ReviewThe oncologist2022
Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.
Review in The oncologist, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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
30 citing papers in PubMed.
- A Predictive MRI Radiomics Model for Histologic Differentiation in Soft Tissue Sarcomas.Cancers · 2026Article
- Machine learning-based models for tumor mutation burden prediction in gastrointestinal cancers: a systematic review and meta-analysis.Discover oncology · 2026Review
- Health economics evaluation of artificial intelligence in the field of oncology: a scoping review.Health economics review · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Clinical advances and challenges of antibody-mediated targeted drug delivery in breast cancer therapeutics.Discover oncology · 2026Review
- Predicting tumor micronecrosis in hepatocellular carcinoma using pretreatment contrast-enhanced CT radiomics model: a multi-center study.NPJ precision oncology · 2025Article
- Multiphase dynamic contrast-enhanced magnetic resonance imaging radiomics nomogram for predicting axillary lymph node metastasis in breast cancer.Gland surgery · 2025Article
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
- Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction.Journal for immunotherapy of cancer · 2025Review
- Pre-Treatment and Pre-Brachytherapy MRI first-order Radiomic Features by a Commercial software as survival predictors in radiotherapy for cervical cancer Objectives.Clinical and translational radiation oncology · 2025Article
- Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning.Journal of imaging · 2025Article
- Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Single-cell spatial immune profiling for precision immunotherapy in Lynch syndrome.Journal of the National Cancer Center · 2025Article
- Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Effective imaging examination evaluation method for surgical pathological complete responds of head and neck squamous cell carcinoma after neoadjuvant immunochemotherapy.Frontiers in oncology · 2025Article
- Application of Pathomic Features for Differentiating Dysplastic Cells in Patients with Myelodysplastic Syndrome.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Just how transformative will AI/ML be for immuno-oncology?Journal for immunotherapy of cancer · 2024Review
- Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics-the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends-for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
Indexed as
Identifiers
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.