ArticleBMC cancer2015
Texture analysis on MR images helps predicting non-response to NAC in breast cancer.
Article in BMC cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 70 citations in OpenAlex.
- Prediction of Chemoresistance in Women Undergoing Neo-Adjuvant Chemotherapy for Locally Advanced Breast Cancer: Volumetric Analysis of First-Order Textural Features Extracted from Multiparametric MRI.Contrast media & molecular imaging · 2018Trial
- The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review.Journal of medical radiation sciences · 2023Article
- Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.European radiology · 2023Article
- Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images.NPJ precision oncology · 2023Article
- Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI.Scientific reports · 2022Article
- Prediction for pathological and immunohistochemical characteristics of triple-negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis.European radiology · 2022Article
- Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.Frontiers in oncology · 2022Review
- Diffusion Breast MRI: Current Standard and Emerging Techniques.Frontiers in oncology · 2022Review
- Article
- Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging.Frontiers in radiology · 2021Review
- Value of Machine Learning with Multiphases CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Invasive Breast Cancer.Cancer management and research · 2021Article
- Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.Frontiers in oncology · 2021Article
- Texture Analysis of Breast DCE-MRI Based on Intratumoral Subregions for Predicting HER2 2+ Status.Frontiers in oncology · 2020Article
- Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls.Annals of translational medicine · 2019Article
- Precision diagnostics based on machine learning-derived imaging signatures.Magnetic resonance imaging · 2019Review
- Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences.Journal of magnetic resonance imaging : JMRI · 2019Article
- Role of texture analysis in breast MRI as a cancer biomarker: A review.Journal of magnetic resonance imaging : JMRI · 2019Review
- The Potential Use of DCE-MRI Texture Analysis to Predict HER2 2+ Status.Frontiers in oncology · 2019Article
- Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.Breast cancer research and treatment · 2019Article
- Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images.Magnetic resonance imaging · 2018Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI.
methodsSixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC.
resultsA model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction.
conclusionPre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.
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