Value of Apparent Diffusion Coefficient Values in Differentiating Malignant and Benign Breast Lesions

Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer

To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

Differentiation of Benign and Malignant Breast Lesions Using ADC Values and ADC Ratio in Breast MRI

Magnetic resonance imaging (MRI) of the breast has been increasingly used for the detailed evaluation of breast lesions. Diffusion-weighted imaging (DWI) gives additional information for the lesions based on tissue cellularity. The aim of our study was to evaluate the possibilities of DWI, apparent diffusion coefficient (ADC) value and ADC ratio (the ratio between the ADC of the lesion and the ADC of normal glandular tissue) to differentiate benign from malignant breast lesions. Materials and methods: Eighty-seven patients with solid breast lesions (52 malignant and 35 benign) were examined on a 1.5 T MR scanner before histopathological evaluation. ADC values and ADC ratios were calculated. Results: The ADC values in the group with malignant tumors were significantly lower (mean 0.88 ± 0.15 × 10−3 mm2/s) in comparison with the group with benign lesions (mean 1.52 ± 0.23 × 10−3 mm2/s). A significantly lower ADC ratio was observed in the patients with malignant tumors (mean 0.66 ± 0.13) versus the patients with benign lesions (mean 1.12 ± 0.23). The cut-off point of the ADC value for differentiating malignant from benign breast tumors was 1.11 × 10−3 mm2/s with a sensitivity of 94.23%, specificity of 94.29%, and diagnostic accuracy of 98%, and an ADC ratio of ≤0.87 with a sensitivity of 94.23%, specificity of 91.43%, and a diagnostic accuracy of 95%. Conclusion: According to the results from our study DWI, ADC values and ADC ratio proved to be valuable additional techniques with high sensitivity and specificity for distinguishing benign from malignant breast lesions.

The diagnostic accuracy of diffusion-weighted magnetic resonance imaging and shear wave elastography in comparison to dynamic contrast-enhanced MRI for diagnosing BIRADS 3 and 4 lesions

Background

Breast cancer is one of the leading causes of female morbidity and mortality. Management options vary between lesions of BIRADS categories 3 and 4. Therefore, reliable differentiation would improve outcome. Although sonomammography and contrast-enhanced breast magnetic resonance imaging (CE-MRI) remain the cornerstone for assessment of breast disease, additional, non-invasive techniques can be used to increase the efficiency of evaluation such as shear wave elastography (SWE) and diffusion-weighted magnetic resonance imaging (DW-MRI). This prospective study included 66 breast lesions that were categorized as BIRADS 3 or 4 by ultrasound ± mammography. All lesions were evaluated by SWE, CE-MRI and DW-MRI. For SWE, lesions were evaluated by both qualitative and quantitative methods. For CE-MRI, both morphological and kinematic evaluations were done and for DW-MRI, both qualitative and quantitative assessments were studied. Results of all imaging modalities were correlated to histopathology.

Results

Thirty-seven out of the examined 66 lesions (56.06%) were categorised as BIRADS 3, out of which 1 (2.7%) turned out to be malignant on histopathology and 36 (97.29%) were proved benign. Twenty-nine (43.93%) were categorized as BIRADS 4, out of which 2 (6.89%) turned out to be benign on pathology and 27 (93.1%) were proved malignant. Morphological and kinematic evaluations of CE-MRI showed 92.59% and 92.86%sensitivity, 94.74% and 84.21% specificity, 92.59 and 81.25%PPV, 94.74 and 94.12% NPV, and 93.85% and 87.88% accuracy respectively. Color-coded scoring of SWE showed indices of 89.29%, 68.42%, 67.57%, 89.66%, and 77.27% respectively. The calculated cut-off value for Emax differentiating benign from malignant was 65.15 kpa, resulting in indices of 96.43%, 57.89%, 95.65%, 62.79%, and 74.24% respectively. For Eratio, the calculated cut-off value was 4.55, resulting in indices of 71.43%, 68.42%, 76.47%, 62.50% and 69.70% respectively. For qualitative evaluation of DW-MRI, indices were 78.57%, 65.79%, 62.86%, 80.65%, and 71.21% respectively. For ADC, the calculated cut-off value was 1.25 × 103 mm2/s, which resulted in indices of 75.00%, 84.21%, 82.05%, 77.78%, and 80.30% respectively.

Conclusion

CE-MRI showed the best diagnostic performance indices. While, SWE and DW-MRI present variable diagnostic performance, both techniques can be used as an adjunct to other imaging modalities to aid the clinical decision and increase its diagnostic confidence.

Accuracy of multiparametric MRI in distinguishing the breast malignant lesions from benign lesions: a meta-analysis

BACKGROUND

The sensitivity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for detecting breast cancer was high and the specificity was relatively low. However, diffusion-weighted imaging (DWI) has a high specificity in the diagnosis of malignant lesions.

PURPOSE

To evaluate the accuracy of the multiparametric MRI (mp-MRI) in distinguishing the breast malignant lesions from the benign lesions.

MATERIAL AND METHODS

A comprehensive search of the PubMed, Embase, and Cochrane Library electronic databases was conducted up to March 2020. Data were analyzed for the following indexes: pooled sensitivity and specificity; positive likelihood ratio; negative likelihood ratio; diagnostic odds ratio; and the area under the curve.

RESULTS

A total of 2356 patients with 1604 malignant and 967 benign breast lesions were included from 22 studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve for mp-MRI were 0.93, 0.85, 6.3, 0.08, 81, and 0.96, respectively. The pooled sensitivity, specificity, and area under the curve for DCE-MRI alone were 0.95, 0.71, and 0.92, respectively. The pooled sensitivity, specificity, and area under the curve for DWI alone were 0.88, 0.84, and 0.93, respectively.

CONCLUSION

The mp-MRI did not improve the sensitivity but increased the specificity for the diagnosis of breast malignant lesions.

Combination Analysis of a Radiomics-Based Predictive Model With Clinical Indicators for the Preoperative Assessment of Histological Grade in Endometrial Carcinoma

BACKGROUND

Histological grade is one of the most important prognostic factors of endometrial carcinoma (EC) and when selecting preoperative treatment methods, conducting accurate preoperative grading is of great significance.

PURPOSE

To develop a magnetic resonance imaging (MRI) radiomics-based nomogram for discriminating histological grades 1 and 2 (G1 and G2) from grade 3 (G3) EC.

METHODS

This was a retrospective study included 358 patients with histologically graded EC, stratified as 250 patients in a training cohort and 108 patients in a test cohort. T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) were performed via 1.5-Tesla MRI. To establish ModelADC, the region of interest was manually outlined on the EC in an apparent diffusion coefficient (ADC) map. To establish the radiomic model (ModelR), EC was manually segmented by two independent radiologists and radiomic features were extracted. The Radscore was calculated based on the least absolute shrinkage and selection operator regression. We combined the Radscore with carbohydrate antigen 125 (CA125) and body mass index (BMI) to construct a mixed model (ModelM) and develop the predictive nomogram. Receiver operator characteristic (ROC) and calibration curves were assessed to verify the prediction ability and the degree of consistency, respectively.

RESULTS

All three models showed some amount of predictive ability. Using ADC alone to predict the histological risk of EC was limited in both the cohort [area under the curve (AUC), 0.715; 95% confidence interval (CI), 0.6509-0.7792] and test cohorts (AUC, 0.621; 95% CI, 0.515-0.726). In comparison with ModelADC, the discrimination ability of ModelR showed improvement (Delong test, P < 0.0001 for both the training and test cohorts). ModelM, established based on the combination of radiomic and clinical indicators, showed the best level of predictive ability in both the training (AUC, 0.925; 95% CI, 0.898-0.951) and test cohorts (AUC, 0.915; 95% CI, 0.863-0.968). Calibration curves suggested a good fit for probability (Hosmer-Lemeshow test, P = 0.673 and P = 0.804 for the training and test cohorts, respectively).

CONCLUSION

The described radiomics-based nomogram can be used to predict EC histological classification preoperatively.

Application of Diffusion Weighted Imaging Techniques for Differentiating Benign and Malignant Breast Lesions

To explore the value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusional kurtosis imaging (DKI) based on diffusion weighted magnetic resonance imaging (DW-MRI) in differentiating benign and malignant breast lesions. A total of 215 patients with breast lesions were prospectively collected for breast MR examination. Single exponential, IVIM, and DKI models were calculated using a series of b values. Parameters including ADC, perfusion fraction (f), tissue diffusion coefficient (D), perfusion-related incoherent microcirculation (D*), average kurtosis (MK), and average diffusivity (MD) were compared between benign and malignant lesions. ROC curves were used to analyze the optimal diagnostic threshold of each parameter, and to evaluate the diagnostic efficacy of single and combined parameters. ADC, D, MK, and MD values were significantly different between benign and malignant breast lesions (P<0.001). Among the single parameters, ADC had the highest diagnostic efficiency (sensitivity 91.45%, specificity 82.54%, accuracy 88.84%, AUC 0.915) and the best diagnostic threshold (0.983 μm²/ms). The combination of ADC and MK offered high diagnostic performance (sensitivity 90.79%, specificity 85.71%, accuracy 89.30%, AUC 0.923), but no statistically significant difference in diagnostic performance as compared with single-parameter ADC (P=0.268). The ADC, D, MK, and MD parameters have high diagnostic value in differentiating benign and malignant breast lesions, and of these individual parameters the ADC has the best diagnostic performance. Therefore, our study revealed that the use of ADC alone should be useful for differentiating between benign and malignant breast lesions, whereas the combination of MK and ADC might improve the diagnostic performance to some extent.

Differentiation of Breast Lesions and Distinguishing Their Histological Subtypes Using Diffusion-Weighted Imaging and ADC Values

Diffusion-weighted imaging (DWI) has not been well explored in differentiation of malignant from benign breast lesions. The aims of this study were to examine the role of apparent diffusion coefficient (ADC) values in differentiation of malignant from benign tumors and distinguishing histological subtypes of malignant lesions, and to determine correlations between ADC values and breast tumors structure. This cohort-study included 174 female patients who underwent contrast-enhanced breast MR examination on a 3T scanner and were divided into two groups: patient group (114 patients with proven tumors) and control group (60 healthy patients). One-hundred-thirty-nine lesions (67 malignant and 72 benign) were detected and pathohistologically analyzed. Differences between variables were tested using chi-square test; correlations were determined using Pearson's correlation test. For determination of cut off values for diagnostic potential, Receiver Operating Characteristic curves were constructed. Statistical significance was set at p < 0.05. Mean ADC values were significantly lower in malignant compared to benign lesions (0.68 × 10^-3mm²/s vs. 1.12 × 10^-3mm²/s, p < 0.001). The cut off value of ADC for benign lesions was 0.792 × 10^-3mm²/s (sensitivity 98.6%, specificity 65.7%), and for malignant 0.993 × 10^-3mm²/s (98.5, 80.6%). There were no significant correlations between malignant lesion subtypes and ADC values. DWI is a clinically useful tool for differentiation of malignant from benign lesions based on mean ADC values. The cut off value for benign lesions was higher than reported recently, due to high amount of fibrosis in included benign lesions. Finally, ADC values might have implications in determination of the biological nature of the malignant lesions.

Unenhanced breast MRI: could it replace dynamic breast MRI in detecting and characterizing breast lesions?

Background

We aimed to evaluate the unenhanced MRI of the breast (UE-MRI) as an effective substitute for dynamic contrast-enhanced breast MRI (DCE-MRI) in both detecting and characterizing breast lesions. We enrolled in our retrospective study 125 females (232 breasts, as 18 patients had unilateral mastectomy) with breast mass at MRI of variable pathologies. Routine DCE-MRI protocol of the breast was conducted. We compared the conventional unenhanced images including STIR, T2, and DWIs to the DCE-MRI by two blinded radiologists, to detect and characterize breast lesions, and then we compared their results with the final reference diagnoses supplied by the histopathology or serial negative follow-ups. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for UE-MRI and DCE-MRI were calculated. UE-MRI results of each observer were also compared with DCE- MRI.

Results

The calculated UE-MRI sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for the first observer were 95%, 80%, 83%, 94%, and 89% respectively, and for the second observer, they were 94%, 79%, 81%, 93%, and 86%. On the other hand, those for the DCE-MRI by the first observer were 98%, 82%, 84%, 98%, and 90% and were 97%, 81%, 84%, 97%, and 89% by the second observer. The intraobserver agreement between the UE-MRI and DCE-MRI results of each observer was 94% and 95%, while the interobserver agreement for each section was 97.4% for UE-MRI and 98.3% for DCE-MRI.

Conclusion

UE-MRI of the breast can be a reliable and effective substitute for breast DCE-MRI. It can be used with comparable accuracy to DCE-MRI whenever contrast administration is not feasible or contraindicated.

Diffusion weighted imaging in breast cancer - Can it be a noninvasive predictor of nuclear grade?

BACKGROUND

DWI and ADC values are noninvasive MRI techniques, which provide quantitative information about tumor heterogeneity.

AIM

To determine the minimum and mean ADC values in breast carcinoma and to correlate ADC values with various prognostic factors.

SETTINGS AND DESIGN

Prospective observational study.

MATERIALS AND METHODS

Fifty-five patients with biopsy-proven breast carcinoma were included in this study. MRI with DWI was performed with Siemens 3T Skyra scanner. ADC values were measured by placing regions of interest (ROIs) within the targeted lesions on ADC maps manually. The histopathological and immunohistochemical analysis of surgical specimen was done to determine the prognostic factors.

STATISTICAL ANALYSIS

Students T test and ANOVA were used to study the difference in ADC between two groups. Pearson correlation coefficient was used to quantify the correlation between ADC values and prognostic factors.

RESULTS

Lower grade (grade I) breast carcinoma had a significantly high ADC value as compared to higher grade carcinoma (grade II and III). For differentiating Grade I tumors from grade II and III, a minimum ADC cut-off value was 0.79 × 10-3 mm2/sec (83% sensitivity and 84% specificity) and a mean ADC cut-off value was 0.82 × 10-3 mm2/sec (83% sensitivity and 71% specificity) was derived. There was no significant correlation between ADC and other prognostic factors.

CONCLUSION

ADC values can be used to differentiate lower grade breast carcinoma (grade I) from higher grades (grade II and III). Minimum ADC values are more accurate in predicting the grade of the breast tumor than mean ADC value.

Is the necrosis/wall ADC ratio useful for the differentiation of benign and malignant breast lesions?

OBJECTIVE

To determine whether the necrosis/wall apparent diffusion coefficient (ADC) ratio is useful for the malignant-benign differentiation of necrotic breast lesions.

METHODS

Breast MRI was performed using a 3-T system. In this retrospective study, calculation of the necrosis/wall ADC ratio was based on ADC values measured from the necrosis and from the wall of malignant and benign breast lesions by diffusion-weighted imaging (DWI). By synchronizing post-contrast T₁ weighted images, the separate parts of wall and necrosis were maintained. All the diagnoses were pathologically confirmed. Statistical analyses were conducted using an independent sample t-test and receiver operating characteristic analysis. The intraclass and interclass correlations were evaluated.

RESULTS

A total of 66 female patients were enrolled, 38 of whom had necrotic breast carcinomas and 28 of whom had breast abscesses. The ADC values were obtained from both the wall and necrosis. The mean necrosis/wall ADC ratio (± standard deviation) was 1.61 ± 0.51 in carcinomas, and it was 0.65 ± 0.33 in abscesses. The area under the curve values for necrosis ADC, wall ADC and the necrosis/wall ADC ratio were 0.680, 0.068 and 0.942, respectively. A wall/necrosis ADC ratio cut-off value of 1.18 demonstrated a sensitivity of 97%, specificity of 93%, a positive-predictive value of 95%, a negative-predictive value of 96% and an accuracy of 95% in determining the malignant nature of necrotic breast lesions. There was a good intra- and interclass reliability for the ADC values of both necrosis and wall.

CONCLUSION

The necrosis/wall ADC ratio appears to be a reliable and promising tool for discriminating breast carcinomas from abscesses using DWI. Advances in knowledge: ADC values of the necrosis obtained by DWI are valuable for malignant-benign differentiation in necrotic breast lesions. The necrosis/wall ADC ratio appears to be a reliable and promising tool in the breast imaging field.