ArticleFrontiers in oncology2026
Latent class analysis of conventional ultrasound features: a novel approach to predicting non-response to neoadjuvant chemotherapy in breast cancer.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To determine if holistic patterns derived from conventional ultrasound features are associated with pathological response to neoadjuvant chemotherapy (NAC) in breast cancer, providing a potential basis for non-invasive treatment monitoring. Methods: This study included 509 female breast cancer patients with unilateral primary lesions who underwent NAC followed by surgery. We employed three clustering methodologies to integrate conventional ultrasound features into holistic patterns: the model-based latent class analysis (LCA) and the distance-based K-modes and hierarchical clustering. The association between the resulting patterns and pathological non-response was assessed using Firth logistic regression, with internal validation performed through 10-fold cross-validation and 10, 000 bootstrap resampling. Results: When comparing the three approaches, LCA was identified as the superior classification framework, providing the best balance of statistical model fit, class separation, and meaningful association with clinical outcomes. The optimal LCA model identified two distinct sonographic patterns, referred to as Pattern A and Pattern B. Pattern B was significantly associated with a 2.51-fold higher (95% CI: 1.05-5.99) likelihood of pathological non-response compared to Pattern A. In subgroup analyses, among defined high-risk groups, Pattern B was linked to an increased risk of pathological non-response ranging from 12.84% (95% CI: 5.74%-23.59%) to 21.34% (95% CI: 13.63%-28.32%), whereas in non-high-risk groups the corresponding increase ranged from 1.12% (95% CI: 0.79%-1.51%) to 3.45% (95% CI: 2.60%-4.47%). Conclusion: Patterns of conventional ultrasound features derived through LCA may offer a preliminary indication for identifying breast cancer patients less likely to benefit from NAC, potentially informing more individualized treatment planning following external validation.
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.