Time-signal Intensity Curve Research Articles

Multiparametric MRI in Diagnosis of Parotid Gland Tumor: An Observational Study in 3-T MRI

Abstract Background Preoperative magnetic resonance imaging (MRI) has an important role in the management and prognostication of parotid gland tumors. We aim to evaluate the role of multiparametric MRI in differentiating the major subgroup of parotid tumors. Material and Methods Multiparametric MRI: T1-weighted imaging (T1WI), T2WI, diffusion-weighted imaging (DWI), pseudo-continuous arterial spin labeling (ASL) and dynamic contrast-enhanced (DCE) imaging were acquired in all patients. Apparent diffusion coefficient (ADC) values and tumor blood flow (TBF) were calculated from DWI and ASL, respectively. Ktrans, Kep, Ve, initial area under the gadolinium enhancement concentration curve (IAUGC), maximum slope, and contrast enhancement ratio (CER) were calculated from DCE-MRI perfusion. The above parameters were compared between three major subgroups of parotid gland tumors, such as non-Warthin benign tumors (NWBT), Warthin's tumors (WT), and malignant parotid tumors (MT). Results The mean ADC of MT (n = 13), WT (n = 5), and NWBT (n = 29) was 1.03 × 10−3 mm2/s, 0.97 × 10−3 mm2/s, and 1.89 × 10−3 mm2/s, respectively. The mean TBF (in mL/100 g/min) was the highest MT (70.33), followed by WT (62.04) and NWBT (21.99). A cutoff of 40.51 mL/100 g/min showed a sensitivity of 96.6% and specificity of 77.8% for predicting NWBT. In DCE-MRI, 96.6% of the NWBT showed a type A time–signal intensity curve. Although the majority of MT and WT had type C and B curves, respectively, there was overlapping. Among the quantitative DCE parameters, Ktrans and Kep were highly sensitive for differentiating NWBT, WT, and MT. Ktrans, Kep, IAUGC, and MS were maximum in WT, followed by MT and NWBT. Kep and Ktrans both had an accuracy of 84.7% for predicting pleomorphic adenoma. Conclusion Multiparametric MRI is useful for differentiating NWBT, WT, and MT. DCE-MRI helps in differentiating benign from malignant tumors. DWI and ASL are useful in differentiating NWBT from parotid malignancy.

Imaging manifestations and treatment efficacy of Granulomatous Lobular Mastitis: An analysis of 29 cases with internal and external traditional Chinese medicine approaches.

Using magnetic resonance imaging (MRI), we analysed the clinical manifestations of granulomatous lobular mastitis (GLM) before and after treatment with traditional Chinese medicine (TCM). This was a retrospective study. Clinical manifestations and imaging features of patients with biopsy-proven GLM before and after treatment were retrospectively analysed from April 2021 to April 2023 at Xiyuan Hospital, China Academy of Chinese Medical Sciences. Among 29 women of childbearing age (mean age, 34.3±3.6 years), Compared to pre-therapy, the number of patients with lumps, ruptures, pain, and menstrual irregularities was significantly reduced (P < 0.05). The Chinese medicine staging demonstrated a transition from bulk to ulcerated (P=0.041). Pre- and post-treatment MRI changes included lesion number, distribution, maximal diameter, time-signal intensity curves (TIC), apparent diffusion coefficients (ADC), and BI-RADS categories 3 and 4a (all P<0.05). Furthermore, substantial changes were seen in chest wall invasion, nipple discharge, increased feeding arteries, and skin edema (all P<0.05). Combining internal and external Traditional Chinese Medicine (TCM) therapy proved successful for GLM. A reliable assessment of therapy efficacy is provided by MRI examination of lesion changes before and after treatment.

Development and Assessment of a Predictive Model for Ki-67 Expression Using Ultrasound Indicators and Non-Morphological Magnetic Resonance Imaging Parameters Before Breast Cancer Therapy.

To formulate a predictive model for assessing Ki-67 expression in breast cancer by integrating pre-treatment ultrasound features with non-morphological magnetic resonance imaging (MRI) parameters, encompassing functional and hemodynamic indicators. A retrospective study was conducted on 167 patients. All patients underwent a breast mass biopsy for histopathological and Ki-67 analysis prior to neoadjuvant chemotherapy (NAC) treatment. Additionally, all patients underwent ultrasonography and MRI examinations prior to the biopsy. The recorded variables were Ki-67, apparent diffusion coefficient (ADC) values, Max Slope, time to peak (TTP), signal enhancement ratio (SER), early enhancement rate (EER), time-signal intensity curve (TIC), tumor maximum diameter, tumor margins and boundaries, aspect ratio, microcalcification, color Doppler flow imaging grading, resistance index (RI), and axillary lymph node metastasis. Statistical analysis was performed using the R software package. Normally distributed continuous data are presented as mean ± standard deviation (SD), skewed continuous data as median, and categorical variables as frequency or percentage. The dataset was randomly divided into a modeling group and a validation group following a 7:3 ratio, employing a predetermined random seed. The selection of variables was conducted using the random forest algorithm. Specifically, in the initial analysis, we trained a random forest model using all available variables. By evaluating the Gini importance scores of each variable, we identified those that contributed the most to predicting Ki-67 expression. The predictive model for Ki-67 expression was constructed using selected variables: Maximum Diameter, ADC value, SER value, Max Slope value, TTP value, and EER value. Within the validation group, the evaluation metrics demonstrated an Area under the curve of 0.961 with a 95% confidence interval ranging from 0.865 to 0.995. The model achieved a kappa score of 1.00, precision of 0.949, recall of 1, an F1 score of 0.974, sensitivity of 100%, specificity of 85.71%, a positive predictive value of 94.87%, and a negative predictive value of 100%. The combination of non-morphological MRI parameters and pre-treatment ultrasound features in a breast cancer prediction model powered by RF machine learning demonstrated favorable clinical outcomes and improved diagnostic performance.

The potential role of breast MRI in evaluation of triple-negative breast cancer and fibroadenoma of less than 3 cm.

The majority of small-sized (<3 cm) triple-negative breast cancer (TNBC) exhibit smooth margins upon palpation and are often oval or rounded masses. Distinguishing these masses preoperatively from fibroadenomas (FAs) would be very meaningful for clinical practice. The aim of our study was to evaluate the magnetic resonance imaging (MRI) appearance of TNBC and differentiate it from FAs. In this retrospective single-center study, we included 37 patients with TNBCs and 36 patients with FAs who underwent breast MRI. We employed the χ2 test and t-test to compare the differences in morphological features, dynamic contrast-enhanced MRI (DCE-MRI) parameters, and apparent diffusion coefficient (ADC) values between the two groups. Additionally, we constructed non-parametric receiver operating characteristic (ROC) curves using ADC values, with pathological results serving as the gold standard. A total of 37 TNBC lesions and 39 FA lesions were included in the final analysis. TNBCs exhibited more frequent irregular shape, irregular margins, peritumoral edema, fast enhancement in the initial phase, rim enhancement, and time-signal intensity curve (TIC) type III compared to FAs (all P<0.05). Conversely, low-signal segregation in T2-weighted imaging (T2WI) and TIC type I were commonly found in FAs. The mean ADC value of TNBCs was significantly lower than that of FAs [(1.104±0.13)×10-3 vs. (1.613±0.16)×10-3 mm2/s, P<0.05]. The cutoff ADC for differentiating TNBCs from FAs was 1.239×10-3 mm2/s, yielding an area under the curve (AUC) of 0.997, a sensitivity of 94.6%, and a specificity of 100%. The morphological presentation of MRI, internal enhancement features of the mass, TIC curves, and ADC values provide valuable differential diagnostic information for TNBC and FA masses with a maximum diameter of less than 3 cm.

The longitudinal changes in multiparametric MRI during neoadjuvant chemotherapy can predict treatment response early in patients with HER2-positive breast cancer

PurposeTo investigate whether longitudinal changes in multiparametric MRI can predict early response to neoadjuvant chemotherapy (NAC) for HER2-positive breast cancer (BC) and to further establish quantitative models based on these features. MethodsA total of 164 HER2-positive BC patients from three centers were included. MRI was performed at baseline and after two cycles of NAC (early post-NAC). Clinicopathological characteristics were enrolled. MRI features were evaluated at baseline and early post-NAC, as well as longitudinal changes in multiparametric MRI, including changes in the largest diameter (LD) of the tumor (ΔLD), apparent diffusion coefficient (ADC) values (ΔADC), and time–signal intensity curve (TIC) (ΔTIC). The patients were divided into a training set (n = 95), an internal validation set (n = 31), and an independent external validation set (n = 38). Univariate and multivariate logistic regression analyses were used to identify the independent indicators of pCR, which were then used to establish the clinicopathologic model and combined model. The AUC was used to evaluate the predictive power of the different models and calibration curves were used to evaluate the consistency of the prediction of pCR in different models. Additionally, decision curve analysis (DCA) was employed to determine the clinical usefulness of the different models. ResultsTwo models were enrolled in this study, including the clinicopathologic model and the combined model. The LD at early post-NAC (OR=0.913, 95 % CI=0.953–0.994 p = 0.026), ΔADC (OR=1.005, 95 % CI=1.005–1.008, p = 0.007), and ΔTIC (OR=3.974, 95 % CI=1.276–12.358, p = 0.017) were identified as the best predictors of NAC response. The combined model constructed by the combination of LD at early post-NAC, ΔADC, and ΔTIC showed good predictive performance in the training set (AUC=0.87), internal validation set (AUC=0.78), and external validation set (AUC=0.79), which performed better than the clinicopathologic model in all sets. ConclusionsThe changes in multiparametric MRI can predict early treatment response for HER2-positive BC and may be helpful for individualized treatment planning.

Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors

BackgroundTo explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs).MethodsThis research included 92 patients (41females, 51 males; age range 16–86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve.ResultsTOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884–0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899–0.915) and the traditional imaging model (AUC, 0.802, 0.712–0.891) alone.ConclusionsVarious TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.

MRI Manifestations of Breast Cancer Stroma and their Role in Predicting Molecular Subtype: A Case-control Study.

This study explored whether breast MRI manifestations could be used to predict the stroma distribution of breast cancer (BC) and the role of tumor stroma-based MRI manifestations in molecular subtype prediction. 57 patients with pathologically confirmed invasive BC (non-special type) who had lumpy BC on MRI within one week before surgery were retrospectively collected in the study. Stroma distributions were classified according to their characteristics in the pathological sections. The stromal distribution patterns among molecular subtypes were compared with the MRI manifestations of BC with different stroma distribution types (SDTs). SDTs were significantly different and depended on the BC hormone receptor (HR) (P<0.001). There were also significant differences among five SDTs on T2WI, ADC map, internal delayed enhanced features (IDEF), marginal delayed enhanced features (MDEF), and time signal intensity (TSI) curves. Spiculated margin and the absence of type-I TSI were independent predictors for BC with star grid type stroma. The appearance frequency of hypo-intensity on T2WI in HR- BCs was significantly lower (P=0.043) than in HR+ BCs. Star grid stroma and spiculated margin were key factors in predicting HR+ BCs, and the AUC was 0.927 (95% CI: 0.867-0.987). Breast MRI can be used to predict BC's stromal distribution and molecular subtypes.

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer.

To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer. A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA). A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models. The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

The mean relative signal intensity ratio (SIR) difference in patients undergoing dynamic contrast enhanced magnetic resonance imaging (DCE MRI) for diagnosis of pituitary microadenoma.

Objective: To determine the mean relative signal intensity ratio (SIR) difference in patients undergoing dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) for the diagnosis of pituitary microadenoma. Study Design: Descriptive, Cross-sectional study. Setting: Radiology Department, Allied Hospital I, Faisalabad. Period: 5th April 2021 to 4th October 2022. Material &amp; Methods: A total of 65 individuals undergoing magnetic resonance imaging with dynamic contrast enhancement of pituitary gland of any gender aged between 20 and 60 years were encompassed. Patients taking any medication (antipsychotics, anti-depressants, opiates or antiemetics), hepatic or renal failure, uncontrolled DM and hypothyroidism were excluded. Senior radiologist interpreted the DCE MR images. Pituitary microadenoma was assessed according to operational definition. For all patients, a region of interest (ROI) was defined on the area displaying varying enhancement, specifically identified as zone A. Amongst the entire patient population, a second ROI was made in the pituitary gland's normal-appearing tissue identified as zone B. Signal intensity time curves were created for all patients using both SIR T and SIR P, and the disparity between SIR T and SIR P was calculated. Relative SIR difference was calculated. Results: In my study, mean relative signal intensity ratio (SIR) difference in patients undergoing the utilization of DCE MRI for detecting pituitary microadenomas was 0.212 ± 0.181. The relative SIR difference on DCE MRI in patients with pituitary microadenoma was 0.35 ± 0.12 and in patients without pituitary microadenoma it was 0.03 ± 0.01. Conclusion: This study concluded that estimation of pre contrast T1 signal intensity ratio of a potentially abnormal lesion in dynamic contrast-enhanced MRI can enhance the accuracy of the diagnosis for locating microadenoma.

Radiomics Nomogram Based on Dual-Sequence MRI for Assessing Ki-67 Expression in Breast Cancer.

Radiomics has been extensively applied in predicting Ki-67 in breast cancer (BC). However, this is often confined to the exploration of a single sequence, without considering the varying sensitivity and specificity among different sequences. To develop a nomogram based on dual-sequence MRI derived radiomic features combined with clinical characteristics for assessing Ki-67 expression in BC. Retrospective. 227 females (average age, 51 years) with 233 lesions and pathologically confirmed BC, which were divided into the training set (n = 163) and test set (n = 70). 3.0-T, T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (EPI sequence). The regions of interest were manually delineated on ADC and DCE-MRI sequences. Three radiomics models of ADC, DCE-MRI, and dsMRI (combined ADC and DCE-MRI sequences) were constructed by logistic regression and the radiomics score (Radscore) of the best model was calculated. The correlation between Ki-67 expression and clinical characteristics such as receptor status, axillary lymph node (ALN) metastasis status, ADC value, and time signal intensity curve was analyzed, and the clinical model was established. The Radscore was combined with clinical predictors to construct a nomogram. The independent sample t-test, Mann-Whitney U test, Chi-squared test, Interclass correlation coefficients (ICCs), single factor analysis, least absolute shrinkage and selection operator (LASSO), logistic regression, receiver operating characteristics, Delong test, Hosmer_Lemeshow test, calibration curve, decision curve. A P-value <0.05 was considered statistically significant. In the test set, the prediction efficiency of the dsMRI model (AUC = 0.862) was higher than ADC model (AUC = 0.797) and DCE-MRI model (AUC = 0.755). With the inclusion of estrogen receptor (ER) and ALN metastasis, the nomogram displayed quality improvement (AUC = 0.876), which was superior to the clinical model (AUC = 0.787) and radiomics model. The nomogram based on dsMRI radiomic features and clinical characteristics may be able to assess Ki-67 expression in BC. 3 TECHNICAL EFFICACY: Stage 3.

Role of Dynamic Contrast-Enhanced MRI in Differentiation Between Malignant and Benign Cervical Lymph Nodes: A Case-Control Study

Background: Changes in signals and parameters of time-signal intensity curve (TIC) resulting from dynamic magnetic resonance imaging (MRI) have been considered for determining malignancy. However, the specific diagnostic criteria of TIC for detecting malignancy in various organs, such as lymph nodes, are not yet well-established. Objectives: This study aimed to evaluate the role of some TIC parameters in identifying malignant cervical lymph nodes. Patients and Methods: In this case-control study, patients with cervical lymphadenopathy were examined by dynamic contrast-enhanced MRI (DCE-MRI) before excisional biopsy. They were then divided into malignant and benign groups based on histopathology. Malignant patients were considered as the case group, while benign cases were considered as the control group. The TIC parameters included the signal intensity (SI) at 45 seconds, 90 seconds, 120 seconds, 5 minutes, and 10 minutes, as well as maximum signal intensity (SImax), the time at which the maximum signal intensity occurs (Tmax), the peak signal intensity (SI peak), and the time at which the peak signal intensity occurs (T peak). Additionally, the enhancement slope (ES), washout ratio (WR), and washout slope (WS) were evaluated at 5 and 10 minutes. Results: A total of 63 patients were enrolled in the study, including 31 (49.2%) patients with benign lymphadenopathies and 32 (50.8%) patients with malignant lymphadenopathies. Among the variables under study, significant differences were observed between the malignant (M) group and the benign (B) group in terms of the SI-5 min, SI-10 min, WR-5 min, WR-10 min, and WS-5 min (SI-5 min: 124.4 ± 145 (B) vs. 219.2 ± 93.8 (M), P = 0.003; SI-10 min: 109.9 ± 134.6 (B) vs. 188.9 ± 83.7 (M), P = 0.007; WR-5 min: 0.58 ± 0.38 (B) vs. 0.15 ± 0.13 (M), P &lt; 0.001; WR-10 min: 0.63 ± 0.36 (B) vs. 0.26 ± 0.17 (M), P &lt; 0.001; and WS-5 min: 0.93 ± 1.20 (B) vs. 1.40 ± 4.44 (M), P = 0.001). In the receiver operating characteristic (ROC) curve analysis, WS-5 min showed specificity of 29.1%, 75%, 90%, and 95% and sensitivity of 88.2%, 70.6%, 47.1%, and 47.1% at cutoff points of 0.1, 0.37, 0.57, and 0.66, respectively. Conclusion: The WR-5 min can be considered as a potential TIC index in DCE-MRI for the diagnosis of malignant cervical lymphadenopathies.

Discrimination between benign and malignant lung lesions using volumetric quantitative dynamic contrast-enhanced MRI.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is considered a promising method in lung lesion assessment. Sixty-four patients with single pulmonary lesions (SPLs) received DCE-MRI at 3.0 T. Of them, 49 cases were diagnosed with lung cancer, and 15 with benign pulmonary nodules (8 inflammatory nodules, 5 tuberculosis, and 2 abscesses). SPLs were quantitatively analyzed to determine the pulmonary lesions-related perfusion parameters, including reflux constant (Kep), volume transfer constant (Ktrans), the maximum slope of increase (MaxSlope), extravascular extracellular space volume fraction (Ve), apparent diffusion coefficient (ADC), the initial area in the signal intensity-time curve (IAUGC), and contrast-enhancement ratio (CER). In addition, a Student's t-test was conducted to calculate statistical significance regarding the quantitatively analyzed perfusion parameters in benign SPLs compared to malignant SPLs. The area under (AUC) the receiver operating characteristic (ROC) curve was studied to investigate the performance of perfusion parameters in diagnosing lung cancer. Values of Ktrans, Kep, Ve, MaxSlope, and IAUGC increased within malignant nodules relative to benign nodules (Ktrans: 0.21 ±0.08 vs. 0.73 ±0.40, P = 0.0001; Kep: 1.21 ±0.66 vs. 1.83 ±0.90, P = 0.0163; Ve: 0.24 ±0.08 vs. 0.47 ±0.18, P < 0.0001; MaxSlope: 0.09 ±0.14 vs. 0.28 ±0.29, P = 0.0166; IAUGC: 0.18 ±0.09 vs. 0.55 ±0.34, P = 0.0001). Meanwhile, malignant nodules presented higher ADC than benign nodules (0.0016 ±0.0006 vs. 0.0012 ±0.0003, P = 0.0019). Ktrans and IAUGC showed the best diagnostic performance with AUCs [1.0, 95%CI (0.99-1.0); 0.93, 95%CI(0.85-1.0), respectively]. Malignant pulmonary lesions had higher values of Ktrans, Ve, Kep, MaxSlope, and IAUGC compared to benign pulmonary lesions. Overall, perfusion parameters of DCE-MRI facilitate discrimination between benign from malignant pulmonary nodules.

Feasibility Analysis of Brain Perfusion Using Polyaspartic Acid Surface-Modified Superparamagnetic Contrast Agent

This research was aimed to construct polyaspartic acid (PASP) surface-modified magnetic resonance imaging (MRI) contrast agent nanoparticles (NPs) and preliminarily demonstrate the feasibility of using the NPs for MRI cerebral perfusion. Ultrasmall superparamagnetic iron oxide (USPIO) NPs were fabricated by a one-step chemical coprecipitation methodology, and surface modification of USPIO NPs was performed using PASP as the surface modifier to prepare PASP-USPIO NPs. The physicochemical properties of the NPs were detected, and their specific structural ability with HUVECs was visualized by Prussian blue staining. With the contrast agent gadolinium-diethylene triamine pentaacetate (Gd-DTPA) as the control group, the intravenous bolus of USPIO and PASP-USPIO was analyzed and a brain MRI scan of New Zealand white rabbits was performed. The relative cerebral blood volume (rCBV) and maximum signal reduction ratio (SRRmax) values of cerebral gray matter and white matter were calculated based on the plotted time-signal intensity. The results showed that the USPIO and PASP-USPIO NPs were successfully prepared. The average particle sizes were 40.1±5.5 nm and 42.7±6.9 nm, respectively, and the specific saturation magnetization was 86.9 A m2 ·kg−1 and 51.3 A m2 ·kg−1, respectively. Relative to USPIO, human umbilical vein endothelial cells (HUVECs) stained with Prussian blue positively in vitro in the PASP-USPIO group were notably increased, while the rate of change in the signal-to-noise ratio of imaging in vivo was substantially decreased. The time-signal intensity curves were plotted, and it was found that the rCBV of gray matter, rCBV of white matter, SRRmax of gray matter, and SRRmax of white matter in the USPIO group and PASP-USPIO group were greatly increased relative to control group (P &lt; 0.05), while the SRRmax ratio of gray matter to white matter was decreased (P &lt; 0.05). Additionally, the rCBV in the gray matter and rCBV in the white matter of the PASP-USPIO group were drastically increased in contrast to the USPIO group (P &lt; 0.05). In short, the constructed PASP surface-modified USPIO NPs can become a novel MRI contrast agent for monitoring hemodynamic changes in brain tissue.

The value of dynamic contrast-enhanced MRI in the differentiation between benign and malignant lacrimal epithelial tumors

Objective: To investigate the diagnostic performance of multiparametric dynamic contrast-enhanced MRI(DCE-MRI) for the differentiation between benign and malignant larcrimal gland epithelial tumors. Methods: The clinical and imaging data of 104 patients with epithelial tumors of the lacrimal gland who underwent orbital MRI scan and met the inclusion criteria in Beijing Tongren Hospital from January 2011 to December 2017 were retrospectively collected, including 48 males and 56 females, aged from 12 to 77 (43±7) years. Sixty-three cases of benign epithelial tumors and 41 cases of malignant epithelial tumors were examined by DCE-MRI. The parameters of semiquantitative analysis including: time to peak enhancement (Tpeak), maximum enhancement ratio (ERmax), Slope, washout ratio (WR) and time-signal intensity curve (TIC) types. The parameters of quantitative analysis including: volume transfer constant (Ktrans), the extravascular extracellular volume fraction (Ve) and rate constant (Kep). Receiver operating characteristic (ROC) curve analysis was performed for DCE-MRI parameters with statistically significant differences, the area under the curve (AUC) was calculated, the diagnostic threshold was determined, and the diagnostic performance was evaluated. Logistic regression analysis was used to determine the best parameters for differential diagnosis of benign and malignant epithelial tumors of the lacrimal gland. Results: For the semiquantitative analysis of DCE-MRI, malignant lacrimal gland epithelial tumor had a significantly shorter Tpeak than benign masses [(103.77±57.87) s vs (187.80±77.01) s,P<0.001)], while had a higher value in ERmax, Slope [M(Q1,Q3)] and WR in malignant masses compared with benign one [1.55±0.39 vs 1.36±0.33; 1.76 (0.97,2.27) vs 0.62 (0.50,0.93); 7.70%(1.40%, 21.60%)% vs 0(0, 0),all P<0.05)].The TICs of benign lacrimal tumors mainly showed a persistent type (49/63),while most malignant lacrimal tumors mainly showed a plateau type (25/41). For the quantitative analysis of DCE-MRI, the values of Ktrans and Kep[M(Q1,Q3)] in malignant tumors were significantly greater than those of benign tumors (0.99±0.52/min vs 0.43±0.23/min, P<0.001; 1.33(0.83, 1.55)/min vs 0.55(0.46, 0.68)/min, P<0.001). No significant difference in Ve was found between the groups (0.76±0.20 vs 0.73±0.22,P=0.467). Through the statistical analysis, TIC types (OR=3.887,95%CI: 1.409-10.725) and Ktrans(OR=50.979,95%CI: 6.046-429.830) can provide superior diagnostic performance for predicting malignant lacrimal gland epithelial tumors, with a sensitivity of 78.05%, specificity of 77.78%,and sensitivity of 70.73%, specificity of 95.24%, respevtively. Furthermore, the comprehensive diagnostic performance of Ktrans in AUC was proven to be significantly better than that of TIC [0.875 (0.796-0.932) vs 0.798 (0.708-0.870),P=0.049]. Conclusions: Multiparametric DCE-MRI is helpful for the differential diagnosis of benign and malignant epithelial tumors of lacrimal gland. TIC type and Ktrans have higher diagnostic value, and the diagnostic performance of Ktrans is better than that of TIC.

Dynamic contrast-enhanced magnetic resonance imaging features and apparent diffusion coefficient value of HER2-positive/HR-negative breast carcinoma.

According to hormone receptor (HR) status, human epidermal growth factor 2 positive (HER2+) breast carcinoma can be divided into HR- and HR+, with different treatment and prognosis. We analyzed the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) findings, apparent diffusion coefficient (ADC) value and the combination of DCE-MRI and ADC value of HER2+/HR- breast carcinoma. Totally 259 cases (96 HR-, 163 HR+) of pathologically verified HER2+ breast carcinoma were collected. Patients underwent DCE-MRI and diffusion weighted imaging (DWI). The morphological characteristics, internal enhancement characteristics, early enhancement rate (EER), and time-signal intensity curves (TIC) were recorded, and ADC values were measured. The relationship between each feature and HER2+/HR- breast cancer was analyzed. Area under the cures (AUC) was used to compare diagnostic performance of DCE-MRI, ADC value and the combination of DCE-MRI and ADC value. HER2+/HR- breast cancer presented as non-mass enhancement (NME), mass with NME, whereas HER2+/HR+ breast cancer presented as mass (P<0.001). HR- cases showed a round or oval shape with circumscribed margins, whereas HR+ cases showed an irregular mass with irregular or spiculated margins (P=0.001, P=0.028). The size of the mass, the internal enhancement characteristics, EER, and TIC did not differ significantly between the two HER2+ breast carcinomas. The ADC values for HR- and HR+ breast cancers were [1.2 (1.14, 1.33)] ×10-3 mm2/s and [1.0 (0.89, 1.11)] ×10-3 mm2/s, respectively, which were statistically significant (Z=-9.119, P<0.001). The ADC value can be used for diagnosing HER2+/HR- breast carcinoma, with the threshold value of 1.095×10-3 mm2/s [negative predictive value (NPV) of 89.8%, sensitivity of 86.5% and specificity of 70.6%]. The AUCs of ADC value, DCE-MRI, and DCE-MRI combined with ADC value were 0.839, 0.689 and 0.860, respectively. AUC of the DCE-MRI combined with ADC value was significantly higher than DCE-MRI alone (P<0.0001). The diagnostic performance of the DCE-MRI combined with ADC value was good in diagnosing HER2+/HR- breast cancers. MRI is an effective tool in diagnosing HER2+/HR- breast carcinoma, which will help select the clinical treatment plan and determine the prognosis.

Diagnostic value of preoperative examination for evaluating margin status in breast cancer.

A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery (BCS). Preoperative imaging examinations are frequently employed to assess the surgical margin. To investigate the role and value of preoperative imaging examinations [magnetic resonance imaging (MRI), molybdenum target, and ultrasound] in evaluating margins for BCS. A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021. The study gathered preoperative imaging data (MRI, ultrasound, and molybdenum target examination) and intraoperative and postoperative pathological information. Based on their BCS outcomes, patients were categorized into positive and negative margin groups. Subsequently, the patients were randomly split into a training set (226 patients, approximately 70%) and a validation set (97 patients, approximately 30%). The imaging and pathological information was analyzed and summarized using R software. Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS. A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis. This study aims to identify the risk factors associated with failure in BCS. The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS. These factors comprise non-mass enhancement (NME) on dynamic contrast-enhanced MRI, multiple focal vascular signs around the lesion on MRI, tumor size exceeding 2 cm, type III time-signal intensity curve, indistinct margins on molybdenum target examination, unclear margins on ultrasound examination, and estrogen receptor (ER) positivity in immunohistochemistry. LASSO regression was additionally employed in this study to identify four predictive factors for the model: ER, molybdenum target tumor type (MT Xmd Shape), maximum intensity projection imaging feature, and lesion type on MRI. The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set. Particularly, the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS. The proposed column chart model effectively predicts the success of BCS for breast cancer. The model utilizes preoperative ultrasound, molybdenum target, MRI, and core needle biopsy pathology evaluation results, all of which align with the real-world scenario. Hence, our model can offer dependable guidance for clinical decision-making concerning BCS.

Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma.

An autoencoder can learn representative time-signal intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility contrast MR imaging. The aim of this study was to investigate whether such an autoencoder-based pattern analysis could provide interpretable tissue labeling and prognostic value in isocitrate dehydrogenase (IDH) wild-type glioblastoma. Preoperative dynamic susceptibility contrast MR images were obtained from 272 patients with IDH wild-type glioblastoma (training and validation, 183 and 89 patients, respectively). The autoencoder was applied to the dynamic susceptibility contrast MR imaging time-signal intensity curves of tumor and peritumoral areas. Representative perfusion patterns were defined by voxelwise K-means clustering using autoencoder latent features. Perfusion patterns were labeled by comparing parameters with anatomic reference tissues for baseline, signal drop, and percentage recovery. In the validation set (n = 89), a survival model was created from representative patterns and clinical predictors using Cox proportional hazard regression analysis, and its performance was calculated using the Harrell C-index. Eighty-nine patients were enrolled. Five representative perfusion patterns were used to characterize tissues as high angiogenic tumor, low angiogenic/cellular tumor, perinecrotic lesion, infiltrated edema, and vasogenic edema. Of these, the low angiogenic/cellular tumor (hazard ratio, 2.18; P = .047) and infiltrated edema patterns (hazard ratio, 1.88; P = .009) in peritumoral areas showed significant prognostic value. The combined perfusion patterns and clinical predictors (C-index, 0.72) improved prognostication when added to clinical predictors (C-index, 0.55). The autoencoder perfusion pattern analysis enabled tissue characterization of peritumoral areas, providing heterogeneity and dynamic information that may provide useful prognostic information in IDH wild-type glioblastoma.

Tiroid Karsinomu Tanısı – Problem Çözücü Olarak Dinamik Manyetik Rezonans Görüntüleme

Objective: Comparison of the diagnostic accuracy of dynamic magnetic resonance imaging (MRI) with color Doppler US and Fine Needle Aspiration Cytology (FNAC) in the differential diagnosis of thyroid carcinoma cases.&#x0D; Materials and methods: Study group comprised 28 women and 6 men and all of them had thyroid hormone disorders in their routine examinations. 38 nodules were examined. After radiologic examinations, FNAC and thyroidectomy were applied.&#x0D; Results: The dynamic MR was found to have the highest sensitivity and specificity between the color-Doppler US, FNAC, and dynamic MR modalities for the diagnosis of carcinoma. In the dynamic MR examination, the difference between the peak-contrast signal intensity values (p = 0.018) and the minimum-contrast signal intensity values (p = 0.023) calculated at the p &lt; 0.05 level were statistically significant between the malignant and benign nodules.&#x0D; Conclusions: We consider that the differential diagnosis of thyroid nodules can be made by analyzing the dynamic MRI data. Malignant thyroid nodules show typical time-signal intensity curves, and we believe that dynamic MRI will be valuable for preoperative assessment of the thyroid nodules in certain conditions like radiology–pathology mismatch.

Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer

ObjectiveTo analyze the factors related to the efficacy of neoadjuvant therapy for breast cancer and find appropriate evaluation methods for evaluating the efficacy of neoadjuvant therapyMethodsA total of 143 patients with breast cancer treated by neoadjuvant chemotherapy at Baotou Cancer Hospital were retrospectively analyzed. The chemotherapy regimen was mainly paclitaxel combined with carboplatin for 1 week, docetaxel combined with carboplatin for 3 weeks, and was replaced with epirubicin combined with cyclophosphamide after evaluation of disease progression. All HER2-positive patients were treated with simultaneous targeted therapy, including trastuzumab single-target therapy and trastuzumab combined with pertuzumab double-target therapy. Combined with physical examination, color Doppler ultrasound, and magnetic resonance imaging (MRI), a systematic evaluation system was initially established—the “triple evaluation method.” A baseline evaluation was conducted before treatment. The efficacy was evaluated by physical examination and color Doppler every cycle, and the efficacy was evaluated by physical examination, color Doppler, and MRI every two cycles.ResultsThe increase in ultrasonic blood flow after treatment could affect the efficacy of monitoring. The presence of two preoperative time–signal intensity curves is a therapeutically effective protective factor for inflow. The triple evaluation determined by physical examination, color Doppler ultrasound, and MRI in determining clinical efficacy is consistent with the effectiveness of the pathological gold standard.ConclusionThe therapeutic effect of neoadjuvant therapy can be better evaluated by combining clinical physical examination, color ultrasound, and nuclear magnetic resonance evaluation. The three methods complement each other to avoid the insufficient evaluation of a single method, which is convenient for most prefecty-level hospitals. Additionally, this method is simple, feasible, and suitable for promotion.

Early Assessment of Neoadjuvant Chemotherapy Response Using Multiparametric Magnetic Resonance Imaging in Luminal B-like Subtype of Breast Cancer Patients: A Single-Center Prospective Study

This study aimed to evaluate the performance of multiparametric breast magnetic resonance imaging (mpMRI) for predicting response to neoadjuvant chemotherapy (NAC) in patients with luminal B subtype breast cancer. The prospective study included thirty-five patients treated with NAC for both early and locally advanced breast cancer of the luminal B subtype at the University Hospital Centre Zagreb between January 2015 and December 2018. All patients underwent breast mpMRI before and after two cycles of NAC. Evaluation of mpMRI examinations included analysis of both morphological (shape, margins, and pattern of enhancement) and kinetic characteristics (initial signal increase and post-initial behavior of the time-signal intensity curve), which were additionally interpreted with a Göttingen score (GS). Histopathological analysis of surgical specimens included grading the tumor response based on the residual cancer burden (RCB) grading system and revealed 29 NAC responders (RCB-0 (pCR), I, II) and 6 NAC non-responders (RCB-III). Changes in GS were compared with RCB classes. A lack of GS decrease after the second cycle of NAC is associated with RCB class and non-responders to NAC.