ArticleNPJ precision oncology2025
Multimodal prediction based on ultrasound for response to neoadjuvant chemotherapy in triple negative breast cancer.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis.Diagnostics (Basel, Switzerland) · 2026Review
- Quantitative Ultrasound Radiomics for Predicting and Monitoring Neoadjuvant Chemotherapy Response in Breast Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- A nomogram to predict disease-free survival in patients with residual triple-negative breast cancer after neoadjuvant chemotherapy based on clinicopathological and sonographic features.Translational cancer research · 2026Article
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- From mechanistic models to artificial intelligence: exploring the potential of digital twins in geriatric oncology.Frontiers in artificial intelligence · 2026Article
- Development and internal validation of a task-conditioned multimodal radiology-pathology model for breast cancer staging and biomarker profiling: a retrospective cohort study.Frontiers in cell and developmental biology · 2026Article
- Latent class analysis of conventional ultrasound features: a novel approach to predicting non-response to neoadjuvant chemotherapy in breast cancer.Frontiers in oncology · 2026Article
- Dual-modal ultrasound-based deep learning radiomics for differentiation of benign and malignant breast lesions.Gland surgery · 2025Article
- Key parameters in intratumoral-peritumoral region fusion models: optimizing deep learning radiomics for breast cancer diagnosis.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
15 authors.
Funding
Abstract
Pathological complete response (pCR) can guide surgical strategy and postoperative treatments in triple-negative breast cancer (TNBC). In this study, we developed a Breast Cancer Response Prediction (BCRP) model to predict the pCR in patients with TNBC. The BCRP model integrated multi-dimensional longitudinal quantitative imaging features, clinical factors and features from the Breast Imaging Data and Reporting System (BI-RADS). Multi-dimensional longitudinal quantitative imaging features, including deep learning features and radiomics features, were extracted from multiview B-mode and colour Doppler ultrasound images before and after treatment. The BCRP model achieved the areas under the receiver operating curves (AUCs) of 0.94 [95% confidence interval (CI), 0.91-0.98] and 0.84 [95%CI, 0.75-0.92] in the training and external test cohorts, respectively. Additionally, the low BCRP score was an independent risk factor for event-free survival (P < 0.05). The BCRP model showed a promising ability in predicting response to neoadjuvant chemotherapy in TNBC, and could provide valuable information for survival.
Identifiers
What Socratic 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 Socratic graph.