Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence.

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

Retrospective comparison of direct in-bore magnetic resonance imaging (MRI)-guided biopsy and fusion-guided biopsy in patients with MRI lesions which are likely or highly likely to be clinically significant prostate cancer.

PURPOSE: To compare clinically significant prostate cancer (csPCa) detection rates between magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) fusion-guided prostate biopsy (FGB) and direct in-bore MRI-guided biopsy (MRGB). METHODS: We performed a comparison of csPCa detection rates between FGB and MRGB. Included patients had (1) at least one prior negative TRUS biopsy; (2) a Prostate Imaging Reporting and Data System (PI-RADS) 4 or 5 lesion and (3) a lesion size of ≥8 mm measured in at least one direction. We considered a Gleason score ≥7 being csPCa. Descriptive statistics with 95% confidence intervals (CI) were used to determine any differences. RESULTS: We included 51 patients with FGB (59 PI-RADS 4 and 41% PI-RADS 5) and 227 patients with MRGB (34 PI-RADS 4 and 66% PI-RADS 5). Included patients had a median age of 69 years (IQR, 65-72) and a median PSA level of 11.0 ng/ml (IQR, 7.4-15.1) and a median age of 67 years (IQR, 61-70), the median PSA 12.8 ng/ml (IQR, 9.1-19.0) within the FGB and the MRGB group, respectively. Detection rates of csPCA did not differ significantly between FGB and MRGB, 49 vs. 61%, respectively. CONCLUSION: We did not detect significant differences between FGB and MRGB in the detection of csPCa. The differences in detection ratios between both biopsy techniques are narrow with an increasing lesion size. This study warrants further studies to optimize selection of best biopsy modality.

Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.

PURPOSE: To determine the best features to discriminate prostate cancer from benign disease and its relationship to benign disease class and cancer grade. MATERIALS AND METHODS: The institutional review board approved this study and waived the need for informed consent. A retrospective cohort of 70 patients (age range, 48-70 years; median, 62 years), all of whom were scheduled to undergo radical prostatectomy and underwent preoperative 3-T multiparametric magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging, were included. The digitized prostatectomy slides were annotated for cancer and noncancerous disease and coregistered to MR imaging with an interactive deformable coregistration scheme. Computer-identified features for each of the noncancerous disease categories (eg, benign prostatic hyperplasia [BPH], prostatic intraepithelial neoplasia [PIN], inflammation, and atrophy) and prostate cancer were extracted. Feature selection was performed to identify the features with the highest discriminatory power. The performance of these five features was evaluated by using the area under the receiver operating characteristic curve (AUC). RESULTS: High-b-value diffusion-weighted images were more discriminative in distinguishing BPH from prostate cancer than apparent diffusion coefficient, which was most suitable for distinguishing PIN from prostate cancer. The focal appearance of lesions on dynamic contrast-enhanced images may help discriminate atrophy and inflammation from cancer. Which imaging features are discriminative for different benign lesions is influenced by cancer grade. The apparent diffusion coefficient appeared to be the most discriminative feature in identifying high-grade cancer. Classification results showed increased performance by taking into account specific benign types (AUC = 0.70) compared with grouping all noncancerous findings together (AUC = 0.62). CONCLUSION: The best features with which to discriminate prostate cancer from noncancerous benign disease depend on the type of benign disease and cancer grade. Use of the best features may result in better diagnostic performance.

Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI.

OBJECTIVES: To investigate the added value of computer-aided diagnosis (CAD) on the diagnostic accuracy of PIRADS reporting and the assessment of cancer aggressiveness. METHODS: Multi-parametric MRI and histopathological outcome of MR-guided biopsies of a consecutive set of 130 patients were included. All cases were prospectively PIRADS reported and the reported lesions underwent CAD analysis. Logistic regression combined the CAD prediction and radiologist PIRADS score into a combination score. Receiver-operating characteristic (ROC) analysis and Spearman's correlation coefficient were used to assess the diagnostic accuracy and correlation to cancer grade. Evaluation was performed for discriminating benign lesions from cancer and for discriminating indolent from aggressive lesions. RESULTS: In total 141 lesions (107 patients) were included for final analysis. The area-under-the-ROC-curve of the combination score was higher than for the PIRADS score of the radiologist (benign vs. cancer, 0.88 vs. 0.81, p = 0.013 and indolent vs. aggressive, 0.88 vs. 0.78, p < 0.01). The combination score correlated significantly stronger with cancer grade (0.69, p = 0.0014) than the individual CAD system or radiologist (0.54 and 0.58). CONCLUSIONS: Combining CAD prediction and PIRADS into a combination score has the potential to improve diagnostic accuracy. Furthermore, such a combination score has a strong correlation with cancer grade. KEY POINTS: • Computer-aided diagnosis helps radiologists discriminate benign findings from cancer in prostate MRI. • Combining PIRADS and computer-aided diagnosis improves differentiation between indolent and aggressive cancer. • Adding computer-aided diagnosis to PIRADS increases the correlation coefficient with respect to cancer grade.

1.5-T multiparametric MRI using PI-RADS: a region by region analysis to localize the index-tumor of prostate cancer in patients undergoing prostatectomy.

BACKGROUND: The use of multiparametric magnetic resonance imaging (mpMRI) to detect and localize prostate cancer has increased in recent years. In 2010, the European Society of Urogenital Radiology (ESUR) published guidelines for mpMRI and introduced the Prostate Imaging Reporting and Data System (PI-RADS) for scoring the different parameters. PURPOSE: To evaluate the reliability and diagnostic performance of endorectal 1.5-T mpMRI using the PI-RADS to localize the index tumor of prostate cancer in patients undergoing prostatectomy. MATERIAL AND METHODS: This institutional review board IRB-approved, retrospective study included 63 patients (mean age, 60.7 years, median PSA, 8.0). Three observers read mpMRI parameters (T2W, DWI, and DCE) using the PI-RADS, which were compared with the results from whole-mount histopathology that analyzed 27 regions of interest. Inter-observer agreement was calculated as well as sensitivity, specificity, positive predictive value (PPV), and negative predicted value (NPV) by dichotomizing the PI-RADS criteria scores ≥3. A receiver-operating curve (ROC) analysis was performed for the different MR parameters and overall score. RESULTS: Inter-observer agreement on the overall score was 0.41. The overall score in the peripheral zone achieved sensitivities of 0.41, 0.60, and 0.55 with an NPV of 0.80, 0.84, and 0.83, and in the transitional zone, sensitivities of 0.26, 0.15, and 0.19 with an NPV of 0.92, 0.91, and 0.92 for Observers 1, 2, and 3, respectively. The ROC analysis showed a significantly increased area under the curve (AUC) for the overall score when compared to T2W alone for two of the three observers. CONCLUSION: 1.5 T mpMRI using the PI-RADS to localize the index tumor achieved moderate reliability and diagnostic performance.

Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance.

PURPOSE: To determine the effect of computer-aided diagnosis (CAD) on less-experienced and experienced observer performance in differentiation of benign from malignant prostate lesions at 3-T multiparametric magnetic resonance (MR) imaging. MATERIALS AND METHODS: The institutional review board waived the need for informed consent. Retrospectively, 34 patients were included who had prostate cancer and had undergone multiparametric MR imaging, including T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced MR imaging prior to radical prostatectomy. Six radiologists less experienced in prostate imaging and four radiologists experienced in prostate imaging were asked to characterize different regions suspicious for cancer as benign or malignant on multiparametric MR images first without and subsequently with CAD software. The effect of CAD was analyzed by using a multiple-reader, multicase, receiver operating characteristic analysis and a linear mixed-model analysis. RESULTS: In 34 patients, 206 preannotated regions, including 67 malignant and 64 benign regions in the peripheral zone (PZ) and 19 malignant and 56 benign regions in the transition zone (TZ), were evaluated. Stand-alone CAD had an overall area under the receiver operating characteristic curve (AUC) of 0.90. For PZ and TZ lesions, the AUCs were 0.92 and 0.87, respectively. Without CAD, less-experienced observers had an overall AUC of 0.81, which significantly increased to 0.91 (P = .001) with CAD. For experienced observers, the AUC without CAD was 0.88, which increased to 0.91 (P = .17) with CAD. For PZ lesions, less-experienced observers increased their AUC from 0.86 to 0.95 (P < .001) with CAD. Experienced observers showed an increase from 0.91 to 0.93 (P = .13). For TZ lesions, less-experienced observers significantly increased their performance from 0.72 to 0.79 (P = .01) with CAD and experienced observers increased their performance from 0.81 to 0.82 (P = .42). CONCLUSION: Addition of CAD significantly improved the performance of less-experienced observers in distinguishing benign from malignant lesions; when less-experienced observers used CAD, they reached similar performance as experienced observers. The stand-alone performance of CAD was similar to performance of experienced observers.

Simulated required accuracy of image registration tools for targeting high-grade cancer components with prostate biopsies.

OBJECTIVES: To estimate the required spatial alignment accuracy for correctly grading 95 % of peripheral zone (PZ) prostate cancers using a system for multiparametric magnetic resonance (MR)-guided ultrasound (US) biopsies. METHODS: PZ prostate tumours were retrospectively annotated on multiparametric MR series using prostatectomy specimens as reference standard. Tumours were grouped based on homogeneous and heterogeneous apparent diffusion coefficient (ADC) values using an automated ADC texture analysis method. The proportion of heterogeneous tumours containing a distinct, high Gleason grade tumour focus yielding low ADC values was determined. Both overall tumour and high-grade focal volumes were calculated. All high-grade target volumes were then used in a simulated US biopsy system with adjustable accuracy to determine the hit rate. RESULTS: An ADC-determined high-grade tumour focus was found in 63 % of the PZ prostate tumours. The focal volumes were significantly smaller than the total tumour volumes (median volume of 0.3 ml and 1.1 ml respectively). To correctly grade 95 % of the aggressive tumour components the target registration error (TRE) should be smaller than 1.9 mm. CONCLUSIONS: To enable finding the high Gleason grade component in 95 % of PZ prostate tumours with MR-guided US biopsies, a technical registration accuracy of 1.9 mm is required. KEY POINTS: • MRI can identify foci of prostatic cancer with reduced apparent diffusion coefficients • Sixty-three per cent of prostatic peripheral zone tumours contain high-grade tumour low ADC foci • The median volume of such foci is 0.3 ml • Biopsy targets are significantly smaller than whole tumour volumes • Simulated registration accuracy is 1.9 mm for correctly grading 95 % of tumours.

Radiomics based automated quality assessment for T2W prostate MR images.

PURPOSE: The guidelines for prostate cancer recommend the use of MRI in the prostate cancer pathway. Due to the variability in prostate MR image quality, the reliability of this technique in the detection of prostate cancer is highly variable in clinical practice. This leads to the need for an objective and automated assessment of image quality to ensure an adequate acquisition and hereby to improve the reliability of MRI. The aim of this study is to investigate the feasibility of Blind/referenceless image spatial quality evaluator (Brisque) and radiomics in automated image quality assessment of T2-weighted (T2W) images. METHOD: Anonymized axial T2W images from 140 patients were scored for quality using a five-point Likert scale (low, suboptimal, acceptable, good, very good quality) in consensus by two readers. Images were dichotomized into clinically acceptable (very good, good and acceptable quality images) and clinically unacceptable (low and suboptimal quality images) in order to train and verify the model. Radiomics and Brisque features were extracted from a central cuboid volume including the prostate. A reduced feature set was used to fit a Linear Discriminant Analysis (LDA) model to predict image quality. Two hundred times repeated 5-fold cross-validation was used to train the model and test performance by assessing the classification accuracy, the discrimination accuracy as receiver operating curve - area under curve (ROC-AUC), and by generating confusion matrices. RESULTS: Thirty-four images were classified as clinically unacceptable and 106 were classified as clinically acceptable. The accuracy of the independent test set (mean ± standard deviation) was 85.4 ± 5.5%. The ROC-AUC was 0.856 (0.851 - 0.861) (mean; 95% confidence interval). CONCLUSIONS: Radiomics AI can automatically detect a significant portion of T2W images of suboptimal image quality. This can help improve image quality at the time of acquisition, thus reducing repeat scans and improving diagnostic accuracy.

Quantifiable Measures of Abdominal Wall Motion for Quality Assessment of Cine-MRI Slices in Detection of Abdominal Adhesions.

Abdominal adhesions present a diagnostic challenge, and classic imaging modalities can miss their presence. Cine-MRI, which records visceral sliding during patient-controlled breathing, has proven useful in detecting and mapping adhesions. However, patient movements can affect the accuracy of these images, despite there being no standardized algorithm for defining sufficiently high-quality images. This study aims to develop a biomarker for patient movements and determine which patient-related factors influence movement during cine-MRI. Included patients underwent cine-MRI to detect adhesions for chronic abdominal complaints, data were collected from electronic patient files and radiologic reports. Ninety slices of cine-MRI were assessed for quality, using a five-point scale to quantify amplitude, frequency, and slope, from which an image-processing algorithm was developed. The biomarkers closely correlated with qualitative assessments, with an amplitude of 6.5 mm used to distinguish between sufficient and insufficient-quality slices. In multivariable analysis, the amplitude of movement was influenced by age, sex, length, and the presence of a stoma. Unfortunately, no factor was changeable. Strategies for mitigating their impact may be challenging. This study highlights the utility of the developed biomarker in evaluating image quality and providing useful feedback for clinicians. Future studies could improve diagnostic quality by implementing automated quality criteria during cine-MRI.

Interpatient variation in normal peripheral zone apparent diffusion coefficient: effect on the prediction of prostate cancer aggressiveness.

PURPOSE: To determine the interpatient variability of prostate peripheral zone (PZ) apparent diffusion coefficient (ADC) and its effect on the assessment of prostate cancer aggressiveness. MATERIALS AND METHODS: The requirement for institutional review board approval was waived. Intra- and interpatient variation of PZ ADCs was determined by means of repeated measurements of normal ADCs at three magnetic resonance (MR) examinations in a retrospective cohort of 10 consecutive patients who had high prostate-specific antigen levels and negative findings at transrectal ultrasonographically-guided biopsy. In these patients, no signs of PZ cancer were found at all three MR imaging sessions. The effect of interpatient variation on the assessment of prostate cancer aggressiveness was examined in a second retrospective cohort of 51 patients with PZ prostate cancer. Whole-mount step-section pathologic evaluation served as reference standard for placement of regions of interest on tumors and normal PZ. Repeated-measures analysis of variance was used to determine the significance of the interpatient variations in ADCs. Linear logistic regression was used to assess whether incorporating normal PZ ADCs improves the prediction of cancer aggressiveness. RESULTS: Analysis of variance revealed that interpatient variability (1.2-2.0×10(-3) mm2/sec) was significantly larger than measurement variability (0.068×10(-3) mm2/sec±0.027 [standard deviation]) (P=.0058). Stand-alone tumor ADCs showed an area under the receiver operating characteristic curve (AUC) of 0.91 for discriminating low-grade versus high-grade tumors. Incorporating normal PZ ADC significantly improved the AUC to 0.96 (P=.0401). CONCLUSION: PZ ADCs show significant interpatient variation, which has a substantial effect on the prediction of prostate cancer aggressiveness. Correcting this effect results in a significant increase in diagnostic accuracy.

Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer.

PURPOSE: To retrospectively determine the relationship between apparent diffusion coefficients (ADCs) obtained with 3.0-T diffusion-weighted (DW) magnetic resonance (MR) imaging and Gleason grades in peripheral zone prostate cancer. MATERIALS AND METHODS: The requirement to obtain institutional review board approval was waived. Fifty-one patients with prostate cancer underwent MR imaging before prostatectomy, including DW MR imaging with b values of 0, 50, 500, and 800 sec/mm(2). In prostatectomy specimens, separate slice-by-slice determinations of Gleason grade groups were performed according to primary, secondary, and tertiary Gleason grades. In addition, tumors were classified into qualitative grade groups (low-, intermediate-, or high-grade tumors). ADC maps were aligned to step-sections and regions of interest annotated for each tumor slice. The median ADC of tumors was related to qualitative grade groups with linear mixed-model regression analysis. The accuracy of the median ADC in the most aggressive tumor component in the differentiation of low- from combined intermediate- and high-grade tumors was summarized by using the area under the receiver operating characteristic (ROC) curve (A(z)). RESULTS: In 51 prostatectomy specimens, 62 different tumors and 251 step-section tumor lesions were identified. The median ADC in the tumors showed a negative relationship with Gleason grade group, and differences among the three qualitative grade groups were statistically significant (P < .001). Overall, with an increase of one qualitative grade group, the median ADC (±standard deviation) decreased 0.18 × 10(-3) mm(2)/sec ± 0.02. Low-, intermediate-, and high-grade tumors had a median ADC of 1.30 × 10(-3) mm(2)/sec ± 0.30, 1.07 × 10(-3) mm(2)/sec ± 0.30, and 0.94 × 10(-3) mm(2)/sec ± 0.30, respectively. ROC analysis showed a discriminatory performance of A(z) = 0.90 in discerning low-grade from combined intermediate- and high-grade lesions. CONCLUSION: ADCs at 3.0 T showed an inverse relationship to Gleason grades in peripheral zone prostate cancer. A high discriminatory performance was achieved in the differentiation of low-, intermediate-, and high-grade cancer.

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.

Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks.

PURPOSE: To investigate whether multi-view convolutional neural networks can improve a fully automated lymph node detection system for pelvic MR Lymphography (MRL) images of patients with prostate cancer. METHODS: A fully automated computer-aided detection (CAD) system had been previously developed to detect lymph nodes in MRL studies. The CAD system was extended with three types of 2D multi-view convolutional neural networks (CNN) aiming to reduce false positives (FP). A 2D multi-view CNN is an efficient approximation of a 3D CNN, and three types were evaluated: a 1-view, 3-view, and 9-view 2D CNN. The three deep learning CNN architectures were trained and configured on retrospective data of 240 prostate cancer patients that received MRL images as the standard of care between January 2008 and April 2010. The MRL used ferumoxtran-10 as a contrast agent and comprised at least two imaging sequences: a 3D T1-weighted and a 3D T2*-weighted sequence. A total of 5089 lymph nodes were annotated by two expert readers, reading in consensus. A first experiment compared the performance with and without CNNs and a second experiment compared the individual contribution of the 1-view, 3-view, or 9-view architecture to the performance. The performances were visually compared using free-receiver operating characteristic (FROC) analysis and statistically compared using partial area under the FROC curve analysis. Training and analysis were performed using bootstrapped FROC and 5-fold cross-validation. RESULTS: Adding multi-view CNNs significantly (p < 0.01) reduced false positive detections. The 3-view and 9-view CNN outperformed (p < 0.01) the 1-view CNN, reducing FP from 20.6 to 7.8/image at 80% sensitivity. CONCLUSION: Multi-view convolutional neural networks significantly reduce false positives in a lymph node detection system for MRL images, and three orthogonal views are sufficient. At the achieved level of performance, CAD for MRL may help speed up finding lymph nodes and assessing them for potential metastatic involvement.

Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI.

A novel automated computerized scheme has been developed for determining a likelihood measure of malignancy for cancer suspicious regions in the prostate based on dynamic contrast-enhanced magnetic resonance imaging (MRI) (DCE-MRI) images. Our database consisted of 34 consecutive patients with histologically proven adenocarcinoma in the peripheral zone of the prostate. Both carcinoma and non-malignant tissue were annotated in consensus on MR images by a radiologist and a researcher using whole mount step-section histopathology as standard of reference. The annotations were used as regions of interest (ROIs). A feature set comprising pharmacokinetic parameters and a T1 estimate was extracted from the ROIs to train a support vector machine as classifier. The output of the classifier was used as a measure of likelihood of malignancy. Diagnostic performance of the scheme was evaluated using the area under the ROC curve. The diagnostic accuracy obtained for differentiating prostate cancer from non-malignant disorders in the peripheral zone was 0.83 (0.75-0.92). This suggests that it is feasible to develop a computer aided diagnosis system capable of characterizing prostate cancer in the peripheral zone based on DCE-MRI.

Elastic Versus Rigid Image Registration in Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy: A Systematic Review and Meta-analysis.

CONTEXT: The main difference between the available magnetic resonance imaging-transrectal ultrasound (MRI-TRUS) fusion platforms for prostate biopsy is the method of image registration being either rigid or elastic. As elastic registration compensates for possible deformation caused by the introduction of an ultrasound probe for example, it is expected that it would perform better than rigid registration. OBJECTIVE: The aim of this meta-analysis is to compare rigid with elastic registration by calculating the detection odds ratio (OR) for both subgroups. The detection OR is defined as the ratio of the odds of detecting clinically significant prostate cancer (csPCa) by MRI-TRUS fusion biopsy compared with systematic TRUS biopsy. Secondary objectives were the OR for any PCa and the OR after pooling both registration techniques. EVIDENCE ACQUISITION: The electronic databases PubMed, Embase, and Cochrane were systematically searched for relevant studies according to the Preferred Reporting Items for Systematic Review and Meta-analysis Statement. Studies comparing MRI-TRUS fusion and systematic TRUS-guided biopsies in the same patient were included. The quality assessment of included studies was performed using the Quality Assessment of Diagnostic Accuracy Studies version 2. EVIDENCE SYNTHESIS: Eleven papers describing elastic and 10 describing rigid registration were included. Meta-analysis showed an OR of csPCa for elastic and rigid registration of 1.45 (95% confidence interval [CI]: 1.21-1.73, p<0.0001) and 1.40 (95% CI: 1.13-1.75, p=0.002), respectively. No significant difference was seen between the subgroups (p=0.83). Pooling subgroups resulted in an OR of 1.43 (95% CI: 1.25-1.63, p<0.00001). CONCLUSIONS: No significant difference was identified between rigid and elastic registration for MRI-TRUS fusion-guided biopsy in the detection of csPCa; however, both techniques detected more csPCa than TRUS-guided biopsy alone. PATIENT SUMMARY: We did not identify any significant differences in prostate cancer detection between two distinct magnetic resonance imaging-transrectal ultrasound fusion systems which vary in their method of compensating for prostate deformation.

Standardized threshold approach using three-dimensional proton magnetic resonance spectroscopic imaging in prostate cancer localization of the entire prostate.

OBJECTIVES: We sought to determine the localization accuracy using 3-dimensional (3D) proton magnetic resonance spectroscopic imaging (MRSI) of the entire prostate with a standardized thresholds approach in prostate cancer patients. MATERIALS AND METHODS: In a prospective study, 32 consecutive patients were examined. Mean age and prostate specific antigen level were 61 years and 7.8 ng/mL, respectively. Median biopsy Gleason score was 6. T2-weighted MRI and 3D MRSI of the entire prostate were performed. Three readers recorded the location of suspicious peripheral zone and central gland cancer nodules on a standardized division of the prostate (14 regions of interest [ROI]) using a standardized thresholds approach. The degree of diagnostic confidence for each ROI was recorded on a 5-point scale. Reconstructed whole-mount section histopathology was the standard of reference. The sensitivity, specificity, positive, and negative predictive value, overall accuracy and interobserver agreement were calculated. Areas under the ROI-based receiver operating characteristic curve (AUC) and diagnostic performance parameters were determined. RESULTS: The standardized thresholds approach had an accuracy of 81% and an AUC of 0.85-0.86 for differentiation between benign and malignant ROIs in the peripheral zone and an accuracy of 87% and an AUC of 0.86-0.91 for this differentiation in the central gland, respectively. Specificities of 81% to 88% were achieved with accompanying sensitivities of 75% to 92% for both peripheral zone and central gland, respectively. Moderate to near-perfect interobserver agreement was demonstrated (kappa=0.42-0.91). CONCLUSION: Our data indicate that a standardized zone-specific threshold approach in MRSI of the prostate is able to prospectively differentiate between benign and malignant tissues in the peripheral zone and the central gland with good accuracy and interobserver agreement.

Segmentation of the heart muscle in 3-D pediatric echocardiographic images.

This study aimed to show segmentation of the heart muscle in pediatric echocardiographic images as a preprocessing step for tissue analysis. Transthoracic image sequences (2-D and 3-D volume data, both derived in radiofrequency format, directly after beam forming) were registered in real time from four healthy children over three heart cycles. Three preprocessing methods, based on adaptive filtering, were used to reduce the speckle noise for optimizing the distinction between blood and myocardium, while preserving the sharpness of edges between anatomical structures. The filtering kernel size was linked to the local speckle size and the speckle noise characteristics were considered to define the optimal filter in one of the methods. The filtered 2-D images were thresholded automatically as a first step of segmentation of the endocardial wall. The final segmentation step was achieved by applying a deformable contour algorithm. This segmentation of each 2-D image of the 3-D+time (i.e., 4-D) datasets was related to that of the neighboring images in both time and space. By thus incorporating spatial and temporal information of 3-D ultrasound image sequences, an automated method using image statistics was developed to perform 3-D segmentation of the heart muscle.

Designing image segmentation studies: Statistical power, sample size and reference standard quality.

Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i.e. the proportion of voxels matching the reference standard) between two algorithms. Furthermore, we derive a formula to relate reference standard errors to their effect on the sample sizes of studies using lower-quality (but potentially more affordable and practically available) reference standards. The accuracy of the derived sample size formula was estimated through Monte Carlo simulation, demonstrating, with 95% confidence, a predicted statistical power within 4% of simulated values across a range of model parameters. This corresponds to sample size errors of less than 4 subjects and errors in the detectable accuracy difference less than 0.6%. The applicability of the formula to real-world data was assessed using bootstrap resampling simulations for pairs of algorithms from the PROMISE12 prostate MR segmentation challenge data set. The model predicted the simulated power for the majority of algorithm pairs within 4% for simulated experiments using a high-quality reference standard and within 6% for simulated experiments using a low-quality reference standard. A case study, also based on the PROMISE12 data, illustrates using the formulae to evaluate whether to use a lower-quality reference standard in a prostate segmentation study.

IMRT boost dose planning on dominant intraprostatic lesions: gold marker-based three-dimensional fusion of CT with dynamic contrast-enhanced and 1H-spectroscopic MRI.

PURPOSE: To demonstrate the theoretical feasibility of integrating two functional prostate magnetic resonance imaging (MRI) techniques (dynamic contrast-enhanced MRI [DCE-MRI] and 1H-spectroscopic MRI [MRSI]) into inverse treatment planning for definition and potential irradiation of a dominant intraprostatic lesion (DIL) as a biologic target volume for high-dose intraprostatic boosting with intensity-modulated radiotherapy (IMRT). METHODS AND MATERIALS: In 5 patients, four gold markers were implanted. An endorectal balloon was inserted for both CT and MRI. A DIL volume was defined by DCE-MRI and MRSI using different prostate cancer-specific physiologic (DCE-MRI) and metabolic (MRSI) parameters. CT-MRI registration was performed automatically by matching three-dimensional gold marker surface models with the iterative closest point method. DIL-IMRT plans, consisting of whole prostate irradiation to 70 Gy and a DIL boost to 90 Gy, and standard IMRT plans, in which the whole prostate was irradiated to 78 Gy were generated. The tumor control probability and rectal wall normal tissue complication probability were calculated and compared between the two IMRT approaches. RESULTS: Combined DCE-MRI and MRSI yielded a clearly defined single DIL volume (range, 1.1-6.5 cm3) in all patients. In this small, selected patient population, no differences in tumor control probability were found. A decrease in the rectal wall normal tissue complication probability was observed in favor of the DIL-IMRT plan versus the plan with IMRT to 78 Gy. CONCLUSION: Combined DCE-MRI and MRSI functional image-guided high-dose intraprostatic DIL-IMRT planned as a boost to 90 Gy is theoretically feasible. The preliminary results have indicated that DIL-IMRT may improve the therapeutic ratio by decreasing the normal tissue complication probability with an unchanged tumor control probability. A larger patient population, with more variations in the number, size, and localization of the DIL, and a feasible mechanism for treatment implementation has to be studied to extend these preliminary tumor control and toxicity estimates.

Automated multistructure atlas-assisted detection of lymph nodes using pelvic MR lymphography in prostate cancer patients.

PURPOSE: To investigate whether atlas-based anatomical information can improve a fully automated lymph node detection system for pelvic MR lymphography (MRL) images of patients with prostate cancer. METHODS: Their data set contained MRL images of 240 prostate cancer patients who had an MRL as part of their clinical work-up between January 2008 and April 2010, with ferumoxtran-10 as contrast agent. Each MRL consisted of at least a 3D T1-weighted sequence, a 3D T2*-weighted sequence, and a FLASH-3D sequence. The reference standard was created by two expert readers, reading in consensus, who annotated and interactively segmented the lymph nodes in all MRL studies. A total of 5089 lymph nodes were annotated. A fully automated computer-aided detection (CAD) system was developed to find lymph nodes in the MRL studies. The system incorporates voxel features based on image intensities, the Hessian matrix, and spatial position. After feature calculation, a GentleBoost-classifier in combination with local maxima detection was used to identify lymph node candidates. Multiatlas based anatomical information was added to the CAD system to assess whether this could improve performance. Using histogram analysis and free-receiver operating characteristic analysis, this was compared to a strategy where relative position features were used to encode anatomical information. RESULTS: Adding atlas-based anatomical information to the CAD system reduced false positive detections both visually and quantitatively. Median likelihood values of false positives decreased significantly in all annotated anatomical structures. The sensitivity increased from 53% to 70% at 10 false positives per lymph node. CONCLUSIONS: Adding anatomical information through atlas registration significantly improves an automated lymph node detection system for MRL images.