ArticleQuantitative imaging in medicine and surgery2022
Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 13 citations in OpenAlex.
- Noninvasive stratification of HER2 status in breast cancer using multiparametric DECT: a prospective cohort study.International journal of surgery (London, England) · 2026Article
- Predict status of axillary lymph node after neoadjuvant chemotherapy with dual-energy CT in breast cancer.BMC medical imaging · 2025Article
- Dual-layer detector spectral computed tomography quantitative parameters for predicting pathological complete remission after neoadjuvant treatment of breast cancer.Quantitative imaging in medicine and surgery · 2025Article
- Integrating deep learning features from mammography with SHAP values for a machine learning model predicting over 5-year recurrence of breast ductal carcinomaFrontiers in immunology · 2025Article
- Value of spectral parameters in the differential diagnosis of benign and malignant breast nodules in non-enhanced chest CT.Quantitative imaging in medicine and surgery · 2024Article
- Differentiating between benign and malignant breast lesions using dual-energy CT-based model: development and validation.Insights into imaging · 2024Article
- Dual-Energy CT in Breast Cancer: Current Applications and Future Outlooks.Precision radiation oncology · 2023Review
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Machine learning-based models for the prediction of breast cancer recurrence risk.BMC medical informatics and decision making · 2023Article
- Quantitative energy spectrum CT in differential diagnosis of aldosterone-producing adenoma and cortisol-producing adenoma.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiparametric dual-energy computed tomography (mpDECT) is widely used to differentiate various kinds of tumors; however, the data regarding its diagnostic performance with machine learning to diagnose breast tumors is limited. We evaluated univariate analysis and machine learning performance with mpDECT to distinguish between benign and malignant breast lesions.
methodsIn total, 172 patients with 214 breast lesions (55 benign and 159 malignant) who underwent preoperative dual-phase contrast-enhanced DECT were included in this retrospective study. Twelve quantitative features were extracted for each lesion, including CT attenuation (precontrast, arterial, and venous phases), the arterial-venous phase difference in normalized effective atomic number (nZ
resultsThe univariate analysis showed that the venous phase λ
conclusionsThe performance of the AdaBoost classifier based on mpDECT data achieved the highest mean accuracy compared to the other machine learning models and univariate analysis in differentiating between benign and malignant breast lesions.
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.