Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates. PURPOSE: To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2). STUDY TYPE: Retrospective. POPULATION: 621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing. FIELD STRENGTH/SEQUENCE: 3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI. ASSESSMENT: Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves. RESULTS: Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set. DATA CONCLUSION: BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low. TECHNICAL EFFICACY: Stage 2.

Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer.

PURPOSE: By radiomic analysis of the postcontrast CT images, this study aimed to predict locoregional recurrence (LR) of locally advanced oropharyngeal cancer (OPC) and hypopharyngeal cancer (HPC). METHODS: A total of 192 patients with stage III-IV OPC or HPC from two independent cohort were randomly split into a training cohort with 153 cases and a testing cohort with 39 cases. Only primary tumor mass was manually segmented. Radiomic features were extracted using PyRadiomics, and then the support vector machine was used to build the radiomic model with fivefold cross-validation process in the training data set. For each case, a radiomics score was generated to indicate the probability of LR. RESULTS: There were 94 patients with LR assigned in the progression group and 98 patients without LR assigned in the stable group. There was no significant difference of TNM staging, treatment strategies and common risk factors between these two groups. For the training data set, the radiomics model to predict LR showed 83.7% accuracy and 0.832 (95% CI 0.72, 0.87) area under the ROC curve (AUC). For the test data set, the accuracy and AUC slightly declined to 79.5% and 0.770 (95% CI 0.64, 0.80), respectively. The sensitivity/specificity of training and test data set for LR prediction were 77.6%/89.6%, and 66.7%/90.5%, respectively. CONCLUSIONS: The image-based radiomic approach could provide a reliable LR prediction model in locally advanced OPC and HPC. Early identification of those prone to post-treatment recurrence would be helpful for appropriate adjustments to treatment strategies and post-treatment surveillance.

Non-Contrast-Enhanced MR Arteriography of Potential Living-Related Liver Donor: Using Contrast Enhanced CT Arteriography as Standard Reference.

BACKGROUND: Contrast-enhanced computed tomography angiography (CTA) and magnetic resonance angiography (MRA) are the primary modalities to assess donors' vessels before transplant surgery. Radiation and contrast medium are potentially harmful to donors. PURPOSE: To compare the image quality and visualization scores of hepatic arteries on CTA and balanced steady-state free-precession (bSSFP) non-contrast-enhanced MRA (NC-MRA), and to evaluate if bSSFP NC-MRA can potentially be a substitute for CTA. STUDY TYPE: Prospective. POPULATION: Fifty-six consecutive potential living-related liver donors (30.9 ± 8.4 years; 31 men). FIELD STRENGTH/SEQUENCE: 1.5T; four bSSFP NC-MRA sequences: respiratory-triggered (Inhance inflow inversion recovery [IFIR]) and three breath-hold (BH); and CTA. ASSESSMENT: The artery-to-liver contrast (Ca-l) was quantified. Three radiologists independently assigned visualization scores using a four-point scale to potential origins, segments, and branches of the hepatic arteries, determined the anatomical variants based on Hiatt's classification, and assessed the image quality of NC-MRA sequences. STATISTICAL TESTS: Fleiss' kappa to evaluate the readers' agreement. Repeat measured ANOVA or Friedman test to compare Ca-l of each NC-MRA. Friedman test to compare overall image quality and visualization scores; post hoc analysis using Wilcoxon signed-rank test. P-value <0.05 was considered statistically significant. RESULTS: Inhance IFIR Ca-l was significantly higher than all BH bSSFP Ca-l (0.56 [0.45-0.64] vs. 0.37 [0.29-0.47] to 0.41 [0.23-0.51]). Overall image quality score of BH bSSFP TI1200 was significantly higher than other NC-MRA (4 [4-4] vs. 4 [3 to 4-4]). The median visualization scores of almost all arteries on CTA were significantly higher than on NC-MRA (4 [3 to 4-4] vs. 1 [1-2] to 4 [4-4]). The median visualization scores were all 4 [4-4 ] on Inhance IFIR with >92.3% observed scores ≥3, except the segment 4 branch (3 [1-4], 53.6%). The identification rates of arterial variants were 92.9%-97% on Inhance IFIR. DATA CONCLUSIONS: Although CTA is superior to the NC-MRA, all NC-MRA depict the donor arterial anatomy well. Inhance IFIR can potentially be an alternative image modality for CTA to evaluate the arterial variants of living donors. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Prediction of poor outcome in stroke patients using radiomics analysis of intraparenchymal and intraventricular hemorrhage and clinical factors.

PURPOSE: To build three prognostic models using radiomics analysis of the hemorrhagic lesions, clinical variables, and their combination, to predict the outcome of stroke patients with spontaneous intracerebral hemorrhage (sICH). MATERIALS AND METHODS: Eighty-three sICH patients were included. Among them, 40 patients (48.2%) had poor prognosis with modified Rankin scale (mRS) of 5 and 6 at discharge, and the prognostic model was built to differentiate mRS ≤ 4 vs. 5 + 6. The region of interest (ROI) of intraparenchymal hemorrhage (IPH) and intraventricular hemorrhage (IVH) were separately segmented. Features were extracted using PyRadiomics, and the support vector machine was applied to select features and build radiomics models based on IPH and IPH + IVH. The clinical models were built using multivariate logistic regression, and then the radiomics scores were combined with clinical variables to build the combined model. RESULTS: When using IPH, the AUC for radiomics, clinical, and combined model was 0.78, 0.82, and 0.87, respectively. When using IPH + IVH, the AUC was increased to 0.80, 0.84, and 0.90, respectively. The combined model had a significantly improved AUC compared to the radiomics by DeLong test. A clinical prognostic model based on the ICH score of 0-1 only achieved AUC of 0.71. CONCLUSIONS: The combined model using the radiomics score derived from IPH + IVH and the clinical factors could achieve a high accuracy in prediction of sICH patients with poor outcome, which may be used to assist in making the decision about the optimal care.

Solid tumor size for prediction of recurrence in large and giant non-functioning pituitary adenomas.

A subset of large non-functioning pituitary adenomas (lNFPA) and giant non-functioning pituitary adenomas (gNFPA) undergoes early progression/recurrence (P/R) after surgery. This study revealed the clinical and image predictors of P/R in lNFPA and gNFPA, with emphasis on solid tumor size. This retrospective study investigated the preoperative MR imaging features for the prediction of P/R in lNFPA (> 3 cm) and gNFPA (> 4 cm). Only the patients with a complete preoperative brain MRI and undergone postoperative MRI follow-ups for more than 1 year were included. From November 2010 to December 2020, a total of 34 patients diagnosed with lNFPA and gNFPA were included (median follow-up time 47.6 months) in this study. A total of twenty-three (23/34, 67.6%) patients had P/R, and the median time to P/R is 25.2 months. Solid tumor diameter (STD), solid tumor volume (STV), and extent of resection are associated with P/R (p < 0.05). Multivariate analysis showed large STV is a risk factor for P/R (p < 0.05) with a hazard ratio of 30.79. The cutoff points of STD and STV for prediction of P/R are 26 mm and 7.6 cm3, with AUCs of 0.78 and 0.79 respectively. Kaplan-Meier analysis of tumor P/R trends showed that patients with larger STD and STV exhibited shorter progression-free survival (p < 0.05). For lNFPA and gNFPA, preoperative STD and STV are significant predictors of P/R. The results offer objective and valuable information for treatment planning in this subgroup.

A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet.

PURPOSE: To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system. METHODS: A total of 190 patients, 50 with malignant and 140 with benign fractures, were studied. The visual diagnosis was made by one senior MSK radiologist, one fourth-year resident, and one first-year resident. The MSK radiologist also gave the binary score for 15 qualitative imaging features. Deep learning was implemented using ResNet50, using one abnormal spinal segment selected from each patient as input. The T1W and T2W images of the lesion slice and its two neighboring slices were considered. The diagnostic performance was evaluated using tenfold cross-validation. RESULTS: The overall reading accuracy was 98, 96, and 66% for the senior MSK radiologist, fourth-year resident, and first-year resident, respectively. Of the 15 imaging features, 10 showed a significant difference between benign and malignant groups with p < = 0.001. The accuracy achieved by using the ResNet50 deep learning model for the identified abnormal vertebral segment was 92%. Compared to the first-year resident's reading, the model improved the sensitivity from 78 to 94% (p < 0.001) and the specificity from 61 to 91% (p < 0.001). CONCLUSION: Our deep learning-based model may provide information to assist less experienced clinicians in the diagnosis of spinal fractures on MRI. Other findings away from the vertebral body need to be considered to improve the model, and further investigation is required to generalize our findings to real-world settings.

Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers.

OBJECTIVES: To apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI. METHODS: A total of 244 patients were analyzed, 99 in training dataset scanned at 1.5 T and 83 in testing-1 and 62 in testing-2 scanned at 3 T. Patients were classified into 3 subtypes based on hormonal receptor (HR) and HER2 receptor: (HR+/HER2-), HER2+, and triple negative (TN). Only images acquired in the DCE sequence were used in the analysis. The smallest bounding box covering tumor ROI was used as the input for deep learning to develop the model in the training dataset, by using a conventional CNN and the convolutional long short-term memory (CLSTM). Then, transfer learning was applied to re-tune the model using testing-1(2) and evaluated in testing-2(1). RESULTS: In the training dataset, the mean accuracy evaluated using tenfold cross-validation was higher by using CLSTM (0.91) than by using CNN (0.79). When the developed model was applied to the independent testing datasets, the accuracy was 0.4-0.5. With transfer learning by re-tuning parameters in testing-1, the mean accuracy reached 0.91 by CNN and 0.83 by CLSTM, and improved accuracy in testing-2 from 0.47 to 0.78 by CNN and from 0.39 to 0.74 by CLSTM. Overall, transfer learning could improve the classification accuracy by greater than 30%. CONCLUSIONS: The recurrent network using CLSTM could track changes in signal intensity during DCE acquisition, and achieved a higher accuracy compared with conventional CNN during training. For datasets acquired using different settings, transfer learning can be applied to re-tune the model and improve accuracy. KEY POINTS: • Deep learning can be applied to differentiate breast cancer molecular subtypes. • The recurrent neural network using CLSTM could track the change of signal intensity in DCE images, and achieved a higher accuracy compared with conventional CNN during training. • For datasets acquired using different scanners with different imaging protocols, transfer learning provided an efficient method to re-tune the classification model and improve accuracy.

Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.

To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson's correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.

Correlation of internal carotid artery diameter and carotid flow with asymmetry of the circle of Willis.

BACKGROUND: The purpose of this study was to clarify the effect of asymmetric COW variants on carotid flow changes, and proposed an easy estimate of the representative carotid flow volume for accurate numerical simulation. METHODS: A total of 210 healthy adults receiving magnetic resonance angiography and carotid duplex sonography were included. Three anterior cerebral artery asymmetry (AA) groups were defined based on the diameter ratio difference (DRD) of bilateral A1 segments: AA1 group, one-side A1 aplasia; AA2, A1 DRD ≥ 50%; AA3, A1 DRD between 10 and 50%. Similarly, 3 posterior communicating artery (PcomA) asymmetry (PA) groups were defined: PA1 group, one fetal-origin posterior cerebral artery and absent contralateral PcomA; PA2, PcomA DRD ≥ 50%; PA3, PcomA DRD between 10 and 50%. RESULTS: With A1 asymmetry, the ICA diameter of the dominant A1 is significantly greater than the contralateral side. Significant differences of bilateral ICA flow were present in the AA1 and AA2 groups (mean flow difference 42.9 and 30.7%, respectively). Significant bilateral ICA diameter and flow differences were only found in the PA1 group. Linear regression analysis of ICA diameter and flow found a moderately positive correlation between ICA diameter and flow in all AA groups, with a 1 mm increment in vessel diameter corresponding to a 62.6 ml increment of flow volume. The product of bilateral ICA diameter and flow volume difference (ICA-PDF) could be a potential discriminator with a cutoff of 4.31 to predict A1 asymmetry ≥50% with a sensitivity of 0.81 and specificity of 0.76. CONCLUSIONS: The study verifies that A1 asymmetry causes unequal bilateral carotid inflow, and consequently different bilateral ICA diameters. Adjustment of the inflow boundary conditions according to the COW variants would be necessary to improve the accuracy of numerical simulation.

Radiomics approach for prediction of recurrence in skull base meningiomas.

PURPOSE: A subset of skull base meningiomas (SBM) may show early progression/recurrence (P/R) as a result of incomplete resection. The purpose of this study is the implementation of MR radiomics to predict P/R in SBM. METHODS: From October 2006 to December 2017, 60 patients diagnosed with pathologically confirmed SBM (WHO grade I, 56; grade II, 3; grade III, 1) were included in this study. Preoperative MRI including T2WI, diffusion-weighted imaging (DWI), and contrast-enhanced T1WI were analyzed. On each imaging modality, 13 histogram parameters and 20 textural gray level co-occurrence matrix (GLCM) features were extracted. Random forest algorithms were utilized to evaluate the importance of these parameters, and the most significant three parameters were selected to build a decision tree for prediction of P/R in SBM. Furthermore, ADC values obtained from manually placed ROI in tumor were also used to predict P/R in SBM for comparison. RESULTS: Gross-total resection (Simpson Grades I-III) was performed in 33 (33/60, 55%) patients, and 27 patients received subtotal resection. Twenty-one patients had P/R (21/60, 35%) after a postoperative follow-up period of at least 12 months. The three most significant parameters included in the final radiomics model were T1 max probability, T1 cluster shade, and ADC correlation. In the radiomics model, the accuracy for prediction of P/R was 90%; by comparison, the accuracy was 83% using ADC values measured from manually placed tumor ROI. CONCLUSIONS: The results show that the radiomics approach in preoperative MRI offer objective and valuable clinical information for treatment planning in SBM.

Noninvasive assessment of intracranial elastance and pressure in spontaneous intracranial hypotension by MRI.

BACKGROUND: Spontaneous intracranial hypotension (SIH) is often misdiagnosed, and can lead to severe complications. Conventional MR sequences show a limited ability to aid in this diagnosis. MR-based intracranial pressure (MR-ICP) may be able to detect changes of intracranial elastance and pressure. PURPOSE: To determine whether MR-ICP is able to differentiate SIH patients from normal subjects, improve diagnostic sensitivity, and provide an insight into the pathophysiology. STUDY TYPE: Prospective. SUBJECTS: Twenty-eight SIH cases with orthostatic headache and 20 healthy volunteers. FIELD STRENGTH/SEQUENCE: Cine phase-contrast MRI on a 1.5T scanner. ASSESSMENT: Intracranial elastance (IE) was derived from the ratio of the peak-to-peak cerebrospinal fluid (CSF) pressure gradient (PGcsf-pp ) and intracranial volume change, obtained by summing all flows before each sequential cardiac frame. STATISTICAL TESTS: Student's t-test was used to compare the MR-ICP indexes and flow parameters between SIH patients and healthy volunteers (P < 0.01). RESULTS: The SIH patients with cervical epidural venous dilatation (EVD) had an IE of 0.121 ± 0.027 mmHg/cm/ml, significantly higher than that of the normal volunteers (0.085 ± 0.027 mmHg/cm/ml; P = 0.002). In contradistinction, the EVD-negative SIH patients, including four with no sign of CSF leaks, had significantly lower IE (0.055 ± 0.012 mmHg/cm/ml) compared with the normal volunteers and the EVD-positive group (P = 0.001, P < 0.001). The EVD-negative patients had significantly lower PGcsf-pp (0.024 ± 0.007 mmHg/cm) compared with the normal volunteers and the EVD-positive group (0.035 ± 0.011 mmHg/cm, 0.040 ± 0.010 mmHg/cm; P = 0.003, P < 0.001). Additionally, the MRI flow study showed a significant decrease in transcranial inflow and outflow of SIH patients (P < 0.01). DATA CONCLUSION: We found that the MR-ICP method is potentially more sensitive than morphological MRI in the early diagnosis of SIH. Also, contrary to common belief, our results suggest that an abnormal craniospinal elastance might be the cause of SIH, instead of CSF leak. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1255-1263.

Prediction of progression in skull base meningiomas: additional benefits of apparent diffusion coefficient value.

A subset of benign (WHO grade I) skull base meningiomas show early progression/recurrence (P/R) in the first years after surgical resection. Besides, complete surgical resection may be difficult to achieve safely in skull base meningiomas due to complex neurovascular structures. The one main challenge in the treatment of skull base meningiomas is to determine factors that correlate with P/R. We retrospectively investigated the preoperative CT and MR imaging features for the prediction of P/R in skull base meningiomas, with emphasis on quantitative ADC values. Only patients had postoperative MRI follow-ups for more than 1 year (at least every 6 months) were included. From October 2006 to December 2015, total 73 patients diagnosed with benign (WHO grade I) skull base meningiomas were included (median follow-up time 41 months), and 17 (23.3%) patients had P/R (median time to P/R 28 months). Skull base meningiomas with spheno-orbital location, adjacent bone invasion, high DWI, and lower ADC value/ratio were significantly associated with P/R (P < 0.05). The cut-off points of ADC value and ADC ratio for prediction of P/R are 0.83 × 10- 3 mm2/s and 1.09 respectively, with excellent area under curve (AUC) values (0.86 and 0.91) (P < 0.05). In multivariate logistic regression, low ADC values (< 0.83 × 10- 3 mm2/s) and adjacent bone invasion are high-risk factors of P/R (P < 0.05), with odds ratios of 31.53 and 17.59 respectively. The preoperative CT and MRI features for prediction of P/R offered clinically vital information for the planning of treatment in skull base meningiomas.

Added value of delayed computed tomography angiography in primary intracranial hemorrhage and hematoma size for predicting spot sign.

Background The computed tomography angiography (CTA) spot sign represents active contrast extravasation within acute primary intracerebral hemorrhage (ICH) and is an independent predictor of hematoma expansion (HE) and poor clinical outcomes. The spot sign could be detected on first-pass CTA (fpCTA) or delayed CTA (dCTA). Purpose To investigate the additional benefits of dCTA spot sign in primary ICH and hematoma size for predicting spot sign. Material and Methods This is a retrospective study of 100 patients who underwent non-contrast CT (NCCT) and CTA within 24 h of onset of primary ICH. The presence of spot sign on fpCTA or dCTA, and hematoma size on NCCT were recorded. The spot sign on fpCTA or dCTA for predicting significant HE, in-hospital mortality, and poor clinical outcomes (mRS ≥ 4) are calculated. The hematoma size for prediction of CTA spot sign was also analyzed. Results Only the spot sign on dCTA could predict high risk of significant HE and poor clinical outcomes as on fpCTA ( P < 0.05). With dCTA, there is increased sensitivity and negative predictive value (NPV) for predicting significant HE, in-hospital mortality, and poor clinical outcomes. The XY value (product of the two maximum perpendicular axial dimensions) is the best predictor (area under the curve [AUC] = 0.82) for predicting spot sign on fpCTA or dCTA in the absence of intraventricular and subarachnoid hemorrhage. Conclusion This study clarifies that dCTA imaging could improve predictive performance of CTA in primary ICH. Furthermore, the XY value is the best predictor for CTA spot sign.

Evaluation of the association between quantitative mammographic density and breast cancer occurred in different quadrants.

BACKGROUND: To investigate the relationship between mammographic density measured in four quadrants of a breast with the location of the occurred cancer. METHODS: One hundred and ten women diagnosed with unilateral breast cancer that could be determined in one specific breast quadrant were retrospectively studied. Women with previous cancer/breast surgery were excluded. The craniocaudal (CC) and mediolateral oblique (MLO) mammography of the contralateral normal breast were used to separate a breast into 4 quadrants: Upper-Outer (UO), Upper-Inner (UI), Lower-Outer (LO), and Lower-Inner (LI). The breast area (BA), dense area (DA), and percent density (PD) in each quadrant were measured by using the fuzzy-C-means segmentation. The BA, DA, and PD were compared between patients who had cancer occurring in different quadrants. RESULTS: The upper-outer quadrant had the highest BA (37 ± 15 cm2) and DA (7.1 ± 2.9 cm2), with PD = 20.0 ± 5.8%. The order of BA and DA in the 4 separated quadrants were: UO > UI > LO > LI, and almost all pair-wise comparisons showed significant differences. For tumor location, 67 women (60.9%) had tumor in UO, 16 (14.5%) in UI, 7 (6.4%) in LO, and 20 (18.2%) in LI quadrant, respectively. The estimated odds and the 95% confidence limits of tumor development in the UO, UI, LO and LI quadrants were 1.56 (1.06, 2.29), 0.17 (0.10, 0.29), 0.07 (0.03, 0.15), and 0.22 (0.14, 0.36), respectively. In these 4 groups of women, the order of quadrant BA and DA were all the same (UO > UI > LO > LI), and there was no significant difference in BA, DA or PD among them (all p > 0.05). CONCLUSIONS: Breast cancer was most likely to occur in the UO quadrant, which was also the quadrant with highest BA and DA; but for women with tumors in other quadrants, the density in that quadrant was not the highest. Therefore, there was no direct association between quadrant density and tumor occurrence.

Breast density quantification using structured-light-based diffuse optical tomography simulations.

We present the feasibility of structured-light-based diffuse optical tomography (DOT) to quantify the breast density with an extensive simulation study. This study is performed on multiple numerical breast phantoms built from magnetic resonance imaging (MRI) images. These phantoms represent realistic tissue morphologies and are given typical breast optical properties. First, synthetic data are simulated at five wavelengths using our structured-light-based DOT forward problem. Afterwards, the inverse problem is solved to obtain the absorption images and subsequently the chromophore concentration maps. Parameters, such as segmented volumes and mean concentrations, are extracted from these maps and used in a regression model to estimate the percent breast densities. These estimations are correlated with the true values from MRI, r=0.97, showing that our new technique is promising in measuring breast density.

Quantitative Magnetic Resonance Diffusion-Weighted Imaging Evaluation of the Supratentorial Brain Regions in Patients Diagnosed with Brainstem Variant of Posterior Reversible Encephalopathy Syndrome: A Preliminary Study.

BACKGROUND AND PURPOSE: Posterior reversible encephalopathy syndrome (PRES) is a clinicoradiologic entity with several causes, characterized by rapid onset of symptoms and typical neuroimaging features, which usually resolve if promptly recognized and treated. Brainstem variant of PRES presents with vasogenic edema in brainstem regions on magnetic resonance (MR) images and there is sparing of the supratentorial regions. Because PRES is usually caused by a hypertensive crisis, which would likely have a systemic effect and global manifestations on the brain tissue, we thus proposed that some microscopic abnormalities of the supratentorial regions could be detected with diffusion-weighted imaging (DWI) using apparent diffusion coefficient (ADC) analysis in brainstem variant of PRES and hypothesized that "normal-looking" supratentorial regions will increase water diffusion. METHODS: We retrospectively identified patients with PRES who underwent brain magnetic resonance imaging studies. We identified 11 brainstem variants of PRES patients, who formed the study cohort, and 11 typical PRES patients and 20 normal control subjects as the comparison cohorts for this study. Nineteen regions of interest were drawn and systematically placed. The mean ADC values were measured and compared among these 3 groups. RESULTS: ADC values of the typical PRES group were consistently elevated compared with those in normal control subjects. ADC values of the brainstem variant group were consistently elevated compared with those in normal control subjects. ADC values of the typical PRES group and brainstem variant group did not differ significantly, except for the pons area. CONCLUSIONS: Quantitative MR DWI may aid in the evaluation of supratentorial microscopic abnormalities in brainstem variant of PRES patients.

US-localized diffuse optical tomography in breast cancer: comparison with pharmacokinetic parameters of DCE-MRI and with pathologic biomarkers.

BACKGROUND: To correlate parameters of Ultrasonography-guided Diffuse optical tomography (US-DOT) with pharmacokinetic features of Dynamic contrast-enhanced (DCE)-MRI and pathologic markers of breast cancer. METHODS: Our institutional review board approved this retrospective study and waived the requirement for informed consent. Thirty seven breast cancer patients received US-DOT and DCE-MRI with less than two weeks in between imaging sessions. The maximal total hemoglobin concentration (THC) measured by US-DOT was correlated with DCE-MRI pharmacokinetic parameters, which included K(trans), k ep and signal enhancement ratio (SER). These imaging parameters were also correlated with the pathologic biomarkers of breast cancer. RESULTS: The parameters THC and SER showed marginal positive correlation (r = 0.303, p = 0.058). Tumors with high histological grade, negative ER, and higher Ki-67 expression ≥ 20% showed statistically higher THC values compared to their counterparts (p = 0.019, 0.041, and 0.023 respectively). Triple-negative (TN) breast cancers showed statistically higher K(trans) values than non-TN cancers (p = 0.048). CONCLUSION: THC obtained from US-DOT and K(trans) obtained from DCE-MRI were associated with biomarkers indicative of a higher aggressiveness in breast cancer. Although US-DOT and DCE-MRI both measured the vascular properties of breast cancer, parameters from the two imaging modalities showed a weak association presumably due to their different contrast mechanisms and depth sensitivities.

Impact of factors affecting the residual tumor size diagnosed by MRI following neoadjuvant chemotherapy in comparison to pathology.

BACKGROUND AND OBJECTIVES: To investigate accuracy of magnetic resonance imaging (MRI) for measuring residual tumor size in breast cancer patients receiving neoadjuvant chemotherapy (NAC). METHODS: Ninety-eight patients were studied. Several MRI were performed during NAC for response monitoring, and the residual tumor size was measured on last MRI after completing NAC. Covariates, including age, tumor characteristics, biomarkers, NAC regimens, MRI scanners, and time from last MRI to operation, were analyzed. Univariate and Multivariate linear regression models were used to determine the predictive value of these covariates for MRI-pathology size discrepancy as the outcome measure. RESULTS: The mean (±SD) of the absolute difference between MRI and pathological residual tumor size was 1.0 ± 2.0 cm (range, 0-14 cm). Univariate regression analysis showed tumor type, morphology, HR status, HER2 status, and MRI scanner (1.5 T or 3.0 T) were significantly associated with MRI-pathology size discrepancy (all P < 0.05). Multivariate regression analyses demonstrated that only tumor type, tumor morphology, and biomarker status considering both HR and HER-2 were independent predictors (P = 0.0014, 0.0032, and 0.0286, respectively). CONCLUSION: The accuracy of MRI in evaluating residual tumor size depends on tumor type, morphology, and biomarker status. The information may be considered in surgical planning for NAC patients.

Computerized breast mass detection using multi-scale Hessian-based analysis for dynamic contrast-enhanced MRI.

This study aimed to investigate a computer-aided system for detecting breast masses using dynamic contrast-enhanced magnetic resonance imaging for clinical use. Detection performance of the system was analyzed on 61 biopsy-confirmed lesions (21 benign and 40 malignant lesions) in 34 women. The breast region was determined using the demons deformable algorithm. After the suspicious tissues were identified by kinetic feature (area under the curve) and the fuzzy c-means clustering method, all breast masses were detected based on the rotation-invariant and multi-scale blob characteristics. Subsequently, the masses were further distinguished from other detected non-tumor regions (false positives). Free-response operating characteristics (FROC) curve and detection rate were used to evaluate the detection performance. Using the combined features, including blob, enhancement, morphologic, and texture features with 10-fold cross validation, the mass detection rate was 100 % (61/61) with 15.15 false positives per case and 91.80 % (56/61) with 4.56 false positives per case. In conclusion, the proposed computer-aided detection system can help radiologists reduce inter-observer variability and the cost associated with detection of suspicious lesions from a large number of images. Our results illustrated that breast masses can be efficiently detected and that enhancement and morphologic characteristics were useful for reducing non-tumor regions.