T2-weighted Multiparametric Magnetic Resonance Imaging Research Articles

Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC.

To develop radiomics signatures from multiparametric magnetic resonance imaging (MRI) scans to detect epidermal growth factor receptor (EGFR) mutations and predict the response to EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in non-small cell lung cancer (NSCLC) patients with brain metastasis (BM). We included 230 NSCLC patients with BM treated at our hospital between January 2017 and December 2021 and 80 patients treated at another hospital between July 2014 and October 2021 to form the primary and external validation cohorts, respectively. All patients underwent contrast-enhanced T1-weighted (T1C) and T2-weighted (T2W) MRI, and radiomics features were extracted from both the tumor active area (TAA) and peritumoral edema area (POA) for each patient. The least absolute shrinkage and selection operator (LASSO) was used to identify the most predictive features. Radiomics signatures (RSs) were constructed using logistic regression analysis. For predicting the EGFR mutation status, the created RS-EGFR-TAA and RS-EGFR- POA showed similar performance. By combination of TAA and POA, the multi-region combined RS (RS-EGFR-Com) achieved the highest prediction performance, with AUCs of 0.896, 0.856, and 0.889 in the primary training, internal validation, and external validation cohort, respectively. For predicting response to EGFR-TKI, the multi-region combined RS (RS-TKI-Com) generated the highest AUCs in the primary training (AUC = 0.817), internal validation (AUC = 0.788), and external validation (AUC = 0.808) cohort, respectively. Our findings suggested values of multiregional radiomics of BM for predicting EGFR mutations and response to EGFR-TKI. TheapplicationofradiomicanalysisofmultiparametricbrainMRIhasproventobeapromisingtooltostratifywhichpatientscanbenefitfromEGFR-TKItherapyandtofacilitatetheprecisetherapeuticsofNSCLCpatientswithbrainmetastases. • Multiregional radiomics can improve efficacy in predicting therapeutic response to EGFR-TKI therapy in NSCLC patients with brain metastasis. • The tumor active area (TAA) and peritumoral edemaarea (POA) may hold complementary information related to the therapeutic response to EGFR-TKI. • The developed multi-region combined radiomics signature achieved the best predictive performance and may be considered as a potential tool for predicting response to EGFR-TKI.

Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer.

PURPOSE This retrospective study aims to evaluate the use of multi-parametric magnetic resonance imaging (MRI) in predicting lymph-vascular space invasion (LVSI) in early-stage cervical cancer using radiomics methods. METHODS A total of 163 patients who underwent contrast-enhanced T1-weighted (CE T1W) and T2-weighted (T2W) MRI scans at 3.0T were enrolled between January 2014 and September 2019. Radiomics features were extracted and selected from the tumoral and peritumoral regions at different dilation distances outside the tumor. Mann-Whitney U test, the least absolute shrinkage and selection operator logistic regression, and logistic regression was applied to select the predictive features and develop the radiomics signature. Univariate analysis was performed on the clinical characteristics. The radiomics nomogram was constructed incorporating the radiomics signature and the selected important clinical predictor. Prediction performance of the radiomics signature, clinical model, and nomogram was evaluated with the area under the curve (AUC), specificity, sensitivity, calibration, and decision curve analysis (DCA). RESULTS A total of 5 features that were selected from the peritumoral regions with 3- and 7-mm dilation distances outside tumors in CE T1W and T2W MRI, respectively, showed optimal discriminative performance. The radiomics signature comprising the selected features was significantly associated with the LVSI status. The radiomics nomogram integrating the radiomics signature and degree of cellular differentiation exhibited the best predictability with AUCs of 0.771 (specificity (SPE)=0.831 and sensitivity (SEN)=0.581) in the training cohort and 0.788 (SPE=0.727, SEN=0.773) in the validation cohort. DCA confirmed the clinical usefulness of our model. CONCLUSION Our results illustrate that the radiomics nomogram based on MRI features from peritumoral regions and the degree of cellular differentiation can be used as a noninvasive tool for predicting LVSI in cervical cancer.

Abstract 5483: An image-based mechanistic computational model for early prediction of organ-confined untreated prostate cancer growth

Abstract According to the American Cancer Society, prostate cancer (PCa) is the most common newly-diagnosed cancer and the second leading cancer-related cause of death among men in the US in 2019. The current clinical protocols of PCa are based on two key strategies: regular screening of men over fifty and patient triaging in risk groups. Prostatic tumors are usually detected at an early organ-confined stage, which poses a low to intermediate risk to the patient and may not produce any symptoms or require treatment for long time. However, most patients are prescribed a radical treatment immediately after diagnosis (e.g., surgery or radiation therapy), which may adversely impact their lives without necessarily prolonging their longevity or quality of life. Moreover, some patients who initially delay their treatment ultimately succumb to PCa due to an inaccurate initial diagnosis. Thus, while regular screening enables the detection of the majority of tumors at an early and mild stage, the limited individualization of patient monitoring and treatment has led to significant rates of both overtreatment and undertreatment. To overcome these fundamental limitations in the clinical management of PCa, we propose the use of a mechanistic mathematical model for which we can perform computer simulations to forecast patient-specific tumor growth. This computational model is based on a set of partial differential equations that describe the main mechanisms involved in organ-confined PCa growth. The model is parameterized using longitudinal multiparametric magnetic resonance (MR) images and the available clinical data for each patient. Tumor growth is simulated over the patient's prostate extracted from T2-weighted MR images. We use isogeometric analysis to accurately and efficiently address the computational challenges arising in this application. Our preliminary simulation results show that our computational technology can predict tumor growth and associated serum Prostate Specific Antigen (PSA, a key biomarker in clinical management of PCa) with reasonable accuracy. We also explore the potential of model parameters and variables to characterize tumor aggressivity. Thus, we believe that our imaging-based modeling approach could be a promising tool capable of being implemented in current PCa protocols to assist physicians in the clinical management of PCa. Citation Format: Guillermo Lorenzo, Thomas J. Hughes, Alessandro Reali, Hector Gomez, Thomas E. Yankeelov. An image-based mechanistic computational model for early prediction of organ-confined untreated prostate cancer growth [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5483.

Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer

ObjectiveThe aim of this study was to investigate whether pretherapeutic, multiparametric magnetic resonance imaging (MRI) radiomic features can be used for predicting non-response to neoadjuvant therapy in patients with locally advanced rectal cancer (LARC).MethodsWe retrospectively enrolled 425 patients with LARC [allocated in a 3:1 ratio to a primary (n = 318) or validation (n = 107) cohort] who received neoadjuvant therapy before surgery. All patients underwent T1-weighted, T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MRI scans before receiving neoadjuvant therapy. We extracted 2424 radiomic features from the pretherapeutic, multiparametric MR images of each patient. The Wilcoxon rank-sum test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression were successively performed for feature selection, whereupon a multiparametric MRI-based radiomic model was established by means of multivariate logistic regression analysis. This feature selection and multivariate logistic regression analysis was also performed on all single-modality MRI data to establish four single-modality radiomic models. The performance of the five radiomic models was evaluated by receiver operating characteristic (ROC) curve analysis in both cohorts.ResultsThe multiparametric, MRI-based radiomic model based on 16 features showed good predictive performance in both the primary (p < 0.01) and validation (p < 0.05) cohorts, and performed better than all single-modality models. The area under the ROC curve of this multiparametric MRI-based radiomic model achieved a score of 0.822 (95% CI 0.752–0.891).ConclusionsWe demonstrated that pretherapeutic, multiparametric MRI radiomic features have potential in predicting non-response to neoadjuvant therapy in patients with LARC.

Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer

Purpose:This study aims to combine multiparametric magnetic resonance imaging (MRI) and digitized pathology with machine learning to generate predictive maps of histologic features for prostate cancer localization.Methods and Materials:Thirty-nine patients underwent MRI prior to prostatectomy. After surgery, tissue was sliced according to MRI orientation using patient-specific 3-dimensionally printed slicing jigs. Whole-mount sections were annotated by our pathologist and digitally contoured to differentiate the lumen and epithelium. Slides were co-registered to the T2-weighted MRI scan. A learning curve was generated to determine the number of patients required for a stable machine-learning model. Patients were randomly stratified into 2 training sets and 1 test set. Two partial least-squares regression models were trained, each capable of predicting lumen and epithelium density. Predicted density values were calculated for each patient in the test dataset, mapped into the MRI space, and compared between regions confirmed as high-grade prostate cancer.Results:The learning-curve analysis showed that a stable fit was achieved with data from 10 patients. Maps indicated that regions of increased epithelium and decreased lumen density, generated from each independent model, corresponded with pathologist-annotated regions of high-grade cancer.Conclusions:We present a radio-pathomic approach to mapping prostate cancer. We find that the maps are useful for highlighting high-grade tumors. This technique may be relevant for dose-painting strategies in prostate radiation therapy. © 2018 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Age-related Changes in Eye, Brain and Visuomotor Behavior in the DBA/2J Mouse Model of Chronic Glaucoma

Although elevated intraocular pressure (IOP) and age are major risk factors for glaucoma, their effects on glaucoma pathogenesis remain unclear. This study examined the onset and progression of glaucomatous changes to ocular anatomy and physiology, structural and physiological brain integrity, and visuomotor behavior in the DBA/2J mice via non-invasive tonometry, multi-parametric magnetic resonance imaging (MRI) and optokinetic assessments from 5 to 12 months of age. Using T2-weighted MRI, diffusion tensor MRI, and manganese-enhanced MRI, increasing IOP elevation at 9 and 12 months old coincided with anterior chamber deepening, altered fractional anisotropy and radial diffusivity of the optic nerve and optic tract, as well as reduced anterograde manganese transport along the visual pathway respectively in the DBA/2J mice. Vitreous body elongation and visuomotor function deterioration were observed until 9 months old, whereas axial diffusivity only decreased at 12 months old in diffusion tensor MRI. Under the same experimental settings, C57BL/6J mice only showed modest age-related changes. Taken together, these results indicate that the anterior and posterior visual pathways of the DBA/2J mice exhibit differential susceptibility to glaucomatous neurodegeneration observable by in vivo multi-modal examinations.

Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology.

Purpose To assess the diagnostic performance and interreader agreement of a standardized diagnostic algorithm in determining the histologic type of small (≤4 cm) renal masses (SRMs) with multiparametric magnetic resonance (MR) imaging. Materials and Methods This single-center retrospective HIPAA-compliant institutional review board-approved study included 103 patients with 109 SRMs resected between December 2011 and July 2015. The requirement for informed consent was waived. Presurgical renal MR images were reviewed by seven radiologists with diverse experience. Eleven MR imaging features were assessed, and a standardized diagnostic algorithm was used to determine the most likely histologic diagnosis, which was compared with histopathology results after surgery. Interreader variability was tested with the Cohen κ statistic. Regression models using MR imaging features were used to predict the histopathologic diagnosis with 5% significance level. Results Clear cell renal cell carcinoma (RCC) and papillary RCC were diagnosed, with sensitivities of 85% (47 of 55) and 80% (20 of 25), respectively, and specificities of 76% (41 of 54) and 94% (79 of 84), respectively. Interreader agreement was moderate to substantial (clear cell RCC, κ = 0.58; papillary RCC, κ = 0.73). Signal intensity (SI) of the lesion on T2-weighted MR images and degree of contrast enhancement (CE) during the corticomedullary phase were independent predictors of clear cell RCC (SI odds ratio [OR]: 3.19; 95% confidence interval [CI]: 1.4, 7.1; P = .003; CE OR, 4.45; 95% CI: 1.8, 10.8; P < .001) and papillary RCC (CE OR, 0.053; 95% CI: 0.02, 0.2; P < .001), and both had substantial interreader agreement (SI, κ = 0.69; CE, κ = 0.71). Poorer performance was observed for chromophobe histology, oncocytomas, and minimal fat angiomyolipomas, (sensitivity range, 14%-67%; specificity range, 97%-99%), with fair to moderate interreader agreement (κ range = 0.23-0.43). Segmental enhancement inversion was an independent predictor of oncocytomas (OR, 16.21; 95% CI: 1.0, 275.4; P = .049), with moderate interreader agreement (κ = 0.49). Conclusion The proposed standardized MR imaging-based diagnostic algorithm had diagnostic accuracy of 81% (88 of 109) and 91% (99 of 109) in the diagnosis of clear cell RCC and papillary RCC, respectively, while achieving moderate to substantial interreader agreement among seven radiologists. © RSNA, 2018 Online supplemental material is available for this article.

Prostate Cancer: A Correlative Study of Multiparametric MR Imaging and Digital Histopathology.

Purpose To correlate multiparametric magnetic resonance (MR) imaging and quantitative digital histopathologic analysis (DHA) of the prostate. Materials and Methods This retrospective study was approved by the local institutional review board and was HIPAA compliant. Forty patients (median age, 60 years; age range, 44-71 years) who underwent prostate MR imaging consisting of T2-weighted and diffusion-weighted (DW) MR imaging along with subsequent robot-assisted radical prostatectomy gave informed consent to be included. Whole-mount tissue specimens were obtained with a patient-specific mold, and DHA was performed to assess the lumen, epithelium, stroma, and epithelial nucleus. These DHA images were registered with MR images and were correlated on a per-voxel basis. The relationship between MR imaging and DHA was assessed by using a linear mixed-effects model and the Pearson correlation coefficient. Results T2-weighted MR imaging, apparent diffusion coefficient (ADC) of DW imaging, and high-b-value DW imaging were significantly related to specific DHA parameters (P < .01). For instance, lumen density (ie, the percentage area of tissue components) was associated with T2-weighted MR imaging (slope = 0.36 ± 0.05 [standard error], γ = 0.35), ADC (slope = 0.47 ± 0.05, γ = 0.50), and high-b-value DW imaging (slope = -0.44 ± 0.05, γ = -0.44). Differences between regions harboring benign tissue and those harboring malignant tissue were observed at MR imaging and DHA (P < .01). Gleason score was significantly associated with MR imaging and DHA parameters (P < .05). For example, it was positively related to high-b-value DW imaging (slope = 0.21 ± 0.16, γ = 0.18) and negatively related to lumen density (slope = -0.19 ± 0.18, γ = -0.35). Conclusion Overall, significant associations were observed between MR imaging and DHA, regardless of prostate anatomy. © RSNA, 2017 Online supplemental material is available for this article.

Multiparametric MRI in differentiating pulmonary artery sarcoma and pulmonary thromboembolism: a preliminary experience.

We aimed to define multiparametric magnetic resonance imaging (MRI) findings to differentiate between pulmonary artery sarcoma (PAS) and pulmonary thromboembolism (PTE). Eleven patients with suspected PTE were prospectively included to undergo pulmonary MRI before surgery or biopsy. MRI protocol included an unenhanced sequence, diffusion-weighted imaging (DWI, b=800 s/mm2) and a dynamic contrast-enhanced sequence. Morphologic characteristics including distribution, filling defect, and intensity were observed on T1-, T2-, and fat-suppressed T2-weighted imaging, DWI, and contrast-enhanced MRI. Apparent diffusion coefficient (ADC) values were calculated. Six patients were pathologically diagnosed as PAS and the other five as chronic PTE. There were no significant differences in age, gender, presenting symptoms, D-dimer, and N-terminal pro-brain natriuretic peptide between the two groups (P > 0.05). Among MRI findings that were tested for their ability to diagnose PAS, area under the curve (AUC) was significantly higher than 0.5 for main pulmonary artery involvement (AUC, 0.83±0.13; P = 0.011), hyperintensity on fat-suppressed T2-weighted imaging (AUC, 0.82±0.14; P = 0.025), hyperintensity on DWI (AUC, 0.88±0.12; P = 0.002), contrast enhancement (AUC, 0.92±0.10; P < 0.001) and pleural effusion (AUC, 0.82±0.14; P = 0.025). Moreover, grape-like appearance in distal pulmonary artery and cardiac invasion had 100% specificity for diagnosis of PAS. However, ADC value of PAS was not significantly different than that of chronic PTE (U, 12.00; P = 0.584). Hyperintense filling defect in main pulmonary artery on fat-suppressed T2-weighted imaging and DWI and contrast enhancement may help to discriminate PAS from PTE.

Automated 2D Segmentation of Prostate in T2-weighted MRI Scans

The prostate cancer is the second most frequent tumor amongst men. Statistics shows that biopsy reveals only 70-80% clinical cancer cases. Multiparametric magnetic resonance imaging (MRI) technique comes to play and is used to help to determine the location to perform a biopsy. With the aim to automating the biopsy localization, prostate segmentation has to be performed in magnetic resonance images. Computer image analysis methods play the key role here. The problem of automated prostate magnetic resonance (MR) image segmentation is burdened by the fact that MRI signal intensity is not standardized: field of view and image appearance is for a large part determined by acquisition protocol, field strength, coil profile and scanner type. Authors overview the most recent Prostate MR image segmentation challenge results and provide insights on T2-weighted MRI scan images automated prostate segmentation problem by comparing the best obtained automatic segmentation algorithms and applying them to 2D prostate segmentation case. The most important benefit of this research will have medical doctors involved in the management of the cancer.

PI-RADS Version 2: A Pictorial Update

The Prostate Imaging Reporting and Data System (PI-RADS) is the result of an extensive international collaborative effort. PI-RADS provides a comprehensive yet practical set of guidelines for the interpretation and reporting of prostate multiparametric magnetic resonance (MR) imaging that will promote the use of this modality for detecting clinically significant prostate cancer. The revised PI-RADS version (PI-RADS version 2) introduces important changes to the original system used for assessing the level of suspicion for clinically significant cancer with multiparametric MR imaging. For peripheral zone abnormalities in PI-RADS version 2, the score obtained from the apparent diffusion coefficient (ADC) map in combination with diffusion-weighted imaging (DWI) performed with high b values (≥1400 sec/mm(2)) is the dominant parameter for determining the overall level of suspicion for clinically significant cancer. For transition zone abnormalities, the score obtained from T2-weighted MR imaging is dominant for overall lesion assessment. Dynamic contrast material-enhanced MR imaging has ancillary roles in the characterization of peripheral zone lesions considered equivocal for clinically significant cancer on the basis of the DWI-ADC combination and in the detection of lesions missed with other multiparametric MR pulse sequences. Assessment with dynamic contrast-enhanced MR imaging is also simplified, being considered positive or negative on the basis of qualitative evaluation for a focal area of rapid enhancement matching an abnormality on DWI-ADC or T2-weighted MR images. In PI-RADS version 2, MR spectroscopic imaging is not incorporated into lesion assessment. In this article, a pictorial overview is provided of the revised PI-RADS version 2 assessment categories for the likelihood of clinically significant cancer. PI-RADS version 2 is expected to evolve with time, with updated versions being released as experience in the use of PI-RADS version 2 increases and as new scientific evidence and technologies emerge. (©)RSNA, 2016.

A Review of Imaging Methods for Prostate Cancer Detection.

Imaging is playing an increasingly important role in the detection of prostate cancer (PCa). This review summarizes the key imaging modalities—multiparametric ultrasound (US), multiparametric magnetic resonance imaging (MRI), MRI–US fusion imaging, and positron emission tomography (PET) imaging—used in the diagnosis and localization of PCa. Emphasis is laid on the biological and functional characteristics of tumors that rationalize the use of a specific imaging technique. Changes to anatomical architecture of tissue can be detected by anatomical grayscale US and T2-weighted MRI. Tumors are known to progress through angiogenesis—a fact exploited by Doppler and contrast-enhanced US and dynamic contrast-enhanced MRI. The increased cellular density of tumors is targeted by elastography and diffusion-weighted MRI. PET imaging employs several different radionuclides to target the metabolic and cellular activities during tumor growth. Results from studies using these various imaging techniques are discussed and compared.

Prostate Cancer: Interobserver Agreement and Accuracy with the Revised Prostate Imaging Reporting and Data System at Multiparametric MR Imaging.

To evaluate accuracy and interobserver variability with the use of the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 for detection of prostate cancer at multiparametric magnetic resonance (MR) imaging in a biopsy-naïve patient population. This retrospective HIPAA-compliant study was approved by the local ethics committee, and written informed consent was obtained from all patients for use of their imaging and histopathologic data in future research studies. In 101 biopsy-naïve patients with elevated prostate-specific antigen levels who underwent multiparametric MR imaging of the prostate and subsequent transrectal ultrasonography (US)-MR imaging fusion-guided biopsy, suspicious lesions detected at multiparametric MR imaging were scored by five readers who were blinded to pathologic results by using to the newly revised PI-RADS and the scoring system developed in-house. Interobserver agreement was evaluated by using κ statistics, and the correlation of pathologic results with each of the two scoring systems was evaluated by using the Kendall τ correlation coefficient. Specimens of 162 lesions in 94 patients were sampled by means of transrectal US-MR imaging fusion biopsy. Results for 87 (54%) lesions were positive for prostate cancer. Kendall τ values with the PI-RADS and the in-house-developed scoring system, respectively, at T2-weighted MR imaging in the peripheral zone were 0.51 and 0.17 and in the transitional zone, 0.45 and -0.11; at diffusion-weighted MR imaging, 0.42 and 0.28; at dynamic contrast material-enhanced MR imaging, 0.23 and 0.24, and overall suspicion scores were 0.42 and 0.49. Median κ scores among all possible pairs of readers for PI-RADS and the in-house-developed scoring system, respectively, for T2-weighted MR images in the peripheral zone were 0.47 and 0.15; transitional zone, 0.37 and 0.07; diffusion-weighted MR imaging, 0.41 and 0.57; dynamic contrast-enhanced MR imaging, 0.48 and 0.41; and overall suspicion scores, 0.46 and 0.55. Use of the revised PI-RADS provides moderately reproducible MR imaging scores for detection of clinically relevant disease.

Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging.

The authors propose a computer-aided diagnosis (CAD) system for prostate cancer to aid in improving the accuracy, reproducibility, and standardization of multiparametric magnetic resonance imaging (MRI). The proposed system utilizes two MRI sequences [T2-weighted MRI and high-b-value (b = 2000 s/mm(2)) diffusion-weighted imaging (DWI)] and texture features based on local binary patterns. A three-stage feature selection method is employed to provide the most discriminative features. The authors included a total of 244 patients. Training the CAD system on 108 patients (78 MR-positive prostate cancers and 105 benign MR-positive lesions), two validation studies were retrospectively performed on 136 patients (68 MR-positive prostate cancers, 111 benign MR-positive lesions, and 117 MR-negative benign lesions). In distinguishing cancer from MR-positive benign lesions, an area under receiver operating characteristic curve (AUC) of 0.83 [95% confidence interval (CI): 0.76-0.89] was achieved. For cancer vs MR-positive or MR-negative benign lesions, the authors obtained an AUC of 0.89 AUC (95% CI: 0.84-0.93). The performance of the CAD system was not dependent on the specific regions of the prostate, e.g., a peripheral zone or transition zone. Moreover, the CAD system outperformed other combinations of MRI sequences: T2W MRI, high-b-value DWI, and the standard apparent diffusion coefficient (ADC) map of DWI. The novel CAD system is able to detect the discriminative texture features for cancer detection and localization and is a promising tool for improving the quality and efficiency of prostate cancer diagnosis.

The role of multi-parametric MR imaging in the detection of early inflammatory sacroiliitis according to ASAS criteria

PurposeTo retrospectively evaluate the accuracy of multi-parametric magnetic resonance (MR) imaging including fat saturated (FS) T2-weighted, short-tau inversion recovery (STIR), diffusion-weighted (DW-MR), and dynamic-contrast-enhanced MR (DCE-MR) imaging techniques in the diagnosis of early inflammatory sacroiliitis and determine the additional value of DW-MR and DCE-MR images according to recently defined ‘Assessment in SpondyloArthritis international Society’ criteria. Materials and methodsThe study included 45 patients with back pain. Two radiologists estimated the likelihood of osteitis in 4 independent viewing sessions including FS T2-weighted, STIR, DW-MR and DCE-MR images. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic (ROC) curve (AUC) were calculated. ResultsOf the 45 patients, 31 had inflammatory back pain. Of 31, 28 (90.3%) patients had inflammatory sacroiliitis diagnosed by clinical and laboratory analysis. FS T2-weighted MR images had the highest sensitivity (42.8% for both radiologists) for detecting osteitis in patients with inflammaory sacroiliitis when compared to other imaging sequences. For specificity, PPV, NPV, accuracy, and AUC levels there were no statistically significant difference between image viewing settings. However, adding STIR, DW-MR and DCE-MR images to the FS T2-weighted MR images did not improve the above stated indices. ConclusionFS T2-weighted MR imaging had the highest sensitivity when compared to other imaging sequences. The addition of DW-MR and DCE-MR images did not significantly improve the diagnostic value of MR imaging in the diagnosis of osteitis for both experienced and less experienced radiologists.

Does 11C-choline PET-CT contribute to multiparametric MRI for prostate cancer localisation?

The aim of this work was to determine whether 11C-choline positron emission tomography (PET)-computed tomography (CT) makes a positive contribution to multiparametric magnetic resonance imaging (MRI) for localisation of intraprostatic tumour nodules. A total of 73 patients with biopsy-proven intermediate- and high-risk prostate cancer were enrolled in a prospective imaging study consisting of T2-weighted (T2w), dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI and 11C-choline PET-CT before radical prostatectomy. Cancerous regions were delineated on the whole-mount prostatectomy sections and on the different MRI modalities and analysed in 24 segments per patient (3 sections, 8 segments each). To analyse PET-CT images, standardized uptake values (SUV) were calculated per segment. In total, 1,752 segments were analyzed of which 708 (40.4%) were found to be malignant. A high specificity (94.7, 93.6 and 92.2%) but relatively low sensitivity (31.2, 24.9 and 44.1%) for tumour localisation was obtained with T2w, DCE and DW MRI, respectively. Sensitivity values significantly increased when combining all MRI modalities (57.2%). For PET-CT, mean SUVmax of malignant octants was significantly higher than mean SUVmax of benign octants (3.68±1.30 vs. 3.12±1.02, p<0.0001). In terms of accuracy, the benefit of adding PET-CT to (multiparametric) MRI was less than 1%. The additional value of 11C-choline PET-CT to MRI in localising intraprostatic tumour nodules is limited, especially when multiparametric MRI is used.

Prostate Cancer: Computer-aided Diagnosis with Multiparametric 3-T MR Imaging—Effect on Observer Performance

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. 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. 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). 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.

Multiparametric MRI for prostate cancer localization in correlation to whole‐mount histopathology

To prospectively evaluate multiparametric magnetic resonance imaging (MRI) for accurate localization of intraprostatic tumor nodules, with whole-mount histopathology as the gold standard. Seventy-five patients with biopsy-proven, intermediate, and high-risk prostate cancer underwent preoperative T2-weighted (T2w), dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI at 1.5T. Localization of suspicious lesions was recorded for each of 24 standardized regions of interest on the different MR images and correlated with the pathologic findings. Generalized estimating equations (GEE) were used to estimate the sensitivity, specificity, accuracy, positive, and negative predictive value for every MRI modality, as well as to evaluate the influence of Gleason score and pT-stage. Tumor volume measurements on histopathological specimens were correlated with those on the different MR modalities (Pearson correlation). DW MRI had the highest sensitivity for tumor localization (31.1% vs. 27.4% vs. 44.5% for T2w, DCE, and DW MRI, respectively; P < 0.005), with more aggressive or more advanced tumors being more easily detected with this imaging modality. Significantly higher sensitivity values were obtained for the combination of T2w, DCE, and DW MRI (58.8%) as compared to each modality alone or any combination of two modalities (P < 0.0001). Tumor volume can most accurately be assessed by means of DW MRI (r = 0.75; P < 0.0001). Combining T2w, DCE, and DW imaging significantly improves prostate cancer localization.

Automated prostate cancer localization without the need for peripheral zone extraction using multiparametric MRI

Multiparametric magnetic resonance imaging (MRI) has been shown to have higher localization accuracy than transrectal ultrasound (TRUS) for prostate cancer. Therefore, automated cancer segmentation using multiparametric MRI is receiving a growing interest, since MRI can provide both morphological and functional images for tissue of interest. However, all automated methods to this date are applicable to a single zone of the prostate, and the peripheral zone (PZ) of the prostate needs to be extracted manually, which is a tedious and time-consuming job. In this paper, our goal is to remove the need of PZ extraction by incorporating the spatial and geometric information of prostate tumors with multiparametric MRI derived from T2-weighted MRI, diffusion-weighted imaging (DWI) and dynamic contrast enhanced MRI (DCE-MRI). In order to remove the need of PZ extraction, the authors propose a new method to incorporate the spatial information of the cancer. This is done by introducing a new feature called location map. This new feature is constructed by applying a nonlinear transformation to the spatial position coordinates of each pixel, so that the location map implicitly represents the geometric position of each pixel with respect to the prostate region. Then, this new feature is combined with multiparametric MR images to perform tumor localization. The proposed algorithm is applied to multiparametric prostate MRI data obtained from 20 patients with biopsy-confirmed prostate cancer. The proposed method which does not need the masks of PZ was found to have prostate cancer detection specificity of 0.84, sensitivity of 0.80 and dice coefficient value of 0.42. The authors have found that fusing the spatial information allows us to obtain tumor outline without the need of PZ extraction with a considerable success (better or similar performance to methods that require manual PZ extraction). Our experimental results quantitatively demonstrate the effectiveness of the proposed method, depicting that the proposed method has a slightly better or similar localization performance compared to methods which require the masks of PZ.

Microplasmin and Tissue Plasminogen Activator: Comparison of Therapeutic Effects in Rat Stroke Model at Multiparametric MR Imaging

To prospectively compare therapeutic and hemorrhagic effects of microplasmin and tissue plasminogen activator (tPA) in stroke therapy by using multiparametric magnetic resonance (MR) imaging in a photothrombotic rat stroke model. The animal experiment complied with institutional regulations for laboratory animals. Stroke was induced in rats with photothrombotic occlusion of middle cerebral artery (MCA). T2-weighted, perfusion-weighted (PW), and diffusion-weighted (DW) MR imaging was performed 1 hour and 24 hours after occlusion. On the basis of PW and DW images at 1 hour, 49 rats with cortex and subcortex involvement and with perfusion-diffusion mismatch were randomly assigned into one of four groups: control group, group treated with 7.5 mg microplasmin, group treated with 10 mg/kg microplasmin, or group treated with 10 mg/kg tPA. Agents were intravenously injected 1.5 hours after occlusion. Infarct size and hemorrhagic transformation were assessed with MR imaging and histomorphologic findings. Neurologic deficit was scored. Measurements were statistically analyzed. There were 13 rats in the control group, 13 in the 7.5 mg/kg microplasmin group, nine in the 10 mg/kg microplasmin group, and 14 in the 10 mg/kg tPA group. Despite similar baseline perfusion-diffusion mismatch, histochemically defined total infarct volume was reduced from 25% +/- 5 (standard deviation) in control group to 21% +/- 2, 20% +/- 4, and 20% +/- 5 in 7.5 mg/kg microplasmin, 10 mg/kg microplasmin, and tPA groups, respectively, as similarly shown on T2-weighted, DW, and PW images at 24 hours (P < .05). Cerebral hemorrhage rate at 24 hours was higher in tPA group than in the other three groups. Bederson score of neurologic deficits was significantly reduced in treated groups compared with that in control group. Perfusion-diffusion mismatch appeared useful in selecting candidates for thrombolytic therapy. Multiparametric MR imaging allowed noninvasive assessment of effects of microplasmin and tPA in rats; microplasmin had a significantly lower hemorrhagic rate.