ReviewCancers2022
Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- The role of deubiquitinating enzymes and their inhibitors in esophageal carcinoma (Review).International journal of oncology · 2026Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents.ESMO real world data and digital oncology · 2026Article
- A framework for a national cancer imaging repository in Nigeria.Scientific reports · 2026Article
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- What are you looking at? Modality contribution in multimodal medical deep learning.International journal of computer assisted radiology and surgery · 2026Article
- Understanding, Recognizing, and Managing Cancer-Related Fatigue Associated with Breast Cancer.Current treatment options in oncology · 2026Review
- From Optical to Molecular Imaging on Human Skin: A Review.Chemical & biomedical imaging · 2026Review
- A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response Analysis.Sensors (Basel, Switzerland) · 2026Article
- VIM-Polyp: Multimodal Colon Polyp Dataset with Video, Histopathology, and Protein Expression.Scientific data · 2025Article
- Navigating Transition Metal-Dependent Cell Death: Mechanisms, Crosstalk, and Future Directions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Interpretable Multiomics Models for Predicting Surgical Interventions and Blood Transfusion Requirements in Traumatic Brain Injury.NPJ digital medicine · 2025Article
- Mode-Specific Coherent Interference of Vibrational Sum-Frequency Generation Imaging: An Approach to Differentiate Lung Tumors through Collagen Interfibrillar Distances.Journal of the American Chemical Society · 2025Article
- From Biomarkers to Behavior: Mapping the Neuroimmune Web of Pain, Mood, and Memory.Biomedicines · 2025Article
- Big Data-Driven Health Portraits for Personalized Management in Noncommunicable Diseases: Scoping Review.Journal of medical Internet research · 2025Article
- Application of Artificial Intelligence Software to Identify Emotions of Lung Cancer Patients in Preoperative Health Education: A Cross-Sectional Study.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunities.Nature communications · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.Frontiers in immunology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression.
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.