Characterization of Suspicious Breast Lesions with Dynamic Contrast enhanced MRI In comparison to Conventional mammography and Ultrasonography

Ghazala, Safieeldin

doi:10.15406/jcpcr.2016.04.00121

Cited by 5 publications

(6 citation statements)

References 29 publications

(54 reference statements)

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“…The kinetic curve of breast MRI showed a persistent increase in the intensity of the enhancement (type 1)However, morphologically both ultrasound and mammography also support the diagnosis in the feature of malignant lesions. Although in this case the kinetic curve indicated the possibility of benign lesions, in the general margin and shape analysis (morphologically) are the most predictive features in the interpretation of MRI images 9. MRI examination showed indistinct lesions and mammography also showed spiculated margins.…”

mentioning

confidence: 60%

“…reaching 91%. 9 The kinetic curve pattern itself cannot be relied upon as a diagnostic determinant, seen in the case of DCIS only about 70% of cases showed enhancement in the initial phase with variable delayed phase enhancement. 11 DCE feature on MRI is more accurate than mammography or ultrasound in identifying the extent of disease but has limitations in distinguishing between malignant and benign lesions.…”

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confidence: 99%

“…13 The study conducted by Ghazala et al, showed that MRI was the most sensitive modality for detecting malignant lesions but had limited specificity due to overlapping characteristics between benign and malignant. 9 Generally, radiology modalities used to detect breast cancer are ultrasonography, mammography, and breast MRI. Each modalities have their own advantages and limitations as explained in this article.…”

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confidence: 99%

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Invasive breast carcinoma with MRI kinetic curve type 1

Daniswara¹,

Manangka²

2020

nsmc

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Breast cancer is the most common cancer in women and is the most significant cause of morbidity and mortality in women cancer patients. Increased awareness of people followed by periodic screening, appropriate supporting examinations, and imaging modalities according to the patient's condition could improve prognosis. We report a case report of a 55-year-old woman with a complaint of a lump in the right breast. Ultrasound examination and mammography showed a single solid lesion in the superior quadrant of the right breast corresponding to Breast Imaging-Reporting and Data System (BI-RADS) 4c. Magnetic Resonance Imaging (MRI) examination revealed a single lesion with enhancement curve type 1 according to bi-rads 4b. Results of the biopsy were grade 2 breast carcinoma without any lymph node metastases. The use of a single modality cannot be used as a benchmark in determining the best diagnosis and therapy. The kinetic feature/enhancement curve of Dynamic Contrast-Enhanced MRI (DCE-MRI) cannot be used as the main guide for determining the diagnosis. The morphology of the lesion either from ultrasound, mammography, or MRI should also be considered in determining the diagnosis and treatment plan

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confidence: 99%

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confidence: 99%

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Invasive breast carcinoma with MRI kinetic curve type 1

Daniswara¹,

Manangka²

2020

nsmc

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“…Our study was conducted in accordance with (56), where the generalization abilities of radiomic models in multi-centric MRI datasets have been recently addressed. It is worth noting that the use of breast lesion classification based on the ACR BI-RADS lexicon also facilitated the sample standardization of the enrolled cases (8).…”

Section: Discussionmentioning

confidence: 99%

“…Among imaging techniques, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) provides both morphological (eg, size, margins, shape) (7) and hemodynamic information, assessing tumor vascularity and defining enhancement kinetic curves, with a reported sensitivity higher than 90% for breast cancer diagnosis (8). However, the specificity of DCE-MRI is still substantially low (about 72%) and it is often necessary to continue the diagnostic work-up with a biopsy (8).…”

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confidence: 99%

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

et al. 2022

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Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radiomics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a manually annotated dataset of 111 patients, which was split into discovery and test sets. A feature calibration and pre-processing step was performed to find only robust non-redundant features. An indepth discovery analysis was performed to define a predictive model: for this purpose, a Support Vector Machine (SVM) was trained in a nested 5-fold cross-validation scheme, by exploiting several unsupervised feature selection methods. The predictive model performance was evaluated in terms of Area Under the Receiver Operating Characteristic (AUROC), specificity, sensitivity, PPV and NPV. The test was performed on unseen held-out data.Results: The model combining Unsupervised Discriminative Feature Selection (UDFS) and SVMs on average achieved the best performance on the blinded test set: AUROC = 0.725 §0.091, sensitivity = 0.709 §0.176, specificity = 0.741 §0.114, PPV = 0.72 §0.093, and NPV = 0.75 §0.114. Conclusion:In this study, we built a radiomic predictive model based on breast DCE-MRI, using only the strongest enhancement phase, with promising results in terms of accuracy and specificity in the differentiation of malignant from benign breast lesions.

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Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study

Maiti,

Nayak,

Hebbar

et al. 2024

F1000Res

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Background Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC). Methods This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF were extracted from the DCE-MRI sequence using a 3D slicer. The relevance of RF for differentiating IDC from ILC was evaluated using the maximum relevance minimum redundancy (mRMR) and Mann-Whitney test. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC. Results Ten DCE MRI-based RFs used in our study showed a significant difference (p <0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC. Conclusions MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.

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Characterization of Suspicious Breast Lesions with Dynamic Contrast enhanced MRI In comparison to Conventional mammography and Ultrasonography

Cited by 5 publications

References 29 publications

Invasive breast carcinoma with MRI kinetic curve type 1

Invasive breast carcinoma with MRI kinetic curve type 1

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study

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