Evaluation of ChatGPT-Generated Educational Patient Pamphlets for Common Interventional Radiology Procedures.

RATIONALE AND OBJECTIVES: This study aimed to evaluate the accuracy and reliability of educational patient pamphlets created by ChatGPT, a large language model, for common interventional radiology (IR) procedures. METHODS AND MATERIALS: Twenty frequently performed IR procedures were selected, and five users were tasked to independently request ChatGPT to generate educational patient pamphlets for each procedure using identical commands. Subsequently, two independent radiologists assessed the content, quality, and accuracy of the pamphlets. The review focused on identifying potential errors, inaccuracies, the consistency of pamphlets. RESULTS: In a thorough analysis of the education pamphlets, we identified shortcomings in 30% (30/100) of pamphlets, with a total of 34 specific inaccuracies, including missing information about sedation for the procedure (10/34), inaccuracies related to specific procedural-related complications (8/34). A key-word co-occurrence network showed consistent themes within each group of pamphlets, while a line-by-line comparison at the level of users and across different procedures showed statistically significant inconsistencies (P < 0.001). CONCLUSION: ChatGPT-generated education pamphlets demonstrated potential clinical relevance and fairly consistent terminology; however, the pamphlets were not entirely accurate and exhibited some shortcomings and inter-user structural variabilities. To ensure patient safety, future improvements and refinements in large language models are warranted, while maintaining human supervision and expert validation.

Read-out Segmented Echo Planar Imaging with Two-Dimensional Navigator Correction (RESOLVE): An Alternative Sequence to Improve Image Quality on Diffusion-Weighted Imaging of Prostate.

OBJECTIVE: We aimed to investigate if the use of read-out segmented echoplanar imaging with additional two-dimensional navigator correction (Readout Segmentation of Long Variable Echo, RESOLVE) for acquiring prostate diffusion-weighted imaging (DWI) improves image quality, compared to single-shot echoplanar imaging (ss-EPI). METHODS: This single-center prospective study cohort included 162 males with suspected prostate cancer, who underwent 3 Tesla multiparametric MRI (3T-mpMRI). Two abdominal radiologists, blinded to the clinical information, separately reviewed each 3T-mpMRI study to rank geometrical distortion, degree of rectal distention, lesion conspicuity, and anatomic details delineation first on ss-EPI-DWI and later on RESOLVE-DWI using 5-point scales (1 = excellent, 5 = poor). The average of the ranking scores given by two readers was generated and used as the final score. RESULTS: There was good-to-excellent interreader agreement for scoring image quality parameters on both ss-EPI and RESOLVE. Geometrical distortion scores > 3 was seen in 12.3% (20/162) of ss-EPI images, with all having geometrical distortion score <3 on RESOLVE (p < .001). The mean image distortion score was significantly less on RESOLVE than ss-EPI (1.16 vs 1.61, p < .01 regardless of rectal gas, p< .05 when stratified by the degree of rectal distention ). RESOLVE was superior to ss-EPI for lesion conspicuity (mean 1.35 vs 1.53, p< .002) and anatomic delineation (2.60 vs 2.68, p< .001) of prostate on DWI. CONCLUSION: Compared to conventional ss-EPI, the use of RESOLVE for acquisition of prostate DWI resulted in significantly enhanced image quality and reduced geometrical distortion. ADVANCES IN KNOWLEDGE: RESOLVE could be an alternative or replacement of ss-EPI for acquiring prostate DWI with significantly less geometrical distortion and significantly improved lesion conspicuity and anatomic delineation.

Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer.

OBJECTIVE: To identify which patient with prostate cancer (PCa) could safely avoid extended pelvic lymph node dissection (ePLND) by predicting lymph node invasion (LNI), via a radiomics-based machine learning approach. METHODS: An integrative radiomics model (IRM) was proposed to predict LNI, confirmed by the histopathologic examination, integrating radiomics features, extracted from prostatic index lesion regions on MRI images, and clinical features via SVM. The study cohort comprised 244 PCa patients with MRI and followed by radical prostatectomy (RP) and ePLND within 6 months between 2010 and 2019. The proposed IRM was trained in training/validation set and evaluated in an internal independent testing set. The model's performance was measured by area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). AUCs were compared via Delong test with 95% confidence interval (CI), and the rest measurements were compared via chi-squared test or Fisher's exact test. RESULTS: Overall, 17 (10.6%) and 14 (16.7%) patients with LNI were included in training/validation set and testing set, respectively. Shape and first-order radiomics features showed usefulness in building the IRM. The proposed IRM achieved an AUC of 0.915 (95% CI: 0.846-0.984) in the testing set, superior to pre-existing nomograms whose AUCs were from 0.698 to 0.724 (p < 0.05). CONCLUSION: The proposed IRM could be potentially feasible to predict the risk of having LNI for patients with PCa. With the improved predictability, it could be utilized to assess which patients with PCa could safely avoid ePLND, thus reduce the number of unnecessary ePLND. KEY POINTS: • The combination of MRI-based radiomics features with clinical information improved the prediction of lymph node invasion, compared with the model using only radiomics features or clinical features. • With improved prediction performance on predicting lymph node invasion, the number of extended pelvic lymph node dissection (ePLND) could be reduced by the proposed integrative radiomics model (IRM), compared with the existing nomograms.

COVID-19 pandemic revisited: lessons the radiology community has learned a year later.

For more than 1 year, COVID-19 pandemic has impacted every aspect of our lives. This paper reviews the major challenges that the radiology community faced over the past year and the impact the pandemic had on the radiology practice, radiologist-in-training education, and radiology research. The lessons learned from COVID-19 pandemic can help the radiology community to be prepared for future outbreaks and new pandemics, preserve good habits, enhance cancer screening programs, and adapt to the changes in radiology education and scientific meetings.

Efficacy of 3T Multiparametric MR Imaging followed by 3T in-Bore MR-Guided Biopsy for Detection of Clinically Significant Prostate Cancer Based on PIRADSv2.1 Score.

PURPOSE: To evaluate the diagnostic yield of 3T in-Bore magnetic resonance-guided biopsy (3T IB-MRGB) for detection of clinically significant prostate cancer (csPCa), based on assessment using the Prostate Imaging Reporting and Data System version 2.1 (PIRADSv2.1). MATERIALS AND METHODS: This single-center study examined individuals who underwent 3T multiparametric prostate magnetic resonance (MR) imaging and subsequent 3T IB-MRGB. The final study cohort included 379 men (with 475 targets) divided into 3 subcohorts: biopsy-naïve men (n = 123), individuals with a history of negative trans-rectal-ultrasonography (TRUS) biopsy results (n = 106), and men with low-grade PCa under active surveillance (n = 150). csPCa was defined as having a Gleason score (GS) ≥3+4. RESULTS: 3T IB-MRGB detected PCa and csPCa in 69.1% (262 of 379) and 50.3% (193 of 379) of patients, respectively. The PCa and csPCa detection rates per target were 64.2% (305 of 475) and 43.8% (208 of 475), respectively. The rate of urosepsis, treated with intravenous antibiotics, was 1% (4 patients). In TRUS biopsy negative results and biopsy-naïve subcohorts, csPCa was found in 36.8% (39 of 106) and 52.8% (65 of 123), respectively. In 50.7% (76 of 150) of the active surveillance subcohort, 3T IB-MRGB upgraded the GS assigned in prior TRUS biopsies. Positive predictive values of PIRADSv2.1 categories 3, 4, and 5 for csPCa detection were 24.8%, 44.4%, and 67.1%, respectively. Higher PIRADSv2.1 categories were significantly associated with PCa (odds ratio [OR], 3.97; 95% confidence interval [CI], 2.98-5.28) and csPCa (OR, 1.41; 95% CI, 1.03-1.94) detection. Of 137 PIRADSv2 category 3 lesions, 28 were downgraded to PIRADSv2.1 category 2, in which there were no occurrences of csPCa in histology. CONCLUSIONS: Use of 3T IB-MRGB resulted in detection of csPCa in 50.9% of individuals. 3T IB-MRGB has a high diagnostic yield in individuals with negative TRUS biopsy results and those under active surveillance. The PIRADSv2.1 category is a strong predictor of PCa and csPCa detection.

3-T Multiparametric MRI Followed by In-Bore MR-Guided Biopsy for Detecting Clinically Significant Prostate Cancer After Prior Negative Transrectal Ultrasound-Guided Biopsy.

OBJECTIVE. The purpose of this study was to evaluate the rate of detection of clinically significant prostate cancer (csPCa), as assessed on the basis of Prostate Imaging Reporting and Data System version 2.1 (PI-RADSv2.1) guidelines, using 3-T in-bore MR-guided biopsy (MRGB) for a cohort of patients suspected of having csPCa despite having a history of recent negative transrectal ultrasound-guided biopsy results. MATERIALS AND METHODS. The cohort in this retrospective, single-center study was derived from a database of 330 patients who underwent multiparametric MRI (mpMRI) followed by in-bore transrectal 3-T MRGB. Seventy-nine patients (mean [± SD] age, 64.1 ± 8.6 years) with prior negative transrectal ultrasound-guided biopsy results and positive pre-MRGB mpMRI results (PI-RADS score ≥ 3) composed the final cohort. The rate of detection of PCa and csPCa (the latter of which was defined by a Gleason score of 3 + 4 or higher) was stratified according to updated PI-RADSv2.1 assessment. RESULTS. MRGB detected PCa in 36 patients (45.6%), 30 (83.3%) of whom had csPCa. The PI-RADSv2.1 score was a strong predictor (odds ratio, 3.97; 95% CI, 1.93-7.47) of csPCa detection. We found two benign transition zone target lesions that were downgraded from PI-RADSv2 category 3 to PI-RADSv2.1 category 2. PCa was detected in 18.4% (7/38), 65.2% (15/23), and 87.5% (14/16) of individuals with PI-RADSv2.1 category 3, 4, and 5 lesions, respectively, with 85.7% (6/7), 86.7% (13/15), and 78.6% (11/14) of these cases found to be csPCa, respectively. Of the seven PI-RADSv2.1 category 3 csPCa lesions, six had prostate-specific antigen density greater than 0.10 ng/mL/cc. CONCLUSION. With the use of 3-T in-bore MRGB, csPCa was detected in 38% of individuals with prior negative transrectal ultrasound-guided biopsy results. PI-RADSv2.1 was a strong predictor of csPCa detection. On the basis of our results, patients with PI-RADSv2.1 category 4 or 5 lesions and patients with PI-RADSv2.1 category 3 lesions and a prostate-specific antigen density greater than or equal to 0.10 ng/mL/cc may benefit from in-bore MRGB.

Prostate Microstructure in Prostate Cancer Using 3-T MRI with Diffusion-Relaxation Correlation Spectrum Imaging: Validation with Whole-Mount Digital Histopathology.

Background Microstructural MRI has the potential to improve diagnosis and characterization of prostate cancer (PCa), but validation with histopathology is lacking. Purpose To validate ex vivo diffusion-relaxation correlation spectrum imaging (DR-CSI) in the characterization of microstructural tissue compartments in prostate specimens from men with PCa by using registered whole-mount digital histopathology (WMHP) as the reference standard. Materials and Methods Men with PCa who underwent 3-T MRI and robotic-assisted radical prostatectomy between June 2018 and January 2019 were prospectively studied. After prostatectomy, the fresh whole prostate specimens were imaged in patient-specific three-dimensionally printed molds by using 3-T MRI with DR-CSI and were then sliced to create coregistered WMHP slides. The DR-CSI spectral signal component fractions (fA, fB, fC) were compared with epithelial, stromal, and luminal area fractions (fepithelium, fstroma, flumen) quantified in PCa and benign tissue regions. A linear mixed-effects model assessed the correlations between (fA, fB, fC) and (fepithelium, fstroma, flumen), and the strength of correlations was evaluated by using Spearman correlation coefficients. Differences between PCa and benign tissues in terms of DR-CSI signal components and microscopic tissue compartments were assessed using two-sided t tests. Results Prostate specimens from nine men (mean age, 65 years ± 7 [standard deviation]) were evaluated; 20 regions from 17 PCas, along with 20 benign tissue regions of interest, were analyzed. Three DR-CSI spectral signal components (spectral peaks) were consistently identified. The fA, fB, and fC were correlated with fepithelium, fstroma, and flumen (all P < .001), with Spearman correlation coefficients of 0.74 (95% confidence interval [CI]: 0.62, 0.83), 0.80 (95% CI: 0.66, 0.89), and 0.67 (95% CI: 0.51, 0.81), respectively. PCa exhibited differences compared with benign tissues in terms of increased fA (PCa vs benign, 0.37 ± 0.05 vs 0.27 ± 0.06; P < .001), decreased fC (PCa vs benign, 0.18 ± 0.06 vs 0.31 ± 0.13; P = .01), increased fepithelium (PCa vs benign, 0.44 ± 0.13 vs 0.26 ± 0.16; P < .001), and decreased flumen (PCa vs benign, 0.14 ± 0.08 vs 0.27 ± 0.18; P = .004). Conclusion Diffusion-relaxation correlation spectrum imaging signal components correlate with microscopic tissue compartments in the prostate and differ between cancer and benign tissue. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lee and Hectors in this issue.

Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome.

OBJECTIVE. Since the outbreak of the novel coronavirus pulmonary illness coronavirus disease 2019 (COVID-19) in China, more than 79,000 people have contracted the virus worldwide. The virus is rapidly spreading with human-to-human transmission despite imposed precautions. Because similar pulmonary syndromes have been reported from other strains of the coronavirus family, our aim is to review the lessons from imaging studies obtained during severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) outbreaks. CONCLUSION. The review of experiences with the MERS and SARS outbreaks will help us better understand the role of the radiologist in combating the outbreak of COVID-19. The known imaging manifestations of the novel coronavirus and the possible unknowns will also be discussed.

Coronavirus (COVID-19) Outbreak: What the Department of Radiology Should Know.

In December 2019, a novel coronavirus (COVID-19) pneumonia emerged in Wuhan, China. Since then, this highly contagious COVID-19 has been spreading worldwide, with a rapid rise in the number of deaths. Novel COVID-19-infected pneumonia (NCIP) is characterized by fever, fatigue, dry cough, and dyspnea. A variety of chest imaging features have been reported, similar to those found in other types of coronavirus syndromes. The purpose of the present review is to briefly discuss the known epidemiology and the imaging findings of coronavirus syndromes, with a focus on the reported imaging findings of NCIP. Moreover, the authors review precautions and safety measures for radiology department personnel to manage patients with known or suspected NCIP. Implementation of a robust plan in the radiology department is required to prevent further transmission of the virus to patients and department staff members.

Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation.

Automatic segmentation of prostatic zones on multiparametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89±0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (Deeplab V3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.