ArticleAmerican journal of cancer research2022
Association of machine learning ultrasound radiomics and disease outcome in triple negative breast cancer.
Article in American journal of cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses 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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature.Technology in cancer research & treatmentPooled it
- Construction of an ultrasound feature-based diagnostic model for predicting triple-negative breast cancer using 108 machine learning algorithm combinations.Journal of clinical imaging science · 2026Article
- AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.Frontiers in oncology · 2026Review
- The Clinical Application of Artificial Intelligence in Breast Imaging: Current Insights, Challenges, and Future Directions.Journal of breast cancer · 2025Review
- Peripheral blood biomarkers in monitoring treatment response in breast cancer patients.Expert review of molecular diagnostics · 2025Article
- Article
- Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study.Breast cancer research : BCR · 2024Article
- Artificial Intelligence Decision Support for Triple-Negative Breast Cancers on Ultrasound.Journal of breast imaging · 2024Article
- Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward.BJR artificial intelligence · 2024Review
- Prediction by a multiparametric magnetic resonance imaging-based radiomics signature model of disease-free survival in patients with rectal cancer treated by surgery.Frontiers in oncology · 2024Article
- A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives.Clinical breast cancer · 2023Review
- Artificial intelligence in breast imaging: Current situation and clinical challenges.Exploration (Beijing, China) · 2023Review
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer.Cancer innovation · 2023Review
- Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based onJournal of personalized medicine · 2023Article
- Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review.Diagnostics (Basel, Switzerland) · 2022Review
- A Review of the Role of Ultrasound Radiomics and Its Application and Limitations in the Investigation of Thyroid Disease.Medical science monitor : international medical journal of experimental and clinical research · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Triple negative breast cancer (TNBC) is a breast cancer subtype with unfavorable prognosis. We aimed to establish a machine learning-based ultrasound radiomics model to predict disease-free survival (DFS) in TNBC. Invasive TNBC>T1b between January 2009 and June 2018 with preoperative ultrasound were enrolled and assigned to training and independent test cohort. Radiomics and clinicopathological features related with DFS were selected by univariate and multivariate regression analysis. Training cohort of combined features was resampled with SMOTEENN to balance distribution and put into classifiers. Areas Under Curves (AUCs) of models were compared by DeLong's test. 562 women were included with 68 DFS events observed. Twenty prognostic radiomics features were extracted. Machine learning model by Naïve Bayes combining radiomics, clinicopathological features, and SMOTEENN had an AUC of 0.86 (95% CI 0.84-0.88), with sensitivity of 74.7% and specificity of 80.1% in training cohort. In independent test cohort, this three-combination model delivered an AUC of 0.90 (95% CI 0.83-0.95), higher than models based on radiomics (AUC=0.69, P=0.016) or radiomics + SMOTEENN (AUC=0.73, P=0.019). Integrating machine learning radiomics model based on ultrasound and clinicopathological features can predict DFS events for TNBC patients.
Indexed as
Identifiers
35141010PMC8822271What 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.