Kinetic Curve Type Research Articles

Follow-up results of BI-RADS 3 lesions on magnetic resonance imaging: a retrospective study

The categorization of Breast Imaging Reporting and Data System (BI-RADS) 3 lesions is not as clear in magnetic resonance imaging (MRI) as it is in mammography (MG). With the increasing number of MRI scans currently being conducted globally, incidentally detected lesions falling into the probably benign category are frequently being observed. In this study, our aim was to investigate the imaging characteristics and follow-up results of BI-RADS 3 lesions detected by MRI and to determine their malignancy rates. Breast MRI scans performed between January 2010 and January 2020 and classified as BI-RADS 3 lesions were retrospectively analyzed. The study included 216 lesions with known biopsy or surgical excision results or with at least one year of radiological follow-up. We assessed the patients' age, the presence of breast cancer, the follow-up interval, and the imaging findings at the beginning and during the follow-up. Lesions that remained stable, disappeared, or decreased in size and had a benign histopathological diagnosis were classified as benign. Lesions with the histopathological diagnosis of malignancy, identified by either biopsy or surgical excision, were classified as malignant. We determined the malignancy rate based on the histopathology and follow-up results. Considering the follow-up results of all cases, 8% of lesions were excised, 0.5% decreased in size, 1.4% became enlarged, 17.1% disappeared, and 73% remained stable. The malignancy rate was 2.8%. A significant relationship was found between lesion shape and malignancy, as progression to malignancy was more likely in round lesions than in other types. An irregular margin, heterogeneous enhancement, and kinetic curve (type 2) features were significant for lesion upgrade to malignancy. The malignancy rate in BI-RADS 3 lesions detected by MRI is low and falls within the accepted cancer rate for MG and sonography. Changes in size, morphology, and enhancement pattern should be considered in terms of malignancy development during follow-up. The follow-up intervals should be determined on a case-by-case basis.

Idiopathic Granulomatous Mastitis or Breast Cancer? A Comparative MRI Study in Patients Presenting with Non-Mass Enhancement.

To compare and determine discriminative magnetic resonance imaging (MRI) findings of idiopathic granulomatous mastitis (IGM) and breast cancer (BC) that present as non-mass enhancement. This retrospective study includes 68 IGM and 75 BC cases that presented with non-mass enhancement on breast MRI. All patients with a previous history of breast surgery, radiotherapy, or chemotherapy due to BC or a previous history of mastitis were excluded. On MRI images, presence of architectural distortion skin thickening, edema, hyperintense ducts containing protein, dilated fat-containing ducts and axillary adenopathies were noted. Cysts with enhancing walls, lesion size, lesion location, fistulas, distribution, internal enhancement pattern and kinetic features of non-mass enhancement were recorded. Apparent diffusion coefficient (ADC) values were calculated. Pearson chi-square test, Fisher's exact test, independent t test and Mann-Whitney U test were used as needed for statistical analysis and comparison. Multivariate logistic regression model was used to determine the independent predictors. IGM patients were significantly younger than BC patients (p < 0.001). Cysts with thin (p < 0.05) or thick walls (p = 0.001), multiple cystic lesions, (p < 0.001), cystic lesions draining to the skin (p < 0.001), and skin fistulas (p < 0.05) were detected more often in IGM. Central (p < 0.05) and periareolar (p < 0.001) location and focal skin thickening (p < 0.05) were significantly more common in IGM. Architectural distortion (p = 0.001) and diffuse skin thickening (p < 0.05) were associated with BC. Multiple regional distribution was more common in IGM, whereas diffuse distribution and clumped enhancement were more common in BC (p < 0.05). In kinetic analysis, persistent enhancement was more common in IGM, whereas plateau and wash-out types were more common in BC (p < 0.001). Independent predictors for BC were age, diffuse skin thickening and kinetic curve types. There was no significant difference in the diffusion characteristics. Based on these findings, MRI had a sensitivity, specificity and accuracy of 88%, 67.65%, and 78.32%, respectively, in differentiating IGM from BC. In conclusion, for non-mass enhancement, MRI can rule out malignancy with a considerably high sensitivity; however, specificity is still low, as many IGM patients have overlapping findings. Final diagnosis should be complemented with histopathology whenever necessary.

DRUG RELEASE FROM POROUS SPHERICAL PARTICLE: DIFFUSION MODEL WITH AN INTERMEDIATE COMPLEX FORMATION

This publication proposes a new model of drug release from a spherical particle that takes into account the transfer of the drug and the porous particle into the solution by forming an intermediate complex with the bio carrier, one of the components of the solution. The mathematical model is reduced to a dimension-less form that is convenient for qualitative analysis of the process. The problem is realized numerically. The initial stage of filling a porous particle with a bio carrier and stage of drug release into environment were analyzed. Two main types of kinetic curves corresponding to diffusive and convective modes were revealed. The kinetic curves with the initial stage of drug release delay were revealed. The model was in effects (unobvious at first glance) related to the interaction of opposite factors affecting the drug release. The different types of kinetic curves obtained on its basis correspond to different observational conditions, types of particles, and properties of bio fluids. This model can be improved to describe and predict drug release kinetics not only from single porous particles, but also from containers of more complex shape.

Value of MRI in Differentiation between Benign and Malignant Breast Mass

Objectives: This study aimed to evaluate the role of MRI in differentiation between benign and malignant well-circumscribed breast masses.&#x0D; Methods: A prospective cross-sectional study conducted on selected female patients with Breast mass who their age older than 35 years. All patients underwent examination using 1.5 Tesla magnetic resonance unit (MAGNETOM AERA, SIMENS).&#x0D; Results: 30 female patients were selected (mean age of 44.2±9.2 years, range between 35-69). Final findings of MRI revealed that 6 (20%) women had malignant breast mass while 24 (80%) women had benign breast mass. The histopathology revealed that 4 (13.3%) women were having malignancy while 26 (86.6%) were having benign breast tumor. There was a highly significant association between malignancy and hyperdense mammography findings of women (p&lt;0.001), multiple lymph nodes for women (p&lt;0.001), increased size of mass by MRI (p&lt;0.001), circumscribed macrolabular lesion on MRI, rapid early kinetic phase of enhanced MRI (p&lt;0.001), washout late kinetic phase enhanced MRI (p&lt;0.001), and MRI kinetic curve type 3(p&lt;0.001). There was a highly significant association between homogenous enhanced MRI findings of women and benign lesions (p&lt;0.001).&#x0D; Conclusion: The validity results of MRI in differentiation between benign and malignant circumscribed breast masses are high, except for positive predictive value, and application of kinetic curve dynamic contrast enhancement increases magnetic resonance in differentiation between benign and malignant circumscribed breast masses.

Correlations between dynamic contrast-enhanced magnetic resonance imaging parameters and histopathologic factors in breast cancer.

This study aimed to correlate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters with prognostic factors in breast cancer. A retrospective analysis was performed in 45 patients who had breast DCE-MRI and were diagnosed with invasive ductal breast cancer following surgery. The following DCE-MRI parameters were calculated: percentage of initial peak enhancement (Epeak), time to initial peak enhancement (TTP), initial slope increase (IS), early signal enhancement ratio (ESER), and time-signal intensity curve (TIC) types. Correlations between Epeak, TTP, IS, ESER, and prognostic factors were determined using the Mann-Whitney U test and the Kruskal-Wallis test. The Chi-square test and Fisher's exact test were used to investigate the relationship between kinetic curve types and prognostic factors. Epeak and IS were significantly higher in the Ki-67 high-expression group than in the Ki-67 low-expression group (p = 0.031 and p = 0.012, respectively). ESER was significantly correlated with the histological type and Ki-67 expression level (p = 0.014 and p = 0.047, respectively). The TIC types were significantly correlated with the Ki-67 expression level (p = 0.009). Tumors with plateaus and washout curves were more likely to be associated with the Ki-67 high-expression group than tumors with a persistent curve (P = 0.027 and P = 0.038, respectively). TTP had no correlation with any prognostic factors (P > 0.05). This study showed that the DCE-MRI parameters of breast cancer were correlated with the expression of histopathological prognostic factors and might be useful for predicting prognosis.

MRI-Guided Breast Biopsy-Histopathological Verification of Suspicious Breast Lesions Visible on MRI Only

Background: Breast cancer is currently the most frequently diagnosed cancer in women. While the range of modalities enabling suspicious lesions detection is wide, MRI remains the most sensitive one. Therefore, the number of methods verifying the lesions seen only on MRI images grows. The aim of this study is to check the usefulness of MRI guided breast biopsy in clinical use. Methods: The study involved 120 patients who underwent diagnostic MRI before the biopsy that revealed suspicious lesions (BI-RADS 4 and 5). Those lesions had not been seen on initial ultrasonography or mammography. In each case, a marker was placed in the biopsy site and histopathological examination of the obtained samples was performed. Results: The study revealed benign lesions in 86 patients (71.7%). The remaining 34 lesions (28.3%) were determined as malignant, including 19 noninfiltrating (15.8%) and 15 infiltrating lesions (12.5%). Study showed correlation between kinetic curve type and lesion malignancy. Breast type, BPE and enhancement type did not have impact on the histopathology result. Conclusion: Breast MRI-guided biopsy is a reliable way to verify lesions not visible on any other diagnostic imaging methods and therefore should be developed.

Invasive breast carcinoma with MRI kinetic curve type 1

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.&#x0D; 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.&#x0D; 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

Molecular subtypes of invasive breast cancer: correlation between PET/computed tomography and MRI findings

The aim of the study was to investigate the diagnostic value of fluorodeoxyglucose-18 (FDG)-PET/computed tomography (CT) and MRI parameters in determining the molecular subtypes of invasive breast cancer. Data from 55 primary invasive breast cancer masses in 51 female patients who underwent pre-treatment PET/CT and MRI scans, and histopathological diagnosis at the authors' center were retrospectively reviewed. The relationship between FDG-PET/CT and MRI parameters, including maximum and mean standard uptake values (SUVmax and SUVmean, respectively), mean metabolic index (MImean) and metabolic tumor volume (MTV) values obtained from FDG-PET, and shape, margin, internal contrast-enhancement characteristics, kinetic curve types, functional tumor volume (FTV), apparent diffusion coefficient (ADC) values obtained from MRI was evaluated. Subsequently, differences among molecular subtypes (i.e. luminal A, luminal B, c-erbB-2 positive, and triple-negative) in terms of PET/CT and MRI parameters were evaluated. The luminal B subtype of invasive breast cancer had higher SUVmax and SUVmean (P = 0.002 and P = 0.017, respectively) values than the luminal A subtype. In addition, the triple-negative subtype had a higher SUVmax (P = 0.028) than the luminal A subtype. There was a statistically significant positive correlation between pathological tumor volume (PTV) and SUVmean (P = 0.019, r = 0.720). SUVmax and ADC were negatively correlated (P = 0.001; r = -0.384). A very strong positive correlation was detected between MTV and FTV (P = 0.000; r = 0.857), and between MTV and PTV (P = 0.006, r = 0.796), and between FTV and PTV (P = 0.006, r = 0.921). Results of the present study suggest that SUVmax was superior to MRI findings in predicting molecular subtypes and that MRI was superior to PET/CT in predicting PTV.

Application of modified temperature-moisture relationships in the calculations of drying kinetics

The paper presents a description of the modified temperature-moisture relationships, selected on the basis of the analysis of the kinetics of drying of liquid dispersed products on substrates and the methods of engineering calculation of the kinetics of drying with their application. Classification of the types of kinetic curves characterizing the change in the type of thermogram, such as a change in the angle of inclination, deformation and degeneration of the wet-bulb thermometer and boiling plateaux, allows obtaining a modified type of temperature-moisture relationships for their use in calculations of the drying kinetics. The choice of the type of temperature-moisture relationship is carried out on the basis of the solution developed by the authors of the forecast model of the kinetic curve type. The engineering method for calculating the kinetics of drying liquid dispersed products on substrates is based on solving the basic equation of drying kinetics, choosing the type of temperature-moisture relationship, calculating the coordinates of the reference points of temperature-moisture relationship, determined on the basis of the drying mode and the properties of the dried material.

Dynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status.

Although sentinel lymph node biopsy (SLNB) is the current standard for identifying lymph metastasis in breast cancer patients, there are complications of SLNB. To evaluate preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) of invasive breast cancer for predicting sentinel lymph node metastasis. Retrospective. In all, 309 patients who underwent clinically node-negative invasive breast cancer surgery FIELD STRENGTH/SEQUENCE: 3.0T, DCE-MRI, DWI. We collected clinicopathologic variables (age, histologic and nuclear grade, extensive intraductal carcinoma component, lymphovascular invasion, and immunohistochemical profiles) and preoperative MRI features (tumor size, background parenchymal enhancement, internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, quantitative kinetic parameters, tumoral apparent diffusion coefficient [ADC], peritumoral maximal ADC, and peritumoral-tumoral ADC ratio). Multivariate logistic regressions were performed to determine independent variables associated with SLN metastasis, and the area under the receiver operating characteristic curve (AUC) was analyzed for those variables. 41 (13.3%) of the patients showed SLN metastasis. With MRI, tumor size (odds ratio [OR], 1.11; 95% confidence interval [CI], 1.06-1.17), heterogeneous (OR, 5.33; 95% CI, 1.71-16.58), and rim (OR, 15.54; 95% CI, 2.12-113.72) enhancement and peritumoral-tumoral ADC ratio (OR, 72.79; 95% CI, 7.15-740.82) were independently associated with SLN metastasis. Clinicopathologic variables independently associated with SLN metastasis included age (OR, 0.96; 95% CI, 0.92-0.99) and CD31 (OR, 2.90; 95% CI, 1.04-8.92). The area under the curve (AUC) of MRI features (0.80; 95% CI, 0.73-0.87) was significantly higher than for clinicopathologic variables (0.68; 95% CI, 0.60-0.77; P = 0.048) and was barely below statistical significance for combined MRI features with clinicopathologic variables (0.84; 95% CI 0.78-0.90, P = 0.057). Preoperative internal enhancement on DCE-MRI and peritumoral-tumoral ADC ratio on DWI might be useful for predicting SLN metastasis in patients with invasive breast cancer. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:615-626.

Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting

PurposeTo develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images. MethodsWe retrospectively assessed 363 breast cancer cases (Luminal A 151, Luminal B 96, HER2 76, and BLBC 40). Eighty-two features defined in the BI-RADS lexicon were visually described. A decision tree model with the Chi-squared automatic interaction detector (CHAID) algorithm was applied for feature selection and classification. A 10-fold cross-validation was performed to investigate the performance (i.e., accuracy, positive predictive value, sensitivity, and F1-score) of the decision tree model. ResultsSeven of the 82 variables were derived from the decision tree-based feature selection and used as features for the classification of molecular subtypes including mass margin calcification on mammography, mass margin types of kinetic curves in the delayed phase, mass internal enhancement characteristics, non-mass enhancement distribution on MRI, and breastfeeding history. The decision tree model accuracy was 74.1%. For each molecular subtype group, Luminal A achieved a sensitivity, positive predictive value, and F1-score of 79.47%, 75.47%, and 77.42%, respectively; Luminal B showed a sensitivity, positive predictive value, and F1-score of 64.58%, 55.86%, and 59.90%, respectively; HER2 had a sensitivity, positive predictive value, and F1-scores of 81.58%, 95.38%, and 87.94%, respectively; BLBC showed sensitivity, positive predictive value, and F1-scores of 62.50%, 89.29%, and 73.53%, respectively. ConclusionsWe applied a complete “white box” machine learning method to predict the molecular subtype of breast cancer based on the BI-RADS feature description in a multi-modal setting. By combining BI-RADS features in both mammography and MRI, the prediction accuracy is boosted and robust. The proposed method can be easily applied widely regardless of variability of imaging vendors and settings because of the applicability and acceptance of the BI-RADS.

Simulation of chlorophyll fluorescence rise and decay kinetics, and P700-related absorbance changes by using a rule-based kinetic Monte-Carlo method.

A model of primary photosynthetic reactions in the thylakoid membrane was developed and its validity was tested by simulating three types of experimental kinetic curves: (1) the light-induced chlorophyll a fluorescence rise (OJIP transients) reflecting the stepwise transition of the photosynthetic electron transport chain from the oxidized to the fully reduced state; (2) the dark relaxation of the flash-induced fluorescence yield attributed to the QA- oxidation kinetics in PSII; and (3) the light-induced absorbance changes near 820 or 705nm assigned to the redox transitions of P700 in PSI. A model was implemented by using a rule-based kinetic Monte-Carlo method and verified by simulating experimental curves under different treatments including photosynthetic inhibitors, heat stress, anaerobic conditions, and very high light intensity.

Characterization of breast lesion using T1-perfusion magnetic resonance imaging: Qualitative vs. quantitative analysis.

The objective of this study was to quantify the hemodynamic parameters using first pass analysis of T1-perfusion magnetic resonance imaging (MRI) data of human breast and to compare these parameters with the existing tracer kinetic parameters, semi-quantitative and qualitative T1-perfusion analysis in terms of lesion characterization. MRI of the breast was performed in 50 women (mean age, 44±11 [SD] years; range: 26-75) years with a total of 15 benign and 35 malignant breast lesions. After pre-processing, T1-perfusion MRI data was analyzed using qualitative approach by two radiologists (visual inspection of the kinetic curve into types I, II or III), semi-quantitative (characterization of kinetic curve types using empirical parameters), generalized-tracer-kinetic-model (tracer kinetic parameters) and first pass analysis (hemodynamic-parameters). Chi-squared test, t-test, one-way analysis-of-variance (ANOVA) using Bonferroni post-hoc test and receiver-operating-characteristic (ROC) curve were used for statistical analysis. All quantitative parameters except leakage volume (Ve), qualitative (type-I and III) and semi-quantitative curves (type-I and III) provided significant differences (P<0.05) between benign and malignant lesions. Kinetic parameters, particularly volume transfer coefficient (Ktrans) provided a significant difference (P<0.05) between all grades except grade-II vs III. The hemodynamic parameter (relative-leakage-corrected-breast-blood-volume [rBBVcorr) provided a statistically significant difference (P<0.05) between all grades. It also provided highest sensitivity and specificity among all parameters in differentiation between different grades of malignant breast lesions. Quantitative parameters, particularly rBBVcorr and Ktrans provided similar sensitivity and specificity in differentiating benign from malignant breast lesions for this cohort. Moreover, rBBVcorr provided better differentiation between different grades of malignant breast lesions among all the parameters.

Can imaging kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging be valuable in predicting clinicopathological prognostic factors of invasive breast cancer?

Background Intrinsic molecular profiling of breast cancer provides clinically relevant information that helps tailor therapy directed to the specific tumor subtype. We hypothesized that dynamic contrast-enhanced MRI (DCE-MRI) derived quantitative kinetic parameters (CD-QKPs) may help predict molecular tumor profiles non-invasively. Purpose To determine the association between DCE-MRI (CD-QKPs) and breast cancer clinicopathological prognostic factors. Material and Methods Clinicopathological factors in consecutive women with biopsy-confirmed invasive breast cancer who underwent breast DCE-MRI were retrospectively reviewed. Analysis of variance was used to examine associations between prognostic factors and CD-QKPs. Fisher's exact test was used to investigate the relationship between kinetic curve type and prognostic factors. Results A total of 198 women with invasive breast cancer were included. High-grade and HER2+ tumors were more likely to have a washout type curve while luminal A tumors were less likely. High-grade was significantly associated with increased peak enhancement (PE; P = 0.01), enhancement maximum slope (MS; P = 0.03), and mean enhancement ( ME, P = 0.03), while high clinical lymph node stage (cN3) was significantly associated with increased MS and time to peak (tP; P = 0.01). HER2+ tumors were associated with a higher PE ( P = 0.03) and ME ( P = 0.06) than HER2- counterparts, and ER-/HER2+ tumors showed higher PE and ME values than ER+/HER2- tumors ( P = 0.06). Conclusion DCE-MRI time-intensity CD-QKPs are associated with high tumor grade, advanced nodal stage, and HER2+ status, indicating their utility as imaging biomarkers.

DEVELOPMENT OF ALGORITHM OF THE RENAL HEMODYNAMICS ANALYSIS BY SCINTIGRAPHY

The aim of this work is the analysis of scintigraphic images of indirect renangiography of patients with chronic pyelonephritis. Indirect angiography is an auxiliary research methodology that is performed in the first seconds after intravenous administration of radio-pharmaceuticals. It allows to evaluate the temporal parameters of renal hemodynamics, to diagnose the presence of renal artery stenosis and to assess the level of vascularization of the kidney tumor.Scintigraphic images of indirect renangiography are characterized by low resolution. Therefore, effective perfusion of tissues is assessed by the kinetics of radiopharmaceuticals in the entire kidney. In this case, the kidney is represented as a concentrated system, that is, as a mathematical point. In turn, this leads to a loss of diagnostic information that characterizes the kidney as a spatially-distributed system.The paper describes the developed algorithm for analyzing the types of kinetic curves of radiopharmaceuticals in the kidneys and their further classification. The algorithm for the analysis of scintigraphic images of the kidneys according to renangioscintigraphy allows us to obtain additional useful information on the functional state of the renal parenchyma. This algorithm allows us to quantify the phenomenon of synchronization of the functional state of the nephron groups, which will provide fundamentally new diagnostic and important information to specialists in nuclear medicine and nephrologists

Dynamic contrast-enhanced breast magnetic resonance imaging for the prediction of early and late recurrences in breast cancer.

The aim of the study was to evaluate dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) features for the prediction of early and late recurrences in patients with breast cancer.Of 1030 breast cancer patients who underwent surgery at our hospital from January 2007 to July 2011, 83 recurrent breast cancer patients were enrolled in this study. We compared MRI features (background parenchymal enhancement [BPE], internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, and quantitative kinetic parameters) and clinico-pathologic variables (age, stage, histologic grade, nuclear grade, existence of lymphovascular invasion and extensive intraductal carcinoma component, and immunohistochemical profiles) between patients with early (≤2.5 years after surgery) and late recurrence (>2.5 years after surgery). Cox proportional hazard regression analysis was performed to evaluate independent risk factors for early and late recurrence.On breast MRI, prominent ipsilateral whole-breast vascularity was independently associated with early recurrence (hazard ratio [HR], 2.86; 95% confidence intervals [CI], 1.39–5.88) and moderate or marked BPE (HR, 2.08; 95% CI, 1.04–4.18) and rim enhancement (HR, 2.14; 95% CI, 1.00–4.59) were independently associated with late recurrence. Clinico-pathologic variables independently associated with early recurrence included negative estrogen receptor (HR, 0.53; 95% CI, 0.29–0.96), whereas T2 stage (HR, 2.08; 95% CI, 1.04–4.16) and nuclear grade III (HR, 2.54; 95% CI, 1.29–4.98) were associated with late recurrence.In DCE-MRI, prominent ipsilateral whole-breast vascularity, moderate or marked BPE, and rim enhancement could be useful for predicting recurrence timing in patients with breast cancer.

High-field MR spectroscopy in the multiparametric MRI evaluation of breast lesions

IntroductionThe aim of this work was to assess the role of 3T-MR spectroscopy (MRS) in the multi-parametric MRI evaluation of breast lesions, using a pattern-recognition based classification method. Methods291 patients (301 lesions, median 2.3cm3) were enrolled in the study (age 18–85y, mean 54.2y). T1-TSE (TR/TE=400/10ms) and T2-STIR imaging (TR/TE=5000/60ms), dynamic-contrast-enhanced MRI (DCE-MRI), apparent diffusion coefficient (ADC) (b=0–800s/mm2), and single-voxel MRS (10×10×10mm3, PRESS, TR/TE=3000ms/135ms) were performed by means of a 3T scanner. MRS results were accepted if the FWHM of the water peak was ⩽45Hz. Total choline (tCho) was considered detected if the signal-to-noise ratio (SNR) of the 3.2ppmpeak was ⩾2. A classifier-based analysis (support-vector-machines, SVM) was performed with 4-dimensional vectors including type of margin, DCE-MRI kinetic curve type, ADC mean value, and tCho SNR. A comparison with 3-dimensional vectors (without tCho SNR) was used to assess MRS impact on sensitivity, specificity, and positive-negative predictive values (PPV-NPV) for malignancy. Results228 lesions (180 malignant/48 benign) showed acceptable spectral quality. Comparison of classification results with histopathological examination of surgical specimens showed sensitivity=93.7%, specificity=84.9%, PPV=95.2%, NPV=81.5% without the inclusion of MRS in the SVM analysis. When MRS was included, the figures increased to 95.1%, 90.7%, 97.2%, and 85.0%, respectively. ConclusionsInclusion of 3T-MRS in the multi-parametric MRI evaluation of breast lesions improved the performance of the SVM-based classifier, showing a possible role of high-field MR spectroscopy in the differential diagnosis between benign and malignant breast lesions. Further research is however needed to confirm this initial evidence.

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

ObjectiveThe aim of this study was to employ a kinetic model with dynamic contrast enhancement-magnetic resonance imaging to develop an approach that can efficiently distinguish malignant from benign lesions.Materials and MethodsA total of 43 patients with 46 lesions who underwent breast dynamic contrast enhancement-magnetic resonance imaging were included in this retrospective study. The distribution of malignant to benign lesions was 31/15 based on histological results. This study integrated a single-compartment kinetic model and dynamic contrast enhancement-magnetic resonance imaging to generate a kinetic modeling curve for improving the accuracy of diagnosis of breast lesions. Kinetic modeling curves of all different lesions were analyzed by three experienced radiologists and classified into one of three given types. Receiver operating characteristic and Kappa statistics were used for the qualitative method. The findings of the three radiologists based on the time-signal intensity curve and the kinetic curve were compared.ResultsAn average sensitivity of 82%, a specificity of 65%, an area under the receiver operating characteristic curve of 0.76, and a positive predictive value of 82% and negative predictive value of 63% was shown with the kinetic model (p = 0.017, 0.052, 0.068), as compared to an average sensitivity of 80%, a specificity of 55%, an area under the receiver operating characteristic of 0.69, and a positive predictive value of 79% and negative predictive value of 57% with the time-signal intensity curve method (p = 0.003, 0.004, 0.008). The diagnostic consistency of the three radiologists was shown by the κ-value, 0.857 (p<0.001) with the method based on the time-signal intensity curve and 0.826 (p<0.001) with the method of the kinetic model.ConclusionsAccording to the statistic results based on the 46 lesions, the kinetic modeling curve method showed higher sensitivity, specificity, positive and negative predictive values as compared with the time-signal intensity curve method in lesion classification.

Differentiation of benign and metastatic axillary lymph nodes in breast cancer: additive value of MRI computer-aided evaluation

To retrospectively determine the qualitative and quantitative cut-off values of the magnetic resonance computer-aided evaluation (MR CAE) parameters to differentiate between benign and metastatic axillary lymph nodes (ALNs) and to investigate the combined diagnostic performance of MR CAE imaging. From July 2011 to June 2014, 124 patients who underwent preoperative conventional MR, diffusion-weighted (DW), and MR CAE imaging were included. Computer-generated qualitative and quantitative features for ALNs were recorded, and two breast imaging radiologists interpreted the MR images for the presence of metastatic ALNs using conventional MR and DW, and in combination with MR CAE images by consensus. The cut-off values of MR CAE and diagnostic performance were derived from the receiver operating characteristic (ROC) curve. Thirty-four (26.4%) were ALN positive and 90 (73.6%) were ALN negative on the final histopathological result. On qualitative analysis, visualization on the colour map (p=0.007) and kinetic curve type (p<0.001) were significantly different between the groups. On quantitative analysis, mean values (%) of persistent, plateau, and washout ratios differed significantly (p<0.001). Of these significant parameters, a washout ratio of >49% showed the greatest diagnostic accuracy (area under the ROC curve, 0.909). With conventional MR and DW images, sensitivity, specificity, and accuracy were 82.4%, 85.6%, and 84.7%, respectively. With added information from MR CAE images, accuracy significantly improved to 93.5% (p=0.043). The sensitivity and specificity improved to 91.2% (p=0.403) and 94.4% (p=0.086), respectively. The additive use of MR CAE improved diagnostic performance and can be helpful for differentiating benign from metastatic ALNs.

The Effects of Applying Breast Compression in Dynamic Contrast Material–enhanced MR Imaging

To evaluate the effects of breast compression on breast cancer masses, contrast material enhancement of glandular tissue, and quality of magnetic resonance (MR) images in the identification and characterization of breast lesions. This was a HIPAA-compliant, institutional review board-approved retrospective study, with waiver of informed consent. Images from 300 MR imaging examinations in 149 women (mean age ± standard deviation, 51.5 years ± 10.9; age range, 22-76 years) were evaluated. The women underwent diagnostic MR imaging (no compression) and MR-guided biopsy (with compression) between June 2008 and February 2013. Breast compression was expressed as a percentage relative to the noncompressed breast. Percentage enhancement difference was calculated between noncompressed- and compressed-breast images obtained in early and delayed contrast-enhanced phases. Breast density, lesion type (mass vs non-masslike enhancement [NMLE]), lesion size, percentage compression, and kinetic curve type were evaluated. Linear regression, receiver operating characteristic (ROC) curve analysis, and κ test were performed. Mean percentage compression was 31.3% ± 9.2 (range, 5.8%-53.2%). Percentage enhancement was higher in noncompressed- versus compressed-breast studies in early (146% ± 66 vs 107% ± 42, respectively; P < .001) and delayed (158% ± 68 vs 107% ± 42, respectively; P = .1) phases. Among breast lesions, 12% (seven of 59) were significantly smaller when compressed, which led to underestimation of TNM classification (P < .001). Breast masses (n = 35) showed significantly higher early percentage enhancement (157% ± 71) than lesions with NMLE (n = 15, 120% ± 40; P = .02) and a percentage enhancement difference (47.5% ± 64 vs 17% ± 28, respectively; P = .023). Kinetic curve performance for identifying invasive cancer decreased after compression (area under ROC curve = 0.53 vs 0.71, respectively; P = .02). Breast compression resulted in complete loss of enhancement of nine of 210 lesions (4%). Breast compression during biopsy affected breast lesion detection, lesion size, and dynamic contrast-enhanced MR imaging interpretation and performance. Limiting the application of breast compression is recommended, except when clinically necessary.