Data System Version Research Articles

Prospective evaluation of PI-RADSv2.1 using multiparametric and biparametric MRI for detecting clinically significant prostate cancer based on MRI/US fusion-guided biopsy.

To evaluate the cancer detection rates for each category of Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1) using multiparametric magnetic resonance imaging (mpMRI) and biparametric MRI (bpMRI) based on MRI/ultrasound (US)-fusion biopsy. This prospective study included participants who underwent mpMRI or bpMRI with a PI-RADSv2.1 interpretation and subsequently received MRI/US-fusion biopsy between August 2022 and December 2023. The lesion-based detection rates of clinically significant prostate cancer (csPCa) in each PI-RADSv2.1 category and the correlation between PI-RADSv2.1 categories and International Society of Urological Pathology (ISUP) grade groups were analyzed. The diagnostic performance of PI-RADSv2.1 in predicting csPCa was evaluated, and diagnostic performance of mpMRI and bpMRI was compared using cut-offs, with PI-RADSv2.1 categories ≥ 3 or ≥ 4 defined as positive. A total of 247 lesions from 216 participants were included in this study. A total of 157 patients underwent mpMRI and the remaining 59 underwent bpMRI. The csPCa detection rates for each PI-RADSv2.1 category of mpMRI and bpMRI were as follows: category 1, 0% (0/11); 2, 13% (3/23); 3, 16% (5/31); 4, 60% (43/72); 5, 65% (26/40), in mpMRI; category 1, 0% (0/4); 2, 33% (1/3); 3, 25% (3/12); 4, 61% (19/31); 5, 75% (15/20) in bpMRI. PI-RADSv2.1 categories were significantly positively associated with csPCa detection rates in both mpMRI and bpMRI (p < 0.0001 and p = 0.00048, respectively). PI-RADSv2.1 categories correlated with ISUP grade groups for mpMRI and bpMRI (p < 0.0001 for both). There were no significant differences in the detection rates between mpMRI and bpMRI for PI-RADS v2.1 positive and negative lesions. PI-RADSv2.1 using mpMRI and bpMRI could stratify the risk of csPCa, and the csPCa detection rate of bpMRI was compatible with that of mpMRI using cut-offs of PI-RADSv2.1 categories ≥ 3 or ≥ 4.

Optimizing Prostate Imaging Practices in Saudi Arabian Hospitals: A Comprehensive Analysis of PI-RADS Compliance in Multiparametric MRI.

Prostate cancer, a significant contributor to male cancer mortality globally, demands improved diagnostic strategies. In Saudi Arabia, where the incidence is expected to double, this study assessed the compliance of multiparametric MRI (mpMRI) practices with Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) guidelines across diverse healthcare institutions. A survey was distributed to the radiology departments of all tertiary referral hospitals in Saudi Arabia (n=60) to assess their compliance with the technical specifications outlined in PI-RADS v2. Statistical analysis included chi-square, Fisher exact, ANOVA, and Student t-tests to examine the collected data. The study revealed an overall commendable compliance rate of 95.23%. However, significant variations were observed in technical parameters, particularly between 1.5 Tesla and 3 Tesla scanners and tertiary versus non-tertiary hospitals. Notable adherence in certain sequences contrasted with discrepancies in T2-weighted and diffusion-weighted imaging parameters. These findings underscore the need for nuanced approaches to optimize prostate imaging protocols, considering field strength and institutional differences. The study contributes to the ongoing refinement of standardized mpMRI practices, aiming to enhance diagnostic accuracy and improve clinical outcomes in prostate cancer.

Using Telehealth to Increase Lung Cancer Screening Referrals for At-Risk Veterans in Rural Communities.

At-risk rural veterans have low rates of lung cancer screening (LCS). This proof-of-principle quality improvement project aimed to determine whether a telehealth intervention would increase referrals for at-risk veterans living in the rural upper Midwest and attending a smoking cessation program to LCS with low-dose computed tomography (LDCT) of the chest. Sixty-eight of 74 LCS-eligible rural veterans who self-enrolled in a smoking cessation program were contacted by telephone. Those who agreed to enroll in LCS were referred to LDCT and followed for 4 months. At the conclusion of the intervention, the number of referrals and screenings performed were tabulated. LDCT reports were reviewed and scored according to Lung CT Screening Reporting and Data System (Lung-RADS) version 1.1. Only 3 of 74 LCS-eligible veterans (4%) underwent LDCT before initiation of this telehealth intervention. By the conclusion of this 4-month project, 19 of 74 veterans (26%) underwent LDCT. Forty-one veterans were successfully contacted and 29 agreed to participate in LCS. Of those who agreed to participate, 19 underwent LDCT within 4 months. Of the veterans who received LDCT, 10 were diagnosed with Lung-RADS 1, 7 with Lung-RADS 2, 1 with Lung-RADS 3, and 1 with Lung-RADS 4B. Annual follow-up LDCT or referral for further evaluation were pursued in each case. Collectively, these data suggest that telehealth intervention could increase referrals of at-risk rural veterans to a centralized LCS program at a regional US Department of Veterans Affairs medical facility.

Evaluating Large Language Models for Automated Reporting and Data Systems Categorization: Cross-Sectional Study.

Large language models show promise for improving radiology workflows, but their performance on structured radiological tasks such as Reporting and Data Systems (RADS) categorization remains unexplored. This study aims to evaluate 3 large language model chatbots-Claude-2, GPT-3.5, and GPT-4-on assigning RADS categories to radiology reports and assess the impact of different prompting strategies. This cross-sectional study compared 3 chatbots using 30 radiology reports (10 per RADS criteria), using a 3-level prompting strategy: zero-shot, few-shot, and guideline PDF-informed prompts. The cases were grounded in Liver Imaging Reporting & Data System (LI-RADS) version 2018, Lung CT (computed tomography) Screening Reporting & Data System (Lung-RADS) version 2022, and Ovarian-Adnexal Reporting & Data System (O-RADS) magnetic resonance imaging, meticulously prepared by board-certified radiologists. Each report underwent 6 assessments. Two blinded reviewers assessed the chatbots' response at patient-level RADS categorization and overall ratings. The agreement across repetitions was assessed using Fleiss κ. Claude-2 achieved the highest accuracy in overall ratings with few-shot prompts and guideline PDFs (prompt-2), attaining 57% (17/30) average accuracy over 6 runs and 50% (15/30) accuracy with k-pass voting. Without prompt engineering, all chatbots performed poorly. The introduction of a structured exemplar prompt (prompt-1) increased the accuracy of overall ratings for all chatbots. Providing prompt-2 further improved Claude-2's performance, an enhancement not replicated by GPT-4. The interrun agreement was substantial for Claude-2 (k=0.66 for overall rating and k=0.69 for RADS categorization), fair for GPT-4 (k=0.39 for both), and fair for GPT-3.5 (k=0.21 for overall rating and k=0.39 for RADS categorization). All chatbots showed significantly higher accuracy with LI-RADS version 2018 than with Lung-RADS version 2022 and O-RADS (P<.05); with prompt-2, Claude-2 achieved the highest overall rating accuracy of 75% (45/60) in LI-RADS version 2018. When equipped with structured prompts and guideline PDFs, Claude-2 demonstrated potential in assigning RADS categories to radiology cases according to established criteria such as LI-RADS version 2018. However, the current generation of chatbots lags in accurately categorizing cases based on more recent RADS criteria.

Clinical characteristics and pathological features of undetectable clinically significant prostate cancer on multiparametric magnetic resonance imaging: A single-center and retrospective study.

The objectives of this study were to clarify the pathological features of clinically significant prostate cancer (csPC) that is undetectable on multiparametric magnetic resonance imaging (mpMRI). This single-center and retrospective study enrolled 33 men with prostate cancer (PC), encompassing 109 PC lesions, who underwent mpMRI before radical prostatectomy. Two radiologists independently assessed the mpMR images of all lesions and compared them with the pathological findings of PC. All PC lesions were marked on resected specimens using prostate imaging reporting and data system version 2.1 and classified into magnetic resonance imaging (MRI)-detectable and MRI-undetectable PC lesions. Each lesion was classified into csPC and clinically insignificant PC. Pathological characteristics were compared between MRI-detectable and MRI-undetectable csPC. Statistical analysis was performed to identify factors associated with MRI detectability. A logistic regression model was used to determine the factors associated with MRI-detectable and MRI-undetectable csPC. Among 109 PC lesions, MRI-detectable and MRI-undetectable PCs accounted for 31% (34/109) and 69% (75/109) of lesions, respectively. All MRI-detectable PCs were csPC. MRI-undetectable PCs included 30 cases of csPC (40%). The detectability of csPC on mpMRI was 53% (34/64). The MRI-undetectable csPC group had a shorter major diameter (10.6 ± 6.6 mm vs. 19.0 ± 6.9 mm, P < 0.001), shorter minor diameter (5.7 ± 2.9 mm vs. 10.7 ± 3.4 mm, P < 0.001), and lower percentage of lesions with Gleason pattern 5 (17% vs. 71%, P < 0.001). Shorter minor diameter (odds ratio [OR], 2.62; P = 0.04) and lower percentage of Gleason pattern 5 (OR, 24; P = 0.01) were independent predictors of MRI-undetectable csPC. The pathological features of MRI-undetectable csPC included shorter minor diameter and lower percentage of Gleason pattern 5. csPC with shorter minor diameter may not be detected on mpMRI. Some MRI-undetectable csPC lesions exhibited sufficient size and Gleason pattern 5, emphasizing the need for further understanding of pathological factors contributing to MRI detectability.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale. In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5-10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4-6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341. Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87-0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83-0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6-63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3-92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3-72·4] vs 69·0% [65·5-72·5]) at the same sensitivity (96·1%, 94·0-98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (-0·04) was greater than the non-inferiority margin (-0·05) and a p value below the significance threshold was reached (p<0·001). An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system. Health~Holland and EU Horizon 2020.

Outcome prediction of SSTR-RADS-3A and SSTR-RADS-3B lesions in patients with neuroendocrine tumors based on 68Ga-DOTATATE PET/MR

PurposeSomatostatin receptor (SSTR)-targeted PET imaging has emerged as a common approach to evaluating those patients with well-differentiated neuroendocrine tumors (NETs). The SSTR reporting and data system (SSTR-RADS) version 1.0 provides a means of categorizing lesions from 1 to 5 according to the likelihood of NET involvement, with SSTR-RADS-3A (soft-tissue) and SSTR-RADS-3B (bone) lesions being those suggestive of but without definitive NET involvement. The goal of the present study was to assess the ability of 68Ga-DOTATATE PET/MR imaging data to predict outcomes for indeterminate SSTR-RADS-3A and 3B lesions.MethodsNET patients with indeterminate SSTR-RADS-3A or SSTR-RADS-3B lesions who underwent 68Ga-DOTATATE PET/MR imaging from April 2020 through August 2023 were retrospectively evaluated. All patients underwent follow-up through December 2023 (median, 17 months; (3–31 months)), with imaging follow-up or biopsy findings ultimately being used to classify lesions as malignant or benign. Lesion maximum standardized uptake value (SUVmax) along with minimum and mean apparent diffusion coefficient (ADCmin and ADCmean) values were measured and assessed for correlations with outcomes on follow-up.ResultsIn total, 33 indeterminate SSTR-RADS-3 lesions from 22 patients (19 SSTR-RADS-3A and 14 SSTR-RADS-3B) were identified based upon baseline 68Ga-DOTATATE PET/MR findings. Over the course of follow-up, 16 of these lesions (48.5%) were found to exhibit true NET positivity, including 9 SSTR-RADS-3A and 7 SSTR-RADS-3B lesions. For SSTR-RADS-3A lymph nodes, a diameter larger than 0.7 cm and an ADCmin of 779 × 10−6mm2/s or lower were identified as being more likely to be associated with metastatic lesions. Significant differences in ADCmin and ADCmean were identified when comparing metastatic and non-metastatic SSTR-RADS-3B bone lesions (P < 0.05), with these parameters offering a high predictive ability (AUC = 0.94, AUC = 0.86).ConclusionBoth diameter and ADCmin can aid in the accurate identification of the nature of lesions associated with SSTR-RADS-3A lymph nodes, whereas ADCmin and ADCmean values can inform the accurate interpretation of SSTR-RADS-3B bone lesions.

A novel model incorporating quantitative contrast-enhanced ultrasound into PI-RADSv2-based nomogram detecting clinically significant prostate cancer

The diagnostic accuracy of clinically significant prostate cancer (csPCa) of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) is limited by subjectivity in result interpretation and the false positive results from certain similar anatomic structures. We aimed to establish a new model combining quantitative contrast-enhanced ultrasound, PI-RADSv2, clinical parameters to optimize the PI-RADSv2-based model. The analysis was conducted based on a data set of 151 patients from 2019 to 2022, multiple regression analysis showed that prostate specific antigen density, age, PI-RADSv2, quantitative parameters (rush time, wash-out area under the curve) were independent predictors. Based on these predictors, we established a new predictive model, the AUCs of the model were 0.910 and 0.879 in training and validation cohort, which were higher than those of PI-RADSv2-based model (0.865 and 0.821 in training and validation cohort). Net Reclassification Index analysis indicated that the new predictive model improved the classification of patients. Decision curve analysis showed that in most risk probabilities, the new predictive model improved the clinical utility of PI-RADSv2-based model. Generally, this new predictive model showed that quantitative parameters from contrast enhanced ultrasound could help to improve the diagnostic performance of PI-RADSv2 based model in detecting csPCa.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

Prognostic performance of MRI LI-RADS version 2018 features and clinical-pathological factors in alpha-fetoprotein-negative hepatocellular carcinoma.

To evaluate the role of the magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinical-pathological factors for predicting the prognosis of alpha-fetoprotein (AFP)-negative (≤ 20ng/ml) hepatocellular carcinoma (HCC) patients, and to compare with other traditional staging systems. We retrospectively enrolled 169 patients with AFP-negative HCC who received preoperative MRI and hepatectomy between January 2015 and August 2020 (derivation dataset:validation dataset = 118:51). A prognostic model was constructed using the risk factors identified via Cox regression analysis. Predictive performance and discrimination capability were evaluated and compared with those of two traditional staging systems. Six risk factors, namely the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade, were associated with recurrence-free survival. The prognostic model constructed using these factors achieved C-index of 0.705 and 0.674 in the derivation and validation datasets, respectively. Furthermore, the model performed better in predicting patient prognosis than traditional staging systems. The model effectively stratified patients with AFP-negative HCC into high- and low-risk groups with significantly different outcomes (p < 0.05). A prognostic model integrating the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade may serve as a valuable tool for refining risk stratification in patients with AFP-negative HCC.

3T multiparametric MRI’s accuracy in detecting prostate cancer using Prostate Imaging Reporting and Data System (PIRADS) version 2.1 with prostate biopsy as a reference

BackgroundMultiparametric magnetic resonance imaging (MRI) is valuable in detecting prostate cancer due to its high sensitivity to malignant lesions. It is commonly utilized to improve the identification of clinically significant cancers within the prostate. This study aimed to correlate the findings from 3T multiparametric MRI of the prostate using the updated Prostate Imaging Reporting and Data System version 2.1 (PIRADSv2.1) from 2019 with reference to prostate biopsy results. Additionally, PIRADSv2.1 was used to calculate the sensitivity, specificity, positive predictive value, and negative predictive value of the 3T multiparametric MRI of the prostate.Methods and materialsA retrospective study was conducted at a tertiary center, wherein we identified patients who underwent a prostate biopsy between June 2019 and June 2021 and had a corresponding MRI of the prostate performed at the same institution, evaluated with PIRADSv2.1 criteria.ResultsA total of 50 patients were eligible for final analysis. The prevalence of prostate cancer was 69% (95% confidence interval (CI) 54–81%). Receiver operating characteristic (ROC) curves were generated for 3T multiparametric MRI of the prostate using PIRADSv2.1 to diagnose prostate cancer; the area under the ROC curve was 0.81 (95% CI 0.68–0.95, p < 0.001). The sensitivity, specificity, positive predictive value, and negative predictive value of the 3T multiparametric prostate MRI using PIRADSv2.1 were 74.0%, 87.0%, 92.9%, and 59.1%, respectively.ConclusionsPIRADSv2.1 exhibited good overall performance in the diagnosis of prostate cancer.

Performance of a Region of Interest–based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database—CAD-FIRST Study

Background and objectiveProstate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest–based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI. MethodsThe lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively. Key findings and limitationsAfter excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70–82) and 80% (CI: 74–85; p = 0.36), respectively. The algorithm’s sensitivity and specificity were 86% (CI: 76–93) and 65% (CI: 54–73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89–100) and 38% (CI: 26–47), and 89% (CI: 79–96) and 47% (CI: 35–57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26–34% of biopsies while missing 5–11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm’s scores would have avoided 44–47% of biopsies while missing 6–9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment. Conclusions and clinical implicationsThe algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy. Patient summaryAn artificial intelligence–based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database.

Significance of atypical nodules upgraded to category 3 in Prostate Imaging Reporting and Data System version 2.1 for prostate cancer diagnosis.

Significance of atypical nodules upgraded to category 3 in Prostate Imaging Reporting and Data System version 2.1 for prostate cancer diagnosis.

Comparison of Lung-RADS Version 1.1 and Lung-RADS Version 2022 in Classifying Airway Nodules Detected at Lung Cancer Screening CT.

Purpose To compare the Lung Imaging Reporting and Data System (Lung-RADS) version 1.1 with version 2022 classification of airway nodules detected at lung cancer screening CT examinations. Materials and Methods This retrospective study included all patients who underwent a lung cancer screening CT examination in the authors' health care network between 2015 and 2021 with a reported airway or endobronchial nodule. A fellowship-trained cardiothoracic radiologist reviewed these CT images and characterized the airway nodules by size, location, multiplicity, morphology, dependent portions of airway, internal air, fluid attenuation, distal changes, outcome at follow-up, and final pathologic diagnosis, if malignant. Sensitivity and specificity of Lung-RADS version 1.1 in detecting malignant nodules were compared with those of Lung-RADS version 2022 using the McNemar test. Results A total of 174 patients were included. Of these, 163 (94%) had airway nodules that were deemed benign, while 11 (6%) had malignant nodules. Airway nodules in the trachea and mainstem bronchi were all benign, while lobar and segmental airway nodules had the highest risk for lung cancer (17.2% and 11.1%, respectively). Of the 12 subsegmental airway nodules that were obstructive, three (25%) were malignant and nine (75%) were benign. Nodules with nonobstructive morphologies, dependent portions of airway, internal air, or fluid attenuation were all benign. Only 10 of the 92 (10.9%) patients with positive Lung-RADS by clinical report had cancer. Lung-RADS version 2022 resulted in higher specificity than version 1.1 (82% vs 50%, P < .001), without sacrificing sensitivity (91% for both). Conclusion Compared with the previous version, Lung-RADS version 2022 reduced the number of false-positive screening CT examinations while still identifying malignant airway nodules. Keywords: CT, Lung, Primary Neoplasms, Pulmonary, Lung Cancer Screening, Lung-RADS, Nodule Risk, Airway Nodule, Endobronchial Nodule © RSNA, 2024.

Evaluation of the Time Difference Method in Identifying Hepatocellular Carcinoma in Current CEUS LR-M Category Nodules

ObjectiveTo explore a potential method for improving the diagnostic detection hepatocellular carcinoma (HCC) based on the contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) version 2017. MethodsWe retrospectively evaluated 585 liver nodules in 427 patients with HCC risk from December 2020 to March 2023. The nodules were categorized as LR-1 to LR-M based on CEUS LI-RADS version 2017 and were randomly subclassified into a developmental cohort (DC) and a validation cohort (VC) at 3:1. In the DC, the cutoff value of the time difference (∆T) for differentiating HCC from other malignancies in LR-M was calculated, which was used to reclassify nodules in the VC. The diagnostic effect on HCC detection before and after reclassification was further assessed. ResultsAccording to the current CEUS LI-RADS, 140 of 426 (32.9%) confirmed HCC nodules were misclassified as LR-M. In the DC (439 nodules), the receiver operating characteristic (ROC) curve showed that the cutoff value of ∆T (washout onset time minus contrast arrival time) recommended for HCC diagnosis was greater than 21 s. In the VC (146 nodules), 34 HCCs were correctly categorized as LR-5 according to the cutoff value and after reclassification LR-5 had higher accuracy (67.1% vs. 89.0%, p < 0.001) and sensitivity (56.0% vs. 87.2%, p < 0.001) for HCC diagnosis with high specificity (100% vs. 94.6%, p = 0.500) remained. ConclusionUsing the time difference method could identify HCC nodules misdiagnosed as LR-M and improve the diagnostic performance for current CEUS LI-RADS.

The significance of PI-RADS v2.1 score combined with quantitative parameters of DWI and DCE-MRI in differentiating between benign prostatic hyperplasia and prostate cancer

This study investigates the efficacy of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score combined with quantitative metrics from Diffusion Weighted Imaging (DWI) and Dynamic Contrast Enhancement Magnetic Resonance Imaging (DCE-MRI) in differentiating between benign prostatic hyperplasia (BPH) and prostate cancer (PCa). The data of 65 patients with prostate diseases were retrospectively analyzed, and they were divided into a BPH group (n = 34) and a PCa group (n = 31). All patients underwent a multiparametric MRI (mpMRI) examination, which included conventional MRI, DWI and DCE-MRI scans. Variables analyzed included the PI-RADS v2.1 score, apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), rate constant (Kep) and extravascular space volume ratio (Ve). The diagnostic performance of the PI-RADS v2.1 score, DWI, DCE-MRI and their combined metrics in differentiating BPH from PCa was assessed using Receiver Operating Characteristic (ROC) curve analysis. The results demonstrated that the PI-RADS v2.1 scores, Ktrans and Kep values of the PCa group were significantly higher than those of the BPH group (p &lt; 0.001), while the ADC value of the PCa group was significantly lower than the BPH group (p &lt; 0.001). No significant difference was observed in the Ve value between the two groups (p = 0.596). ROC analysis indicated that the area under the curve (AUC) values for the PI-RADS v2.1 score, ADC, Ktrans, Kep, Ve and their combination parameters were 0.824, 0.916, 0.903, 0.904, 0.625 and 0.990, respectively, with the combined parameters showing higher sensitivity and specificity than any single parameter. These findings suggest that the PI-RADS v2.1 score, along with DWI and DCE-MRI sequences, are valuable tools for differentiating BPH from PCa. The quantitative parameters, including ADC, Ktrans and Kep values, offer significant imaging references for clinical assessment, and the combination parameters can significantly enhance diagnostic accuracy.

Radiologic Features of Nodules Attached to the Mediastinal or Diaphragmatic Pleura at Low-Dose CT for Lung Cancer Screening.

Background Pulmonary noncalcified nodules (NCNs) attached to the fissural or costal pleura with smooth margins and triangular or lentiform, oval, or semicircular (LOS) shapes at low-dose CT are recommended for annual follow-up instead of immediate workup. Purpose To determine whether management of mediastinal or diaphragmatic pleura-attached NCNs (M/DP-NCNs) with the same features as fissural or costal pleura-attached NCNs at low-dose CT can follow the same recommendations. Materials and Methods This retrospective study reviewed chest CT examinations in participants from two databases. Group A included 1451 participants who had lung cancer that was first present as a solid nodule with an average diameter of 3.0-30.0 mm. Group B included 345 consecutive participants from a lung cancer screening program who had at least one solid nodule with a diameter of 3.0-30.0 mm at baseline CT and underwent at least three follow-up CT examinations. Radiologists reviewed CT images to identify solid M/DP-NCNs, defined as nodules 0 mm in distance from the mediastinal or diaphragmatic pleura, and recorded average diameter, margin, and shape. General descriptive statistics were used. Results Among the 1451 participants with lung cancer in group A, 163 participants (median age, 68 years [IQR, 61.5-75.0 years]; 92 male participants) had 164 malignant M/DP-NCNs 3.0-30.0 mm in average diameter. None of the 164 malignant M/DP-NCNs had smooth margins and triangular or LOS shapes (upper limit of 95% CI of proportion, 0.02). Among the 345 consecutive screening participants in group B, 146 participants (median age, 65 years [IQR, 59-71 years]; 81 female participants) had 240 M/DP-NCNs with average diameter 3.0-30.0 mm. None of the M/DP-NCNs with smooth margins and triangular or LOS shapes were malignant after a median follow-up of 57.8 months (IQR, 46.3-68.1 months). Conclusion For solid M/DP-NCNs with smooth margins and triangular or LOS shapes at low-dose CT, the risk of lung cancer is extremely low, which supports the recommendation of Lung Imaging Reporting and Data System version 2022 for annual follow-up instead of immediate workup. © RSNA, 2024 See also the editorial by Goodman and Baruah in this issue.

Individual Participant Data Meta-Analysis of LR-5 in LI-RADS Version 2018 versus Revised LI-RADS for Hepatocellular Carcinoma Diagnosis.

Background A simplification of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 (v2018), revised LI-RADS (rLI-RADS), has been proposed for imaging-based diagnosis of hepatocellular carcinoma (HCC). Single-site data suggest that rLI-RADS category 5 (rLR-5) improves sensitivity while maintaining positive predictive value (PPV) of the LI-RADS v2018 category 5 (LR-5), which indicates definite HCC. Purpose To compare the diagnostic performance of LI-RADS v2018 and rLI-RADS in a multicenter data set of patients at risk for HCC by performing an individual patient data meta-analysis. Materials and Methods Multiple databases were searched for studies published from January 2014 to January 2022 that evaluated the diagnostic performance of any version of LI-RADS at CT or MRI for diagnosing HCC. An individual patient data meta-analysis method was applied to observations from the identified studies. Quality Assessment of Diagnostic Accuracy Studies version 2 was applied to determine study risk of bias. Observations were categorized according to major features and either LI-RADS v2018 or rLI-RADS assignments. Diagnostic accuracies of category 5 for each system were calculated using generalized linear mixed models and compared using the likelihood ratio test for sensitivity and the Wald test for PPV. Results Twenty-four studies, including 3840 patients and 4727 observations, were analyzed. The median observation size was 19 mm (IQR, 11-30 mm). rLR-5 showed higher sensitivity compared with LR-5 (70.6% [95% CI: 60.7, 78.9] vs 61.3% [95% CI: 45.9, 74.7]; P < .001), with similar PPV (90.7% vs 92.3%; P = .55). In studies with low risk of bias (n = 4; 1031 observations), rLR-5 also achieved a higher sensitivity than LR-5 (72.3% [95% CI: 63.9, 80.1] vs 66.9% [95% CI: 58.2, 74.5]; P = .02), with similar PPV (83.1% vs 88.7%; P = .47). Conclusion rLR-5 achieved a higher sensitivity for identifying HCC than LR-5 while maintaining a comparable PPV at 90% or more, matching the results presented in the original rLI-RADS study. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sirlin and Chernyak in this issue.

Per-patient Negative Predictive Value of the CT and MRI Liver Imaging Reporting and Data System Version 2018 Treatment Response Algorithm for Hepatocellular Carcinoma.

Background The Liver Imaging Reporting and Data System version 2018 (LI-RADS) treatment response algorithm (TRA) is a high-specificity, lower-sensitivity grading system to diagnose hepatocellular carcinoma (HCC) and recurrence after local-regional therapy. However, the emphasis on specificity can result in disease understaging, potentially leading to poorer posttransplant outcomes. Purpose To determine the negative predictive value (NPV) of pretransplant CT and MRI assessment for viable HCC on a per-patient basis using the LI-RADS TRA, considering explant pathology as the reference standard. Materials and Methods Patient records from 218 consecutive adult patients from a single institution with HCC who underwent liver transplant from January 2011 to November 2017 were retrospectively reviewed. Two readers blinded to the original report reviewed immediate (within 90 days) pretransplant imaging and characterized observations according to the LI-RADS TRA. Based on this, patients with LR-4, LR-5, or LR-TR (treatment response) viable tumors were designated as viable tumor; patients with solely LR-3 or LR-TR equivocal tumors were designated as equivocal; and patients with only LR-TR nonviable lesions were designated as no viable disease. Patients were designated as within or outside the Milan criteria. These per-patient designations were compared with the presence of viable disease at explant pathology. Fisher exact test was used to compare the differences between CT and MRI. Weighted κ values were used to calculate interreader reliability. Results Final study sample consisted of 206 patients (median age, 61 years [IQR, 57-65 years]; 157 male patients and 49 female patients). Per-patient LI-RADS TRA assessment of pretransplant imaging had an NPV of 32% (95% CI: 27, 38) and 26% (95% CI: 20, 33) (readers 1 and 2, respectively) for predicting viable disease. Seventy-five percent (reader 1) and 77% (reader 2) of patients deemed equivocal had residual tumors at explant pathology. Weighted interreader reliability was substantial (κ = 0.62). Conclusion Patient-based stratification of viable, equivocal, and nonviable disease at pretransplant CT or MRI, based on LI-RADS TRA, demonstrated low negative predictive value in excluding HCC at explant pathology. © RSNA, 2023 See also the editorial by Tamir and Tau in this issue.

Zone-specific computer-aided diagnosis system aimed at characterizing ISUP ≥ 2 prostate cancers on multiparametric magnetic resonance images: evaluation in a cohort of patients on active surveillance.

To assess a region-of-interest-based computer-assisted diagnosis system (CAD) in characterizing aggressive prostate cancer on magnetic resonance imaging (MRI) from patients under active surveillance (AS). A prospective biopsy database was retrospectively searched for patients under AS who underwent MRI and subsequent biopsy at our institution. MRI lesions targeted at baseline biopsy were retrospectively delineated to calculate the CAD score that was compared to the Prostate Imaging-Reporting and Data System (PI-RADS) version 2 score assigned at baseline biopsy. 186 patients were selected. At baseline biopsy, 51 and 15 patients had International Society of Urological Pathology (ISUP) grade ≥ 2 and ≥ 3 cancer respectively. The CAD score had significantly higher specificity for ISUP ≥ 2 cancers (60% [95% confidence interval (CI): 51-68]) than the PI-RADS score (≥ 3 dichotomization: 24% [CI: 17-33], p = 0.0003; ≥ 4 dichotomization: 32% [CI: 24-40], p = 0.0003). It had significantly lower sensitivity than the PI-RADS ≥ 3 dichotomization (85% [CI: 74-92] versus 98% [CI: 91-100], p = 0.015) but not than the PI-RADS ≥ 4 dichotomization (94% [CI:85-98], p = 0.104). Combining CAD findings and PSA density could have avoided 47/184 (26%) baseline biopsies, while missing 3/51 (6%) ISUP 2 and no ISUP ≥ 3 cancers. Patients with baseline negative CAD findings and PSAd < 0.15ng/mL2 who stayed on AS after baseline biopsy had a 9% (4/44) risk of being diagnosed with ISUP ≥ 2 cancer during a median follow-up of 41months, as opposed to 24% (18/74) for the others. The CAD could help define AS patients with low risk of aggressive cancer at baseline assessment and during subsequent follow-up.