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"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite bp-MRI datasets. Materials and Methods This retrospective study included data from 5,150 patients (14,191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bp-MRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual DW images acquired using various b-values, to align with the style of images acquired using b-values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1,692 test cases (2,393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. Results For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 (P < .001), respectively, for PI-RADS ≥ 3, and 0.77 and 0.80 (P < .001) for PI-RADS ≥ 4 PCa lesions. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 (P < .001) for PI-RADS ≥ 3, and 0.50 and 0.77 (P < .001) for PI-RADS ≥ 4 PCa lesions. Conclusion UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS recommended DWI protocol (eg, with an extremely high b-value). ©RSNA, 2024.
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AIM: To investigate the associations between the hour of the day and Prostate Imaging-Reporting and Data System (PI-RADS) scores assigned by radiologists in prostate MRI reports. MATERIALS AND METHODS: Retrospective single-center collection of prostate MRI reports over an 8-year period. Mean PI-RADS scores assigned between 0800 and 1800 h were examined with a regression model. RESULTS: A total of 35'004 prostate MRI interpretations by 26 radiologists were included. A significant association between the hour of day and mean PI-RADS score was identified (ß2 = 0.005, p < 0.001), with malignant scores more frequently assigned later in the day. CONCLUSION: These findings suggest chronobiological factors may contribute to variability in radiological assessments. Though the magnitude of the effect is small, this may potentially add variability and impact diagnostic accuracy.
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Imagen por Resonancia Magnética , Neoplasias de la Próstata , Masculino , Humanos , Imagen por Resonancia Magnética/métodos , Neoplasias de la Próstata/diagnóstico por imagen , Estudios Retrospectivos , Persona de Mediana Edad , Anciano , Radiólogos , Variaciones Dependientes del Observador , Sistemas de Información Radiológica/estadística & datos numéricosRESUMEN
OBJECTIVE: The aim of the study is to evaluate the performance of the ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) score and perform individual MRI feature analysis for differentiating between benign and malignant ovarian teratomas. METHODS: In this institutional review board-approved retrospective study, consecutive patients with a pathology-proven fat-containing ovarian mass imaged with contrast-enhanced MRI (1.5T or 3T) from 2013 to 2022 were included. Two blinded radiologists independently evaluated masses per the O-RADS MRI lexicon, including having a "characteristic" or "large" Rokitansky nodule (RN). Additional features analyzed included the following: nodule size/percentage volume relative to total teratoma volume, presence of bulk/intravoxel fat in the nodule, diffusion restriction in the nodule, angular interface, nodule extension through the teratoma border, presence/type of nodule enhancement pattern (solid versus peripheral), and evidence for metastatic disease. An overall O-RADS MRI score was assigned. Patient and lesion features associated with malignancy were evaluated and used to create a malignant teratoma score. χ2, Fisher's exact tests, receiver operating characteristic curve, and κ analysis was performed. RESULTS: One hundred thirty-seven women (median age 34, range 9-84 years) with 123 benign and 14 malignant lesions were included. Mean teratoma size was 7.3 cm (malignant: 14.4 cm, benign: 6.5 cm). 18/123 (14.6%) of benign teratomas were assigned an O-RADS 4 based on the presence of a "large" (11/18) or "noncharacteristic" (12/18) RN. 12/14 malignant nodules occupied >25% of the total teratoma volume (P = 0.09). Features associated with malignancy included the following: age <18 years, an enhancing noncharacteristic RN, teratoma size >12 cm, irregular cystic border, and extralesional extension; these were incorporated into a malignant teratoma score, with a score of 2 or more associated with area under the curve of 0.991 for reviewer 1 and 0.993 for reviewer 2. Peripheral enhancement in a RN was never seen with malignancy (64/123 benign, 0/14 malignant) and would have appropriated downgraded 9/18 overcalled O-RADS 4 benign teratomas. CONCLUSIONS: O-RADS MRI overcalled 15% (18/123) benign teratomas as O-RADS 4 but correctly captured all malignant teratomas. We propose defining a "characteristic" RN as an intravoxel or bulk fat-containing nodule. Observation of a peripheral rim of enhancement in a noncharacteristic RN allowed more accurate prediction of benignity and should be added to the MRI lexicon for improved O-RADS performance.
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Background: Uterine sarcomas are rare; however, they display imaging features that overlap those of leiomyomas. The potential for undetected uterine sarcomas is clinically relevant because minimally invasive treatment of leiomyomas may lead to cancer dissemination. ADC values have shown potential for differentiating benign and malignant uterine masses. Objective: The purpose of this study was to perform a systematic review of the diagnostic performance of ADC values in differentiating uterine sarcomas from leiomyomas. Evidence acquisition: We searched three electronic databases (MEDLINE, EMBASE, and Cochrane databases) for studies distinguishing uterine sarcomas from leiomyomas using MRI, including ADC, with pathologic tissue confirmation or imaging follow-up as the reference standard. Data extraction and QUADAS-2 quality assessment were performed. Sensitivity and specificity were pooled using hierarchic models, including bivariate and hierarchic summary ROC models. Metaregression was used to assess the impact of various factors on heterogeneity. Evidence synthesis: Twenty-one studies met study inclusion criteria. Pooled sensitivity and specificity were 89% (95% CI, 82-94%) and 86% (95% CI, 78-92%), respectively. Area under the summary ROC curve was 94% (95% CI, 92-96%). Context of ADC interpretation (i.e., standalone vs part of multiparametric MRI [mpMRI]) was the only factor found to account significantly for heterogeneity (p = .01). Higher specificity (95% [95% CI, 92-99%] vs 82% [95% CI, 75-89%]) and similar sensitivity (94% [95% CI, 89-99%] vs 88% [95% CI, 82-93%]) were observed when ADC was evaluated among mpMRI features as compared with standalone ADC assessment. ADC cutoff values ranged (0.87-1.29 × 10-3 mm2/s) but were not associated with statistically different performance (p = .37). Pooled mean ADC values in sarcomas and leiomyomas were 0.904 × 10-3 mm2/s and 1.287 × 10-3 mm2/s, respectively. Conclusion: As part of mpMRI evaluation of uterine masses, mass ADC value less than 0.904 × 10-3 mm2/s may be a useful test-positive threshold for uterine sarcoma, consistent with a prior expert consensus statement. Institutional protocols may influence locally selected ADC values. Clinical Impact: Using ADC as part of mpMRI assessment improves detection of uterine sarcoma, which could influence candidate selection for minimally invasive treatments.
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OBJECTIVE: To perform image quality comparison between deep learning-based multiband diffusion-weighted sequence (DL-mb-DWI), accelerated multiband diffusion-weighted sequence (accelerated mb-DWI), and conventional multiband diffusion-weighted sequence (conventional mb-DWI) in patients undergoing clinical liver magnetic resonance imaging (MRI). METHODS: Fifty consecutive patients who underwent clinical MRI of the liver at a 1.5-T scanner, between September 1, 2021, and January 31, 2022, were included in this study. Three radiologists independently reviewed images using a 5-point Likert scale for artifacts and image quality factors, in addition to assessing the presence of liver lesions and lesion conspicuity. RESULTS: DL-mb-DWI acquisition time was 65.0 ± 2.4 seconds, significantly (P < 0.001) shorter than conventional mb-DWI (147.5 ± 19.2 seconds) and accelerated mb-DWI (94.3 ± 1.8 seconds). DL-mb-DWI received significantly higher scores than conventional mb-DWI for conspicuity of the left lobe (P < 0.001), sharpness of intrahepatic vessel margin (P < 0.001), sharpness of the pancreatic contour (P < 0.001), in-plane motion artifact (P = 0.002), and overall image quality (P = 0.005) by reader 2. DL-mb-DWI received significantly higher scores for conspicuity of the left lobe (P = 0.006), sharpness of the pancreatic contour (P = 0.020), and in-plane motion artifact (P = 0.042) by reader 3. DL-mb-DWI received significantly higher scores for strength of fat suppression (P = 0.004) and sharpness of the pancreatic contour (P = 0.038) by reader 1. The remaining quality parameters did not reach statistical significance for reader 1. CONCLUSIONS: Novel diffusion-weighted MRI sequence with deep learning-based image reconstruction demonstrated significantly decreased acquisition times compared with conventional and accelerated mb-DWI sequences, while maintaining or improving image quality for routine abdominal MRI. DL-mb-DWI offers a potential alternative to conventional mb-DWI in routine clinical liver MRI.
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Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.
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Imagen por Resonancia Magnética , Próstata , Neoplasias de la Próstata , Humanos , Masculino , Inteligencia Artificial , Aprendizaje Automático , Imagen por Resonancia Magnética/métodos , Próstata/diagnóstico por imagen , Neoplasias de la Próstata/diagnóstico por imagen , Neoplasias de la Próstata/patologíaRESUMEN
OBJECTIVE: Poor medication adherence remains highly prevalent and adversely affects health outcomes. Patients frequently describe properties of the pills themselves, like size and shape, as barriers, but this has not been evaluated objectively. We sought to determine the extent to which oral medication properties thought to be influential translate into lower objectively-measured adherence. DESIGN: Retrospective cohort study. SETTING: US nationwide commercial claims database, 2016-2019. PARTICIPANTS: Among patients initiating first-line hypertension, diabetes or hyperlipidaemia treatment based on clinical guidelines, we measured pill size, shape, colour and flavouring, number of pills/day and fixed-dose combination status as properties. OUTCOME MEASURES: Outcomes included discontinuation after the first fill (ie, never filling again over a minimum of 1-year follow-up) and long-term non-adherence (1-year proportion of days covered <0.80). We estimated associations between each property and outcomes, by therapeutic class (eg, statins), with multivariable logistic regression. RESULTS: Across 604 323 patients, 14.6% discontinued after filling once (ie, were non-persistent), and 54.0% were non-adherent over 1-year follow-up. Large pill size was associated with non-adherence, except for thiazides (eg, metformin adjusted OR (aOR): 1.12, 95% CI: 1.06 to 1.18). Greater pill burden was associated with a higher risk of non-adherence across all classes (eg, metformin aOR: 1.58, 95% CI: 1.53 to 1.64 for two pills/day). Taking less than one pill/day was also associated with higher risk of non-adherence and non-persistence (eg, non-persistence statin aOR: 1.29, 95% CI: 1.20 to 1.38). Pill shape, colour, flavouring and combination status were associated with mixed effects across classes. CONCLUSIONS: Pill burden and pill size are key properties affecting adherence for almost all classes; others, like size and combination, could modestly affect medication adherence. Clinical interventions could screen patients for potential intolerance to medication and potentially implement more convenient dosing schedules.
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Inhibidores de Hidroximetilglutaril-CoA Reductasas , Hipertensión , Metformina , Humanos , Estados Unidos , Estudios Retrospectivos , Hipertensión/tratamiento farmacológico , Inhibidores de Hidroximetilglutaril-CoA Reductasas/uso terapéutico , Cumplimiento de la Medicación , Metformina/uso terapéuticoRESUMEN
INTRODUCTION: Prostate cancer diffusion weighted imaging (DWI) MRI is typically performed at high-field strength (3.0 T) in order to overcome low signal-to-noise ratio (SNR). In this study, we demonstrate the feasibility of prostate DWI at low field enabled by random matrix theory (RMT)-based denoising, relying on the MP-PCA algorithm applied during image reconstruction from multiple coils. METHODS: Twenty-one volunteers and 2 prostate cancer patients were imaged with a 6-channel pelvic surface array coil and an 18-channel spine array on a prototype 0.55 T system created by ramping down a commercial magnetic resonance imaging system (1.5 T MAGNETOM Aera Siemens Healthcare) with 45 mT/m gradients and 200 T/m/s slew rate. Diffusion-weighted images were acquired with 4 non-collinear directions, for which b = 50 s/mm 2 was used with 8 averages and b = 1000 s/mm 2 with 40 averages; 2 extra b = 50 s/mm 2 were used as part of the dynamic field correction. Standard and RMT-based reconstructions were applied on DWI over different ranges of averages. Accuracy/precision was evaluated using the apparent diffusion coefficient (ADC), and image quality was evaluated over 5 separate reconstructions by 3 radiologists with a 5-point Likert scale. For the 2 patients, we compare image quality and lesion visibility of the RMT reconstruction versus the standard one on 0.55 T and on clinical 3.0 T. RESULTS: The RMT-based reconstruction in this study reduces the noise floor by a factor of 5.8, thereby alleviating the bias on prostate ADC. Moreover, the precision of the ADC in prostate tissue after RMT increases over a range of 30%-130%, with the increase in both signal-to-noise ratio and precision being more prominent for a low number of averages. Raters found that the images were consistently of moderate to good overall quality (3-4 on the Likert scale). Moreover, they determined that b = 1000 s/mm 2 images from a 1:55-minute scan with the RMT-based reconstruction were on par with the corresponding images from a 14:20-minute scan with standard reconstruction. Prostate cancer was visible on ADC and calculated b = 1500 images even with the abbreviated 1:55-minute scan reconstructed with RMT. CONCLUSIONS: Prostate imaging using DWI is feasible at low field and can be performed more rapidly with noninferior image quality compared with standard reconstruction.
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Próstata , Neoplasias de la Próstata , Masculino , Humanos , Próstata/diagnóstico por imagen , Próstata/patología , Estudios de Factibilidad , Neoplasias de la Próstata/patología , Imagen de Difusión por Resonancia Magnética/métodos , Relación Señal-Ruido , Reproducibilidad de los ResultadosRESUMEN
The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.
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BACKGROUND: Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2-weighted imaging (T2WI). PURPOSE: To compare conventional bpMRIs (CL-bpMRI) with bpMRIs including a deep learning-accelerated T2WI (DL-bpMRI) in diagnosing prostate cancer. STUDY TYPE: Retrospective. POPULATION: Eighty consecutive men, mean age 66 years (47-84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow-up included prostate biopsy or stability of prostate-specific antigen (PSA) for 1 year. FIELD STRENGTH AND SEQUENCES: A 3 T MRI. Conventional axial and coronal T2 turbo spin echo (CL-T2), 3-fold deep learning-accelerated axial and coronal T2-weighted sequence (DL-T2), diffusion weighted imaging (DWI) with b = 50 sec/mm2 , 1000 sec/mm2 , calculated b = 1500 sec/mm2 . ASSESSMENT: CL-bpMRI and DL-bpMRI including the same conventional diffusion-weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer-assisted detection algorithm (DL-CAD). The readers evaluated image quality using a 4-point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI-RADS) v2.1. DL-CAD identified and assigned lesions of PI-RADS 3 or greater. STATISTICAL TESTS: Quality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test. SIGNIFICANCE: P = 0.05. RESULTS: Eighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL-T2, DL-T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient-based analysis, the reader results of AUC are (CL-bpMRI, DL-bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL-CAD (CL-bpMRI, DL-bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48). CONCLUSION: Deep learning-accelerated T2-weighted imaging may potentially be used to decrease acquisition time for bpMRI. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.
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Aprendizaje Profundo , Neoplasias de la Próstata , Masculino , Humanos , Anciano , Persona de Mediana Edad , Imagen por Resonancia Magnética/métodos , Próstata/diagnóstico por imagen , Próstata/patología , Neoplasias de la Próstata/diagnóstico por imagen , Neoplasias de la Próstata/patología , Estudios RetrospectivosRESUMEN
BACKGROUND: Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency. METHODS: This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant. RESULTS: DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUCpatient: 0.89 vs. 0.86; AUClesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P ≤ .03) of DL-CAD. CONCLUSIONS: Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD. TRIAL REGISTRATION: ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.
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Aprendizaje Profundo , Neoplasias de la Próstata , Masculino , Humanos , Estudios Retrospectivos , Reproducibilidad de los Resultados , Neoplasias de la Próstata/diagnóstico por imagen , Neoplasias de la Próstata/patología , Imagen por Resonancia Magnética/métodos , Imagen de Difusión por Resonancia Magnética/métodosRESUMEN
This work aimed to develop a modified stack-of-stars golden-angle radial sampling scheme with variable-density acceleration along the slice (kz) dimension (referred to as VD-stack-of-stars) and to test this new sampling trajectory with multi-coil compressed sensing reconstruction for rapid motion-robust 3D liver MRI. VD-stack-of-stars sampling implements additional variable-density undersampling along the kz dimension, so that slice resolution (or volumetric coverage) can be increased without prolonging scan time. The new sampling trajectory (with increased slice resolution) was compared with standard stack-of-stars sampling with fully sampled kz (with standard slice resolution) in both non-contrast-enhanced free-breathing liver MRI and dynamic contrast-enhanced MRI (DCE-MRI) of the liver in volunteers. For both sampling trajectories, respiratory motion was extracted from the acquired radial data, and images were reconstructed using motion-compensated (respiratory-resolved or respiratory-weighted) dynamic radial compressed sensing reconstruction techniques. Qualitative image quality assessment (visual assessment by experienced radiologists) and quantitative analysis (as a metric of image sharpness) were performed to compare images acquired using the new and standard stack-of-stars sampling trajectories. Compared to standard stack-of-stars sampling, both non-contrast-enhanced and DCE liver MR images acquired with VD-stack-of-stars sampling presented improved overall image quality, sharper liver edges and increased hepatic vessel clarity in all image planes. The results have suggested that the proposed VD-stack-of-stars sampling scheme can achieve improved performance (increased slice resolution or volumetric coverage with better image quality) over standard stack-of-stars sampling in free-breathing DCE-MRI without increasing scan time. The reformatted coronal and sagittal images with better slice resolution may provide added clinical value.
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Aumento de la Imagen , Imagenología Tridimensional , Humanos , Imagenología Tridimensional/métodos , Aumento de la Imagen/métodos , Medios de Contraste , Imagen por Resonancia Magnética/métodos , Respiración , ArtefactosRESUMEN
OBJECTIVES: To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. METHODS: In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. RESULTS: In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023). CONCLUSIONS: DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. KEY POINTS: ⢠DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. ⢠With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. ⢠DL-CAD assistance increases the correlation between PI-RADS category and cancer grade.
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Aprendizaje Profundo , Imágenes de Resonancia Magnética Multiparamétrica , Neoplasias de la Próstata , Masculino , Humanos , Próstata/diagnóstico por imagen , Próstata/patología , Imagen por Resonancia Magnética , Estudios Retrospectivos , Neoplasias de la Próstata/patología , Clasificación del Tumor , Biopsia Guiada por Imagen , Radiólogos , ComputadoresRESUMEN
OBJECTIVE: Despite increasing overall health care spending over the past several decades, little is known about long-term patterns of spending among US patients with gout. Current approaches to assessing spending typically focus on composite measures or patients agnostic to disease state; in contrast, examining spending using longitudinal measures may better discriminate patients and target interventions to those in need. We used a data-driven approach to classify and predict spending patterns in patients with gout. METHODS: Using insurance claims data from 2017-2019, we used group-based trajectory modeling to classify patients ages 40 years or older diagnosed with gout and treated with urate-lowering therapy (ULT) by their total health care spending over 2 years. We assessed the ability to predict membership in each spending group using logistic and generalized boosted regression with split-sample validation. Models were estimated using different sets of predictors and evaluated using C statistics. RESULTS: In 57,980 patients, the mean ± SD age was 71.0 ± 10.5 years, and 17,194 patients (29.7%) were female. The best-fitting model included the following groups: minimal spending (13.2%), moderate spending (37.4%), and high spending (49.4%). The ability to predict groups was high overall (e.g., boosted C statistics with all predictors: minimal spending [0.89], moderate spending [0.78], and high spending [0.90]). Although average adherence was relatively high in the population, for the high-spending group, the most influential predictors were greater gout medication adherence and diabetes melllitus diagnosis. CONCLUSION: We identified distinct long-term health care spending patterns in patients with gout using ULT with high accuracy. Several clinical predictors could be key areas for intervention, such as gout medication use or diabetes melllitus.
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Gota , Ácido Úrico , Humanos , Femenino , Persona de Mediana Edad , Anciano , Anciano de 80 o más Años , Masculino , Supresores de la Gota/uso terapéutico , Gastos en Salud , Gota/diagnóstico , Gota/tratamiento farmacológico , Cumplimiento de la MedicaciónAsunto(s)
Disentimientos y Disputas , Seguridad del Paciente , Humanos , Estados Unidos , RadiólogosRESUMEN
PURPOSE: Fat-suppressed T2-weighted imaging (T2-FS) requires a long scan time and can be wrought with motion artifacts, urging the development of a shorter and more motion robust sequence. We compare the image quality of a single-shot T2-weighted MRI prototype with deep-learning-based image reconstruction (DL HASTE-FS) with a standard T2-FS sequence for 3 T liver MRI. METHODS: 41 consecutive patients with 3 T abdominal MRI examinations including standard T2-FS and DL HASTE-FS, between 5/6/2020 and 11/23/2020, comprised the study cohort. Three radiologists independently reviewed images using a 5-point Likert scale for artifact and image quality measures, while also assessing for liver lesions. RESULTS: DL HASTE-FS acquisition time was 54.93 ± 16.69, significantly (p < .001) shorter than standard T2-FS (114.00 ± 32.98 s). DL HASTE-FS received significantly higher scores for sharpness of liver margin (4.3 vs 3.3; p < .001), hepatic vessel margin (4.2 vs 3.3; p < .001), pancreatic duct margin (4.0 vs 1.9; p < .001); in-plane (4.0 vs 3.2; p < .001) and through-plane (3.9 vs 3.4; p < .001) motion artifacts; other ghosting artifacts (4.3 vs 2.9; p < .001); and overall image quality (4.0 vs 2.9; p < .001), in addition to receiving a higher score for homogeneity of fat suppression (3.7 vs 3.4; p = .04) and liver-fat contrast (p = .03). For liver lesions, DL HASTE-FS received significantly higher scores for sharpness of lesion margin (4.4 vs 3.7; p = .03). CONCLUSION: Novel single-shot T2-weighted MRI with deep-learning-based image reconstruction demonstrated superior image quality compared with the standard T2-FS sequence for 3 T liver MRI, while being acquired in less than half the time.
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Aprendizaje Profundo , Neoplasias Hepáticas , Humanos , Imagen por Resonancia Magnética/métodos , Procesamiento de Imagen Asistido por Computador , ArtefactosRESUMEN
Endometriosis is the presence of ectopic endometrial glands outside of the uterus. MR imaging is particularly useful for characterizing deep infiltrating endometriosis but can also be useful in characterizing endometriomas and hematosalpinges, characterizing broad ligament deposits, assessing for endometriosis-associated malignancy, and differentiating malignancy from decidualized endometriomas. Masses and cysts with hemorrhagic or proteinaceous contents can sometimes be difficult to distinguish from endometriomas. Imaging protocols should include pre-contrast T1-weighted imaging with fat saturation, T2-weighted imaging without fat saturation, opposed- and in-phase or Dixon imaging, administration of contrast media, and subtraction imaging.
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Endometriosis , Femenino , Humanos , Endometriosis/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Diagnóstico Diferencial , Endometrio/patología , Medios de ContrasteRESUMEN
BACKGROUND: Pro re nata (PRN) antipsychotics and benzodiazepines are routinely used for the rapid stabilization of acutely agitated patients. Despite the popular use of PRN medications in mental health units, primary literature supporting efficacy and safety is poor, and there is no single universally accepted practice guideline. PRN psychotropic medications have the potential to cause adverse effects when used inappropriately. AIMS: Our objective was to characterize the prescribing, administration, and documentation practices of PRN psychotropic medications in a psychiatric intensive care unit. METHODS: We conducted a retrospective chart review of patients admitted to a 12-bed psychiatric intensive care unit between June and September 2018. All PRN antipsychotic and benzodiazepine orders, administrations, documentation practices, and attempted nonpharmacological strategies were assessed for each order and patient. Descriptive statistics were used to analyze data. RESULTS: Thirty-two patients with a total of 123 physicians' orders and 1,179 PRN administrations of antipsychotics and benzodiazepines were reviewed. Of the total administrations, 720 (61%) were combinations with at least two psychotropic agents. Forty-one (33%) physicians' orders had a prescribed indication, and 559 (47%) administrations had an attempted nonpharmacological method prior to PRN administration. Eight patients (25%) had antipsychotic PRN orders, which exceeded the total daily maximum dose. Three adverse drug effects were attributed to PRN administration. CONCLUSIONS: Areas of improvement that we identified included documentation practices of effectiveness of administered PRNs, prescriptions to include clear indications and dosage within the 24-hour maximum limits, and documentation of nonpharmacological methods utilized.
Asunto(s)
Antipsicóticos , Trastornos Mentales , Humanos , Antipsicóticos/uso terapéutico , Trastornos Mentales/tratamiento farmacológico , Estudios Retrospectivos , Canadá , Psicotrópicos/uso terapéutico , Benzodiazepinas/uso terapéuticoRESUMEN
PURPOSE: The objective of the study was to determine whether Axumin (18F-Fluciclovine) PET/MRI informs the decision to perform an early repeat biopsy of PI-RADS 4/5 region of interest (ROI) exhibiting no clinically significant prostate cancer (csPCa) on initial biopsy. METHODS: This prospective study enrolled men with at least one PI-RADS 4/5 ROI on multi-parametric MRI and no csPCa on prior biopsy defined as Gleason grade group (GGG) > 1. All men underwent an Axumin PET/MRI and only-persistent PI-RADS > 2 ROI were advised to undergo a repeat biopsy. A PET cancer suspicion score (PETCSS) was internally developed to stratify PET avid lesions according to their suspicion of harboring csPCa. The sensitivity, specificity, positive (PPV) and negative predictive value (NPV) of the PETCSS for predicting csPCa were assessed. Relative risk was calculated to analyze the association of baseline variables with csPCa on repeat biopsy. RESULTS: Thirty-eight ROI on 36 enrolled men were analyzed. Fourteen (36.8%) were downgraded to PI-RADS 1/2 and were not subjected to repeat biopsy. Thirteen (92.9%) of these downgraded scans also exhibited low-risk PETCSS. Overall, 18/22 (81.2%) subjects underwent a repeat per protocol biopsy. Of the 20 ROI subjected to repeat biopsy, eight (40%) were found to harbour csPCa. The sensitivity, specificity, PPV and NPV of the PETCSS were 50, 50, 40, and 60%, respectively. No predictor of csPCa was found in the risk analysis. CONCLUSION: Our pilot study showed that both MRI and PET sequences have limited performance for identifying those persistently suspicious PI-RADS 4/5 ROI that are found to harbor csPCa on repeat biopsy.