ReviewCancer management and research2019
Application of Radiomics for Personalized Treatment of Cancer Patients.
Review in Cancer management and research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review.Abdominal radiology (New York) · 2024Pooled it
- Integrating MRI radiomics and transcriptomics to predict IDH mutation status and prognosis in glioma.Cancer cell international · 2026Article
- MRI Based Radiomics as an Imaging Biomarker for Locally Advanced Carcinoma Rectum: Predicting Tumor Response Following Neoadjuvant Chemoradiotherapy as an Organ Preservation Strategy.Cancer reports (Hoboken, N.J.) · 2026Article
- Monitoring Pharmacological Treatment of Breast Cancer with MRI.Current issues in molecular biology · 2025Review
- Prioritizing Trust in Podiatrists' Preference for AI in Supportive Roles Over Diagnostic Roles in Health Care: Qualitative Interview and Focus Group Study.JMIR human factors · 2025Article
- Radiomics of Vascular Structures in Pulmonary Ground-glass Nodules: A Predictor of InvasivenessCurrent medical imaging · 2025Article
- Radiomics Analysis for Clinical Decision Support in 177Lu-DOTATATE Therapy of Metastatic Neuroendocrine Tumors using CT Images.Journal of biomedical physics & engineering · 2024Article
- Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.Radiology. Artificial intelligence · 2024Article
- Prediction of High-Risk Neuroblastoma Among Neuroblastic Tumors Using Radiomics Features Derived from Magnetic Resonance Imaging: A Pilot Study.Yonsei medical journal · 2024Article
- Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI.BMC medical imaging · 2023Article
- Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer.Japanese journal of radiology · 2023Review
- Targeted Therapy and Personalized Medicine.Cancer treatment and research · 2023Article
- Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [European journal of hybrid imaging · 2022Article
- Article
- Review
- Article
- Identification of marker genes and cell subtypes in castration-resistant prostate cancer cells.Journal of Cancer · 2021Article
- Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches.Applied bionics and biomechanics · 2021Article
- A Non-invasive Method to Diagnose Lung Adenocarcinoma.Frontiers in oncology · 2020Article
- Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer.Frontiers in oncology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics is a novel concept that relies on obtaining image data from examinations such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). With the appropriate algorithm, the extracted results have broad applicability and potential for a massive positive impact in radiology. For example, clinicians can verify treatment efficiency, predict the location of tumor metastasis, correlate results with a histopathological examination, or more accurately define the type of cancer. Combining radiomics with other testing techniques allows every patient to have a personalized treatment plan that is essential for advanced examination and treatment. This article explains the process of radiomics, including data collection mechanisms, combined use with genomics, and artificial intelligence and immunology techniques, which may solve many of the challenges faced by doctors in diagnosing and treating their patients.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.