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A review in radiomics: Making personalized medicine a reality via routine imaging

Julien Guiot, Akshayaa Vaidyanathan, Louis Deprez, Fadila Zerka, Denis Danthine, Anne‐Noelle Frix, Philippe Lambin, Fabio Bottari, Nathan Tsoutzidis, Benjamin Miraglio, Sean Walsh, Wim Vos (+4 others)
2021 Medicinal research reviews (Print)  
Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts.  ...  For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making  ...  in a fast and reproducible way. 5 Radiomics is the result of several decades of computer-aided diagnosis, prognosis, and therapeutics research. 6, 7 A robust radiomics approach consists in the identification  ... 
doi:10.1002/med.21846 pmid:34309893 fatcat:xrqzahven5hnlg6n2ia37amdia

Radiomics and radiogenomics for precision radiotherapy

Jia Wu, Khin Khin Tha, Lei Xing, Ruijiang Li
2018 Journal of Radiation Research  
Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response.  ...  We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy.  ...  The radiomic model may have the potential to allow for personalization of chemoradiation treatments for head-and-neck cancer patients.  ... 
doi:10.1093/jrr/rrx102 pmid:29385618 pmcid:PMC5868194 fatcat:jgwi4xhp3zcvvg7tzeoubyter4

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

Xingping Zhang, Yanchun Zhang, Guijuan Zhang, Xingting Qiu, Wenjun Tan, Xiaoxia Yin, Liefa Liao
2022 Frontiers in Oncology  
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research  ...  Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability  ...  and robustness of radiomic features derived from the ROI outlined by the region growth algorithm.  ... 
doi:10.3389/fonc.2022.773840 pmid:35251962 pmcid:PMC8891653 fatcat:3h5tnm3aznb33k5ylkcd6tvs4e

Radiomics in Oncology: A 10-Year Bibliometric Analysis

Haoran Ding, Chenzhou Wu, Nailin Liao, Qi Zhan, Weize Sun, Yingzhao Huang, Zhou Jiang, Yi Li
2021 Frontiers in Oncology  
Artificial intelligence (AI), segmentation methods, and the use of radiomics for classification and diagnosis in oncology are major areas of focus in this field.  ...  ObjectivesTo date, radiomics has been applied in oncology for over a decade and has shown great progress.  ...  AUTHOR CONTRIBUTIONS YL conceived and designed the structure of this manuscript. HD, CW, NL, WS, QZ, YH, and ZJ wrote the paper. YL revised the paper.  ... 
doi:10.3389/fonc.2021.689802 pmid:34616671 pmcid:PMC8488302 fatcat:dl2mujapujci3my5g34oer37rm

Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction

Yun Qin, Yiqi Deng, Hanyu Jiang, Na Hu, Bin Song
2021 Frontiers in Oncology  
Gastric cancer (GC) is one of the most common cancers and one of the leading causes of cancer-related death worldwide.  ...  We also summarized the current clinical applications of AI in GC research, which include characterization, differential diagnosis, treatment response monitoring, and prognosis prediction.  ...  authors contributed to writing of the manuscript and approved the final manuscript. YQ and YD contributed equally to this work.  ... 
doi:10.3389/fonc.2021.631686 fatcat:xwltrbc7zbeejm5mumd47a2ofa

The Role of CT-Based Radiomics in Precise Imaging of Renal Cancer

Marta Ligero, Kinga Bernatowicz, Raquel Perez-Lopez
2021 Journal of Nephrological Science  
In this review, we briefly introduce the methodology for radiomics analysis and the main challenges for implementation of radiomics-based tools in clinical practice.  ...  , prognosis), gene expression prediction (radiogenomics) and response evaluation.  ...  Application of CT-Based Radiomics Towards Improving Renal Cancer Care: Diagnosis, Prognosis and Prediction of Response Applying CT-based radiomics towards improving renal cancer care has two main goals  ... 
doi:10.29245/2767-5149/2021/2.1111 fatcat:5kxfjrnnxzhp5k222j3gwklo3i

The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up

Radouane El Ayachy, Nicolas Giraud, Paul Giraud, Catherine Durdux, Philippe Giraud, Anita Burgun, Jean Emmanuel Bibault
2021 Frontiers in Oncology  
Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.  ...  Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade.  ...  Some studies demonstrated that delta radiomics seem to be more robust than radiomics features with the potential of using delta features for early assessment of treatment response and developing tailored  ... 
doi:10.3389/fonc.2021.603595 pmid:34026602 pmcid:PMC8131863 fatcat:qfszkyfmtnfp7d7ixvwyiz2vme

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics [article]

Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim
2022 arXiv   pre-print
This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks.  ...  Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring.  ...  Acknowledgements This project was in part supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2019-06467, and the Canadian Institutes of Health Research  ... 
arXiv:2110.10332v4 fatcat:vmpxhoolarbrve5ddyfn5umfim

Discovery of stable and prognostic CT-based radiomic features independent of contrast administration and dimensionality in oesophageal cancer

Concetta Piazzese, Kieran Foley, Philip Whybra, Chris Hurt, Tom Crosby, Emiliano Spezi, Jason Chia-Hsun Hsieh
2019 PLoS ONE  
The aim of this work was to investigate radiomic analysis of contrast and non-contrast enhanced planning CT images of oesophageal cancer (OC) patients in terms of stability, dimensionality and contrast  ...  A total of 238 2D and 3D radiomic features were computed from oesophageal CT images.  ...  Rhys Carrington for providing the baseline characteristics of the clinical cohort.  ... 
doi:10.1371/journal.pone.0225550 pmid:31756181 pmcid:PMC6874382 fatcat:a72jt7dodnf3zlsudvuo2m4vgy

Machine and deep learning methods for radiomics

Michele Avanzo, Lise Wei, Joseph Stancanello, Martin Vallières, Arvind Rao, Olivier Morin, Sarah A. Mattonen, Issam El Naqa
2020 Medical Physics (Lancaster)  
The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis  ...  In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working  ...  clustering. 76 Unsupervised consensus clustering identified robust imaging subtypes using dynamic CE-MRI data for patients with breast cancer. 77 t-distributed stochastic neighbor embedding is a dimension  ... 
doi:10.1002/mp.13678 pmid:32418336 pmcid:PMC8965689 fatcat:m2fed2mssvbzzpomhtfkem65ty

Multimodality MRI-based radiomics for aggressiveness prediction in papillary thyroid cancer

Zedong Dai, Ran Wei, Hao Wang, Wenjuan Hu, Xilin Sun, Jie Zhu, Hong Li, Yaqiong Ge, Bin Song
2022 BMC Medical Imaging  
Sparse representation method is used for radiation feature selection and classification model establishment.  ...  Thyroid nodules were manually segmented on three modal MR images, and then radiomics features were extracted.  ...  Acknowledgements We thank all members of the Department of Radiology, Pathology and General Surgery (Minhang Hospital, Fudan University) for constructive advice in manuscript preparation.  ... 
doi:10.1186/s12880-022-00779-5 pmid:35331162 pmcid:PMC8952254 fatcat:dzkf3yrrubfe5hhjtfak2erxau

Delta radiomics analysis of Magnetic Resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer

M. R. Tomaszewski, K. Latifi, E. Boyer, R. F. Palm, I. El Naqa, E. G. Moros, S. E. Hoffe, S. A. Rosenberg, J. M. Frakes, R. J. Gillies
2021 Radiation Oncology  
Stability analyses revealed a wide distribution of feature sensitivities to ROI delineation and was able to identify features that were robust to variability in contouring.  ...  Methods Histogram and texture radiomic features (n = 73) were extracted from the Gross Tumor Volume (GTV) in 0.35T MRgRT scans of 26 locally advanced and borderline resectable PDAC patients treated with  ...  Analysis of spatial robustness of MRgRT radiomics was performed for both histogram and texture features through quantification of feature changes following small changes in GTV boundary position.  ... 
doi:10.1186/s13014-021-01957-5 pmid:34911546 pmcid:PMC8672552 fatcat:mexpluey6ffhzegyf3tunfxq34

Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future

Filippo Pesapane, Anna Rotili, Giorgio Maria Agazzi, Francesca Botta, Sara Raimondi, Silvia Penco, Valeria Dominelli, Marta Cremonesi, Barbara Alicja Jereczek-Fossa, Gianpaolo Carrafiello, Enrico Cassano
2021 Current Oncology  
practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment.  ...  In this review, we discuss the current limitations and promises of radiomics for improvement in further research.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/curroncol28040217 pmid:34202321 fatcat:queca2zkkrg7dok6osuglbltay

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Ahmad Chaddad, Michael Jonathan Kucharczyk, Paul Daniel, Siham Sabri, Bertrand J. Jean-Claude, Tamim Niazi, Bassam Abdulkarim
2019 Frontiers in Oncology  
These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response.  ...  Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis.  ...  Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images.  ... 
doi:10.3389/fonc.2019.00374 pmid:31165039 pmcid:PMC6536622 fatcat:hdwtwfw6qja7zattkgblorbeku

Radiomics: Images Are More than Pictures, They Are Data

Robert J. Gillies, Paul E. Kinahan, Hedvig Hricak
2016 Radiology  
A major strength of a radiomics approach for cancer is that digital radiologic images are obtained for almost every patient with cancer, and all of these images are potential sources for and prognosis.  ...  In the evaluation of lung cancer and in the evaluation of glioblastoma multiforme, radiomics has been shown to be a tool with which to assess patient prognosis (7).  ...  Activities not related to the present article: is on the advisory board of and is an investor in Health Myne. Other relationships: has a patent pending for Radiology Reading Room of the Future.  ... 
doi:10.1148/radiol.2015151169 pmid:26579733 pmcid:PMC4734157 fatcat:hhnbd2mm6jhavfp7ghinakrlda
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