ReviewFrontiers in oncology2021
Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review.
Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Flexible Integration of Structurally Diverse Spatial Multi-Omics With AgaeSMO.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The Bioinformatics and Experimental Analysis of CD276 for Prognosis and Immune Infiltrates in Colon Adenocarcinoma.Cancer reports (Hoboken, N.J.) · 2026Article
- A multi-task deep learning framework for simultaneous prediction of microsatellite instability and tumor mutational burden in gastric cancer from histopathological images.Frontiers in oncology · 2026Article
- A novel four-serum marker model for early detection and therapeutic monitoring of breast cancer.Scientific reports · 2025Article
- Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach.Scientific reports · 2025Article
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- Application of Metabolic Biomarkers in Breast Cancer: A Literature Review.Annals of laboratory medicine · 2025Review
- A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.Scientific reports · 2025Article
- A Review on Recent Trends in Photo-Drug Efficiency of Advanced Biomaterials in Photodynamic Therapy of Cancer.Mini reviews in medicinal chemistry · 2025Review
- Article
- Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science.Diagnostics (Basel, Switzerland) · 2024Article
- A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning.Scientific reports · 2024Article
- Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.BMC gastroenterology · 2024Article
- Detection and isolation of brain tumors in cancer patients using neural network techniques in MRI images.Scientific reports · 2024Article
- Selective Naked-Eye Detection of Lung Squamous Cell Carcinoma Mediated by lncRNA SOX2OT Targeted Nanoplasmonic Probe.ACS omega · 2024Article
- Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction.PloS one · 2024Article
- A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme.Frontiers in cardiovascular medicine · 2024Article
- Review
- Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.
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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.