Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging

Breast MRI-the Importance of Type II and III Dynamic Curves Evaluation and Framing in BI-RADS 4C and 5 Score

BACKGROUND

Breast Magnetic Resonance Imaging (MRI) offers the highest sensitivity in detecting breast cancer among existing clinical and imaging techniques, making it a crucial component of breast imaging protocols. This study aims to investigate MRI importance in correlation with previous imaging discordant procedures performed as echography and/or mammography to evaluate characteristics and framing in high-risk BI-RADS 4C or 5 categories based on morphological features and kinetic curves of masses found in the breasts of patients from our database.

METHODS

A retrospective study with related statistical analysis was performed on a group of 33 cases, selected from a total of 488 patients who underwent breast MRI examinations at SPAD Imaging International S.R.L. Craiova, between 01.01.2021 and 31.12.2023, aged between 33 and 75 years. In all patients, MRI images parameters were analysed.

RESULTS

In 33 patients, 23 had a single lesion and 10 had multiple lesions, 9 of them in the ipsilateral breast and, as a particularity, one of them, located in the contralateral breast. In 21 of the total patients with multiple or single lesions they had type III curves, which were classified in the BI-RADS 5 category, considering both criteria-morphology and type of curve, where the other previous techniques had not mentioned an increased risk, hence revealing that the situation in a percentage of 63.63 in the case of MRI investigation proved to be clearly superior.

CONCLUSION

Combining both kinetic and morphologic criteria can enhance the diagnostic accuracy of MRI in breast lesion evaluation.

Reduction of Abnormal Blood Flow in Frozen Shoulder after Shoulder Manipulation under Ultrasound-guided Cervical Nerve Root Block: Semi-quantitative Analysis using Dynamic MRI

Background

We previously reported a characteristic dynamic magnetic resonance imaging (MRI) change in patients with frozen shoulder (FS) and named this abnormal blood flow pattern the “burning sign”. In this study, a semiquantitative method was used to evaluate changes in this abnormal blood flow pattern on dynamic MRI after shoulder manipulation under ultrasound-guided cervical nerve root block (MUC) in patients with FS.

Methods

Nineteen patients with FS underwent MUC, with dynamic MRI performed before and after. We used dynamic MRI to semiquantitatively assess changes in the burning sign at the axillary pouch (AP) and rotator interval (RI) by examining the enhancement rate in the signal intensity and the enhancement velocity. Functional assessments included a numeric rating scale score, the range of shoulder motion, the American Shoulder and Elbow Surgeons score, and the Constant score.

Results

The burning sign in the AP and RI was observed with dynamic MRI in all patients before MUC. The average interval from MUC until dynamic MRI was 8.2 months (range, 6-12). Clinical results for all patients improved after MUC. The before and after MUC enhancement rates (%) were 217 ± 51 and 85 ± 36 in the AP and 233 ± 61 and 73 ± 40 in the RI, respectively (both P < .001). The before and after MUC enhancement velocities (ms/s) were 902 ± 335 and 203 ± 125 in the AP and 1249 ± 634 and 213 ± 146 in the RI, respectively (both P < .001).

Conclusion

Dynamic MRI semiquantitatively demonstrated a reduction in abnormal blood flow and improvement in clinical results after MUC in patients with FS.

Time density curve of dynamic contrast-enhanced computed tomography correlates with histological characteristics of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is characterized by an infiltrative growth pattern with intense desmoplastic stroma comprised of cancer-associated fibroblasts (CAFs). Additionally, the histological characteristics are considered to play a vital role in the poor prognosis of PDAC. However, the density of cancer cells, degree of desmoplasia and vascular proliferation varies in individual cases. We hypothesized that preoperative radiological images would reflect histological characteristics, such as cancer cell density, CAF density and microvessel density. To clarify the association between the histological characteristics and radiological images of PDAC, the cancer cell density, CAF density and microvessel density from surgical specimens were measured with immunostaining, and the time density curve of dynamic contrast-enhanced computed tomography (CECT) was analyzed. Overall, the initial slope between non-enhanced and arterial phases was correlated with microvessel density, and the second slope between arterial and portal phases was correlated with CAF and cancer cell densities. In conclusion, the present study suggested the possibility of estimating cancer cell, CAF and microvessel densities using the TDC of dynamic CECT.

Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. The algorithms are based on recent advances in multi-dimensional signal processing and aim to advance current state-of-the-art computer-aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi-parametric computer-aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI. Since some of the approaches discussed are also based on time-lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.

The Role of Combined Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MRI for Differentiating Malignant From Benign Breast Lesions Presenting Washout Curve

PURPOSE

The aim of this study is to evaluate the diagnostic performance of combined breast magnetic resonance imaging (MRI) protocol including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in patients with enhancing lesions that demonstrated washout curve and to determine whether applying apparent diffusion coefficient (ADC) cutoff value could improve the diagnostic value of breast MRI.

METHODS

The retrospective study included 116 patients with 116 suspicious breast lesions, which showed washout curve on DCE-MRI, who underwent subsequent biopsy. Morphologic characteristics on DCE-MRI and ADC values on DWI were evaluated. Apparent diffusion coefficient values and morphologic features of benign and malignant lesions were compared. Diagnostic values of DCE-MRI and combined MRI, including DCE-MRI and DWI (applying an ADC cutoff value) for distinguishing malignancy from benign lesions, were calculated.

RESULTS

Of the 116 breast lesions, 79 were malignant and 37 were benign. The ADC value of malignant tumors (median ADC, 0.72 × 10^-3 mm²/s) was significantly lower than that of benign lesions (median ADC, 1.03 × 10^-3 mm²/s; P < .000). The sensitivity and specificity of an ADC cutoff value of 0.89 × 10^-3 mm²/s were 92% and 95%, respectively. Dynamic contrast-enhanced MRI alone presented 100% sensitivity and 59.4% specificity. Adding an ADC cutoff value of 0.89 × 10^-3 mm²/s provided 100% sensitivity and 81% specificity, which would have prevented biopsy for 21.6% of benign lesions without missing any malignancies.

CONCLUSION

Applying an ADC cutoff value to DCE-MRI provides an improvement in the diagnostic value of breast MRI for differentiating among lesions presenting washout curve.