ReviewComputational and mathematical methods in medicine2021
Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.
Review in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it, 136 citations in OpenAlex.
- Diagnostic predictive models for cancer-related fatigue: current evidence and future directions.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Application of Deep Learning in Cancer Prognosis Prediction Model.Technology in cancer research & treatmentPooled it
- The prediction of cancer-specific mortality in T1 non-muscle-invasive bladder cancer: comparison of logistic regression and artificial neural network: a SEER population-based study.International urology and nephrology · 2023Trial
- Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.Scientific reports · 2026Article
- A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response Analysis.Sensors (Basel, Switzerland) · 2026Article
- Effective transfer of tumor annotations from hematoxylin and eosin to fluorescence images of breast and lung tissues.Journal of biomedical optics · 2026Article
- Deep learning-based predictive models for assessing the impact of clinical factors and second primary malignancy on survival in patients with colorectal cancer.European journal of medical research · 2025Article
- Enhancing bone cancer detection through optimized pre trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset.Scientific reports · 2025Article
- Leveraging machine learning with dynamic 18F-FDG PET/CT: integrating metabolic and flow features for lung cancer differential diagnosis.European journal of nuclear medicine and molecular imaging · 2025Article
- Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis.Discover oncology · 2025Article
- Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries.Discover oncology · 2025Review
- Towards unbiased skin cancer classification using deep feature fusion.BMC medical informatics and decision making · 2025Article
- Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.Scientific reports · 2025Article
- HED-Net: a hybrid ensemble deep learning framework for breast ultrasound image classification.Frontiers in artificial intelligence · 2025Article
- Explainable Diagnosis Prediction through Neuro-Symbolic Integration.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification.Scientific reports · 2024Article
- A Method for Real-Time Lung Nodule Instance Segmentation Using Deep Learning.Life (Basel, Switzerland) · 2024Article
- Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.Heliyon · 2024Review
- Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.International journal of molecular sciences · 2024Article
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 at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.
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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.