Placing Regions Of Interest Research Articles

The cut-off value of ADC to differentiate benign from malignant breast lesions

Background: Diffusion-weighted MRI is a fast, unenhanced modality that shows promise in identifying mammographically occult malignancy and warrants further investigation as an alternative supplemental breast cancer screening tool.Aim of study: To estimate the diagnostic accuracy of diffusion wighted imaging and apparent diffusion coeffient (ADC) value in the differentiation between benign and malignant breast lesions, and to obtain a cutoff ADC value between benign and malignant breast lesions along with histopathological correlation. Materials and Methods: A prospective study of 56 patients with present with breast mass who examined at 1.5 tesla MRI & proved by histopathology. ADC value were a quired at b values of 0, 600, 850 s/mm² after identification of ROI (Region of interest) placement. ADC value with histopathology correlation was analyzed. Results: From total 56 lesions, 24 lesions were histologically malignant and 32 were histologically benign. With an ADC cut‑off value of 1.31 × 10−3 mm2/s for malignant lesions, a sensitivity of 100% and specificity of 72.1% was obtained. The false‑postive lesions were one intraductal papilloma & 3 abcesses in which a low ADC value was obtained. Purely DCIS (Ductal carcinoma in situ) lesions presenting as non‑mass‑like enhancement had a high ADC value of 1.2‑1.3 × 10−3 mm2/s, thereby decreasing specificity. Conclusion: Diffusion‑weighted Imaging is a functionl quantitative assessment by ADC value, may act as an efficient tool in increasing the diagnostic accuracy and specificity of breast MRI in the characterization of different breast lesions.

Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function: CHanges in Alps index on Multiple conditiON acquIsition eXperiment (CHAMONIX) study

PurposeThe diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was developed to evaluate the brain’s glymphatic function or interstitial fluid dynamics. This study aimed to evaluate the reproducibility of the DTI-ALPS method and the effect of modifications in the imaging method and data evaluation.Materials and methodsSeven healthy volunteers were enrolled in this study. Image acquisition was performed for this test–retest study using a fixed imaging sequence and modified imaging methods which included the placement of region of interest (ROI), imaging plane, head position, averaging, number of motion-proving gradients, echo time (TE), and a different scanner. The ALPS-index values were evaluated for the change of conditions listed above.ResultsThis test–retest study by a fixed imaging sequence showed very high reproducibility (intraclass coefficient = 0.828) for the ALPS-index value. The bilateral ROI placement showed higher reproducibility. The number of averaging and the difference of the scanner did not influence the ALPS-index values. However, modification of the imaging plane and head position impaired reproducibility, and the number of motion-proving gradients affected the ALPS-index value. The ALPS-index values from 12-axis DTI and 3-axis diffusion-weighted image (DWI) showed good correlation (r = 0.86). Also, a shorter TE resulted in a larger value of the ALPS-index.ConclusionALPS index was robust under the fixed imaging method even when different scanners were used. ALPS index was influenced by the imaging plane, the number of motion-proving gradient axes, and TE in the imaging sequence. These factors should be uniformed in the planning ALPS method studies. The possibility to develop a 3-axis DWI-ALPS method using three axes of the motion-proving gradient was also suggested.

Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis

Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This study aims to develop an automated method of aqueduct CSF flow analysis using convolution neural networks (CNNs), which can replace the current standard involving manual segmentation of aqueduct region of interest (ROI). Retrospective analysis was performed on 333 patients who underwent PC-MRI, totaling 353 imaging studies. Aqueduct flow measurements using manual ROI placement was performed independently by two radiologists. Two types of CNNs, MultiResUNet and UNet, were trained using ROI data from the senior radiologist, with PC-MRI studies being randomly divided into training (80%) and validation (20%) datasets. Segmentation performance was assessed using Dice similarity coefficient (DSC), and CSF flow parameters were calculated from both manual and CNN-derived ROIs. MultiResUNet, UNet and second radiologist (Rater 2) had DSCs of 0.933, 0.928, and 0.867, respectively, with p < 0.001 between CNNs and Rater 2. Comparison of CSF flow parameters showed excellent intraclass correlation coefficients (ICCs) for MultiResUNet, with lowest correlation being 0.67. For UNet, lower ICCs of −0.01 to 0.56 were observed. Only 3/353 (0.8%) studies failed to have appropriate ROIs placed by MultiResUNet, compared to 12/353 (3.4%) failed cases for UNet. In conclusion, CNNs were able to measure aqueductal CSF flow with similar performance to a senior neuroradiologist. MultiResUNet demonstrated fewer cases of segmentation failure and more consistent flow measurements compared to the widely adopted UNet.

Subsegmentation of the Kidney in Experimental MR Images Using Morphology-Based Regions-of-Interest or Multiple-Layer Concentric Objects.

Functional renal MRI promises access to a wide range of physiologically relevant parameters such as blood oxygenation, perfusion, tissue microstructure, pH, and sodium concentration. For quantitative comparison of results, representative values must be extracted from the parametric maps obtained with these different MRI techniques. To improve reproducibility of results this should be done based on regions-of-interest (ROIs) that are clearly and objectively defined.Semiautomated subsegmentation of the kidney in magnetic resonance images represents a simple but very valuable approach for the quantitative analysis of imaging parameters in multiple ROIs that are associated with specific anatomic locations. Thereby, it facilitates comparing MR parameters between different kidney regions, as well as tracking changes over time.Here we provide detailed step-by-step instructions for two recently developed subsegmentation techniques that are suitable for kidneys of small rodents: i) the placement of ROIs in cortex, outer and the inner medulla based on typical kidney morphology and ii) the division of the kidney into concentrically oriented layers.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers.

An Automated Method for Quality Control in MRI Systems: Methods and Considerations

Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.

Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification

Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

Free‐Breathing Volumetric Liver and Proton Density Fat Fraction Quantification in Pediatric Patients Using Stack‐of‐Radial MRI With Self‐Gating Motion Compensation

Stack-of-radial multiecho gradient-echo MRI is promising for free-breathing liver quantification and may benefit children. To validate stack-of-radial MRI with self-gating motion compensation in phantoms, and to evaluate it in children. Prospective. Four vials with different driven by a motion stage. Sixteen pediatric patients with suspected nonalcoholic fatty liver disease or steatohepatitis (five females, 13 ± 4 years, body mass index 29.2 ± 8.6 kg/m2 ). Stack-of-radial, and 2D and 3D Cartesian multiecho gradient-echo sequences at 3T. Ungated and gated stack-of-radial proton density fat fraction (PDFF) and maps were reconstructed without and with self-gating motion compensation. Stack-of-radial measurements of phantoms without and with motion were validated against reference 2D Cartesian results of phantoms without motion. In subjects, free-breathing stack-of-radial and reference breath-hold 3D Cartesian were acquired. Subject inclusion for statistical analysis and region of interest placement were determined independently by two observers. Phantom results were fitted with a weighted linear model. Demographic differences between excluded and included subjects were tested by multivariate analysis of variance. PDFF and measurements were compared using Bland-Altman analysis. Interobserver agreement was assessed by the intraclass correlation coefficient (ICC). Ungated stack-of-radial inside moving phantom vials showed a significant positive bias of 64.3 s-1 (P < 0.00001), unlike gated results (P > 0.31). Subject inclusion decisions for statistical analysis from two observers were consistent. No significant differences were found between four excluded and 12 included subjects (P = 0.14). Compared to breath-hold Cartesian, ungated and gated free-breathing stack-of-radial exhibited mean differences of 18.5 s-1 and 3.6 s-1 . Mean PDFF differences were 1.1% and 1.0% for ungated and gated measurements, respectively. Interobserver agreement was excellent (ICC for PDFF = 0.99, ICC for = 0.90; P < 0.0003). Stack-of-radial MRI with self-gating motion compensation seems to allow free-breathing liver and PDFF quantification in children. 2 TECHNICAL EFFICACY STAGE: 2.

The validity and reliability of the exposure index as a metric for estimating the radiation dose to the patient

IntroductionWith the introduction of digital radiography, the feedback between image quality and over-exposure has been partly lost which in some cases has led to a steady increase in dose. Over the years the introduction of exposure index (EI) has been used to resolve this phenomenon referred to as ‘dose creep’. Even though EI is often vendor specific it is always a related of the radiation exposure to the detector. Due to the nature of this relationship EI can also be used as a patient dose indicator, however this is not widely investigated in literature. MethodsA total of 420 dose-area-product (DAP) and EI measurements were taken whilst varying kVp, mAs and body habitus on two different anthropomorphic phantoms (pelvis and chest). Using linear regression, the correlation between EI and DAP were examined. Additionally, two separate region of interest (ROI) placements/per phantom where examined in order to research any effect on EI. ResultsWhen dividing the data into subsets, a strong correlation between EI and DAP was shown with all R-squared values > 0.987. Comparison between the ROI placements showed a significant difference between EIs for both placements. ConclusionThis research shows a clear relationship between EI and radiation dose which is dependent on a wide variety of factors such as ROI placement, body habitus. In addition, pathology and manufacturer specific EI’s are likely to be of influence as well. Implications for practiceThe combination of DAP and EI might be used as a patient dose indicator. However, the influencing factors as mentioned in the conclusion should be considered and examined before implementation.

Computation of the Texture Features on T2-Weighted Images as a Novel Method to Assess the Function of the Transplanted Kidney: Primary Research

Texture in medical images describes the internal structure of human tissues or organs. We hypothesize that textural analysis (TA) could be applied to assess renal function after kidney transplantation (KTx). This preliminary study aims to find a statistical difference between texture features in transplanted kidneys with different placement of region of interest (ROI). Also, we aimed at comparing results of TA with transplanted kidney function. For analysis, we used 9 retrospective examinations in patients with a transplanted kidney. All patients underwent a diagnostic magnetic resonance imaging (MRI) scan, including T2-weighted images. All MRI acquisition was performed using a 1.5T MRI (MAGNETOM Aera, Siemens Healthineers AG, Erlangen, Germany). Examinations were performed from indications other than KTx and in various times after KTx. We found an association between the texture parameters and the estimated glomerular filtration rate (4p estimate formula: Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]) and between texture parameters and creatinine in ROI location in the cortex. Our findings make TA a promising tool for the assessment of the function of the transplanted kidney. However, the effect of binning, ROI size, and placement of ROI in the organ are yet to be determined and need further study.

Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of Osteoarthritis Initiative (OAI) (9012 knee radiographs) and Multicenter Osteoarthritis Study (MOST) (3,644 knee radiographs) datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. Subsequently, we built logistic regression models to identify and compare the performances of several texture descriptors and each ROI placement method using 5-fold cross validation. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset. We used area under the receiver operating characteristic curve (ROC AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs non-OA) over the commonly-used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, Local Binary Pattern (LBP) yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.

Shear wave elasticity by tracing total nodule showed high reproducibility and concordance with fibrosis in thyroid cancer

BackgroundAlthough shear wave elastography (SWE) is reported to be useful in detecting malignant thyroid nodules, it shows a wide range of cut-off values of elasticity index (EI) in detecting malignant nodules. The cause of discrepancy remains unclear. Fibrosis of the tumors is known to increase the EI in SWE, and matching of SWE and surgical histopathology has not been fully illustrated in thyroid cancer. We aimed to evaluate the reproducibility of the new total nodular region of interest (ROI) method excluding the subjective features of focal circular ROI placement and to determine the lesion that causes the elevation of EI on SWE and on histopathology.MethodsA total of 29 thyroid cancers from 28 patients were included. We evaluated the reproducibility of EI in the new total nodular ROI using Q-Box Trace program and compared the EI in focal nodular ROI using a 3-mm circular area. We analyzed the correlation between fibrosis and EI.ResultThe coefficient of variation (CV) of the intrarater assay was significantly lower in total nodular ROI than in focal nodular ROI within the image in rater 1 (1.7% vs. 13.4%, p < 0.001) and in rater 2 (1.4% vs. 16.9%, p < 0.001) and in different images in rater 1 (7.6% vs. 12.3%, p = 0.040) and in rater 2 (7.5% vs. 19.8%, p = 0.004). Moreover, CV of the interrater assay showed similar results (14.9% vs. 19%, p = 0.030). Interrater intraclass correlation coefficient showed better agreement in total nodular ROI than in focal nodular ROI (0.863 vs. 0.783). The degree of fibrosis on histopathology showed significant correlations with EI (EMean, p < 0.001; EMax, p = 0.027), and the location of fibrosis was concordant with the high EI area on SWE.ConclusionOur study revealed that the new total nodular ROI method showed higher reproducibility and better agreement in intra- and interrater assay than the focal nodular ROI method, suggesting a valuable and standardized method in clinical practice. Moreover, our results showed that fibrosis in the histopathology increased EI on SWE and might lead to the discrepancy of the cut-off values in detecting thyroid cancer.

To investigate the sensitivity of chemoradiotherapy on nasopharyngeal carcinoma using intravoxel incoherent motion MRI

Objective To investigate the application value of intravoxel incoherent motion (IVIM) MRI for evaluating the sensitivity of chemoradiotherapy on nasopharyngeal carcinoma (NPC). Methods Sixty eight consecutive patients newly diagnosed with NPC in the stage of T3 (30 patients) or T4 (38 patients) were retrospectively enrolled. They were divided into effective group (45 patients) and poor-effective group (23 patients) clinically after a standard chemoradiotherapy according to the RECIST criteria. IVIM with 13 b-values (range,0 to 800 s/mm2) and general MRI were performed at 3.0 T MR scanner before and after chemoradiotherapy. Two radiologists major in MRI diagnose analyzed all images independently and placed regions of interest (ROIs). Intraclass correlation coefficient (ICC) was used to evaluate intra-observer and inter-observer agreement. And Mann-Whitney test was used to assess the differences between the two groups. Results The reproducibility between intra-observer and inter-observer was relatively good. Statistically,D [ (0.69±0.06) ×10-3 mm2/s vs. (0.52±0.10) ×10-3 mm2/s; U=51.5, P 0.05). Conclusions IVIM may be potentially useful in assessing the chemoradiotherapy on NPC. The higher D value combined with higher D* value might indicate the chemoradiotherapy on NPC is more sensitive,and the higher D* value might reflect increased blood vessel generation and parenchymal perfusion in NPC. Key words: Nasopharyngeal neoplasms; Treatment outcome; Diffusion magnetic resonance imaging; Forecasting

MRI-based methodology to monitor the impact of positional changes on the airway caliber in obstructive sleep apnea patients

PurposeTo develop a non-invasive MRI-based methodology to visually and quantitatively assess the impact of head and chest rotations on the airway caliber. MethodsAn MRI table set-up was developed for independent rotations of the head and chest along B0 field and tested for feasibility using phantom scans. The accuracy of the head and chest rotations was validated with ten volunteer scans. A 3T MRI protocol was optimized to image the regions of interest (ROIs) that were the retropalatal (RP) and retroglossal (RG) sections of the upper airway. A workflow for data analysis was developed to assess the changes of the airway caliber following the independent head and chest rotations. ResultsA prototype MRI table setup was established with two separate plates each supporting and rotating the head or chest independently. Subject positioning and image acquisition were finished within seven minutes for each position. Thus, each subject MRI was set up with seven positions and completed for less than one hour. The implemented angles were within 0.3-degree deviation from the targeted angles. The data analysis workflow provided 2D and 3D visualization and quantification with the measurements of cross-sectional area, lateral and anterior-posterior distances of the ROIs. Sharp contrast of the airway and its surrounding tissues facilitated an automatic approach to ROI placement to minimize subjectivity. ConclusionsThe 3T MRI data acquisition and analysis methodology could reliably assess the impact of head and chest rotations on the upper airway caliber to identify the optimal position for obstructive sleep apnea patients.

Automatic ROI Placement in the Upper Trapezius Muscle in B-mode Ultrasound Images.

Research involving B-mode ultrasound imaging often requires user defined regions of interest (ROIs) for analysis, traditionally drawn/selected by a trained operator. This manual process is incredibly time consuming and subjective. Here, we propose a fast and simple method of detecting the average location of aponeurosis layers in ultrasound images of the upper trapezius to place a rectangular ROI for quantitative image analysis. A total of 56 B-mode ultrasound images were analyzed, where rectangular ROIs were manually placed in the skeletal muscle by two trained operators. Interoperator agreement was determined between the ROI border locations using intercorrelation coefficient (ICC). Next, our automatic algorithm was applied (image thresholding, binary masking, and pixel intensity peak detection), estimating the mean position of both aponeurosis layers for rectangular ROI placement. The automatic estimation method was compared with the manual (visual) method by various statistics ( t test, linear correlation, Bland-Altman plot). The performance was also evaluated under additive noise conditions (Speckle). Finally, agreement of the overlapping ROI area between the manual and automatic methods was also computed. Performance of the automatic method compared with manual placement was excellent for both the superficial and deep ROI borders, performing consistently even with additive noise (error <0.674 ± 1.69 mm). Manual measurements indicated excellent consensus (ICC = 0.902) between operators. The overlapping area between the manual and automatic measurements demonstrated good agreement (90.65 ± 11.3%). With constraints, our method is robust even under large levels of noise addition making the automatic algorithm an acceptable replacement for manual ROI placement in the upper trapezius.

Discrimination Between Solitary Brain Metastasis and Glioblastoma Multiforme by Using ADC-Based Texture Analysis: A Comparison of Two Different ROI Placements

To explore the value of texture analysis based on the apparent diffusion coefficient (ADC) value and the effect of region of interest (ROI) placements in distinguishing glioblastoma multiforme (GBM) from solitary brain metastasis (sMET). Sixty-two patients with pathologically confirmed GBM (n = 36) and sMET (n = 26) were retrospectively included. All patients underwent diffusion-weighted imaging with b values of 0 and 1000 s/mm2, and the ADC maps were generated automatically. ROIs were placed on the largest whole single-slice tumor (ROI1) and the enhanced solid portion (ROI2) of the ADC maps, respectively. The texture feature metrics of the histogram and gray-level co-occurrence matrix were then extracted by using in-house software. The parameters of the texture analysis were compared between GBM and sMET, using the Mann-Whitney U test. A receiver operating characteristic (ROC) curve analysis was performed to determine the best parameters for distinguishing between GBM from sMET. Homogeneity and the inverse difference moment (IDM) of GBM were significantly higher than those of sMET in both ROIs (ROI1, p = 0.014 for homogeneity and p = 0.048 for IDM; ROI2, p< 0.001 for homogeneity and p = 0.029 for IDM). According to the ROC curve analysis, the area under the ROC curve (AUC) of homogeneity in ROI1 (AUC, 0.682, sensitivity, 72.2%, specificity, 61.5%) was significantly lower than that of ROI2 (AUC, 0.886, sensitivity, 83.3%, specificity, 76.9%; p= 0.012), whereas the IDM showed no statistical significance between two ROIs (p> 0.05). The ADC-based texture analysis can help differentiate GBM from sMET, and the ROI on the solid portion would be recommended to calculate the ADC-based texture metrics.

Do regions of interest location and type influence liver stiffness measurement using magnetic resonance elastography?

To assess the variability of liver stiffness measurements using magnetic resonance elastography (MRE) at 1.5T, depending on different approaches of regions of interest (ROIs) drawing. Fifty consecutive patients with successful liver MRE were included. There were 32 men and 18 women with a mean age of 52±14 (SD) years (range: 20-85 years). MRE was acquired using a gradient recalled-echo MRE sequence. At the level of the portal bifurcation, one observer drawn in the right liver first 3 elliptical ROI and then one free-hand ROI, as large as possible based on the confidence map and the anatomy. Three additional elliptical ROIs were further drawn on the slice above and 3 other on the slice below, for a total of 9 elliptical ROIs. The average value of liver stiffness in the 3 elliptical ROIs of the central slice and the one from the 9 elliptical ROIs were computed. Three liver stiffness values were obtained for each patient from the 3 measurement methods (one free-hand ROI, 3 elliptical ROIs and 9 elliptical ROIs). Inter-method variability was assessed using the intra-class correlation coefficient (ICC) and Bland-Altman analysis. The variability between the 3 methods was excellent with ICC>0.978 (P<0.0001). The Bland-Altman analysis revealed high agreement between the 3 methods with bias<0.45kPa and limits of agreement<±1.13kPa. The variability was lower when comparing a large free-hand ROI and the 3-elliptical ROIs, than when comparing the 9-elliptical ROIs to one of the other methods. Our results show that the variability between the 3 methods of ROI drawing and placement is very low.

Magnetic Resonance Imaging-Apparent Diffusion Coefficient Assessment of Vestibular Schwannomas: Systematic Approach, Methodology, and Pitfalls

To investigate the validity of various approaches to extract quantitative measurements of diffusion imaging (i.e., apparent diffusion coefficient [ADC]) to investigate tumors of the central nervous system. In current studies, the region of interest (ROI) for the quantitative measurements are placed arbitrarily according to morphology. Our aim is to investigate how placement patterns influence the ADC estimation in intracranial tumors. Twenty consecutive patients affected by vestibular schwannoma were studied using diffusion imaging. ADC values were obtained using different ROI placement methods: segmentation ADC values of the entire volume (vADC), random ADC values were obtained in 10 different ROI points, and a single ROI in the ADC of the internal auditory canal portion of the tumor. ADC of the internal auditory canal portion of the tumor and vADC differed significantly (P < 0.01). vADC was different between cystic and microcystic schwannomas (P= 0.009) and between cystic and solid schwannomas (P= 0.006). The positioning of ROI in these measurements is pivotal. Although "whole tumor volume" measurements represent the largest amount of information, multiple seed points can be used as well. However, there must be multiple seeds and their placement must be reported. ADC can be used as a versatile tool for tumor assessment but must be used judiciously and structured to yield comparable results.

Diagnostic value of quantitative dual-source CT dual-energy iodine maps combined with morphological CT features in assessing histological subtypes of lung cancer

Objective To investigate the clinical usefulness of quantitative dual-source dual-energy CT (DECT) iodine enhancement metrics combined with morphological CT features in distinguishing different lung cancer subtypes. Methods One hundred and sixty-two consecutive patients suspected with lung cancer were prospectively enrolled and underwent DECT in arterial phase prior to biopsy or surgery. Tumor histological subtypes were determined in 110 patients. Two radiologists interpreted CT morphologic features of 110 lesions in a consensual manner. In addition, two radiologists independently contoured lesions and placed regions of interest in descending aorta or subclavian artery on the same section for normalization, from which automated computer measurements were generated: iodine density and iodine ratio (the ratio of iodine density of lesion to that of artery on the same section). DECT metrics and morphological CT features were compared among different lung cancer subtypes. Chi-square was used to compare qualitative parameters. One way ANOVA was used to compare quantitative parameters satisfying normal distribution, while those parameters not satisfying normal distribution or ranked data were compared by Kruskal-Wallis rank sum test. Multinomial logistic regression models were used to differentiate the histological subtypes of lung cancer: adenocarcinoma, squamous cell carcinoma (SCC), small cell lung cancer (SCLC). Results There were 48 cases of adenocarcinomas, 36 cases of SCC and 26 cases of SCLC. In analysis of CT features, tumor diameter, distribution, spiculation, pleural retraction, vascular involvement, confluent mediastinal lymphadenopathy, encasement of mediastinal structures and enhancement heterogeneity showed statistical difference (all P<0.05). The diameter of SCC[(5.73±3.67)cm] and SCLC [(6.08±4.39)cm] were larger than adenocarcinoma [(3.75±2.80 cm)] (H=13.806, P<0.05). Adenocarcinomas were mostly located in the periphery (31 cases), while SCC (26 cases) and SCLC (21 cases) were mainly centrally located. Spiculation was mostly found in adenocarcinoma (44 cases) rather than SCLC (13 cases). Pleural retraction was mostly observed in adenocarcinoma (36 cases) rather than SCC (10 cases) and SCLC (5 cases). Vascular involvement was mostly found in SCLC (19 cases) rather than adenocarcinoma (15 cases). Confluent mediastinal lymphadenopathy was more frequently found in SCLC (15 cases) compared with adenocarcinoma (3 cases) and SCC (4 cases). Encasement of mediastinal structures was mostly found in SCLC (13 cases) rather than adenocarcinoma (7 cases). Homogeneous enhancement was more frequently found in SCLC (10 cases) than SCC (6 cases). No significant differences were observed in other CT features between any other two groups. Iodine density and iodine ratio were statistically different among these three subtypes lung cancer (H=16.817, 20.338, P<0.001). Iodine density of adenocarcinoma and SCC was (1.50±0.80) and (1.40±0.40) mg/ml, respectively, higher than the (1.20±0.40) mg/ml for SCLC (P<0.01). Iodine ratio of adenocarcinoma and SCC was (16.10±7.02)% and (15.05±4.62)%, respectively, higher than the (11.55±3.15)% for SCLC (P<0.01). No significant difference was observed between adenocarcinoma and SCC. Accuracy of the model based on CT features was 69.1%, accuracy of the model based on CT features combined with DECT parameters was 80.9%. Conclusions Quantitative DECT metrics are different among adenocarcinoma, SCC and SCLC, when combined with morphological CT features, higher diagnostic accuracy can be achieved. Key words: Lung neoplasms; Tomography, X-ray computed; Diagnosis

Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines.

We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user’s role while achieving good segmentation accuracy.

Apparent diffusion coefficient measurement in luminal breast cancer: will tumour shrinkage patterns affect its efficacy of evaluating the pathological response?

To determine which region of interest (ROI) placement method of apparent diffusion coefficient (ADC) measurement has the best performance for predicting pathological complete response (PCR) at two cycles of neoadjuvant chemotherapy (NAC) according to different tumour shrinkage patterns of luminal breast cancer and to assess the evaluative accuracy of ADC value combined with other clinicopathological indicators. Sixty-one patients who underwent NAC for histopathologically confirmed breast cancer were enrolled in this retrospective study. The ADC values of different shrinkage patterns (concentric shrinkage, nest or dendritic shrinkage, and mixed shrinkage) for tumours shown by diffusion-weighted imaging (DWI) were measured independently using three ROI placement methods (single-round, three-round, and freehand). Intraclass correlation coefficients (ICCs) were used to assess the interobserver variability in the ADC values. Multivariate logistic regression analysis was performed to identify the independent predictors of PCR. The best placement method found was single-round ROI in all the patients (AUC=0.863). When analysed separately, the effectiveness results differed: the single-round method was optimal for concentrically shrinking tumours (AUC=0.970); the freehand method was optimal for nest or dendritically shrinking tumours (AUC=0.714); and the three-round method was optimal for mixed shrinking tumours (AUC=0.975). Multivariate logistic analysis showed that oestrogen receptor (ER), ΔADC% and tumour diameter reduction (ΔD%) were independent factors in evaluating the PCR. The methods for measuring ADC values vary across different shrinkage patterns of luminal tumours. ΔADC%, ER and ΔD% were independent factors for evaluating the PCR.