Sana D Karam; Eugene Yu; Wei Xu
Journal:  Lancet Oncol       Date:  2016-02-27       Impact factor: 41.316

3.  Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma.

Authors:  Marta Bogowicz; Oliver Riesterer; Luisa Sabrina Stark; Gabriela Studer; Jan Unkelbach; Matthias Guckenberger; Stephanie Tanadini-Lang
Journal:  Acta Oncol       Date:  2017-08-18       Impact factor: 4.089

4.  Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics.

Authors:  Michael R Folkert; Jeremy Setton; Aditya P Apte; Milan Grkovski; Robert J Young; Heiko Schöder; Wade L Thorstad; Nancy Y Lee; Joseph O Deasy; Jung Hun Oh
Journal:  Phys Med Biol       Date:  2017-06-12       Impact factor: 3.609

5.  Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA.

Authors:  Kyle J Lafata; Michael N Corradetti; Junheng Gao; Corbin D Jacobs; Jingxi Weng; Yushi Chang; Chunhao Wang; Ace Hatch; Eric Xanthopoulos; Greg Jones; Chris R Kelsey; Fang-Fang Yin
Journal:  Radiol Imaging Cancer       Date:  2021-04

6.  Quantifying metabolic heterogeneity in head and neck tumors in real time: 2-DG uptake is highest in hypoxic tumor regions.

Authors:  Erica C Nakajima; Charles Laymon; Matthew Oborski; Weizhou Hou; Lin Wang; Jennifer R Grandis; Robert L Ferris; James M Mountz; Bennett Van Houten
Journal:  PLoS One       Date:  2014-08-15       Impact factor: 3.240

7.  Glycolysis, tumor metabolism, cancer growth and dissemination. A new pH-based etiopathogenic perspective and therapeutic approach to an old cancer question.

Authors:  Khalid O Alfarouk; Daniel Verduzco; Cyril Rauch; Abdel Khalig Muddathir; H H Bashir Adil; Gamal O Elhassan; Muntaser E Ibrahim; Julian David Polo Orozco; Rosa Angela Cardone; Stephan J Reshkin; Salvador Harguindey
Journal:  Oncoscience       Date:  2014-12-18

8.  A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer.

Authors:  Haonan Lu; Mubarik Arshad; Andrew Thornton; Giacomo Avesani; Paula Cunnea; Ed Curry; Fahdi Kanavati; Jack Liang; Katherine Nixon; Sophie T Williams; Mona Ali Hassan; David D L Bowtell; Hani Gabra; Christina Fotopoulou; Andrea Rockall; Eric O Aboagye
Journal:  Nat Commun       Date:  2019-02-15       Impact factor: 14.919

9.  Plasma Circulating Tumor HPV DNA for the Surveillance of Cancer Recurrence in HPV-Associated Oropharyngeal Cancer.

Authors:  Bhishamjit S Chera; Sunil Kumar; Colette Shen; Robert Amdur; Roi Dagan; Rebecca Green; Emily Goldman; Jared Weiss; Juneko Grilley-Olson; Shetal Patel; Adam Zanation; Trevor Hackman; Jeff Blumberg; Samip Patel; Brian Thorp; Mark Weissler; Wendell Yarbrough; Nathan Sheets; William Mendenhall; Xianming M Tan; Gaorav P Gupta
Journal:  J Clin Oncol       Date:  2020-02-04       Impact factor: 44.544

10.  Dynamic Changes in Circulating Tumor DNA During Chemoradiation for Locally Advanced Lung Cancer.

Authors:  Michael N Corradetti; Jordan A Torok; Ace J Hatch; Eric P Xanthopoulos; Kyle Lafata; Corbin Jacobs; Christel Rushing; John Calaway; Greg Jones; Chris R Kelsey; Andrew B Nixon
Journal:  Adv Radiat Oncol       Date:  2019-05-22
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  5 in total

Review 1.  Radiomics: a primer on high-throughput image phenotyping.

Authors:  Kyle J Lafata; Yuqi Wang; Brandon Konkel; Fang-Fang Yin; Mustafa R Bashir
Journal:  Abdom Radiol (NY)       Date:  2021-08-25

Review 2.  Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.

Authors:  David Morland; Elizabeth Katherine Anna Triumbari; Luca Boldrini; Roberto Gatta; Daniele Pizzuto; Salvatore Annunziata
Journal:  Diagnostics (Basel)       Date:  2022-05-27

3.  Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

Authors:  Hangjie Ji; Kyle Lafata; Yvonne Mowery; David Brizel; Andrea L Bertozzi; Fang-Fang Yin; Chunhao Wang
Journal:  Front Oncol       Date:  2022-05-13       Impact factor: 5.738

4.  Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden.

Authors:  Alex J Allphin; Yvonne M Mowery; Kyle J Lafata; Darin P Clark; Alex M Bassil; Rico Castillo; Diana Odhiambo; Matthew D Holbrook; Ketan B Ghaghada; Cristian T Badea
Journal:  Tomography       Date:  2022-03-10

5.  A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images.

Authors:  Zongsheng Hu; Zhenyu Yang; Kyle J Lafata; Fang-Fang Yin; Chunhao Wang
Journal:  Med Phys       Date:  2022-03-15       Impact factor: 4.506

  5 in total

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