ArticleBMC cancer2025
Predicting breast cancer response to neoadjuvant chemotherapy with ultrasound-based deep learning radiomics models -- dual-center study.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Assessment of Early Breast Cancer Response to Chemotherapy with Ultrasound Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
purposeThe aim of this study is to develop and validate a deep learning fusion model based on early (two-cycle) ultrasound images, using stacking fusion technology for the intratumoral and peritumoral regions, to predict early tumor response in breast cancer patients receiving neoadjuvant chemotherapy (NAC). MATERIALS AND
methodsThis study enrolled breast cancer patients who received 6 to 8 cycles of neoadjuvant chemotherapy (NAC) between May 2019 and September 2023 from two medical centers. In the training set, the ResNet deep learning method was employed to construct a deep learning model based on ultrasound image data from intratumoral and peritumoral regions. A deep learning fusion model was established using stacking technology, which was tested on internal and external validation sets to calculate the areas under the receiver operating characteristic curve (AUC) and clinical decision curve (DCA).
resultsA total of 469 breast cancer patients were included in this study. The AUC values of the fusion model DLRS3 in the training set for 3 mm ROI (ROI3mm), 5 mm ROI (ROI5mm), 10 mm ROI (ROI10mm), and intratumoral region (ROIin) were 0.858, 0.872, 0.919, and 0.848, respectively. The AUC values in the internal validation set were 0.890, 0.965, 0.915, and 0.840, respectively, while in the external validation set, they were 0.844, 0.886, 0.938, and 0.905, respectively.
conclusionThis study successfully developed a fusion model for predicting early tumor response in breast cancer patients receiving NAC. The model combines deep learning methods based on early NAC ultrasound images and demonstrates high predictive performance.
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.