Radiomics: the bridge between medical imaging and personalized medicine
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Citations
Interreader reproducibility of the Neck Imaging Reporting and Data system (NI-RADS) lexicon for the detection of residual/recurrent disease in treated head and neck squamous cell carcinoma (HNSCC).
Transcriptomics and Epigenomics in head and neck cancer: available repositories and molecular signatures.
Toward Generalizability in the Deployment of Artificial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification
A Radiomics Model for Predicting the Response to Bevacizumab in Brain Necrosis after Radiotherapy.
Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.
References
Global cancer statistics
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer
Pembrolizumab for the treatment of non-small cell lung cancer
Radiomics: Images Are More than Pictures, They Are Data.
Related Papers (5)
Radiomics: Images Are More than Pictures, They Are Data.
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Computational Radiomics System to Decode the Radiographic Phenotype
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Frequently Asked Questions (19)
Q2. What have the authors contributed in "Radiomics: the bridge between medical imaging and personalized medicine" ?
Herein, the authors describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Herein, the authors provide guidance for investigations to meet this urgent need in the field of radiomics.
Q3. What are the future works in "Radiomics: the bridge between medical imaging and personalized medicine" ?
Picture archiving and radiomics knowledge systems ( PARKS ) of the future will identify, segment, and extract features from regions of interest. If previous images associated with the same patient are accessible, the earlier identified regions of interest will be automatically identified by the PARKS software. Quantitative image features that are uploaded to a shared database and compared with previous images will be automatically extracted by the PARKS to enhance CDSS for diagnosis, prognosis, and treatment, resulting in improved personalization and precision medicine ( FIG. 7 ).
Q4. What is the promising research area in oncology?
In the field of oncology, a promising research area is that of biomarkers — in particular, biomarkers for immunotherapy and imaging biomarkers90,91.
Q5. what is the need for standardized methodology in tumor texture analysis?
The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis.
Q6. What is the current method of quantification of the radiosensitivity of human tumours?
The quantification of the radiosensitivity of human tumours is presently performed on the basis of the ex vivo tumour survival fraction, and the detection of unrepaired DNA double-strand breaks82,83.
Q7. What is the way to achieve a holistic model?
To achieve holistic models, features beyond radiomics (such as data from clinical records, data obtained during treatment or biological and/or genetic) should also be incorporated.
Q8. What are the main benefits of a standardized RLHC?
In addition, universal streamlined solutions through advanced information communication technologies have been central to the realization of this endeavor, readily facilitating synchronized RLHC in each centre without inclusion of sensitive data, which overcomes the classic barriers to data sharing.
Q9. What is the purpose of phantom studies?
In essence, phantom studies provide a risk-mitigation strategy to help navigate from the current clinical imaging scenario to the desired optimal imaging scenario.
Q10. Why should a radiomic study incorporate reproducibility assessments?
Radiomic studies should incorporate reproducibility assessments owing to the beneficial ethical, economic and logistical effects they have (such asinforming power calculations and required samples sizes, multicentric trial duration and trial cost).
Q11. What is the way to evaluate the features of a radiomics dataset?
Feature selection should be data-driven owing to the vast in- human range of possible radiomics features; such analysis should be performed in a robust and transparent manner.
Q12. What drives the research and clinical communities?
A pressing need to embrace knowledge and data-sharing technology106, which transcends institutional and national boundaries107, drives both the research and clinical communities.
Q13. What is the effect of IGF-1R inhibition on radiosensitivity?
85. Chitnis, M. M. et al. IGF-1R inhibition enhances radiosensitivity and delays double-strand break repair by both non-homologous end-joining and homologous recombination.
Q14. What can be done to improve the quality of radiomics?
Although the minute technical details of radiomics are tedious, they can greatly influence robustness, generalizability, and confound meta- analyses.
Q15. What are some examples of robust features that can be observed?
Examples that enable robust features to be observed21 include: evaluation by multiple clinicians, perturb segmentations with noise, combination of diverse algorithms, or use different stages of the breathing cycle.
Q16. What is the main advantage of using the ontology terms?
Exploiting this technique, the ontology terms serve as a common reference for the data at each institutional site, permitting a unified process for information retrieval enabled by a semantic gateway to the data.
Q17. What is the main reason why these approaches have been undermined?
these approaches have been undermined by the presence of substantial experimental variability rather than by the existence of interpatient variations in radiosensitivity.
Q18. How can groups of highly correlated radiomics features be identified?
Groups of highly correlated radiomics features can be identified via clustering, and these features can be reduced to single archetypal features per cluster.
Q19. What are the capabilities of the current picture archiving and communication systems?
Such capabilities are on the technological, scientific, and clinical horizons, as most current picture archiving and communication systems have the capability to co-register current images with previous images and perform user-interactive segmentation.