Texture Analysis with 3.0-T MRI for Association of Response to                    Neoadjuvant Chemotherapy in Breast Cancer

Eun, Na Lae; Kang, Daesung; Son, Eun Ju; Park, Jeong Seon; Youk, Ji Hyun; Kim, Jeong Ah; Gweon, Hye Mi

doi:10.1148/radiol.2019182718

Cited by 84 publications

(62 citation statements)

References 35 publications

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“…The results of subsequent studies were similar, and our TA results were no exception, with the AUC, sensitivity, and speci city all lower than those with CRMC, indicating that the diagnostic e ciency of TA alone in NME diagnosis was not high. Some investigators had used TA combined with breast MRI morphology features to distinguish between phyllodes and broadenomas tumors, while others had combined TA with DWI parameters to predict the response to neoadjuvant chemotherapy for breast cancer, and their results demonstrated that combined TA could improve the diagnostic performance [30,31]. On the basis of previous studies, we tried to use the combination of TA and CRMC in NME diagnosis.…”

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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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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“…Numerous radiomics studies have attempted to predict pCR using features extracted mostly from DCE‐MRI, with some studies also evaluating T 2 and DWI parameters 110 . Among the most studied parameters are texture features, 111 which are mathematically extracted quantitative statistical features of an image, as well as morphologic and kinetic features (Fig. 14).…”

Section: Future Directionsmentioning

confidence: 99%

Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

Reig

Heacock

Lewin

et al. 2020

Magnetic Resonance Imaging

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The goals of imaging after neoadjuvant therapy for breast cancer are to monitor the response to therapy and facilitate surgical planning. MRI has been found to be more accurate than mammography, ultrasound, or clinical exam in evaluating treatment response. However, MRI may both overestimate and underestimate residual disease. The accuracy of MRI is dependent on tumor morphology, histology, shrinkage pattern, and molecular subtype. Emerging MRI techniques that combine functional information such as diffusion, metabolism, and hypoxia may improve MR accuracy. In addition, machine-learning techniques including radiomics and radiogenomics are being studied with the goal of predicting View this article online at wileyonlinelibrary.com.

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“…Texture analysis was an established technique，which was beneficial to diagnoses, by extracting a large amount of texture information from medical images [19]. Until now, texture analyses have been used in identifying the differentiated degree and characteristics of tumor, evaluating the therapeutic effect, etc [20][21][22].…”

Section: Backgroudmentioning

confidence: 99%

Combined Texture Analysis of MR images and Glypican-3 to Identify the Differentiated Degree of Hepatocellular Carcinoma: a retrospective study

Feng¹,

Zhang²,

Liu³

et al. 2020

Preprint

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Abstract BACKGROUND To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) and the glypican-3 (GPC-3) to identify the differentiated degree of hepatocellular carcinoma (HCC). METHOD In this retrospective study, 104 participants were enrolled (GPC-3 data obtained in 51 participants). Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture analysis was calculated by MaZda and then using the B11 program for data analysis and classification. The performance of texture features and GPC-3 in identifying the differentiated degree of HCC was assessed by receiver operating characteristic (ROC) analysis. RESULTS There were no statistically significances for the expression of GPC-3 between poorly-, well- and moderately-differentiated HCC. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively. With GPC-3 combined, the AUC was increased to 0.868, while accuracy was decreased, in poorly- verse well-differentiated HCC, and the AUC and accuracy were the same as those without GPC-3 combined in poorly- verse moderately-differentiated HCC. Although the AUC was increased to 0.818 with GPC-3 combined in moderately- verse well-differentiated HCC, there were no statistical significance for the value change (p>0.05). CONCLUSION S Texture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI are valuable in identifying the differentiated degree of HCC. There is no significant effect of GPC-3 in identifying the differentiated degree of HCC, suggesting the promising value of texture analysis of MR images in the precise presurgical diagnosis of HCC.

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Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer

Cited by 84 publications

References 35 publications

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

Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

Combined Texture Analysis of MR images and Glypican-3 to Identify the Differentiated Degree of Hepatocellular Carcinoma: a retrospective study

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