Preoperative predicting malignancy in breast mass-like lesions: value of adding histogram analysis of apparent diffusion coefficient maps to dynamic contrast-enhanced magnetic resonance imaging for improving confidence level

Liu, Hongli; Zong, Min‐Hua; Han, Wei; Lou, Jianjuan; Wang, Siqi; Zou, Qigui; Shi, Haibin; Jiang, Yanni

doi:10.1259/bjr.20170394

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…The latter quantifies iodine concentration as an imaging marker for tissue vascularization, thus providing a wide range of additional applications ( 10 ). In parallel, histogram analysis is an objective and reproducible method based on tissue volume and provides more extensive information on tumor characteristics, especially when the intensity distribution is not normal ( 12 , 13 ). The changes of histogram shape, asymmetry, and variation reflect underlying molecular processes such as proliferation and aggressiveness ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

Zeng¹,

Chen²,

Lin³

et al. 2022

Quant Imaging Med Surg

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Background: Variations in axillary lymph node (ALN) metastatic potential between different breast cancers lead to microscopical alterations in tumor perfusion heterogeneity. This study investigated the usefulness of histogram metrics from iodine maps in the preoperative diagnosis of metastatic ALNs in patients with earlystage breast cancer.Methods: Between October 2020 and November 2021 enhanced spectral computed tomography (CT) was performed in female patients with breast cancer. Quantitative spectral CT parameters and histogram parameters (mean, median, maximum, minimum, 10th percentiles, 90th percentiles, kurtosis, skewness, energy, range, and variance) from iodine maps were compared between patients with metastatic and nonmetastatic ALNs. Continuous variables were compared using Student's t-test or Mann-Whitney U test.Categorical variables were compared using Pearson's chi-square tests or Fisher's exact tests. Associations between ALN status and imaging features were evaluated using Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis.Results: This study included 113 female patients (62 and 51 in the ALN-negative and ALN-positive groups, respectively). Tumor size, molecular subtypes, and location differed significantly between the ALN-negative and ALN-positive groups (P<0.05). None of the quantitative spectral CT parameters of mass between metastatic and nonmetastatic ALN groups were significantly different (P>0.05). Histogram parameters of iodine maps for breast cancers, including maximum, 10th percentile, range, and energy, were significantly higher in the metastatic ALNs group compared with the nonmetastatic ALNs group (P<0.05). Multivariable logistic regression analyses showed that tumor location and energy were independent predictors of metastatic ALNs in breast cancers. The combination of independent predictors yielded an area under the curve (AUC) of 0.824 (sensitivity 72.5%; specificity 74.2%).Conclusions: Whole-lesion histogram parameters derived from spectral CT iodine maps may be used as a complementary noninvasive means for the preoperative identification of ALN metastases in patients with early-stage breast cancer.

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Section: Introductionmentioning

confidence: 99%

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

Zeng¹,

Chen²,

Lin³

et al. 2022

Quant Imaging Med Surg

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“…Moreover, it is very useful for early cancer detection and distinguishing benign lesions from malignant ones. Dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) are two common functional imaging sequences used to differentiate between benign and malignant breast disease in clinical practice [3][4]. DCE-MRI analyzes images during and after intravenous contrast agent injection and can re ect tumor vascularity; The semi-quantitative time-intensity curve (TIC) characteristics derived from DCE-MRI can re ect the hemodynamic characteristics of breast lesions [2].…”

Section: Introductionmentioning

confidence: 99%

The dynamic contrast-enhanced MR imaging of breast cancer: the correlation between mono-exponential DWI parameters and semi-quantitative TIC characteristics

Li¹,

Zhu²,

Fan³

et al. 2022

Preprint

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Background To investigate the relationship between mono-exponential diffusion- weighted imaging (DWI) parameters and time-intensity curve (TIC) characteristics generated from dynamic contrast-enhanced (DCE) MRI of breast cancer. Materials and Methods A total of 108 patients with breast cancer were prospectively enrolled in this study. All patients underwent DWI and DCE-MRI examination. Patients were divided into untypical TIC and untypical TIC groups; untypical TIC pattern was defined as a wash-in/plateau curve, and the typical TIC pattern of breast cancer as a wash-out curve. The relationships between DWI parameters and semi-quantitative TIC characteristics were investigated by using multivariate logistic regression analysis. Results The BI-RADS grade and the incidence of lymph node metastasis were higher in the typical TIC group (all P < 0.05). The ADCmean and ADCmin values were lower in the typical TIC group than in the untypical TIC group (all P < 0.05). There was a significantly negative correlation between ADCmean or ADCmin value and SIslope2 (r=-0.535,-0.514; P = 0.02, 0.04). According to logistic regression analysis, micro-vessel density (MVD), tumor stroma ratio (TSR), ADCmin, histological grading, and ADCmean were independently correlated with TIC patterns. Conclusion The water diffusion restriction reflected by DWI parameters may contribute to the TIC characteristics at the late phase after enhancement, which provides theoretical guidance for the diagnosis of breast cancer based on a combination of DWI and TIC characteristics.

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“…It remains difficult to accurately identify fibroadenoma and IDC. Liu et al [14] implemented the addition of histogram analysis to apparent diffusion coefficient maps in DCE-MRI for predicting breast malignancy, in which most lesions were found to be fibroadenoma or IDC. Moreover, a typical breast MRI study consists of several acquired and postprocessed image series, each composed of several dozen slices.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning-Based DCE-MRI Method for Identifying Differentiation Between Benign and Malignant Breast Tumors

et al. 2020

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In this paper, we propose a transfer learning-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) method for classifying fibroadenoma and invasive ductal carcinoma (IDC) in breast tumors. A total of 207 breast tumors from patients were collected and identified by pathologic diagnosis within 15 days after enhanced DCE-MRI examination; 119 patients (average age 50.52±10.33 years) had fibroadenomas, and 88 patients (average age 42.20±10.10 years) had IDCs. Two lesion-level models were built based on the InceptionV3 and VGG19 models, which were pretrained with the ImageNet dataset. The effects of different depths of transfer learning were examined. The network's performance was evaluated through five-fold cross validation. In the lesion-level models, the model based on Inception V3 obtained better results (area under the receiver operating characteristic curve (AUC) = 0.89) when the weights were frozen before layer-276. The other model based on VGG19 obtained better results (AUC = 0.87) when the weights were frozen before layer-13. Compared with the image-level models, both lesion-level models displayed better discrimination (accuracy increased by 13% and 14%) based on the VGG19 and Inception V3 models, respectively. Our research confirms that transfer learning can be utilized to classify fibroadenomas and IDCs in DCE-MRI images. Different depths of transfer learning result in different performances, and our proposed lesion-level model notably improves the classification accuracy. INDEX TERMS Magnetic resonance imaging, invasive ductal carcinoma, fibroadenoma, transfer learning, lesion classification.

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Preoperative predicting malignancy in breast mass-like lesions: value of adding histogram analysis of apparent diffusion coefficient maps to dynamic contrast-enhanced magnetic resonance imaging for improving confidence level

Cited by 9 publications

References 26 publications

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

The dynamic contrast-enhanced MR imaging of breast cancer: the correlation between mono-exponential DWI parameters and semi-quantitative TIC characteristics

Transfer Learning-Based DCE-MRI Method for Identifying Differentiation Between Benign and Malignant Breast Tumors

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