Dual-energy CT quantitative parameters for the differentiation of benign from malignant lesions and the prediction of histopathological and molecular subtypes in breast cancer

Wang, Xiaoxia; Liu, Dai‐Hong; Zeng, Xiangfei; Jiang, Shixi; Li, Lan; Yu, Tao; Zhang, Jiuquan

doi:10.21037/qims-20-825

Cited by 28 publications

(28 citation statements)

References 34 publications

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“…Quantitative iodine parameters have been considered a promising tool for distinguishing malignant from benign tumors (12)(13)(14)(15). Spectral curves and the effective atomic number (Z eff ) have helped identify benign and malignant tumors, histological type, and differentiation degree (16)(17)(18)(19). Quantitative dualenergy CT (DECT) analysis has distinguished invasive adenocarcinoma from noninvasive or minimally invasive adenocarcinoma among pulmonary ground-glass opacity nodules (13).…”

Section: Introductionmentioning

confidence: 99%

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Jiang¹,

Liu²,

Long³

et al. 2022

Quant Imaging Med Surg

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Background: This study aimed to investigate the ability of quantitative parameter-derived dual-source dual-energy computed tomography (DS-DECT) combined with machine learning to distinguish between benign and malignant thyroid nodules.Methods: Patients with thyroid nodules and pathological surgical results who underwent preoperative DS-DECT were selected. Quantitative parameter-derived DS-DECT was applied to classify benign and malignant nodules. Then, machine learning and binary logistic regression analysis models were constructed using the DS-DECT quantitative parameters to distinguish between benign and malignant nodules. The receiver operating characteristic curve was used to assess the diagnostic performance. The DeLong test was used to compare the diagnostic efficacy. Results: One hundred and thirty patients with 139 confirmed thyroid nodules were involved in the study. The malignant group had a significantly higher iodine concentration nodule (arterial phase) (P=0.001), normalized iodine concentration (arterial phase) (P=0.002), iodine concentration difference (P<0.001), spectral curve slope (nonenhancement) (P=0.007), spectral curve slope (arterial phase) (P=0.001), effective atomic number (nonenhancement) (P<0.001), and effective atomic number (arterial phase) (P=0.039) than the benign group. The binary logistic regression analysis model had an AUC (area under the curve) of 0.76, a sensitivity of 0.821, and a specificity of 0.667. The machine learning model had an AUC of 0.86, a sensitivity of 0.822, specificity of 0.791 in the training cohort, an AUC of 0.84, a sensitivity of 0.727, and specificity of 0.750 in the testing cohort. Conclusions: Multiple quantitative parameters of DS-DECT combined with machine learning could differentiate between benign and malignant thyroid nodules.

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Section: Introductionmentioning

confidence: 99%

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Jiang¹,

Liu²,

Long³

et al. 2022

Quant Imaging Med Surg

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“…In contrast to prior research (11), we used multiple quantitative parameters extracted from DECT. We extracted and calculated 12 features per lesion and used eight machine learning algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…DECT can provide various quantitative parameters for objective quantitative analysis of breast tumors while screening for lung metastases and inflammation lesions compared with mammograms and ultrasounds. Currently, some studies have investigated the role of DECT in the diagnosis of breast cancer via multiple quantitative parameters (11). One study using DECT quantitative parameters showed that the iodine concentration was higher in breast tumors than in the pectoral muscle and normal breast tissue (12).…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions

Xiao¹,

Wang²,

Qi³

et al. 2022

Quant Imaging Med Surg

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Background: Multiparametric dual-energy computed tomography (mpDECT) is widely used to differentiate various kinds of tumors; however, the data regarding its diagnostic performance with machine learning to diagnose breast tumors is limited. We evaluated univariate analysis and machine learning performance with mpDECT to distinguish between benign and malignant breast lesions.Methods: In total, 172 patients with 214 breast lesions (55 benign and 159 malignant) who underwent preoperative dual-phase contrast-enhanced DECT were included in this retrospective study. Twelve quantitative features were extracted for each lesion, including CT attenuation (precontrast, arterial, and venous phases), the arterial-venous phase difference in normalized effective atomic number (nZ eff ), normalized iodine concentration (NIC), and slope of the spectral Hounsfield unit (HU) curve (λ Hu ).Predictive models were developed using univariate analysis and eight machine learning methods [logistic regression, extreme gradient boosting (XGBoost), stochastic gradient descent (SGD), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), random forest (RF), decision tree, and linear support vector machine (SVM)]. Classification performances were assessed based on the area under the receiver operating characteristic curve (AUROC). The best performances of the conventional univariate analysis and machine learning methods were compared using the Delong test.Results: The univariate analysis showed that the venous phase λ Hu had the highest AUROC (0.88).Machine learning with mpDECT achieved an excellent and stable diagnostic performance, as shown by the mean classification performances in the training dataset (AUROC, 0.88-0.99) and testing (AUROC, 0.83-0.96) datasets. The performance of the AdaBoost model based on mpDECT was more stable than the other machine learning models and superior to the univariate analysis (AUROC, 0.96 vs. 0.88; P<0.001). Conclusions:The performance of the AdaBoost classifier based on mpDECT data achieved the highest mean accuracy compared to the other machine learning models and univariate analysis in differentiating between benign and malignant breast lesions.

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“…A previous study (20) showed that vasculature remodeling or neoangiogenesis is an initiating event in the early stage of tumor development; iodine distribution in the tissue is strongly related to local blood volume and vascular density. Therefore, NIC may be used as a surrogate imaging marker for assessing angiogenesis in breast cancer (21) . Studies have shown that there is no statistical significance between the measured iodine content and the true value (22) .…”

Section: Discussionmentioning

confidence: 99%

A preliminary application research of dual-energy spectral CT in breast cancer diagnosis

Chen

Wang²,

Wang³

et al. 2022

Int J Radiat Res

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Background:The objective of this study was to retrospectively analyze the application of dual-energy spectral computerized tomography (DECT) to accurately diagnose breast cancer and lymph node metastasis. Materials and Methods: Between May 2018 and December 2019, 37 patients (22 with breast cancer and 15 with normal breast cancer) who underwent spectral CT imaging were analyzed. Metastatic lymph nodes were identified in 14 patients with breast cancer. Twelve patients who underwent traditional CT were included randomly as the control group to compare the radiation dose with spectral CT. Monochromatic levels with an optimal contrast-to-noise ratio for normal breast tissue were obtained. Quantitative parameters of spectral CT were compared between normal breast and breast cancer patients. The spectral curve, histogram, and scatter plot features of metastatic lymph nodes and primary lesions were analyzed. Results: The monochromatic level with the optimal contrast-to-noise ratio of the breast was approximately 65keV. All quantitative parameters, including values at 40keV-140keV, the concentrations of iodine, spectral curve slope (λ HU ), and relative iodine concentration were increased in breast cancer compared to those in healthy breasts. Metastatic lymph nodes were more consistent with primary breast cancer lesions in the spectral curve, histogram, and scatter plot, especially in the venous phase. Additionally, the radiation of spectral CT was decreased compared to that of traditional CT. Conclusion: Spectral CT can be used to identify breast cancer and metastatic lymph nodes.

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Dual-energy CT quantitative parameters for the differentiation of benign from malignant lesions and the prediction of histopathological and molecular subtypes in breast cancer

Cited by 28 publications

References 34 publications

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions

A preliminary application research of dual-energy spectral CT in breast cancer diagnosis

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