AIMC Topic: Neoplasm Metastasis
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Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network.
International journal of molecular sciences
Apr 11, 2024
Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, su...
Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine Learning Algorithm.
Chinese journal of integrative medicine
Mar 27, 2024
OBJECTIVE: To establish the dynamic treatment strategy of Chinese medicine (CM) for metastatic colorectal cancer (mCRC) by machine learning algorithm, in order to provide a reference for the selection of CM treatment strategies for mCRC.
Artificial intelligence algorithm accurately assesses oestrogen receptor immunohistochemistry in metastatic breast cancer cytology specimens: A pilot study.
Cytopathology : official journal of the British Society for Clinical Cytology
Mar 22, 2024
OBJECTIVE: The Visiopharm artificial intelligence (AI) algorithm for oestrogen receptor (ER) immunohistochemistry (IHC) in whole slide images (WSIs) has been successfully validated in surgical pathology. This study aimed to assess its efficacy in cyt...
Machine learning application identifies plasma markers for proteinuria in metastatic colorectal cancer patients treated with Bevacizumab.
Cancer chemotherapy and pharmacology
Feb 25, 2024
BACKGROUND AND OBJECTIVES: Proteinuria is a common complication after the application of bevacizumab therapy in patients with metastatic colorectal cancer, and severe proteinuria can lead to discontinuation of the drug. There is a lack of sophisticat...
Machine learning based on SEER database to predict distant metastasis of thyroid cancer.
Endocrine
Dec 29, 2023
OBJECTIVE: Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk as early as possible. This paper aimed to predict distant metastasis of...
DiaDeL: An Accurate Deep Learning-Based Model With Mutational Signatures for Predicting Metastasis Stage and Cancer Types.
IEEE/ACM transactions on computational biology and bioinformatics
Jun 3, 2022
Mutational signatures help identify cancer-associated genes that are being involved in tumorigenesis pathways. Hence, these pathways guide precision medicine approaches to find appropriate drugs and treatments. The pattern of mutations varies in diff...
Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the e...
Incorporating artificial intelligence in urology: Supervised machine learning algorithms demonstrate comparative advantage over nomograms in predicting biochemical recurrence after prostatectomy.
The Prostate
Dec 2, 2021
OBJECTIVE: After radical prostatectomy (RP), one-third of patients will experience biochemical recurrence (BCR), which is associated with subsequent metastasis and cancer-specific mortality. We employed machine learning (ML) algorithms to predict BCR...
Reproducibility of Results
Male
Middle Aged
Risk Assessment
Nomograms
Regression Analysis
Predictive Value of Tests
Prostatic Neoplasms
Algorithms
Biomarkers
Computer Simulation
Recurrence
Prognosis
Humans
Neoplasm Staging
Comparative Effectiveness Research
Artificial Intelligence
Supervised Machine Learning
Neoplasm Metastasis
Prostatectomy
Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors.
Nature communications
Nov 18, 2021
Metastatic cancer is associated with poor patient prognosis but its spatiotemporal behavior remains unpredictable at early stage. Here we develop MetaNet, a computational framework that integrates clinical and sequencing data from 32,176 primary and ...
Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning.
PloS one
Nov 17, 2021
We define cell morphodynamics as the cell's time dependent morphology. It could be called the cell's shape shifting ability. To measure it we use a biomarker free, dynamic histology method, which is based on multiplexed Cell Magneto-Rotation and Mach...
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