ArticleJournal of imaging2024
Decoding Breast Cancer: Using Radiomics to Non-Invasively Unveil Molecular Subtypes Directly from Mammographic Images.
Article in Journal of imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature.Technology in cancer research & treatmentPooled it
- Interpretable Whole-Breast Radiomic Biomarkers for Exploratory Assessment of HER2 + Breast Cancer in Digital Mammography.Journal of imaging informatics in medicine · 2026Article
- Self-knowledge distillation for prediction of breast cancer molecular subtypes based on digital breast tomosynthesis.Medical & biological engineering & computing · 2025Article
- Review
- Integrating multimodal ultrasound imaging and machine learning for predicting luminal and non-luminal breast cancer subtypes.Frontiers in oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Breast cancer is the most commonly diagnosed cancer worldwide. The therapy used and its success depend highly on the histology of the tumor. This study aimed to explore the potential of predicting the molecular subtype of breast cancer using radiomic features extracted from screening digital mammography (DM) images. A retrospective study was performed using the OPTIMAM Mammography Image Database (OMI-DB). Four binary classification tasks were performed: luminal A vs. non-luminal A, luminal B vs. non-luminal B, TNBC vs. non-TNBC, and HER2 vs. non-HER2. Feature selection was carried out by Pearson correlation and LASSO. The support vector machine (SVM) and naive Bayes (NB) ML classifiers were used, and their performance was evaluated with the accuracy and the area under the receiver operating characteristic curve (AUC). A total of 186 patients were included in the study: 58 luminal A, 35 luminal B, 52 TNBC, and 41 HER2. The SVM classifier resulted in AUCs during testing of 0.855 for luminal A, 0.812 for luminal B, 0.789 for TNBC, and 0.755 for HER2, respectively. The NB classifier showed AUCs during testing of 0.714 for luminal A, 0.746 for luminal B, 0.593 for TNBC, and 0.714 for HER2. The SVM classifier outperformed NB with statistical significance for luminal A (
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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.