# Radiomics and Machine Learning in Medical Imaging

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of radiomics, a quantitative analysis of medical images, particularly in the context of cancer imaging and precision medicine. It explores the extraction of advanced features from medical images, the use of machine learning for predictive modeling, and the assessment of tumor heterogeneity through texture analysis. |
| Domain | Health Sciences |
| Field | Medicine |
| OpenAlex ID | t12422 |
| Works | 613 |

## Topic papers all

Showing 15 of 613.

- [Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial](https://scholariq.org/papers/pembrolizumab-versus-docetaxel-for-previously-treated-pd-l1-positive-advanced/)
- [Radiomics: Extracting more information from medical images using advanced feature analysis](https://scholariq.org/papers/radiomics-extracting-more-information-from-medical-images-using-advanced-feature/)
- [Radiomics: the bridge between medical imaging and personalized medicine](https://scholariq.org/papers/radiomics-the-bridge-between-medical-imaging-and-personalized-medicine/)
- [Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach](https://scholariq.org/papers/decoding-tumour-phenotype-by-noninvasive-imaging-using-a-quantitative-radiomics/)
- [Artificial intelligence in healthcare: past, present and future](https://scholariq.org/papers/artificial-intelligence-in-healthcare-past-present-and-future/)
- [An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics](https://scholariq.org/papers/an-integrated-tcga-pan-cancer-clinical-data-resource-to-drive-high-quality/)
- [Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial](https://scholariq.org/papers/pembrolizumab-versus-chemotherapy-for-previously-untreated-pd-l1-expressing/)
- [International evaluation of an AI system for breast cancer screening](https://scholariq.org/papers/international-evaluation-of-an-ai-system-for-breast-cancer-screening/)
- [Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden](https://scholariq.org/papers/nivolumab-plus-ipilimumab-in-lung-cancer-with-a-high-tumor-mutational-burden/)
- [Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic](https://scholariq.org/papers/development-of-tumor-mutation-burden-as-an-immunotherapy-biomarker-utility-for/)
- [First-Line Nivolumab in Stage IV or Recurrent Non–Small-Cell Lung Cancer](https://scholariq.org/papers/first-line-nivolumab-in-stage-iv-or-recurrent-non-small-cell-lung-cancer/)
- [Radiomics: the process and the challenges](https://scholariq.org/papers/radiomics-the-process-and-the-challenges/)
- [Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)](https://scholariq.org/papers/reporting-recommendations-for-tumor-marker-prognostic-studies-remark-3/)
- [AACR Project GENIE: Powering Precision Medicine through an International Consortium](https://scholariq.org/papers/aacr-project-genie-powering-precision-medicine-through-an-international/)
- [Meta-Analysis of Concomitant Versus Sequential Radiochemotherapy in Locally Advanced Non–Small-Cell Lung Cancer](https://scholariq.org/papers/meta-analysis-of-concomitant-versus-sequential-radiochemotherapy-in-locally/)

## Topic primary papers

Showing 15 of 127.

- [Radiomics: Extracting more information from medical images using advanced feature analysis](https://scholariq.org/papers/radiomics-extracting-more-information-from-medical-images-using-advanced-feature/)
- [Radiomics: the bridge between medical imaging and personalized medicine](https://scholariq.org/papers/radiomics-the-bridge-between-medical-imaging-and-personalized-medicine/)
- [Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach](https://scholariq.org/papers/decoding-tumour-phenotype-by-noninvasive-imaging-using-a-quantitative-radiomics/)
- [An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics](https://scholariq.org/papers/an-integrated-tcga-pan-cancer-clinical-data-resource-to-drive-high-quality/)
- [Radiomics: the process and the challenges](https://scholariq.org/papers/radiomics-the-process-and-the-challenges/)
- [Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)](https://scholariq.org/papers/reporting-recommendations-for-tumor-marker-prognostic-studies-remark-3/)
- [Texture analysis of medical images](https://scholariq.org/papers/texture-analysis-of-medical-images/)
- [Repeatability and Reproducibility of Radiomic Features: A Systematic Review](https://scholariq.org/papers/repeatability-and-reproducibility-of-radiomic-features-a-systematic-review/)
- [The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions](https://scholariq.org/papers/the-uk-biobank-imaging-enhancement-of-100-000-participants-rationale-data/)
- [REporting recommendations for tumor MARKer prognostic studies (REMARK)](https://scholariq.org/papers/reporting-recommendations-for-tumor-marker-prognostic-studies-remark-2/)
- [Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention](https://scholariq.org/papers/feasibility-of-blood-testing-combined-with-pet-ct-to-screen-for-cancer-and-guide/)
- [From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment](https://scholariq.org/papers/from-patterns-to-patients-advances-in-clinical-machine-learning-for-cancer/)
- [From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge](https://scholariq.org/papers/from-detection-of-individual-metastases-to-classification-of-lymph-node-status/)
- [Non–Small Cell Lung Cancer: Histopathologic Correlates for Texture Parameters at CT](https://scholariq.org/papers/non-small-cell-lung-cancer-histopathologic-correlates-for-texture-parameters-at/)
- [Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability](https://scholariq.org/papers/stability-of-fdg-pet-radiomics-features-an-integrated-analysis-of-test-retest/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.