Application of Radiomics in Radiotherapy: Challenges and Future Prospects
Bibliographic record
Abstract
Background and Objective: Specific treatment for each patient based on their clinical data is one of the medical prospects of the future. Using data mining and machine learning techniques based on computer science in extracting the quantitative features of an image to improve the process of diagnosis, prognosis, prediction and response to cancer treatment is known as radiomics. This article examines the workflow, findings, challenges ahead, and the role of radiomics in precision medicine and individual therapy. Methods: In this review article, we searched well-known indexes such as ISC, web of science, Google Scholar, Scopus, PubMed without time limit and based on the keywords radiomics, radiotherapy, cancer and quantitative imaging and relevant articles were collected. Findings: Radiomics is a combination of everyday computer-aided diagnosis, machine learning methods, deep learning and human skills that can be used for quantitative description of the phenotypes of cancerous tumors. Image collection and processing, tumor segmentation, extraction of features, processing and modeling are some of the basic steps of the process of radiomics. Computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound (US) methods are among the used images. Conclusion: According to the results of this study, the prerequisite for the clinical implementation of radiomics is the elimination of deficiencies such as the dependence of the features on the imaging parameters, and the unrepeatability of the features. Therefore, a comprehensive approach should be adopted, stable and reproducible patterns should be developed to accept radiomics as a clinical prognostic tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".