BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study.

Background The performance of publicly available large language models (LLMs) remains unclear for complex clinical tasks. Purpose To evaluate the agreement between human readers and LLMs for Breast Imaging Reporting and Data System (BI-RADS) categories assigned based on breast imaging reports written in three languages and to assess the impact of discordant category assignments on clinical management. Materials and Methods This retrospective study included reports for women who underwent MRI, mammography, and/or US for breast cancer screening or diagnostic purposes at three referral centers. Reports with findings categorized as BI-RADS 1-5 and written in Italian, English, or Dutch were collected between January 2000 and October 2023. Board-certified breast radiologists and the LLMs GPT-3.5 and GPT-4 (OpenAI) and Bard, now called Gemini (Google), assigned BI-RADS categories using only the findings described by the original radiologists. Agreement between human readers and LLMs for BI-RADS categories was assessed using the Gwet agreement coefficient (AC1 value). Frequencies were calculated for changes in BI-RADS category assignments that would affect clinical management (ie, BI-RADS 0 vs BI-RADS 1 or 2 vs BI-RADS 3 vs BI-RADS 4 or 5) and compared using the McNemar test. Results Across 2400 reports, agreement between the original and reviewing radiologists was almost perfect (AC1 = 0.91), while agreement between the original radiologists and GPT-4, GPT-3.5, and Bard was moderate (AC1 = 0.52, 0.48, and 0.42, respectively). Across human readers and LLMs, differences were observed in the frequency of BI-RADS category upgrades or downgrades that would result in changed clinical management (118 of 2400 [4.9%] for human readers, 611 of 2400 [25.5%] for Bard, 573 of 2400 [23.9%] for GPT-3.5, and 435 of 2400 [18.1%] for GPT-4; P < .001) and that would negatively impact clinical management (37 of 2400 [1.5%] for human readers, 435 of 2400 [18.1%] for Bard, 344 of 2400 [14.3%] for GPT-3.5, and 255 of 2400 [10.6%] for GPT-4; P < .001). Conclusion LLMs achieved moderate agreement with human reader-assigned BI-RADS categories across reports written in three languages but also yielded a high percentage of discordant BI-RADS categories that would negatively impact clinical management. © RSNA, 2024 Supplemental material is available for this article.

Transrectal ultrasound features and biopsy outcomes of transition PI-RADS 5.

BACKGROUND: Transition Prostate Imaging and Reporting and Data System (PI-RADS) 5 is easily detected owing to typical magnetic resonance imaging features. However, it is unclear as to how transition PI-RADS 5 appears on transrectal ultrasound (TRUS). PURPOSE: To assess TRUS features of transition PI-RADS 5 and outcomes of TRUS-guided target biopsy. MATERIAL AND METHODS: Between March 2014 and November 2018, 186 male patients underwent TRUS-guided biopsy of PI-RADS 5. Of them, 82 and 104were transition and peripheral PI-RADS 5, respectively. Transition and peripheral PI-RADS 5 were compared according to echogenicity (hyperechoic or hypoechoic) and hypoechoic rim (present or absent). Each tumor was targeted with TRUS based on TRUS features. Significant (Gleason score ≥7) and insignificant (Gleason score 6) cancer detection rates (CDRs) were compared between transition and peripheral PI-RADS 5. Standard reference was biopsy examination. Fisher's exact test was used for statistical analysis. RESULTS: Transition PI-RADS 5 was hyperechoic in 89.0% (73/82) and had a hypoechoic rim in 97.6% (80/82), whereas peripheral PI-RADS 5 was hypoechoic in 99.0% (103/104) and had a hypoechoic rim in 26.9% (28/104) (both, P<0.0001). The significant CDRs of transition and peripheral PI-RADS 5 were 56.1% (46/82) and 65.4% (68/104), respectively (P=0.2263). However, the insignificant CDRs of these categories were 22.0% (18/82) and 8.7% (9/104), respectively (P=0.0123). CONCLUSION: Transition PI-RADS 5 tends to have hyperechoic echogenicity and a hypoechoic rim. These findings help to target the transition PI-RADS 5 using TRUS. However, transition PI-RADS 5 is confirmed more frequently as insignificant cancer than peripheral PI-RADS 5.

US LI-RADS Visualization Score: Interobserver Variability and Association With Cause of Liver Disease, Sex, and Body Mass Index.

OBJECTIVES: To evaluate the interobserver agreement between radiologists using the Ultrasound Liver Reporting And Data System (US LI-RADS) visualization score and assess association between visualization score and cause of liver disease, sex, and body mass index (BMI). METHODS: This retrospective, single institution, cross-sectional study evaluated 237 consecutive hepatocellular carcinoma surveillance US examinations between March 4, 2017 and September 4, 2017. Five abdominal radiologists independently assigned a US LI-RADS visualization score (A, no or minimal limitations; B, moderate limitations; C, severe limitations). Interobserver agreement was assessed with a weighted Kappa statistic. Association between US visualization score (A vs B or C) and cause of liver disease, sex, and BMI (< or ≥ 25 kg/m2) was evaluated using univariate and multivariate analyses. RESULTS: The average weighted Kappa statistic for all raters was 0.51. A score of either B or C was assigned by the majority of radiologists in 148/237 cases and was significantly associated with cause of liver disease (P = 0.014) and elevated BMI (P < 0.001). Subjects with viral liver disease were 3.32 times (95% CI: 1.44-8.38) more likely to have a score of A than those with non-alcoholic steatohepatitis (P = 0.007). The adjusted odds ratio of visualization score A was 0.249 (95% CI: 0.13-0.48) among those whose BMI was ≥25 kg/m2 vs. BMI < 25 kg/m2. CONCLUSION: Interobserver agreement between radiologists using US LI-RADS score was moderate. The majority of US examinations were scored as having moderate or severe limitations, and this was significantly associated with non-alcoholic steatohepatitis and increased BMI.

Using new criteria to improve the differentiation between HCC and non-HCC malignancies: clinical practice and discussion in CEUS LI-RADS 2017.

PURPOSE: Using contrast-enhanced ultrasound (CEUS) to evaluate the diagnostic performance of liver imaging reporting and data system (LI-RADS) version 2017 and to explore potential ways to improve the efficacy. METHODS: A total of 315 nodules were classified as LR-1 to LR-5, LR-M, and LR-TIV. New criteria were applied by adjusting the early washout onset (< 45 s) and the time of marked washout (within 3 min). Two subgroups of the LR-M nodules were recategorized as LR-5, respectively. The diagnostic performance was evaluated by calculating the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: By adjusting early washout onset to < 45 s, the LR-5 as a standard for diagnosing HCC had an improved sensitivity (74.1% vs. 56.1%, P < 0.001) without significant change in PPV (93.3% vs. 96.1%, P = 0.267), but the specificity was decreased (48.3% vs. 78.5%, P = 0.018). The LR-M as a standard for the diagnosis of non-HCC malignancies had an increase in specificity (89.2% vs. 66.2%, P < 0.001) but a decrease in sensitivity (31.5% vs. 68.4%, P = 0.023). After reclassification according to the time of marked washout, the sensitivity of the LR-5 increased (80% vs. 56.1%, P < 0.001) without a change in PPV (94.9% vs. 96.1%, P = 0.626) and specificity (80% vs. 78.5%, P = 0.879). For reclassified LR-M nodules, the specificity increased (87.5% versus 66.2%, P < 0.001) with a non-significant decrease in sensitivity (47.3% vs. 68.4%, P = 0.189). CONCLUSIONS: The CEUS LI-RADS showed good confidence in diagnosing HCC while tended to misdiagnose HCC as non-HCC malignancies. Adjusting the marked washout time within 3 min would reduce the possibility of this misdiagnosis.

Application of a shear-wave elastography prediction model to distinguish between benign and malignant breast lesions and the adjustment of ultrasound Breast Imaging Reporting and Data System classifications.

AIM: To explore a real-time shear-wave elastography (SWE) prediction model distinguishing benign from malignant breast lesions and to determine its application in adjusting ultrasound Breast Imaging Reporting and Data System (BI-RADS) classifications. MATERIALS AND METHODS: Four hundred and sixty-eight patients with 488 breast lesions were enrolled. Patients underwent hollow-needle puncture or surgical resection for histopathological examinations. Ultrasound examinations, both conventional ultrasound and real-time SWE, were performed <2 weeks prior to sampling. Statistical analyses were implemented to distinguish benign from malignant breast lesions and adjust ultrasound BI-RADS 3 and 4a classifications. RESULTS: The real-time SWE indicators Emax and Ecol showed the highest diagnostic efficiency in distinguishing between benign and malignant lesions through quantitative and qualitative indicators, respectively. The area under the curve (AUC) for Emax was 0.837 while that for Ecol was 0.828. The AUC of the real-time SWE prediction model, constructed by multivariate logistic regression, for diagnosing benign and malignant breast lesions was 0.850. CONCLUSION: The real-time SWE prediction model aids in the differential diagnosis of benign and malignant breast lesions but cannot replace conventional ultrasound. The model improves the diagnostic performance of ultrasound BI-RADS 3 and 4a classifications.

Automatic PI-RADS assignment by means of formal methods.

INTRODUCTION AND OBJECTIVES: The Prostate Imaging Reporting and Data System (PI-RADS) version 2 emerged as standard in prostate magnetic resonance imaging examination. The Pi-RADS scores are assigned by radiologists and indicate the likelihood of a clinically significant cancer. The aim of this paper is to propose a methodology to automatically mark a magnetic resonance imaging with its related PI-RADS. MATERIALS AND METHODS: We collected a dataset from two different institutions composed by DWI ADC MRI for 91 patients marked by expert radiologists with different PI-RADS score. A formal model is generated starting from a prostate magnetic resonance imaging, and a set of properties related to the different PI-RADS scores are formulated with the help of expert radiologists and pathologists. RESULTS: Our methodology relies on the adoption of formal methods and radiomic features, and in the experimental analysis, we obtain a specificity and sensitivity equal to 1. Q CONCLUSIONS: The proposed methodology is able to assign the PI-RADS score by analyzing prostate magnetic resonance imaging with a very high accuracy.

Ultrasound (US) LI-RADS: Outcomes of Category US-3 Observations.

OBJECTIVE. The purpose of the study is to evaluate the outcomes of ultrasound (US) LI-RADS category US-3 observations detected at US performed for hepatocellular carcinoma (HCC) screening and surveillance on the basis of subsequently performed multi-phase MRI or CT or histopathology. MATERIALS AND METHODS. In this retrospective analysis, 267 patients at high risk for HCC (161 men and 106 women; mean [± SD] age, 58.6 ± 12.2 years) underwent screening liver US between January 2017 and June 2019 and were assigned US-3 observations on a prospective clinical basis using the US LI-RADS algorithm. The results of follow-up imaging studies and/or histopathology were analyzed. RESULTS. Visualization scores assigned at US were A (40.8% [109/267]), B (52.8% [141/267]), and C (6.4% [17/267]). Reasons for US-3 observations included a measurable mass of 1 cm or larger (88.8% [237/267]; mean size, 1.8 ± 1.0 cm; range, 1.0-6.9 cm), an area of parenchymal distortion of 1 cm or greater (7.9% [21/267]; mean size, 1.8 ± 0.9 cm; range, 1.0-4.0 cm), or a new venous thrombus (3.4% [9/267]). Confirmatory testing with multiphase contrast-enhanced MRI or CT or with histopathology was available for 81.6% (218/267) of patients. Causes of US-3 observations included no abnormality at MRI or CT (41.3% [90/218]), a benign lesion (32.6% [71/218]), a LI-RADS category 3 (LR-3) observation at MRI or CT (5.5% [12/218]), a LI-RADS category 4 or 5 (LR-4 or LR-5) observation at MRI or CT or identification of HCC at histopathology (18.8% [41/218]), and an LR-M (denoting probably or definitely malignant but without specific features for HCC) observation at MRI or CT or other malignancy at histopathology (1.8% [4/218]). The PPV of a US-3 observation for probable or definite HCC was 18.8%, and for any malignancy it was 20.6%. CONCLUSION. In the HCC screening population, approximately one in five US-3 observations represents probable or definite HCC at multiphase MRI or CT or HCC at histopathology. These findings support current US LI-RADS guidelines to pursue further evaluation with multiphase cross-sectional imaging for US-3 observations.

Osseous Tumor Reporting and Data System-Multireader Validation Study.

OBJECTIVE: To develop and validate an Osseous Tumor Reporting and Data System (OT-RADS) with the hypothesis that the proposed guideline is reliable and assists in separating benign from malignant osseous tumors with a good area under the curve, and that could assist further patient management. METHODS: In this multireader cross-sectional validation study, an agreement was reached for OT-RADS categories based on previously described magnetic resonance imaging features and consensus of expert musculoskeletal radiologists. World Health Organization classification was used, and a wide spectrum of benign and malignant osseous tumors was evaluated. Magnetic resonance imaging categories were as follows: OT-RADS 0-incomplete imaging; OT-RADS I-negative; OT-RADS II-definitely benign; OT-RADS III-probably benign; OT-RADS IV-suspicious for malignancy or indeterminate; OT-RADS V-highly suggestive of malignancy; and OT-RADS VI-known biopsy-proven malignancy or recurrent malignancy in the tumor bed. Four blinded readers categorized each tumor according to OT-RADS classification. Intraclass correlation (ICC) and Conger κ were used. Diagnostic performance measures including area under the receiver operating curve were reported. Osseous Tumor Reporting and Data System was dichotomized as benign (I-III) and malignant (IV and V) for calculating sensitivity and specificity. RESULTS: Interreader agreement for OT-RADS (ICC = 0.78) and binary distinction of benign versus malignant (κ = 0.67) were good to excellent, while agreement for individual tumor feature characteristics were poor to fair (ICC = 0.25-0.36; κ = 0.16-0.39). The sensitivities, specificities, and area under the receiver operating curve of the readers ranged from 0.93-1.0, 0.71-0.86, and 0.92-0.97, respectively. CONCLUSIONS: Osseous Tumor Reporting and Data System lexicon is reliable and helps stratify tumors into benign and malignant categories. It can be practically used by radiologists to guide patient management, improve multidisciplinary communications, and potentially impact outcomes.

The impact of TI-RADS in detecting thyroid malignancies: a prospective study.

INTRODUCTION: Thyroid ultrasonography (US) is the first-step noninvasive and easily accessible diagnostic method widely used in the detection and characterization of nodular thyroid disease. We aimed to develop a TI-RADS, which is easy to apply and only relies on the counting of suspicious criteria. In order to measure the reliability of the system, we investigated its correlation with fine needle aspiration biopsy (FNAB) and post-surgery histological results. MATERIALS AND METHODS: In this prospective study, 242 patients who had undergone FNAB with simultaneous cytopathologist in the radiology department between April and August 2016 were analyzed. Before FNAB, the thyroid gland was re-evaluated with US, and TI-RADS classification was made. Demographic characteristics, family thyroid cancer history and radiotherapy history to the neck region were noted. RESULTS: Of the 242 patients, 17.3% were male (42 males/200 females). US-guided FNAB was applied to all patients. Mean age was 50 ± 13 years (min: 19, max: 82). Both FNAB and final post-surgery histology results showed that sex and age were not statistically significantly associated with malignancy (p = 0.193) TI-RADS criteria and FNAB results revealed a statistically significant association between irregular contours, the state of anteroposterior diameter being longer than transverse diameter, microcalcifications, marked hypoechogenicity, and malignancy (p < 0,05). Thirty patients were TI-RADS ≥ 4, 206 patients were TI-RADS ≤ 3 and there was a significant correlation between TI-RADS and Bethesda classification (p = 0.001). In addition, statistically significant associations were found between malignancy and family history of thyroid cancer (p = 0.035) and radiotherapy history to the neck region (p = 0.01). CONCLUSION: TI-RADS system after nodule identification is based only on the counting of suspicious criteria. It will be safe and effective to recommend follow-up with low score TI-RADS, benign characters and insufficient FNAB results, and thus, unnecessary thyroidectomy operations will be prevented. It will be easier for surgeons to recommend surgery and persuade the patients for it when patients have high TI-RADS scores. TI-RADS has high power in detecting malignancy by recommending biopsy of suspicious nodules.

Comparison of 2 fracture risk estimation processes in Alberta: a cross-sectional chart review.

BACKGROUND: In Canada, decisions regarding osteoporosis pharmacotherapy are based on estimated 10-year risk of osteoporotic fracture. We aimed to determine how frequently 2 common approaches (Canadian Association of Radiologists and Osteoporosis Canada [CAROC] tool and Fracture Risk Assessment Tool [FRAX]) produced different estimates and to seek possible explanations for differences. METHODS: We conducted a cross-sectional chart review at a tertiary osteoporosis centre (Dr. David Hanley Osteoporosis Centre in Calgary). Included patients were women referred for consideration of osteoporosis pharmacotherapy who attended a consultation between 2016 and 2019 and whose charts contained 10-year osteoporotic fracture risk estimates using both the CAROC tool (based on bone mineral density [BMD] results) and FRAX (based on BMD results and clinically assessed fracture risk factors). Risk estimates provided on BMD reports (calculated with CAROC) and generated through osteoporosis clinic consultation (calculated with FRAX, including BMD) were categorized as low (< 10.0%), moderate (10.0%-19.9%) or high (≥ 20.0%). Estimates were considered discordant when they placed the patient in different risk categories. RESULTS: Of 190 patients evaluated, 99 (52.1%) had discordant risk estimates. Although a similar proportion were considered high risk by BMD reports using the CAROC tool (17.9%) and clinic charts using FRAX (19.5%), the 2 methods identified different patients as being high risk. Around the crucial high-risk (20.0%) treatment threshold, discordance was present in 37 patients (19.5%, 95% confidence interval [CI] 14.5%-25.7%); discordance around the moderate-risk (10.0%) threshold was present in 69 (36.3%, 95% CI 29.5%-43.2%) patients. Disagreement regarding fracture history between BMD reports and clinic charts was observed in 19.8% of patients. INTERPRETATION: Fracture risk estimates on BMD reports (using the CAROC tool) and those calculated in the clinical setting (using FRAX) frequently result in different risk classification. Osteoporosis treatment decisions may differ in up to half of patients depending on which estimate is used, highlighting the need for a consistent and accurate assessment process for fracture risk.

Evaluation of a prospective adverse event reporting system in interventional radiology.

AIM: To assess the performance of a prospective adverse event (AE) reporting system. MATERIALS AND METHODS: Four hundred and seventy-one consecutive arterial procedures were performed in 465 patients (median age, 65 years; interquartile range, 54-77; 276 men) over 2 years by four interventional radiologists at a single centre where clinical follow-up was not performed routinely by interventional radiology (IR). AEs were reported prospectively using a radiology information system or in interventional radiologists' electronic records and combined in a departmental listing of adverse events (DLAE). A retrospective medical record review was performed to identify a reference standard list of AEs for this observational cohort study. AEs were graded according to the Society of Interventional Radiology AE classification system. Descriptive statistics were calculated for the performance of the DLAE. A model comparing the rate of reporting of AEs with and without integration of clinical follow-up was tested for significance. RESULTS: Thirty-eight of the 471 (8%) IR procedures had an AE according to the reference standard. The DLAE identified 20/38 (53%) of AEs (K=0.67 [good agreement], 95% confidence interval [CI] agreement=0.53-0.81; p=0.0001; sensitivity 52.6% [95% CI, 36-69%], specificity 100% [95% CI, 99-100%], positive predictive value [PPV] 100%, negative predictive value [NPV] 96 [95% CI, 94.5-97%], accuracy 96% [95% CI, 94-97%]). The performance of the AE reporting system will improve with integration of clinical follow-up (p=0.0015). CONCLUSION: A prospective AE reporting system without clinical integration will not detect all procedure complications.

Inclusion of Thyroid Nodule Location in American College of Radiology TI-RADS Scoring: Impact on System Performance.

Emerging data suggest that the location of thyroid nodules influences malignancy risk. The purpose of this study was to explore the impact of including location in American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) scoring. Four of five revised scoring algorithms that added 1 or 2 points to higher-risk locations were associated with lowered accuracy due to lower specificity. However, an algorithm that added 1 point to isthmic nodules did not differ significantly from ACR TI-RADS in accuracy; one additional isthmic cancer was diagnosed for each 10.3 additional benign nodules recommended for biopsy.

Multimodality Imaging of Hepatocellular Carcinoma: From Diagnosis to Treatment Response Assessment in Everyday Clinical Practice.

The Liver Imaging Reporting and Data System (LI-RADS) is a recently developed classification aiming to improve the standardization of liver imaging assessment in patients at risk of developing hepatocellular carcinoma (HCC). The LI-RADS v2017 implemented new algorithms for ultrasound (US) screening and surveillance, contrast-enhanced US diagnosis and computed tomography/magnetic resonance imaging treatment response assessment. A minor update of LI-RADS was released in 2018 to comply with the American Association for the Study of the Liver Diseases guidance recommendations. The scope of this review is to provide a practical overview of LI-RADS v2018 focused both on the multimodality HCC diagnosis and treatment response assessment.

Impact of mandated prospectively reported apparent diffusion coefficient values on the rates of positivity for clinically significant prostate cancer by PI-RADS score.

BACKGROUND: Prior research has shown that retrospectively measured apparent diffusion coefficient (ADC) of prostate magnetic resonance imaging (MRI) lesions is associated with clinically significant prostate cancer (csPCa) on targeted biopsy suggesting that ADC should be measured and reported prospectively. PURPOSE: To assess the impact of mandatory prospective measurement of ADC on the rates of positivity across PI-RADS scores for csPCa. MATERIAL AND METHODS: Consecutive patients who underwent ultrasound (US)-MRI fusion prostate biopsy from August 2018 to July 2019 and who had prospectively reported ADC were compared to control patients who did not. Rates of positivity by PI-RADS category were computed and compared using Chi-square. Multivariable regression was performed. RESULTS: In total, 126 patients (median age 65 years) with 165 prostate lesions (19, 51, 70, and 25 PI-RADS 2, 3, 4, and 5, respectively) and prospectively reported ADC values were compared to 113 control patients (median age 66 years) with 157 prostate lesions (17, 42, 64, and 34 PI-RADS 2, 3, 4, and 5, respectively). Rates of positivity across PI-RADS scores were similar between the two cohorts; 11%, 25%, 55%, and 76% and 0%, 21%, 56%, and 62% for PI-RADS 2, 3, 4, and 5 in the test and control cohorts, respectively (Chi-square P = 0.78). Multivariate logistic regression showed no significant association between the presence of prospectively measured ADC and csPCa (odds ratio 1.1, 95% confidence interval 0.7-1.7, P = 0.82). CONCLUSION: Prospective ADC measurement may not impact PI-RADS category assignments or positivity rates for csPCa under current guidelines. Future versions of PI-RADS may need to incorporate ADC into scoring rules to realize their potential.

Liver Imaging Reporting and Data System (LI-RADS) v2018: Review of the CT/MRI Diagnostic Categories.

Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system that provides standardization in the interpretation and reporting of observations in patients at risk of developing hepatocellular carcinoma (HCC). Computed tomography/magnetic resonance imaging (CT/MRI) LI-RADS v2018 includes 8 diagnostic categories, which reflect the probability of benignity, malignancy in general, or HCC specifically. This article reviews the diagnostic categories of CT/MRI LI-RADS v2018, highlighting the key imaging features, diagnostic criteria, and management implications.

Subcategory classifications of Breast Imaging and Data System (BI-RADS) category 4 lesions on MRI.

PURPOSE: Category 4 in BI-RADS for magnetic resonance imaging (MRI) has a wide range of probabilities of malignancy, extending from > 2 to < 95%. We classified category 4 lesions into three subcategories and analyzed the positive predictive value (PPV) of malignancy in a tertiary hospital. MATERIALS AND METHODS: This retrospective study included 346 breast MRIs with 434 category 2-5 lesions. All enhancing lesions were classified as category 2 (0% probability of malignancy), 3 (> 0%, ≤ 2%), 4 (> 2%, < 95%) and 5 (≥ 95%); category 4 lesions were further subcategorized into 4A (> 2%, ≤ 10%), 4B (> 10%, ≤ 50%) and 4C (> 50%, < 95%) at the time of diagnosis. Radiological and pathological reports were retrospectively analyzed, and the PPVs were calculated. RESULTS: We included 149 malignant and 285 benign lesions. The PPVs of subcategories 4A, 4B and 4C were 1.8%, 11.8% and 67.5%, respectively. The PPVs were higher for lesions coexisting with category 5 or 6 lesions compared with those for isolated lesions. CONCLUSION: Category 4 lesions can be classified into three subcategories depending on the likelihood of malignancy. Lesions coexisting with category 5 or 6 lesions are more likely to be malignant.

Impact of PI-RADS Category 3 lesions on the diagnostic accuracy of MRI for detecting prostate cancer and the prevalence of prostate cancer within each PI-RADS category: A systematic review and meta-analysis.

OBJECTIVE: To evaluate Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions' impact on the diagnostic test accuracy (DTA) of MRI for prostate cancer (PC) and to derive the prevalence of PC within each PI-RADS category. METHODS: MEDLINE and Embase were searched until April 10, 2020 for studies reporting on the DTA of MRI by PI-RADS category. Accuracy metrics were calculated using a bivariate random-effects meta-analysis with PI-RADS three lesions treated as a positive test, negative test, and excluded from the analysis. Differences in DTA were assessed utilizing meta-regression. PC prevalence within each PI-RADS category was estimated with a proportional meta-analysis. RESULTS: In total, 26 studies reporting on 12,913 patients (4,853 with PC) were included. Sensitivities for PC in the positive, negative, and excluded test groups were 96% (95% confidence interval [CI] 92-98), 82% (CI 75-87), and 95% (CI 91-97), respectively. Specificities for the positive, negative, and excluded test groups were 33% (CI 23-44), 71% (CI 62-79), and 52% (CI 37-66), respectively. Meta-regression demonstrated higher sensitivity (p < 0.001) and lower specificity (p < 0.001) in the positive test group compared to the negative group. Clinically significant PC prevalences were 5.9% (CI 0-17.1), 11.4% (CI 6.5-17.3), 24.9% (CI 18.4-32.0), 55.7% (CI 47.8-63.5), and 81.4% (CI 75.9-86.4) for PI-RADS categories 1, 2, 3, 4 and 5, respectively. CONCLUSION: PI-RADS category 3 lesions can significantly impact the DTA of MRI for PC detection. A low prevalence of clinically significant PC is noted in PI-RADS category 1 and 2 cases. ADVANCES IN KNOWLEDGE: Inclusion or exclusion of PI-RADS category 3 lesions impacts the DTA of MRI for PC detection.

Radiological Society of North America (RSNA) Expert Consensus Statement Related to Chest CT Findings in COVID-19 Versus CO-RADS: Comparison of Reporting System Performance Among Chest Radiologists and End-User Preference.

PURPOSE: The RSNA expert consensus statement and CO-RADS reporting system assist radiologists in describing lung imaging findings in a standardized manner in patients under investigation for COVID-19 pneumonia and provide clarity in communication with other healthcare providers. We aim to compare diagnostic performance and inter-/intra-observer among chest radiologists in the interpretation of RSNA and CO-RADS reporting systems and assess clinician preference. METHODS: Chest CT scans of 279 patients with suspected COVID-19 who underwent RT-PCR testing were retrospectively and independently examined by 3 chest radiologists who assigned interpretation according to the RSNA and CO-RADS reporting systems. Inter-/intra-observer analysis was performed. Diagnostic accuracy of both reporting systems was calculated. 60 clinicians participated in a survey to assess end-user preference of the reporting systems. RESULTS: Both systems demonstrated almost perfect inter-observer agreement (Fleiss kappa 0.871, P < 0.0001 for RSNA; 0.876, P < 0.0001 for CO-RADS impressions). Intra-observer agreement between the 2 scoring systems using the equivalent categories was almost perfect (Fleiss kappa 0.90-0.92, P < 0.001). Positive predictive values were high, 0.798-0.818 for RSNA and 0.891-0.903 CO-RADS. Negative predictive value were similar, 0.573-0.585 for RSNA and 0.573-0.58 for CO-RADS. Specificity differed between the 2 systems, 68-73% for CO-RADS and 52-58% for RSNA with superior specificity of CO-RADS. Of 60 survey participants, the majority preferred the RSNA reporting system rather than CO-RADS for all options provided (66.7-76.7%; P < 0.05). CONCLUSIONS: RSNA and CO-RADS reporting systems are consistent and reproducible with near perfect inter-/intra-observer agreement and excellent positive predictive value. End-users preferred the reporting language in the RSNA system.

How does PI-RADS v2.1 impact patient classification? A head-to-head comparison between PI-RADS v2.0 and v2.1.

BACKGROUND: PI-RADS classification has recently been updated, with the magnitude of changes implemented currently unknown. PURPOSE: To quantify the categorization shifts between PI-RADS v2.0 and v2.1. MATERIAL AND METHODS: Retrospective review of 535 consecutive diagnostic magnetic resonance imaging (MRI) studies performed over 18 months, assigning to each case a PI-RADS category in the peripheral zone (PZ), the transition zone (TZ), and the whole gland using both PI-RADS v2.0 and v2.1. Significance of changes in category assignments and of differences in the number of positive or negative MRIs were evaluated using the McNemar test. RESULTS: Comparing v2.0 to v2.1 for the whole gland, 11.2% of PI-RADS 2 categories shifted to PI-RADS 1 (6.9% in the PZ, 56.8% in the TZ), 16.1% of PI-RADS 3 categories shifted to PI-RADS 2 (15.0% in the PZ, 20.0% in the TZ), and 2.1% of PI-RADS 2 categories shifted to PI-RADS 3 (0.3% in the PZ, 1.9% in the TZ). The proportion of PI-RADS 1 significantly increased from 0.6% to 7.3%, PI-RADS 2 significantly decreased from 60.0% to 53.8%, and PI-RADS 3 non-significantly decreased from 11.6% to 11.0%. The total number of positive exams (PI-RADS 3-5) did not change significantly (39.4% versus 38.8%). CONCLUSION: The most prominent change between v2.0 and v2.1 was observed in the TZ with the downgrading of typical benign prostatic hyperplasia nodules from category 2 into category 1. Overall, there were no significant changes in the number of positive and negative MRI results, with an expected low influence in clinical management.