Correlative study between ADC value and grading of invasive breast cancer

Azzam, Heba; Mansour, Sahar; Salem, Noha Y.; El-Assaly, Hany

doi:10.1186/s43055-019-0124-6

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…We considered high grades to be grade III and low grades to be grades I and II. There was no significant difference between grades II and III using ADC in an earlier study 55 . Similar results were found in our analysis.…”

Section: Discussionsupporting

confidence: 92%

“…In the characterization of low‐ versus high‐grade tumors, the SVM classifier using the WBFS method displayed 0.94 sensitivity, 0.80 specificity, an F1 score of 0.92, and 0.24 RMSE with 10‐fold CV over 10 repetitions in our cohort. Several studies have reported on the relationship between ADC value and the histological grade of breast cancer, with varying results 52–55 . This variation may be attributable to the discrepancy in the sample size and the MRI technique, especially the use of different b‐values.…”

Section: Discussionmentioning

confidence: 99%

“…Several research studies have reported the use of texture or radiomic features in the diagnosis and grading of cancer. [56][57][58] However, methods based upon texture features suffer from a lack of standardization, external verification of multicenter data, and large sample size, 59 therefore making reproducibility of the results a challenge. The feature nomenclature is not homogenous among publications.…”

Section: Different Features Selection Methodsmentioning

confidence: 99%

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Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features

2021

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Magnetic resonance imaging (MRI) is playing an important role in the classification of breast tumors. MRI can be used to obtain multiparametric (mp) information, such as structural, hemodynamic, and physiological information. Quantitative analysis of mp‐MRI data has shown potential in improving the accuracy of breast tumor classification. In general, a large set of quantitative and texture features can be generated depending upon the type of methodology used. A suitable combination of selected quantitative and texture features can further improve the accuracy of tumor classification. Machine learning (ML) classifiers based upon features derived from MRI data have shown potential in tumor classification. There is a need for further research studies on selecting an appropriate combination of features and evaluating the performance of different ML classifiers for accurate classification of breast tumors. The objective of the current study was to develop and optimize an ML framework based upon mp‐MRI features for the characterization of breast tumors (malignant vs. benign and low‐ vs. high‐grade). This study included the breast mp‐MRI data of 60 female patients with histopathology results. A total of 128 features were extracted from the mp‐MRI tumor data followed by features selection. Five ML classifiers were evaluated for tumor classification using 10‐fold crossvalidation with 10 repetitions. The support vector machine (SVM) classifier based on optimum features selected using a wrapper method with an adaptive boosting (AdaBoost) technique provided the highest sensitivity (0.96 ± 0.03), specificity (0.92 ± 0.09), and accuracy (94% ± 2.91%) in the classification of malignant versus benign tumors. This method also provided the highest sensitivity (0.94 ± 0.07), specificity (0.80 ± 0.05), and accuracy (90% ± 5.48%) in the classification of low‐ versus high‐grade tumors. These findings suggest that the SVM classifier outperformed other ML methods in the binary classification of breast tumors.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Different Features Selection Methodsmentioning

confidence: 99%

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Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features

2021

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“…In ADC maps, tissues with high cellularity showed lower ADC values [20,21]. Recent literature has demonstrated an inverse correlation between ADC values and histological grade of the tumor, proving its usefulness in identifying high-grade invasive breast cancer prior to the surgery [22,23].…”

Section: Introductionmentioning

confidence: 99%

On the Additional Information Provided by 3T-MRI ADC in Predicting Tumor Cellularity and Microscopic Behavior

Santucci

Faiella

Calabrese

et al. 2021

Cancers

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Background: to evaluate whether Apparent Diffusion Coefficient (ADC) values of invasive breast cancer, provided by 3T Diffusion Weighted-Images (DWI), may represent a non-invasive predictor of pathophysiologic tumor aggressiveness. Methods: 100 Patients with histologically proven invasive breast cancers who underwent a 3T-MRI examination were included in the study. All MRI examinations included dynamic contrast-enhanced and DWI/ADC sequences. ADC value were calculated for each lesion. Tumor grade was determined according to the Nottingham Grading System, and immuno-histochemical analysis was performed to assess molecular receptors, cellularity rate, on both biopsy and surgical specimens, and proliferation rate (Ki-67 index). Spearman’s Rho test was used to correlate ADC values with histological (grading, Ki-67 index and cellularity) and MRI features. ADC values were compared among the different grading (G1, G2, G3), Ki-67 (<20% and >20%) and cellularity groups (<50%, 50–70% and >70%), using Mann–Whitney and Kruskal-Wallis tests. ROC curves were performed to demonstrate the accuracy of the ADC values in predicting the grading, Ki-67 index and cellularity groups. Results: ADC values correlated significantly with grading, ER receptor status, Ki-67 index and cellularity rates. ADC values were significantly higher for G1 compared with G2 and for G1 compared with G3 and for Ki-67 < 20% than Ki-67 > 20%. The Kruskal-Wallis test showed that ADC values were significantly different among the three grading groups, the three biopsy cellularity groups and the three surgical cellularity groups. The best ROC curves were obtained for the G3 group (AUC of 0.720), for G2 + G3 (AUC of 0.835), for Ki-67 > 20% (AUC of 0.679) and for surgical cellularity rate > 70% (AUC of 0.805). Conclusions: 3T-DWI ADC is a direct predictor of cellular aggressiveness and proliferation in invasive breast carcinoma, and can be used as a supporting non-invasive factor to characterize macroscopic lesion behavior especially before surgery.

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“…However, there was no significant difference between grade II and III tumors (p ¼ 0.979). 5 We thank the authors for showing the potential value of utilizing ADC maps with specific cut-off values as an additional useful predictive marker for malignant breast lesions, as well as a potentially helpful tool for tumor grading.…”

mentioning

confidence: 99%