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Metastasis

Metastasis

Metastasis is the spread of cancer cells from the original site to distant locations in the body. Cancer cells can spread via blood vessels (hematogenous) as well as lymph vessels in the body.
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...

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Updated: Oct 18, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Machine learning and deep learning methods that use omics data for metastasis prediction.

Somayah Albaradei^1,2, Maha Thafar^1,3, Asim Alsaedi^4,5

¹Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.

Computational and Structural Biotechnology Journal

|September 30, 2021

Summary

This summary is machine-generated.

Predicting cancer metastasis onset is crucial for survival. This review covers machine learning and deep learning methods using molecular data to improve cancer diagnostics and therapies.

Keywords:

AE, autoencoder ANN, Artificial Neural Network AUC, area under the curve Acc, Accuracy Artificial intelligence BC, Betweenness centrality BH, Benjamini-Hochberg BioGRID, Biological General Repository for Interaction Datasets CCP, compound covariate predictor CEA, Carcinoembryonic antigen CNN, convolution neural networks CV, cross-validation Cancer DBN, deep belief network DDBN, discriminative deep belief network DEGs, differentially expressed genes DIP, Database of Interacting Proteins DNN, Deep neural network DT, Decision Tree Deep learning EMT, epithelial-mesenchymal transition FC, fully connected GA, Genetic Algorithm GANs, generative adversarial networks GEO, Gene Expression Omnibus HCC, hepatocellular carcinoma HPRD, Human Protein Reference Database KNN, K-nearest neighbor L-SVM, linear SVM LIMMA, linear models for microarray data LOOCV, Leave-one-out cross-validation LR, Logistic Regression MCCV, Monte Carlo cross-validation MLP, multilayer perceptron Machine learning Metastasis NPV, negative predictive value PCA, Principal component analysis PPI, protein-protein interaction PPV, positive predictive value RC, ridge classifier RF, Random Forest RFE, recursive feature elimination RMA, robust multi‐array average RNN, recurrent neural networks SGD, stochastic gradient descent SMOTE, synthetic minority over-sampling technique SVM, Support Vector Machine Se, sensitivity Sp, specificity TCGA, The Cancer Genome Atlas k-CV, k-fold cross validation mRMR, minimum redundancy maximum relevance

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Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Metastasis is the leading cause of cancer mortality, driving extensive research into its complex cellular mechanisms.
High-throughput sequencing technologies have significantly advanced our understanding of metastatic processes.
This knowledge is pivotal for developing novel therapeutic strategies and improving patient diagnostics.

Purpose of the Study:

To review machine learning (ML) and deep learning (DL) based methods for predicting cancer metastasis onset.
To detail the types of molecular data utilized in these predictive models.
To identify critical molecular signatures and discuss challenges and future directions in ML/DL for metastasis prediction.

Main Methods:

Systematic review of existing literature on ML and DL approaches for metastasis prediction.
Analysis of various molecular data types (e.g., genomic, transcriptomic) employed in model development.
Evaluation of derived molecular signatures and their clinical relevance.

Main Results:

Summary of diverse ML and DL algorithms applied to metastasis prediction.
Identification of key molecular features and biomarkers associated with metastatic potential.
Discussion of the successes and limitations of current AI-driven prediction models.

Conclusions:

ML and DL show significant promise in predicting metastasis onset, aiding in early diagnosis and personalized treatment.
Further research is needed to overcome challenges such as data heterogeneity and model interpretability.
Improving predictive performance requires robust datasets and advanced algorithmic approaches for better cancer patient outcomes.