DCE‐MRI texture analysis with tumor subregion partitioning for predicting Ki‐67 status of estrogen receptor‐positive breast cancers

Fan, Ming; Hu, Cheng; Zhang, Peng; Gao, Xin; Zhang, Juan; Shao, Guoliang; Li, Lihua

doi:10.1002/jmri.25921

Cited by 65 publications

(73 citation statements)

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“…For example, the feature of difference entropy, which measures heterogeneous textural appearance, i.e., randomness of the difference in neighboring voxel gray levels, was high in low Ki-67 expression tumors [49]. Moreover, a related study found that luminal A tumors had a higher value of this feature than luminal B tumors did [20]. Our results are partly consistent with those of the previous study because the Ki-67 index has also been used as a proliferation marker to distinguish between the luminal A and luminal B molecular subtypes of ER-positive breast cancer [50].…”

Section: Discussionmentioning

confidence: 98%

“…However, to predict tumor characteristics, only a single parametric MRI is usually used. For example, studies indicate that radiomic analysis of MRI may be helpful in predicting breast cancer subtypes [13][14][15][16], neoadjuvant chemotherapy responses [17][18][19], Ki-67 expression level [20][21][22], pathological stage [23], histological grade [24], tumor malignancy [25,26], and breast cancer risk [27] based on either DCE-MRI or DWI. These studies were performed without considering complementary information that could be provided by different parametric images.…”

Section: Introductionmentioning

confidence: 99%

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Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics

Fan

Yuan

Zhao

et al. 2020

IEEE J. Biomed. Health Inform.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics

Fan

Yuan

Zhao

et al. 2020

IEEE J. Biomed. Health Inform.

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“…Several kinds of quantitative methods have been applied to the differentiation of benign from malignant breast lesions based on DCE‐MRI 28‐32 . However, most previous studies showed that heterogeneity investigations based on post‐contrast images were commonly separate from analyses based on kinetic parametric maps 33‐35 …”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

Jiang

Yin

2020

Journal of Surgical Oncology

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Background and Objectives: To investigate the performance of texture analysis based on enhancement kinetic parametric maps derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in discriminating benign from malignant tumors.Methods: A total of 192 cases confirmed by histopathology were retrospectively selected from our Picture Archiving and Communication System, including 93 benign and 99 malignant tumors. Lesion areas were delineated semi-automatically, and six kinetic parametric maps reflecting the perfusion information were generated, namely the maximum slope of increase (MSI), slope of signal intensity (SI slope ), initial percentage of peak enhancement (E initial ), percentage of peak enhancement (E peak ), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) maps. A total of 286 texture features were extracted from those quantitative parametric maps. The Student t test or Mann-Whitney U test was used to select features that were statistically significantly different between the benign and malignant groups. A support vector machine was employed with a leave-one-out crossvalidation method to establish the classification model. Classification performance was evaluated according to the receiver operating characteristic (ROC) theory. Results:The areas under ROC curves (AUCs) indicating the diagnostic performance were 0.925 for MSI, 0.854 for SI slope , 0.756 for E initial , 0.923 for E peak , 0.871 for ESER and 0.881 for SEP. Significant differences in AUCs were found between E initial vs MSI, E initial vs E peak and E initial vs SEP (P < .05). There were no significant differences in other pairwise comparisons.Conclusion: Texture analysis of the kinetic parametric maps derived from breast DCE-MRI can contribute to the discrimination between malignant and benign lesions. It can be considered as a supplementary tool for breast diagnosis. K E Y W O R D S breast cancer, kinetics, machine learning, magnetic resonance imaging, receiver operating characteristic

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“…Consequently, quantifying the spatial heterogeneity in vascularization of the whole tumor volume from DCE‐MRI data may be more informative and realistic. In that sense, radiomics approaches on DCE‐MRI have recently shown encouraging results, alone or with other MRI sequences, in order to improve the detection of prostate cancer, to distinguish benign and malignant adnexal masses, to identify relevant molecular subtypes of breast cancers, to detect lymph node metastases in breast cancers, or to predict response to neoadjuvant treatment for rectum, breast, and nasopharyngeal cancers . Soft‐tissue sarcomas (STS) are malignant mesenchymal tumors with important inter‐ and intratumoral heterogeneity known to be associated with high grade .…”

mentioning

confidence: 99%

“…In that sense, radiomics approaches on DCE-MRI have recently shown encouraging results, alone or with other MRI sequences, in order to improve the detection of prostate cancer, to distinguish benign and malignant adnexal masses, to identify relevant molecular subtypes of breast cancers, to detect lymph node metastases in breast cancers, or to predict response to neoadjuvant treatment for rectum, breast, and nasopharyngeal cancers. [5][6][7][8][9][10][11][12] Soft-tissue sarcomas (STS) are malignant mesenchymal tumors with important inter-and intratumoral heterogeneity known to be associated with high grade. 13 Previous studies have shown that DCE-MRI could be useful to predict response to chemotherapy in high-grade STS, [14][15][16][17] as well as radiomics approaches.…”

mentioning

confidence: 99%

Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization

Crombé

Saut

Guigui

et al. 2019

Magnetic Resonance Imaging

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Background Evaluating heterogeneity in tumor vascularization through texture analysis could improve predictions of patients' outcome and response evaluation. Purpose To investigate the influence of temporal parameters on texture features extracted from dynamic contrast‐enhanced (DCE)‐MRI parametric maps. Study type Prospective cross‐sectional study. Subjects Twenty‐five adults with soft‐tissue sarcoma (STS), median age: 68 years. Field Strength/Sequence DCE‐MRI acquisition using a CAIPIRINHA‐Dixon‐TWIST‐VIBE sequence at 1.5T (temporal resolutions: 2 sec, duration: 5 min). Assessment The area under time–intensity curve (AUC) and Ktrans maps were generated for several temporal resolution (dt = 2 sec, 4 sec, 6 sec, 8 sec, 10 sec, 12 sec, 20 sec) and scan durations (T = 3 min, 4 min, 5 min for a 6‐sec sampling) by downsampling and truncating the initial DCE‐MRI sequence. Tumor volume was manually segmented and propagated on all parametric maps. Thirty‐two first‐ and second order‐texture features were extracted per map to quantify the intratumoral heterogeneity. Statistical Tests The influence of temporal parameters on texture features was studied with repeated‐measures analysis of variance (or nonparametric equivalent). The dispersion of each texture feature depending on temporal parameters was estimated with coefficients of variation (CVs). The performances of multivariate models to predict the response to chemotherapy (ie, binary logistic regression based on the baseline texture features) were compared. Results The temporal resolution had a significant influence on 12/32 (37.5%) and 14/32 (43.8%) texture features evaluated on AUC and Ktrans maps, respectively (range of P < 0.0001–0.0395). Scan duration had a significant influence on 23/32 (71.9%) texture features from Ktrans map (range of P < 0.0001–0.0321). Dispersion was high (mean CV >0.5) with sampling for 2/32 (6.3%) and 10/32 (31.3%) features from AUC and Ktrans maps, respectively; and with truncating for 6/32 (18.8%) features from Ktrans map. The area under the receiver operating characteristics curve of predictive models ranged from 0.77 (95% confidence interval [CI] = [0.54–1.00], with dt = 6 sec T = 4 min) to 0.90 (95% CI = [0.74–1.00], with dt = 6 sec T = 5 min). Data Conclusion The values of texture features extracted from DCE‐MRI parametric maps can be influenced by temporal parameters, which can lead to variations in performance of predictive models. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1773–1788.

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DCE‐MRI texture analysis with tumor subregion partitioning for predicting Ki‐67 status of estrogen receptor‐positive breast cancers

Abstract: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.

Cited by 65 publications

References 41 publications

Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics

Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization

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