Trial reportScientific reports2019
Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration.
Trial report in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Assessing Treatment Response in Soft Tissue Sarcoma Using Dynamic Contrast-Enhanced MRI: A Systematic Review.Korean journal of radiology · 2026Pooled it
- A dual-tower multimodal framework with feature quality enhancement for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer: a multicenter study.Translational oncology · 2026Article
- A multicenter study of a predictive model for pathological complete response after neoadjuvant therapy in breast cancer using multimodal digital biomarkers.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Anatomical zone and tissue type impacts the repeatability of quantitative MRI parameters and radiomic features for longitudinal monitoring of treatment response in the prostate.Magma (New York, N.Y.) · 2025Article
- Microstructural diffusion MRI for differentiation of breast tumors and prediction of prognostic factors in breast cancer.Frontiers in oncology · 2025Article
- Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.Cancer medicine · 2024Article
- AI Applications to Breast MRI: Today and Tomorrow.Journal of magnetic resonance imaging : JMRI · 2024Review
- Longitudinal registration of TMagnetic resonance imaging · 2024Article
- Review
- Dynamic 2-deoxy-D-glucose-enhanced multispectral optoacoustic tomography for assessing metabolism and vascular hemodynamics of breast cancer.Photoacoustics · 2023Article
- Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy.Communications medicine · 2023Article
- Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast.Magnetic resonance in medicine · 2023Article
- Article
- Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI.Journal of magnetic resonance imaging : JMRI · 2023Article
- Supine magnetic resonance image registration for breast surgery: insights on material mechanics.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.Cancers · 2022Review
- Article
- Assessment of Cone-Beam Breast Computed Tomography for Predicting Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer: A Prospective Study.Journal of oncology · 2022Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
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
Abstract
We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p < 0.05 and RFS (C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p < 0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p < 0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer.
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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.