Malignant breast tumours: Correlation of apparent diffusion coefficient values using diffusion-weighted images and dynamic contrast-enhancement ratio with histologic grading

Value of apparent diffusion coefficient factor in correlation with the molecular subtypes, tumor grade, and expression of Ki-67 in breast cancer

Background

Breast cancer is known to be the most common cancer in women; in the last decade, contrast-enhanced magnetic resonance imaging has become an important tool in the diagnosis of cancer breast. Numerous studies have analyzed associations between imaging and histopathological features as well as the proliferation potential of breast cancer. The purpose of this study was to evaluate the relationship between the apparent diffusion coefficient (ADC) and expression of Ki-67 as well as tumor molecular subtype in breast cancer.

Results

No significant difference between the mean ADC value of tumors of grade I, II, and III was found. However, there was a significant difference between the mean ADC value of tumors of molecular type A and molecular type B (P = 0.000), HER2 overexpression (P = 0.018), and TN (P = 0.000), respectively. However, there was no significant difference between molecular type B, HER2 overexpression and TN. Also, no significant difference was found between the Ki-67 value of tumors of grade I, II, and III. Yet there was a significant difference between the mean ADC value of tumors of molecular type A and molecular type B (P = 0.000), HER2 overexpression (P = 0.014), and TN (P = 0.000), respectively. However, there was no significant difference between molecular type B, HER2 overexpression, and TN.

Conclusions

There is a significant inverse correlation between ADC values and Ki-67 expression. DWI and Ki-67 could be a good discriminator between tumors of molecular subtype A from other subtypes, yet it did not show a correlation with the tumor grade.

Correlative study between ADC value and grading of invasive breast cancer

Background

Studying breast carcinoma is of great importance as it is the commonest female malignancy. Accurate preoperative assessment of disease characteristics and prognosis would be of great help in the diagnosis and treatment planning of breast cancer. The aim of this study was to evaluate the role of the apparent diffusion coefficient (ADC) value in detecting the grading of invasive breast carcinoma prior to management.

Results

There was a significant difference between the mean ADC value of tumors of grade I and III (p = 0.001) and between grade I and II (p = 0.002). However, there was no significant difference between grade II and III (p = 0.979). High ADC values were associated with low-grade tumors. The mean ADC value of 0.93 × 10–3 mm2/s showed sensitivity 98%, specificity 100%, PPV 100%, NPV 83.3%, accuracy 98.2%, AUC = 0.994, and 95% confidence interval of 0.978 to 1.000.

Conclusion

DWI is a contrast-free modality that allows for both morphological and quantitative analysis. ADC value may not be the proper modality to determine the prognosis of breast cancer due to overlap values, yet it could be a good discriminator between low- and high-grade tumors and hence predictor of breast cancer cells that would respond to chemotherapy.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

The role of diffusion weighted imaging as supplement to dynamic contrast enhanced breast MRI: Can it help predict malignancy, histologic grade and recurrence?

RATIONALE AND OBJECTIVES

To evaluate the value of adding Diffusion Weighted Imaging (DWI) with Apparent Diffusion Coefficient (ADC) mapping to dynamic contrast enhanced (DCE-MRI) to distinguish benign from malignant pathology subtypes and tumor recurrence.

METHOD AND MATERIALS

In this retrospective IRB approved study, 956 consecutive patients underwent bilateral breast MRI between 1/2015 and 12/2015, with 156 BIRADS 4, 5, or 6 lesions detected in 111 patients. DWI imaging at B0, B100, B600, B1000 was performed with DCE-MRI. Values for diffusion and ADC images were recorded by two fellowship-trained breast radiologists. Mean ADC and signal intensity (SI) values were correlated with histology, tumor grade, hormone receptors (ER, PR, and HER-2)and Oncotype DX scores, when available. p ≤ 0.05 was considered significant.

RESULTS

Of 156 lesions, there were 59 (38%) benign lesions, 24 (15%) Ductal Carcinoma In-Situ, 47 (30%) Invasive Ductal Carcinoma (IDC), 15 (10%) Invasive Lobular Carcinoma (ILC) and 2 (2%) Mucinous carcinoma (MC), five (5%) mixed IDC and ILC, and four (4%) other, including tubular and rare types of malignancy. Mean ADC values for malignancy were significantly lower than for benign lesions (1085 ± 343 × 10^-6 vs 1481 ± 276 × 10^-6 mm²/s), which is highly predictive (area under curve = 0.82). In addition, tumors with PR negativity and Oncotype score ≥18 (intermediate to high risk for recurrence) demonstrated significantly lower ADC values. SI at B100 and B600 was helpful in distinguishing benign versus IDC. There was no significant correlation between ADC values and tumor grade or ER/HER2 status.

CONCLUSION

ADC value is important factor in distinguishing malignancy, differentiating tumors with higher Oncotype score, and PR negativity. Therefore, it can be used as an important tool to assist appropriate treatment selection.