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

[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 .