You-Ling Gong1, Zhi-Gang Yang2, Xin Tang3, Wen-Lei Qian3, Wei-Feng Yan3, Tong Pang3. 1. Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China. gongyouling@hotmail.com. 2. Department of Radiology, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China. yangzg666@163.com. 3. Department of Radiology, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
Abstract
BACKGROUND: Tumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA). We aim at investigating radiomic features' value in predicting the TMB status of BLCA patients. METHODS: Totally, 75 patients with BLCA were enrolled. Radiomic features extracted from the volume of interest of preoperative pelvic contrast-enhanced computed tomography (CECT) were obtained for each case. Unsupervised hierarchical clustering analysis was performed based on radiomic features. Sequential univariate Logistic regression, the least absolute shrinkage and selection operator (LASSO) regression and the backward stepwise regression were used to develop a TMB-predicting model using radiomic features. RESULTS: The unsupervised clustering analysis divided the total cohort into two groups, i.e., group A (32.0%) and B (68.0%). Patients in group A had a significantly larger proportion of having high TMB against those in group B (66.7% vs. 41.2%, p = 0.039), indicating the intrinsic ability of radiomic features in TMB-predicting. In univariate analysis, 27 radiomic features could predict TMB. Based on six radiomic features selected by logistic and LASSO regression, a TMB-predicting model was built and visualized by nomogram. The area under the ROC curve of the model reached 0.853. Besides, the calibration curve and the decision curve also revealed the good performance of the model. CONCLUSIONS: Our work firstly proved the feasibility of using radiomics to predict TMB for patients with BLCA. The predictive model based on radiomic features from pelvic CECT has a promising ability to predict TMB. Future study with a larger cohort is needed to verify our findings.
BACKGROUND:Tumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA). We aim at investigating radiomic features' value in predicting the TMB status of BLCA patients. METHODS: Totally, 75 patients with BLCA were enrolled. Radiomic features extracted from the volume of interest of preoperative pelvic contrast-enhanced computed tomography (CECT) were obtained for each case. Unsupervised hierarchical clustering analysis was performed based on radiomic features. Sequential univariate Logistic regression, the least absolute shrinkage and selection operator (LASSO) regression and the backward stepwise regression were used to develop a TMB-predicting model using radiomic features. RESULTS: The unsupervised clustering analysis divided the total cohort into two groups, i.e., group A (32.0%) and B (68.0%). Patients in group A had a significantly larger proportion of having high TMB against those in group B (66.7% vs. 41.2%, p = 0.039), indicating the intrinsic ability of radiomic features in TMB-predicting. In univariate analysis, 27 radiomic features could predict TMB. Based on six radiomic features selected by logistic and LASSO regression, a TMB-predicting model was built and visualized by nomogram. The area under the ROC curve of the model reached 0.853. Besides, the calibration curve and the decision curve also revealed the good performance of the model. CONCLUSIONS: Our work firstly proved the feasibility of using radiomics to predict TMB for patients with BLCA. The predictive model based on radiomic features from pelvic CECT has a promising ability to predict TMB. Future study with a larger cohort is needed to verify our findings.
Authors: Matthew D Hellmann; Margaret K Callahan; Mark M Awad; Emiliano Calvo; Paolo A Ascierto; Akin Atmaca; Naiyer A Rizvi; Fred R Hirsch; Giovanni Selvaggi; Joseph D Szustakowski; Ariella Sasson; Ryan Golhar; Patrik Vitazka; Han Chang; William J Geese; Scott J Antonia Journal: Cancer Cell Date: 2019-02-11 Impact factor: 31.743
Authors: Alexandra Snyder; Vladimir Makarov; Taha Merghoub; Jianda Yuan; Jedd D Wolchok; Timothy A Chan; Jesse M Zaretsky; Alexis Desrichard; Logan A Walsh; Michael A Postow; Phillip Wong; Teresa S Ho; Travis J Hollmann; Cameron Bruggeman; Kasthuri Kannan; Yanyun Li; Ceyhan Elipenahli; Cailian Liu; Christopher T Harbison; Lisu Wang; Antoni Ribas Journal: N Engl J Med Date: 2014-11-19 Impact factor: 91.245
Authors: Robert M Samstein; Chung-Han Lee; Alexander N Shoushtari; Matthew D Hellmann; Ronglai Shen; Yelena Y Janjigian; David A Barron; Ahmet Zehir; Emmet J Jordan; Antonio Omuro; Thomas J Kaley; Sviatoslav M Kendall; Robert J Motzer; A Ari Hakimi; Martin H Voss; Paul Russo; Jonathan Rosenberg; Gopa Iyer; Bernard H Bochner; Dean F Bajorin; Hikmat A Al-Ahmadie; Jamie E Chaft; Charles M Rudin; Gregory J Riely; Shrujal Baxi; Alan L Ho; Richard J Wong; David G Pfister; Jedd D Wolchok; Christopher A Barker; Philip H Gutin; Cameron W Brennan; Viviane Tabar; Ingo K Mellinghoff; Lisa M DeAngelis; Charlotte E Ariyan; Nancy Lee; William D Tap; Mrinal M Gounder; Sandra P D'Angelo; Leonard Saltz; Zsofia K Stadler; Howard I Scher; Jose Baselga; Pedram Razavi; Christopher A Klebanoff; Rona Yaeger; Neil H Segal; Geoffrey Y Ku; Ronald P DeMatteo; Marc Ladanyi; Naiyer A Rizvi; Michael F Berger; Nadeem Riaz; David B Solit; Timothy A Chan; Luc G T Morris Journal: Nat Genet Date: 2019-01-14 Impact factor: 38.330
Authors: Jonathan E Rosenberg; Jean Hoffman-Censits; Tom Powles; Michiel S van der Heijden; Arjun V Balar; Andrea Necchi; Nancy Dawson; Peter H O'Donnell; Ani Balmanoukian; Yohann Loriot; Sandy Srinivas; Margitta M Retz; Petros Grivas; Richard W Joseph; Matthew D Galsky; Mark T Fleming; Daniel P Petrylak; Jose Luis Perez-Gracia; Howard A Burris; Daniel Castellano; Christina Canil; Joaquim Bellmunt; Dean Bajorin; Dorothee Nickles; Richard Bourgon; Garrett M Frampton; Na Cui; Sanjeev Mariathasan; Oyewale Abidoye; Gregg D Fine; Robert Dreicer Journal: Lancet Date: 2016-03-04 Impact factor: 79.321
Authors: Arjun V Balar; Matthew D Galsky; Jonathan E Rosenberg; Thomas Powles; Daniel P Petrylak; Joaquim Bellmunt; Yohann Loriot; Andrea Necchi; Jean Hoffman-Censits; Jose Luis Perez-Gracia; Nancy A Dawson; Michiel S van der Heijden; Robert Dreicer; Sandy Srinivas; Margitta M Retz; Richard W Joseph; Alexandra Drakaki; Ulka N Vaishampayan; Srikala S Sridhar; David I Quinn; Ignacio Durán; David R Shaffer; Bernhard J Eigl; Petros D Grivas; Evan Y Yu; Shi Li; Edward E Kadel; Zachary Boyd; Richard Bourgon; Priti S Hegde; Sanjeev Mariathasan; AnnChristine Thåström; Oyewale O Abidoye; Gregg D Fine; Dean F Bajorin Journal: Lancet Date: 2016-12-08 Impact factor: 79.321
Authors: Hira Rizvi; Francisco Sanchez-Vega; Konnor La; Walid Chatila; Philip Jonsson; Darragh Halpenny; Andrew Plodkowski; Niamh Long; Jennifer L Sauter; Natasha Rekhtman; Travis Hollmann; Kurt A Schalper; Justin F Gainor; Ronglai Shen; Ai Ni; Kathryn C Arbour; Taha Merghoub; Jedd Wolchok; Alexandra Snyder; Jamie E Chaft; Mark G Kris; Charles M Rudin; Nicholas D Socci; Michael F Berger; Barry S Taylor; Ahmet Zehir; David B Solit; Maria E Arcila; Marc Ladanyi; Gregory J Riely; Nikolaus Schultz; Matthew D Hellmann Journal: J Clin Oncol Date: 2018-01-16 Impact factor: 44.544