ArticleBMC bioinformatics2020
A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images.Journal of cancer research and clinical oncology · 2023Article
- The application of traditional machine learning and deep learning techniques in mammography: a review.Frontiers in oncology · 2023Review
- A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation.Journal of digital imaging · 2022Article
- Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach.Journal of digital imaging · 2022Article
- Contrast-Enhanced Mammography (CEM) Capability to Distinguish Molecular Breast Cancer Subtypes.Biomedicines · 2022Article
- Review
- Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification.Computers in biology and medicine · 2022Article
- Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer.Frontiers in medicine · 2022Article
- Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications.European radiology experimental · 2021Article
- Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images.Diagnostics (Basel, Switzerland) · 2021Article
- Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study.Cancers · 2021Article
- A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results.Frontiers in oncology · 2021Article
- Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features.Cancer management and research · 2021Article
- Predicting Molecular Subtypes of Breast Cancer with Mammography and Ultrasound Findings: Introduction of Sono-Mammometry Score.Radiology research and practice · 2021Article
- Mammary hamartoma: is ultrasound-guided vacuum-assisted breast biopsy sufficient for its treatment?Gland surgery · 2020Article
- Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome.Diagnostics (Basel, Switzerland) · 2020Article
- Article
- Breast cancer survivors' risk of interval cancers and false positive results in organized mammography screening.Cancer medicine · 2020Article
- Precision Science on Incidence and Progression of Early-Detected Small Breast Invasive Cancers by Mammographic Features.Cancers · 2020Article
- Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images.Diagnostics (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundScreening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features.
resultsFor each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance.
conclusionsThe best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.
Indexed as
Identifiers
What OpenQuestion holds
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.