Objective: To explore the efficacy of the quantitative parameter histogram analysis of intravoxel incoherent motion (IVIM) for different molecular prognostic factors of breast cancer.
Materials And Methods: A total of 72 patients with breast cancer who were confirmed by surgical pathology and underwent preoperative magnetic resonance imaging (MRI) were analyzed retrospectively. A region of interest (ROI) was drawn in each slice of the IVIM images. Whole-tumor histogram parameters were obtained with Firevoxel's software by accumulating all ROIs. Next, Kolmogorov-Smirnov test, Student's t-test, Mann-Whitney U test, receiver operating characteristic curve analysis and spearman rank correlation analysis were used to assess the relationship between histogram parameters and molecular prognostic factors of breast cancer.
Results: Among estrogen receptor (ER)-negative ROCs, the apparent diffusion coefficient (ADC) 10th percentile had the highest ROC of 0.792, with a cut-off value of 0.788 × 10 mm/s, and sensitivity and specificity of 0.714 and 0.867, respectively. The negative correlation between lymph node metastasis status and ADC standard deviation was significant (ρ = -0.44, the correlation coefficients was represented by ρ). Positive correlations were observed between hormonal expression of ER and progesterone receptor (PR) with heterogeneity metrics of ADC or perfusion fraction (f), such as ADC inhomogeneity (ρ = 0.37, ρ = 0.29) and f skewness (ρ = 0.32, ρ = 0.28). Negative correlations were observed with numerical metrics, such as the ADC median (ρ = -0.31, ρ = -0.34) and f 45th percentile (ρ = -0.35, ρ = -0.28). The positive correlations between human epidermal receptor factor-2 (HER2) and pseudo-diffusivity (Dp) numerical metrics, Ki-67 expression, and heterogeneity metrics of Dp were high.
Conclusions: The ADC 10th percentile had the largest area under the curve in the ER-negative ROC analysis, and the ADC standard deviation was the most valuable in the correlation analysis of lymph node metastasis. Whole-lesion quantitative histogram parameters of IVIM could, therefore, provide a scientific basis for radiomics to further guide clinical practice in the prognosis of breast cancer.
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http://dx.doi.org/10.1016/j.mri.2021.10.027 | DOI Listing |
Magn Reson Med
December 2024
Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Purpose: To develop and validate a novel analytical approach simplifying , , proton density (PD), and off-resonance quantifications from phase-cycled balanced steady-state free precession (bSSFP) data. Additionally, to introduce a method to correct aliasing effects in undersampled bSSFP profiles.
Theory And Methods: Off-resonant-encoded analytical parameter quantification using complex linearized equations (ORACLE) provides analytical solutions for bSSFP profiles.
Phys Med
December 2024
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China. Electronic address:
Purpose: Automated treatment plan generation is essential for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART) to ensure standardized treatment-plan quality. We proposed a novel cross-technique transfer learning (CTTL)-based strategy for online MRIgART autoplanning.
Method: We retrospectively analyzed the data from 210 rectal cancer patients.
Cancer Imaging
December 2024
Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
Background: The complex interactions of the tumor micromilieu may be reflected by diffusion-weighted imaging (DWI) derived from the magnetic resonance imaging (MRI). The present study investigated the association between apparent diffusion coefficient (ADC) values and histopathologic features in uterine cervical cancer.
Methods: In this retrospective study, prebiopsy MRI was used to analyze histogram ADC-parameters.
Neural Netw
December 2024
School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100190, China. Electronic address:
Optimal transport (OT) is an effective tool for measuring discrepancies in probability distributions and histograms of features. To reduce its high computational complexity, entropy-regularized OT is proposed, which is computed through Sinkhorn algorithm and can be readily integrated into neural networks. However, each time the parameters of networks are updated, both the value and derivative of OT need to be calculated.
View Article and Find Full Text PDFJ Appl Clin Med Phys
December 2024
Department of Radiation Oncology, and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Purpose: Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential advantages of treatment methods with the risks of harm to healthy tissues, including the heart. There is currently a lack of comprehensive, data-driven evidence on effective risk stratification strategies.
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