ReviewInsights into imaging2018
Radiomics and liquid biopsy in oncology: the holons of systems medicine.
Review in Insights into imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06108310 (ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors), which is not on this map. Cited by 48 papers, 2 of them syntheses 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.
ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors
Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.European radiology · 2026Pooled it
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Liquid Biopsy for Minimal Residual Disease Assessment in Endometrial and Cervical Cancers: Molecular Rationale, Clinical Evidence, and Translational Barriers.International journal of molecular sciences · 2026Review
- Radiogenomic approach combining CT-based radiomics and liquid biopsy improves prognostic stratification in patients with advanced NSCLC.The journal of liquid biopsy · 2026Article
- Early Cancer Detection: What's Going on and What's Next.MedComm · 2026Review
- Risk-stratified classification of pulmonary nodule malignancy via a machine learning model integrating imaging and cell-free DNA: a model development and validation study (DECIPHER-NODL).The Lancet regional health. Western Pacific · 2025Article
- Radiomics-clinical nomogram for preoperative tumor-node-metastasis staging prediction in breast cancer patients using dynamic enhanced magnetic resonance imaging.Translational cancer research · 2025Article
- Cholangiocarcinoma: The era of liquid biopsy.World journal of gastroenterology · 2025Review
- Artificial intelligence for response prediction and personalisation in radiation oncology.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Review
- Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives.Therapeutic advances in vaccines and immunotherapy · 2024Review
- Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences.Breast cancer (Dove Medical Press) · 2024Article
- Radiologic Technology Students' Perceptions on Adoption of Artificial Intelligence Technology in Radiology.International journal of general medicine · 2024Article
- Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives.View (Beijing, China) · 2023Article
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Application of radiomics in lung immuno-oncology.Precision radiation oncology · 2023Review
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think?International journal of environmental research and public health · 2023Article
- Additively manufactured, solid object structures for adjustable image contrast in Magnetic Resonance Imaging.Zeitschrift fur medizinische Physik · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics is a process of extraction and analysis of quantitative features from diagnostic images. Liquid biopsy is a test done on a sample of blood to look for cancer cells or for pieces of tumourigenic DNA circulating in the blood. Radiomics and liquid biopsy have great potential in oncology, since both are minimally invasive, easy to perform, and can be repeated in patient follow-up visits, enabling the extraction of valuable information regarding tumour type, aggressiveness, progression, and response to treatment. Both methods are in their infancy, with major evidence of application in lung and gastrointestinal cancer, while still undergoing evaluation in other cancer types. In this paper, the main oncologic applications of radiomics and liquid biopsy are reviewed, and a synergistic approach incorporating both tests for cancer diagnosis and follow-up is discussed within the context of systems medicine. TEACHING POINTS: • Radiomics is a process of extraction and analysis of quantitative features from diagnostic images. • Most clinical applications of radiomics are in the field of oncologic imaging. • Radiomics applies to all imaging modalities. • A cluster of radiomic features is a "radiomic signature". • Machine learning may improve the efficacy of radiomics analysis.
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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.