Objective: This study aimed to evaluate axillary pathologic complete response (pCR) after neoadjuvant systemic therapy (NST) in clinically node-positive breast cancer (BC) patients based on post-NST multiple-parameter MRI and clinicopathological characteristics.
Methods: In this retrospective study, females with clinically node-positive BC who received NST and followed by surgery between January 2017 and September 2021 were included. All axillary lymph nodes (ALNs) on MRI were matched with pathology by ALN markers or sizes. MRI morphological parameters, signal intensity curve (TIC) patterns and apparent diffusion coefficient (ADC) values of post-NST ALNs were measured. The clinicopathological characteristics was also collected and analyzed. Univariable and multivariable logistic regression analyses were performed to evaluate the independent predictors of axillary pCR.
Results: Pathologically confirmed 137 non-pCR ALNs in 71 patients and 87 pCR ALNs in 87 patients were included in this study. Cortical thickness, fatty hilum, and TIC patterns of ALNs, hormone receptor, and human epidermal growth factor receptor 2 (HER2) status were significantly different between the two groups (all, < 0.05). There was no significant difference for ADC values ( = 0.875). On multivariable analysis, TIC patterns (odds ratio [OR], 2.67, 95% confidence interval [CI]: 1.33, 5.34, = 0.006), fatty hilum (OR, 2.88, 95% CI:1.39, 5.98, = 0.004), hormone receptor (OR, 8.40, 95% CI: 2.48, 28.38, = 0.001) and HER2 status (OR, 8.57, 95% CI: 3.85, 19.08, < 0.001) were identified as independent predictors associated with axillary pCR. The area under the curve of the multivariate analysis using these predictors was 0.85 (95% CI: 0.79, 0.91).
Conclusion: Combining post-NST multiple-parameter MRI and clinicopathological characteristics allowed more accurate identification of BC patients who had received axillary pCR after NST.
Advances In Knowledge: A combined model incorporated multiple-parameter MRI and clinicopathologic features demonstrated good performance in evaluating axillary pCR preoperatively and non-invasively.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9793477 | PMC |
| http://dx.doi.org/10.1259/bjr.20220533 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multi-parameter quantitative magnetic resonance imaging for early detecting skeletal muscle involvement and predicting functional decline in children with Becker muscular dystrophy.
Pediatr Radiol
December 2024
Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Chuanshan Road No. 69, 421001, Hengyang, China.
Article Synopsis
- The study investigates how to effectively assess muscle involvement in children with Becker muscular dystrophy using quantitative magnetic resonance imaging (qMRI).
- It compares different qMRI parameters (fat fraction, T1, and T2) between affected children and healthy controls, finding that fat fraction in the gluteus maximus is the most effective marker.
- The results suggest that measuring fat fraction can predict functional decline in these children, making it a valuable biomarker for monitoring disease progression.
Improved MRF Reconstruction via Structure-Preserved Graph Embedding Framework.
IEEE Trans Image Process
October 2024
Highly undersampled schemes in magnetic resonance fingerprinting (MRF) typically lead to aliasing artifacts in reconstructed images, thereby reducing quantitative imaging accuracy. Existing studies mainly focus on improving the reconstruction quality by incorporating temporal or spatial data priors. However, these methods seldom exploit the underlying MRF data structure driven by imaging physics and usually suffer from high computational complexity due to the high-dimensional nature of MRF data.
View Article and Find Full Text PDFDeep magnetic resonance fingerprinting based on Local and Global Vision Transformer.
Med Image Anal
July 2024
The School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China. Electronic address:
To mitigate systematic errors in magnetic resonance fingerprinting (MRF), the precomputed dictionary is usually computed with minimal granularity across the entire range of tissue parameters. However, the dictionary grows exponentially with the number of parameters increase, posing significant challenges to the computational efficiency and matching accuracy of pattern-matching algorithms. Existing works, primarily based on convolutional neural networks (CNN), focus solely on local information to reconstruct multiple parameter maps, lacking in-depth investigations on the MRF mechanism.
View Article and Find Full Text PDFTensor radiomics: paradigm for systematic incorporation of multi-flavoured radiomics features.
Quant Imaging Med Surg
December 2023
Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada.
Background: Radiomics features hold significant value as quantitative imaging biomarkers for diagnosis, prognosis, and treatment response assessment. To generate radiomics features and ultimately develop signatures, various factors can be manipulated, including image discretization parameters (e.g.
View Article and Find Full Text PDFDiagnostic value of combined magnetic resonance imaging techniques in the evaluation of Parkinson disease.
Quant Imaging Med Surg
October 2023
Department of Nuclear Medicine, The First People's Hospital of Yancheng, The Fourth Affiliated Hospital of Nantong University, Yancheng, China.
Background: The incidence of Parkinson disease (PD) has been increasing each year. The development of new magnetic resonance imaging (MRI) technology can help understand its pathogenesis and identify more effective imaging-based biological indicators.
Methods: The clinical and MRI imaging data of 40 patients with PD and 40 healthy controls were analyzed.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!