ArticleBMC cancer2020
Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
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Who cites it
77 citing papers in PubMed, 106 citations in OpenAlex.
- Prediction of the efficacy of first transarterial chemoembolization for advanced hepatocellular carcinomaWorld journal of clinical cases · 2025Trial
- Magnetic resonance imaging-based artificial intelligence model predicts neoadjuvant therapy response in triple-negative breast cancer.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Decoding the Rotation Effect: A Retrospective Analysis of Lesion Orientation and Its Impact on Wavelet-Based Radiomics Feature Extraction and Lung Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- A radiomics-deep learning nomogram integrating intratumoral and peritumoral DCE-MRI features for pCR prediction in breast cancer.Frontiers in oncology · 2026Article
- Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer.Frontiers in oncology · 2026Article
- K-means clustering-based analysis of quantitative ultrafast DCE-MRI for predicting breast cancer response to neoadjuvant chemotherapy.Journal of applied clinical medical physics · 2026Article
- Biparametric magnetic resonance imaging-based radiomics model can improve the detection of dense and sparse prostate cancers.Quantitative imaging in medicine and surgery · 2025Article
- Segmentation variability and radiomics stability for predicting triple-negative breast cancer subtype using magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific.The Lancet regional health. Western Pacific · 2025Review
- Gradient-Based Radiomics for Outcome Prediction and Decision-Making in PULSAR: A Preliminary Study.International journal of particle therapy · 2025Article
- Multi-omics spatial characteristics of CD8Frontiers in immunology · 2025Article
- Predictive modeling of acute radiation-induced dermatitis in nasopharyngeal carcinoma patients undergoing tomotherapy using machine learning with multimodal data integration.Frontiers in oncology · 2025Article
- Integrating intratumoral, peritumoral, and clinical features in an ultrasound-based radiomics model: contributions and synergies for predicting microvascular invasion in hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy.Frontiers in oncology · 2025Article
- Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy.Frontiers in oncology · 2025Article
- Intratumoral and peritumoral multiparametric MRI-based radiomics nomogram for preoperative risk stratification in patients with endometrial cancer.Frontiers in oncology · 2025Article
- Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease.European radiology · 2024Article
- Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features.Science advances · 2024Article
- Multiomics-Based Outcome Prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR).Cancers · 2024Article
- Multimodal Ultrasound Radiomic Technology for Diagnosing Benign and Malignant Thyroid Nodules of Ti-Rads 4-5: A Multicenter Study.Sensors (Basel, Switzerland) · 2024Article
17 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundThe purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC).
methodsFifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC).
resultsThe AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance.
conclusionsWavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.
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