scholarly journals A Comparative Assessment of MR BI-RADS 4 Breast Lesions With Kaiser Score and Apparent Diffusion Coefficient Value

2021 ◽  
Vol 11 ◽  
Author(s):  
Lingsong Meng ◽  
Xin Zhao ◽  
Lin Lu ◽  
Qingna Xing ◽  
Kaiyu Wang ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Breast Imaging ◽  
Diagnostic Performance ◽  
Area Under The Curve ◽  
Lower Sensitivity ◽  
Breast Lesions ◽  
Roc Curve Analysis ◽  
Apparent Diffusion Coefficient Value ◽  
Apparent Diffusion

ObjectivesTo investigate the diagnostic performance of the Kaiser score and apparent diffusion coefficient (ADC) to differentiate Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions at dynamic contrast-enhanced (DCE) MRI.MethodsThis was a single-institution retrospective study of patients who underwent breast MRI from March 2020 to June 2021. All image data were acquired with a 3-T MRI system. Kaiser score of each lesion was assigned by an experienced breast radiologist. Kaiser score+ was determined by combining ADC and Kaiser score. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of Kaiser score+, Kaiser score, and ADC. The area under the curve (AUC) values were calculated and compared by using the Delong test. The differences in sensitivity and specificity between different indicators were determined by the McNemar test.ResultsThe study involved 243 women (mean age, 43.1 years; age range, 18–67 years) with 268 MR BI-RADS 4 lesions. Overall diagnostic performance for Kaiser score (AUC, 0.902) was significantly higher than for ADC (AUC, 0.81; p = 0.004). There were no significant differences in AUCs between Kaiser score and Kaiser score+ (p = 0.134). The Kaiser score was superior to ADC in avoiding unnecessary biopsies (p < 0.001). Compared with the Kaiser score alone, the specificity of Kaiser score+ increased by 7.82%, however, at the price of a lower sensitivity.ConclusionFor MR BI-RADS category 4 breast lesions, the Kaiser score was superior to ADC mapping regarding the potential to avoid unnecessary biopsies. However, the combination of both indicators did not significantly contribute to breast cancer diagnosis of this subgroup.

scholarly journals Nomograms of Combining Apparent Diffusion Coefficient Value and Radiomics for Preoperative Risk Evaluation in Endometrial Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Kaiyue Zhang ◽  
Yu Zhang ◽  
Xin Fang ◽  
Mengshi Fang ◽  
Bin Shi ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Endometrial Carcinoma ◽  
Area Under The Curve ◽  
Myometrial Invasion ◽  
Diagnostic Efficiency ◽  
Preoperative Risk ◽  
Adc Value ◽  
Apparent Diffusion Coefficient Value ◽  
Apparent Diffusion

ObjectivesTo evaluate the value of nomogram models combining apparent diffusion coefficient (ADC) value and radiomic features on magnetic resonance imaging (MRI) in predicting the type, grade, deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) of endometrial carcinoma (EC) preoperatively.MethodsThis study included 210 EC patients. ADC value was calculated, and radiomic features were measured on T2-weighted images. The univariate and multivariate logistic regressions and cross-validations were performed to reduce valueless features, then radiomics signatures were developed. Nomogram models using ADC combined with radiomic features were developed in the training cohort. The receiver operating characteristic (ROC) curve was performed to estimate the diagnostic efficiency of nomogram models by the area under the curve (AUC) in the training and validation cohorts.ResultsThe ADC value was significantly different between each subgroup. Radiomic features were ultimately limited to four features for type, six features for grade, six features for DMI, four features for LVSI, and eight features for LNM for the nomogram models. The AUC of the nomogram model combining ADC value and radiomic features in the training and validation cohorts was 0.851 and 0.867 for type, 0.959 and 0.880 for grade, 0.839 and 0.766 for DMI, 0.816 and 0.746 for LVSI, and 0.910 and 0.897 for LNM.ConclusionsThe nomogram models of ADC value combined with radiomic features were associated with the type, grade, DMI, LVSI, and LNM of EC, and provide an effective, non-invasive method to evaluate preoperative risk stratification for EC.

Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis

2020 ◽  
Vol 61 (9) ◽  
pp. 1165-1175
Author(s):  
Fan Xu ◽  
Ying-ying Liang ◽  
Yuan Guo ◽  
Zhi-ping Liang ◽  
Mei Wu ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Diagnostic Performance ◽  
Meta Analysis ◽  
Histogram Analysis ◽  
Breast Lesions ◽  
Adc Value ◽  
Apparent Diffusion ◽  
Malignant Breast ◽  
Meta Regression

Background Although whole-lesion apparent diffusion coefficient (ADC) histogram has been increasingly used for breast lesions, it has not been routinely used in clinical practice as an emergent promising imaging tool. Purpose To evaluate the performance of whole-lesion ADC histogram analysis metrics for differentiating benign and malignant breast lesions. Material and Methods A systematic PubMed/EMBASE/Cochrane electronic database search was performed for original diagnostic studies from 1 January 1970 to 2 January 2019. Summary estimates of diagnostic accuracy were generated and meta-regression was performed to explore sources of heterogeneity according to study and magnetic resonance imaging characteristics. Results Five original articles involving 493 patients were included in the meta-analysis. The pooled sensitivity and specificity of whole-lesion ADC histogram analysis were 0.85 (95% confidence interval [CI] = 0.81–0.89) and 0.79 (95% CI = 0.72–0.84) for distinguishing benign and malignant breast lesions, respectively. The area under the curve (AUC) was 0.9178. No publication bias was detected ( P = 0.51). In subgroup analysis, the summary sensitivity and specificity of 50th percentile ADC value were 0.81 (95% CI = 0.71–0.88) and 0.86 (95% CI = 0.74–0.94), respectively. Meta-regression analysis indicated no covariates were sources of heterogeneity ( P > 0.05). Conclusion Whole-lesion ADC histogram analysis demonstrated good diagnostic performance for differentiating between benign and malignant breast lesions, with 50th percentile ADC value showing higher diagnostic accuracy than other histogram parameters. Given the limited number of studies included in the analysis, the findings from our meta-analysis will need further confirmation in future research.

scholarly journals Region of interest demarcation for quantification of the apparent diffusion coefficient in breast lesions and its interobserver variability

2015 ◽  
Vol 21 (2) ◽  
pp. 123-127 ◽  
Author(s):  
Luísa Nogueira ◽  
Sofia Brandão ◽  
Eduarda Matos ◽  
Rita Gouveia Nunes ◽  
Hugo Alexandre Ferreira ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Interobserver Variability ◽  
Region Of Interest ◽  
Breast Lesions ◽  
Apparent Diffusion

Low diffusion level within a fibroadenoma as the sole sign of ductal carcinoma in situ: A case report

2021 ◽  
pp. 1-4
Author(s):  
Corrado Tagliati ◽  
Giuseppe Lanni ◽  
Federico Cerimele ◽  
Antonietta Di Martino ◽  
Valentina Calamita ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Surgical Excision ◽  
Small Region ◽  
Apparent Diffusion Coefficient Value ◽  
Apparent Diffusion

We present a case of ductal carcinoma in situ within a fibroadenoma. Breast cancer arising within fibroadenoma incidence ranges from 0.125% to 0.02%, and ductal carcinoma in situ is not the most frequent malignancy that can be found within a fibroadenoma. Dynamic contrast-enhanced magnetic resonance imaging showed an oval mass with circumscribed margins and dark internal septations, suspicious for fibroadenoma. According to European Society of Breast Radiology diffusion-weighted imaging consensus, mean apparent diffusion coefficient value obtained by drawing a small region of interest on the lesion apparent diffusion coefficient map showed a low diffusion level. Therefore, ductal carcinoma in situ within a fibroadenoma was diagnosed at final pathology after surgical excision.

Diagnostic value of apparent diffusion coefficient in differentiating between high-grade gliomas and brain metastases

2017 ◽  
Vol 59 (5) ◽  
pp. 599-605 ◽  
Author(s):  
Ionut Caravan ◽  
Cristiana Augusta Ciortea ◽  
Alexandra Contis ◽  
Andrei Lebovici
Keyword(s):  
Diffusion Coefficient ◽  
Brain Metastases ◽  
Apparent Diffusion Coefficient ◽  
Curve Analysis ◽  
High Grade ◽  
Peritumoral Edema ◽  
Roc Curve Analysis ◽  
Apparent Diffusion ◽  
Adc Values ◽  
The Mean

Background High-grade gliomas (HGGs) and brain metastases (BMs) can display similar imaging characteristics on conventional MRI. In HGGs, the peritumoral edema may be infiltrated by the malignant cells, which was not observed in BMs. Purpose To determine whether the apparent diffusion coefficient values could differentiate HGGs from BMs. Material and Methods Fifty-seven patients underwent conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) before treatment. The minimum and mean ADC in the enhancing tumor (ADCmin, ADCmean) and the minimum ADC in the peritumoral region (ADCedema) were measured from ADC maps. To determine whether there was a statistical difference between groups, ADC values were compared. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff ADC value for distinguishing between HGGs and BMs. Results The mean ADCmin values in the intratumoral regions of HGGs were significantly higher than those in BMs. No differences were observed between groups regarding ADCmean values. The mean ADCmin values in the peritumoral edema of HGGs were significantly lower than those in BMs. According to ROC curve analysis, a cutoff value of 1.332 × 10−3 mm2/s for the ADCedema generated the best combination of sensitivity (95%) and specificity (84%) for distinguishing between HGGs and BMs. The same value showed a sensitivity of 95.6% and a specificity of 100% for distinguishing between GBMs and BMs. Conclusion ADC values from DWI were found to distinguish between HGGs and solitary BMs. The peritumoral ADC values are better than the intratumoral ADC values in predicting the tumor type.

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