Literature DB >> 33477723

Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma.

Giulia Buizza1, Chiara Paganelli1, Emma D'Ippolito2, Giulia Fontana3, Silvia Molinelli4, Lorenzo Preda5,6, Giulia Riva2, Alberto Iannalfi2, Francesca Valvo2, Ester Orlandi2, Guido Baroni1,3.   

Abstract

Skull-base chordoma (SBC) can be treated with carbon ion radiotherapy (CIRT) to improve local control (LC). The study aimed to explore the role of multi-parametric radiomic, dosiomic and clinical features as prognostic factors for LC in SBC patients undergoing CIRT. Before CIRT, 57 patients underwent MR and CT imaging, from which tumour contours and dose maps were obtained. MRI and CT-based radiomic, and dosiomic features were selected and fed to two survival models, singularly or by combining them with clinical factors. Adverse LC was given by in-field recurrence or tumour progression. The dataset was split in development and test sets and the models' performance evaluated using the concordance index (C-index). Patients were then assigned a low- or high-risk score. Survival curves were estimated, and risk groups compared through log-rank tests (after Bonferroni correction α = 0.0083). The best performing models were built on features describing tumour shape and dosiomic heterogeneity (median/interquartile range validation C-index: 0.80/024 and 0.79/0.26), followed by combined (0.73/0.30 and 0.75/0.27) and CT-based models (0.77/0.24 and 0.64/0.28). Dosiomic and combined models could consistently stratify patients in two significantly different groups. Dosiomic and multi-parametric radiomic features showed to be promising prognostic factors for LC in SBC treated with CIRT.

Entities:  

Keywords:  dosiomics; machine learning; oncology; particle therapy; personalized medicine; radiology; radiomics

Year:  2021        PMID: 33477723      PMCID: PMC7832399          DOI: 10.3390/cancers13020339

Source DB:  PubMed          Journal:  Cancers (Basel)        ISSN: 2072-6694            Impact factor:   6.639


  49 in total

1.  Dose prescription in carbon ion radiotherapy: How to compare two different RBE-weighted dose calculation systems.

Authors:  Silvia Molinelli; Giuseppe Magro; Andrea Mairani; Naruhiro Matsufuji; Nobuyuki Kanematsu; Taku Inaniwa; Alfredo Mirandola; Stefania Russo; Edoardo Mastella; Azusa Hasegawa; Hiroshi Tsuji; Shigeru Yamada; Barbara Vischioni; Viviana Vitolo; Alfredo Ferrari; Mario Ciocca; Tadashi Kamada; Hirohiko Tsujii; Roberto Orecchia; Piero Fossati
Journal:  Radiother Oncol       Date:  2016-07-06       Impact factor: 6.280

2.  Optic nerve constraints for carbon ion RT at CNAO - Reporting and relating outcome to European and Japanese RBE.

Authors:  Jon Espen Dale; Silvia Molinelli; Viviana Vitolo; Barbara Vischioni; Maria Bonora; Giuseppe Magro; Helge Egil Seime Pettersen; Andrea Mairani; Azusa Hasegawa; Olav Dahl; Francesca Valvo; Piero Fossati
Journal:  Radiother Oncol       Date:  2019-07-13       Impact factor: 6.280

3.  MR Imaging Grading System for Skull Base Chordoma.

Authors:  K Tian; L Wang; J Ma; K Wang; D Li; J Du; G Jia; Z Wu; J Zhang
Journal:  AJNR Am J Neuroradiol       Date:  2017-04-20       Impact factor: 3.825

4.  MRI Signal Intensity and Electron Ultrastructure Classification Predict the Long-Term Outcome of Skull Base Chordomas.

Authors:  J Bai; J Shi; S Zhang; C Zhang; Y Zhai; S Wang; M Li; C Li; P Zhao; S Geng; S Gui; L Jing; Y Zhang
Journal:  AJNR Am J Neuroradiol       Date:  2020-05-07       Impact factor: 3.825

Review 5.  Comparison of the Effectiveness of Radiotherapy with Photons and Particles for Chordoma After Surgery: A Meta-Analysis.

Authors:  Jinpeng Zhou; Bowen Yang; Xin Wang; Zhitao Jing
Journal:  World Neurosurg       Date:  2018-06-05       Impact factor: 2.104

6.  Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma.

Authors:  Longfei Li; Ke Wang; Xiujian Ma; Zhenyu Liu; Shuo Wang; Jiang Du; Kaibing Tian; Xuezhi Zhou; Wei Wei; Kai Sun; Yusong Lin; Zhen Wu; Jie Tian
Journal:  Eur J Radiol       Date:  2019-07-05       Impact factor: 3.528

7.  Machine Learning methods for Quantitative Radiomic Biomarkers.

Authors:  Chintan Parmar; Patrick Grossmann; Johan Bussink; Philippe Lambin; Hugo J W L Aerts
Journal:  Sci Rep       Date:  2015-08-17       Impact factor: 4.379

8.  Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization.

Authors:  David Molina; Julián Pérez-Beteta; Alicia Martínez-González; Juan Martino; Carlos Velasquez; Estanislao Arana; Víctor M Pérez-García
Journal:  PLoS One       Date:  2017-06-06       Impact factor: 3.240

9.  Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis.

Authors:  Bin Liang; Hui Yan; Yuan Tian; Xinyuan Chen; Lingling Yan; Tao Zhang; Zongmei Zhou; Lvhua Wang; Jianrong Dai
Journal:  Front Oncol       Date:  2019-04-12       Impact factor: 6.244

10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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  3 in total

1.  MRI-Based Radiomics Differentiates Skull Base Chordoma and Chondrosarcoma: A Preliminary Study.

Authors:  Erika Yamazawa; Satoshi Takahashi; Masahiro Shin; Shota Tanaka; Wataru Takahashi; Takahiro Nakamoto; Yuichi Suzuki; Hirokazu Takami; Nobuhito Saito
Journal:  Cancers (Basel)       Date:  2022-07-03       Impact factor: 6.575

2.  Multi-Organ Omics-Based Prediction for Adaptive Radiation Therapy Eligibility in Nasopharyngeal Carcinoma Patients Undergoing Concurrent Chemoradiotherapy.

Authors:  Sai-Kit Lam; Yuanpeng Zhang; Jiang Zhang; Bing Li; Jia-Chen Sun; Carol Yee-Tung Liu; Pak-Hei Chou; Xinzhi Teng; Zong-Rui Ma; Rui-Yan Ni; Ta Zhou; Tao Peng; Hao-Nan Xiao; Tian Li; Ge Ren; Andy Lai-Yin Cheung; Francis Kar-Ho Lee; Celia Wai-Yi Yip; Kwok-Hung Au; Victor Ho-Fun Lee; Amy Tien-Yee Chang; Lawrence Wing-Chi Chan; Jing Cai
Journal:  Front Oncol       Date:  2022-01-31       Impact factor: 6.244

Review 3.  Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

Authors:  Xingping Zhang; Yanchun Zhang; Guijuan Zhang; Xingting Qiu; Wenjun Tan; Xiaoxia Yin; Liefa Liao
Journal:  Front Oncol       Date:  2022-02-17       Impact factor: 6.244

  3 in total

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