Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study
暂无分享,去创建一个
D. Koh | M. Orton | S. Doran | J. Larkin | C. Messiou | S. Turajlic | J. López | F. Comito | J. Shur | S. Shepherd | Charlotte Spencer | V. Albarrán-Artahona | Derfel Ap Dafydd | Evan Hann | Hannah R Warren | C. Spencer
[1] A. Demircioğlu. Benchmarking Feature Selection Methods in Radiomics , 2022, Investigative radiology.
[2] Yue Zhao,et al. Spatial patterns of tumour growth impact clonal diversification in a computational model and the TRACERx Renal study , 2021, Nature Ecology & Evolution.
[3] D. Koh,et al. Radiomics in Oncology: A Practical Guide , 2021, Radiographics : a review publication of the Radiological Society of North America, Inc.
[4] K. Takeda,et al. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients , 2021, Radiation Oncology.
[5] K. Takeda,et al. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients , 2021, Radiation oncology.
[6] R. Gillies,et al. The Biological Meaning of Radiomic Features. , 2021, Radiology.
[7] S. Horswell,et al. Selection of metastasis competent subclones in the tumour interior , 2020, Nature Ecology & Evolution.
[8] H. Alkadhi,et al. Radiomics in medical imaging—“how-to” guide and critical reflection , 2020, Insights into Imaging.
[9] D. Koh,et al. How to develop a meaningful radiomic signature for clinical use in oncologic patients , 2020, Cancer Imaging.
[10] Zhan Feng,et al. Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings , 2020, Frontiers in Oncology.
[11] E. S. Durmaz,et al. Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status. , 2019, AJR. American journal of roentgenology.
[12] A. Boulesteix,et al. Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data , 2018, BMC Bioinformatics.
[13] Xi Chen,et al. Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model , 2018, Physics in medicine and biology.
[14] G. Mayhew,et al. Tracking Cancer Evolution Reveals Constrained Routes to Metastases: TRACERx Renal , 2018, Cell.
[15] Zoltan Szallasi,et al. Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal , 2018, Cell.
[16] Aaron J. Fisher,et al. All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously , 2018, J. Mach. Learn. Res..
[17] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[18] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[19] C. Swanton,et al. TRACERx Renal: tracking renal cancer evolution through therapy , 2017, Nature Reviews Urology.
[20] Stefan Leger,et al. Image biomarker standardisation initiative version 1 . 4 , 2016, 1612.07003.
[21] G. C. Garriga,et al. 2009 Ninth IEEE International Conference on Data Mining Permutation Tests for Studying Classifier Performance , 2022 .
[22] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[23] L. Breiman. Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.
[24] Leonardo Vanneschi,et al. Regularization Techniques in Radiomics: A Case Study on the Prediction of pCR in Breast Tumours and the Axilla , 2019, CIBB.
[25] Timothy R. Olsen,et al. The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. , 2007, Neuroinformatics.
[26] Hilde van der Togt,et al. Publisher's Note , 2003, J. Netw. Comput. Appl..
[27] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .