Xiaokai Mo1,2, Xiangjun Wu3,4, Di Dong3,4, Baoliang Guo1, Changhong Liang1, Xiaoning Luo1,5, Bin Zhang6, Lu Zhang1, Yuhao Dong1,2, Zhouyang Lian1, Jing Liu1, Shufang Pei1, Wenhui Huang1, Fusheng Ouyang1, Jie Tian7,8,9, Shuixing Zhang10. 1. Department of Radiology, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, People's Republic of China. 2. Shantou University Medical College, Shantou, Guangdong, People's Republic of China. 3. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing, 100190, People's Republic of China. 4. University of Chinese Academy of Sciences, Beijing, 100190, People's Republic of China. 5. Department of Otolaryngology-Head and Neck Surgery, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, People's Republic of China. 6. Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, Guangdong, 510627, People's Republic of China. 7. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing, 100190, People's Republic of China. jie.tian@ia.ac.cn. 8. University of Chinese Academy of Sciences, Beijing, 100190, People's Republic of China. jie.tian@ia.ac.cn. 9. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, People's Republic of China. jie.tian@ia.ac.cn. 10. Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, Guangdong, 510627, People's Republic of China. shui7515@126.com.
Abstract
PURPOSE: To develop a radiomics-based model to stratify the risk of early progression (local/regional recurrence or metastasis) among patients with hypopharyngeal cancer undergoing chemoradiotherapy and modify their pretreatment plans. MATERIALS AND METHODS: We randomly assigned 113 patients into two cohorts: training (n = 80) and validation (n = 33). The radiomic significant features were selected in the training cohort using least absolute shrinkage and selection operator and Akaike information criterion methods, and they were used to build the radiomic model. The concordance index (C-index) was applied to evaluate the model's prognostic performance. A Kaplan-Meier analysis and the log-rank test were used to assess risk stratification ability of models in predicting progression. A nomogram was plotted to predict individual risk of progression. RESULTS: Composed of four significant features, the radiomic model showed good performance in stratifying patients into high- and low-risk groups of progression in both the training and validation cohorts (log-rank test, p = 0.00016, p = 0.0063, respectively). Peripheral invasion and metastasis were selected as significant clinical variables. The combined radiomic-clinical model showed good discriminative performance, with C-indices 0.804 (95% confidence interval (CI), 0.688-0.920) and 0.756 (95% CI, 0.605-0.907) in the training and validation cohorts, respectively. The median progression-free survival (PFS) in the high-risk group was significantly shorter than that in the low-risk group in the training (median PFS, 9.5 m and 19.0 m, respectively; p [log-rank] < 0.0001) and validation (median PFS, 11.3 m and 22.5 m, respectively; p [log-rank] = 0.0063) cohorts. CONCLUSIONS: A radiomics-based model was established to predict the risk of progression in hypopharyngeal cancer with chemoradiotherapy. KEY POINTS: • Clinical information showed limited performance in stratifying the risk of progression among patients with hypopharyngeal cancer. • Imaging features extracted from CECT and NCCT images were independent predictors of PFS. • We combined significant features and valuable clinical variables to establish a nomogram to predict individual risk of progression.
RCT Entities:
PURPOSE: To develop a radiomics-based model to stratify the risk of early progression (local/regional recurrence or metastasis) among patients with hypopharyngeal cancer undergoing chemoradiotherapy and modify their pretreatment plans. MATERIALS AND METHODS: We randomly assigned 113 patients into two cohorts: training (n = 80) and validation (n = 33). The radiomic significant features were selected in the training cohort using least absolute shrinkage and selection operator and Akaike information criterion methods, and they were used to build the radiomic model. The concordance index (C-index) was applied to evaluate the model's prognostic performance. A Kaplan-Meier analysis and the log-rank test were used to assess risk stratification ability of models in predicting progression. A nomogram was plotted to predict individual risk of progression. RESULTS: Composed of four significant features, the radiomic model showed good performance in stratifying patients into high- and low-risk groups of progression in both the training and validation cohorts (log-rank test, p = 0.00016, p = 0.0063, respectively). Peripheral invasion and metastasis were selected as significant clinical variables. The combined radiomic-clinical model showed good discriminative performance, with C-indices 0.804 (95% confidence interval (CI), 0.688-0.920) and 0.756 (95% CI, 0.605-0.907) in the training and validation cohorts, respectively. The median progression-free survival (PFS) in the high-risk group was significantly shorter than that in the low-risk group in the training (median PFS, 9.5 m and 19.0 m, respectively; p [log-rank] < 0.0001) and validation (median PFS, 11.3 m and 22.5 m, respectively; p [log-rank] = 0.0063) cohorts. CONCLUSIONS: A radiomics-based model was established to predict the risk of progression in hypopharyngeal cancer with chemoradiotherapy. KEY POINTS: • Clinical information showed limited performance in stratifying the risk of progression among patients with hypopharyngeal cancer. • Imaging features extracted from CECT and NCCT images were independent predictors of PFS. • We combined significant features and valuable clinical variables to establish a nomogram to predict individual risk of progression.
Entities:
Keywords:
Chemoradiotherapy; Head and neck cancer; Hypopharynx; Prognosis; Recurrence
Authors: Jonathan J Beitler; Qiang Zhang; Karen K Fu; Andy Trotti; Sharon A Spencer; Christopher U Jones; Adam S Garden; George Shenouda; Jonathan Harris; Kian K Ang Journal: Int J Radiat Oncol Biol Phys Date: 2014-03-07 Impact factor: 7.038
Authors: Allen S Ho; Sungjin Kim; Mourad Tighiouart; Cynthia Gudino; Alain Mita; Kevin S Scher; Anna Laury; Ravi Prasad; Stephen L Shiao; Nabilah Ali; Chrysanta Patio; Jon Mallen-St Clair; Jennifer E Van Eyk; Zachary S Zumsteg Journal: JAMA Oncol Date: 2018-07-01 Impact factor: 31.777
Authors: Michael Bartoschek; Nikolay Oskolkov; Matteo Bocci; John Lövrot; Christer Larsson; Mikael Sommarin; Chris D Madsen; David Lindgren; Gyula Pekar; Göran Karlsson; Markus Ringnér; Jonas Bergh; Åsa Björklund; Kristian Pietras Journal: Nat Commun Date: 2018-12-04 Impact factor: 14.919
Authors: Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin Journal: Nat Commun Date: 2014-06-03 Impact factor: 14.919
Authors: Maryam Gul; Kimberley-Jane C Bonjoc; David Gorlin; Chi Wah Wong; Amirah Salem; Vincent La; Aleksandr Filippov; Abbas Chaudhry; Muhammad H Imam; Ammar A Chaudhry Journal: Front Oncol Date: 2021-07-07 Impact factor: 6.244