Diffusion-weighted imaging or dynamic contrast-enhanced curve: a retrospective analysis of contrast-enhanced magnetic resonance imaging–based differential diagnoses of benign and malignant breast lesions

Yang, Xiaoping; Dong, Mengshi; Li, Shu; Chai, Ruimei; Zhang, Zheng; Li, Nan; Zhang, Lína

doi:10.1007/s00330-020-06883-w

Cited by 15 publications

(20 citation statements)

References 42 publications

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“…The high diagnostic performance of the TIC model combined of morphologic analysis and TIC types was achieved in the training cohort and validated in the validation cohort. In this study, diagnostic performance of the TIC model was rather superior than the ADC model, which is different from the previously reported results in the literature, 14 which showed that the diagnostic abilities of the two models were similar. There may be some reasons for this difference: First, this may be because of a bias to the different sample sizes.…”

Section: Discussioncontrasting

confidence: 99%

“…12 A previous study reported that the DWI model (established on the scored ADC values, distribution, and enhancement characteristics) was superior or equal to the TIC model (established on the scored TIC types, distribution, and enhancement characteristics) on the basis of CE-MRI in the differentiation between benign and malignant breast lesions. 14 However, lesions enrolled in the study were predominantly mass lesions, while the sample size of NME lesions was relatively small (71 in the training set and 16 in the validation set). Therefore, the aim of the current study is to assess and compare the role of DWI and TIC for detecting NME malignant lesions in a relatively large sample size.…”

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confidence: 99%

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Contrasts Between Diffusion‐Weighted Imaging and Dynamic Contrast‐Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment

Yang

Fan

et al. 2023

Magnetic Resonance Imaging

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Background: Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable. Purpose: To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images. Study Type: Retrospective. Subjects: A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results. Field Strength/Sequence: A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm 2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence. Assessment: In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort. Statistical Tests: Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chisquare test were performed. P < 0.05 was considered statistically significant. Results: For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635. Data Conclusion: Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions. Evidence Level: 4. Technical Efficacy: Stage 2.

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confidence: 99%

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Contrasts Between Diffusion‐Weighted Imaging and Dynamic Contrast‐Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment

Yang

Fan

et al. 2023

Magnetic Resonance Imaging

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“…The pathological and pharmacokinetic mechanisms differed in benign and malignant lesions. Malignant tumors had abundant vascularity and highly permeable vessel walls that allowed easier transfer of the contrast agent from vessels to the extravascular space was easier; thus, malignant lesions had shorter TTE and larger MS, while the benign lesions showed the opposite findings [33,34]. This could explain why in the combined model, the texture features extracted from the 2nd and 5th contrast sequences were independently relevant to discriminate benign and malignant NME.…”

Section: Discussionmentioning

confidence: 99%

Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women

et al. 2021

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Background Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. Methods Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann–Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis. Results The combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising. Conclusions With the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

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“…The pathological and pharmacokinetic mechanisms differed in benign and malignant lesions. Malignant tumors had abundant vascularity and highly permeable vessel walls that allowed easier transfer of the contrast agent from vessels to the extravascular space was easier; thus, malignant lesions had shorter TTE and larger MS, while the benign lesions showed the opposite ndings [32,33]. This could explain why in the combined model, the texture features extracted from the 2nd and 5th contrast sequences were independently relevant to discriminate benign and malignant NME.…”

Section: Discussionmentioning

confidence: 99%

Additive Value of Texture Analysis Based on Breast MRI for Distinguishing Between Benign and Malignant Non-mass Enhancement in Premenopausal Women

Tan

Mai

Huang

et al. 2020

Preprint

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Background: Non-mass enhancement (NME) is a diagnostic dilemma. Texture analysis (TA) could serve as an objective method to quantify tumor characteristics and growth patterns. However, there are few reports about TA use in NME diagnosis. To our knowledge, NME diagnosis based on the combination of the features noted on routine MRI and TA has not been reported.. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. Methods: Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann-Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis.Results: The combination model showed a superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising.Conclusion: The combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

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Diffusion-weighted imaging or dynamic contrast-enhanced curve: a retrospective analysis of contrast-enhanced magnetic resonance imaging–based differential diagnoses of benign and malignant breast lesions

Cited by 15 publications

References 42 publications

Contrasts Between Diffusion‐Weighted Imaging and Dynamic Contrast‐Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment

Contrasts Between Diffusion‐Weighted Imaging and Dynamic Contrast‐Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment

Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women

Additive Value of Texture Analysis Based on Breast MRI for Distinguishing Between Benign and Malignant Non-mass Enhancement in Premenopausal Women

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