ArticlePhysics in medicine and biology2018
Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.
Article in Physics in medicine and biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 98 citations in OpenAlex.
- Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature.Breast cancer research : BCR · 2022Pooled it
- Diagnostic Accuracy of Different Machine Learning Algorithms for Breast Cancer Risk Calculation: a Meta-AnalysisAsian Pacific journal of cancer prevention : APJCP · 2018Pooled it
- Integrating DNA methylation biomarkers for breast cancer risk prediction using artificial intelligence.Scientific reports · 2026Article
- Optimised Convolution Layers of DnCNN using Vedic Multiplier and Hyperparameter Tuning in Cancer Detection on Field Programmable Gate ArrayCurrent medical imaging · 2025Article
- Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients.Breast cancer (Dove Medical Press) · 2025Article
- Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet.PloS one · 2023Article
- MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images.Health information science and systems · 2022Article
- A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.Bioengineering (Basel, Switzerland) · 2022Article
- Developing new quantitative CT image markers to predict prognosis of acute ischemic stroke patients.Journal of X-ray science and technology · 2022Article
- Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.IEEE transactions on bio-medical engineering · 2021Article
- COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.Scientific reports · 2021Article
- Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.International journal of molecular sciences · 2021Review
- Impact of image compression on deep learning-based mammogram classification.Scientific reports · 2021Article
- Additive value of exposure parameters for breast cancer diagnosis in digital mammography.European radiology · 2021Article
- Breast Cancer Type Classification Using Machine Learning.Journal of personalized medicine · 2021Article
- Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network.Computational intelligence and neuroscience · 2021Article
- Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.International journal of medical informatics · 2020Article
- Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations.British journal of cancer · 2020Article
- COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images.medRxiv : the preprint server for health sciences · 2020Article
- Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases.IEEE transactions on medical imaging · 2020Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.
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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.