ArticleEuropean journal of nuclear medicine and molecular imaging2024
Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast [
Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2026Pooled it
- [Breast (Edinburgh, Scotland) · 2026Trial
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.BMC medical imaging · 2026Article
- Article
- A unified multi modal transformer framework for breast cancer recurrence prediction and survival analysis.Scientific reports · 2026Article
- Machine learning-derived diagnostic model of epithelial ovarian cancer based on gut microbiome signatures.Journal of translational medicine · 2025Article
- Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy.Frontiers in oncology · 2025Article
- The use of machine and deep learning to model the relationship between discomfort temperature and labor productivity loss among petrochemical workers.BMC public health · 2024Article
- Interpretable machine learning models for predicting short-term prognosis in AChR-Ab+ generalized myasthenia gravis using clinical features and systemic inflammation index.Frontiers in neurology · 2024Article
- Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo evaluate if a machine learning prediction model based on clinical and easily assessable imaging features derived from baseline breast [
methodsAltogether 143 women with newly diagnosed breast cancer (54 ± 12 years) were retrospectively enrolled. All women underwent a breast [
resultsNested-cross-validation yielded a mean ROC-AUC of 80.4 ± 6.0% for prediction of pCR. Mean sensitivity, specificity, PPV, and NPV of 54.5 ± 21.3%, 83.6 ± 4.2%, 63.6 ± 8.5%, and 77.6 ± 8.1% could be achieved. Histopathological data were the most important features for classification of the XGBoost model followed by PET, MRI, and sociodemographic/anthropometric features.
conclusionThe evaluated multi-source XGBoost model shows promising results for reliably predicting pathological complete response in breast cancer patients prior to NAST. However, yielded performance is yet insufficient to be implemented in the clinical decision-making process.
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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.