Patient Perceptions of Standardized Risk Language Used in ACR Prostate MRI PI-RADS Scores.

INTRODUCTION: Prostate MRI reports use standardized language to describe risk of clinically significant prostate cancer (csPCa) from "equivocal" (Prostate Imaging Reporting and Data System [PI-RADS] 3), "likely" (PI-RADS 4), to "highly likely" (PI-RADS 5). These terms correspond to risks of 11%, 37%, and 70% according to American Urological Association guidelines, respectively. We assessed how men perceive risk associated with standardized PI-RADS language. METHODOLOGY: We conducted a crowdsourced survey of 1,204 men matching a US prostate cancer demographic. We queried participants' risk perception associated with standardized PI-RADS language across increasing contexts: words only, PI-RADS sentence, full report, and full report with numeric estimate. Median perceived risk (interquartile range) and absolute under/overestimation compared with American Urological Association standards were reported. Multivariable linear mixed-effects analysis identified factors associated with accuracy of risk perception. RESULTS: Median perceived risks of csPCa (interquartile range) for the word-only context were "equivocal" 50% (50%-74%), "likely" 75% (68%-85%), and "highly likely" 87% (78%-92%), corresponding to +39%, +38%, and +17% overestimation, respectively. Median perceived risks for the PI-RADS-sentence context were 50% (50%-50%), 75% (68%-81%), and 90% (80%-94%) for PI-RADS 3, 4, and 5, corresponding to +39%, +38%, and +20% overestimation, respectively. Median perceived risks for the full-report context were 50% (35%-70%), 72% (50%-80%), and 84% (54%-91%) for PI-RADS 3, 4, and 5, corresponding to +39%, +35%, and +14% overestimation, respectively. For the full-report-with-numeric-estimate context describing a PI-RADS 4 lesion, median perceived risk was 70% (50%-%80), corresponding to +33% overestimation. Including numeric estimates increased correct perception of risk from 3% to 11% (P < .001), driven by men with higher numeracy (odds ratio 1.24, P = .04). CONCLUSION: Men overestimate risk of csPCa associated with standardized PI-RADS language regardless of context, especially for PI-RADS 3 and 4 lesions. Changes to PI-RADS language or data-sharing policies for imaging reports should be considered.

High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance.

PURPOSE: Majority of men with low-risk prostate cancer can be managed with active surveillance (AS). This study evaluates a high-resolution diffusion-weighted imaging (HR-DWI) technique to predict adverse biopsy histology (AH), defined as Gleason score ≥7 on any biopsy or ≥3 increase in number of positive biopsy cores on systematic biopsies. We test the hypothesis that high-grade disease and progressing disease undergo subtle changes during even short intervals that can be detected by HR-DWI. EXPERIMENTAL DESIGN: In a prospective clinical trial, serial multiparametric MRIs, incorporating HR-DWI and standard DWI (S-DWI) were performed approximately 12 months apart prior to prostate biopsy (n = 59). HR-DWI, which uses reduced field-of-view and motion compensation techniques, was compared with S-DWI. RESULTS: HR-DWI had a 3-fold improvement in spacial resolution compared with S-DWI as confirmed using imaging phantoms. For detecting AH, multiparametric MRI using HR-DWI had a sensitivity of 75% and specificity of 83.9%, and MRI using S-DWI had a sensitivity of 71.4% and specificity of 54.8%. The AUC for HR-DWI was significantly higher (0.794 vs. 0.631, P = 0.014). Secondary analyses of univariable predictors of AH showed tumor size increase [OR 16.8; 95% confidence interval (CI): 4.06-69.48; P < 0.001] and apparent diffusion coefficient (ADC) decrease (OR 5.06; 95% CI: 1.39-18.38; P = 0.014) on HR-DWI were significant predictors of AH. CONCLUSION: HR-DWI outperforms S-DWI in predicting AH. Patient with AH have tumors that change in size and ADC that could be detected using HR-DWI. Future studies with longer follow-up should assess HR-DWI for predicting disease progression during AS. SIGNIFICANCE: We report on a prospective clinical trial using a MRI that has three times the resolution of standard MRI. During AS for prostate cancer, two high-resolution MRIs performed approximately a year apart can detect tumor changes that predict the presence of aggressive cancers that should be considered for curative therapy such as prostatectomy or radiation.

Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.

Background and aims: With the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer. Materials and Methods: We employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application. Results: We identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p < 0.0001). Eight (53%) of the top 15 journals with the most publications were radiology journals. The largest number of publications were from China (n=1156), the US (n=719), and Germany (n=236). The three most common publication categories were "medical image analysis for diagnosis" (37%), "diagnostic or prognostic biomarkers modeling & bioinformatics" (19%), and "genomic or molecular analysis" (18%). Conclusion: Our study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.

Development and validation of a biomarker index for HCC treatment response.

BACKGROUND: Serum AFP-L3%, AFP, and DCP are useful biomarkers for HCC detection, but their utility in assessing treatment response remains unknown. We aim to evaluate the accuracy of a biomarker model in the detection of posttreatment viable tumors. METHODS: For model derivation, recipients with HCC undergoing liver transplant from 2018 to 2022 who had biomarkers collected within 3 months before transplant were included. We developed a generalized linear model for detecting posttreatment viable tumors with the 3 biomarkers as covariates, which we termed the "LAD Score." An independent cohort of 117 patients with HCC was used for external validation. RESULTS: Among 205 recipients of transplant, 70.2% had evidence of viable tumor on explant. The median LAD score was higher among patients with viable versus nonviable tumors (1.06 vs. 0.465, p < 0.001). The LAD score had a sensitivity of 55.6% and a specificity of 85.1% at the cutoff of 0.927, which was more accurate than imaging for detecting posttreatment viable tumors (AUROC 0.736 vs. 0.643, respectively; p = 0.045). The superior performance of the LAD score over imaging is primarily driven by its greater accuracy in detecting tumors <2 cm in diameter (AUROC of the LAD score 0.721 vs. imaging 0.595, p = 0.02). In the validation data set, the LAD score had an AUROC of 0.832 (95% CI: 0.753, 0.911) with a sensitivity of 72.5% and a specificity of 89.4% at the cutoff of 0.927. CONCLUSIONS: Our findings suggest the utility of LAD score in treatment response assessment after locoregional therapy for HCC, particularly in detecting small tumors. A larger prospective study is in progress to validate its accuracy and evaluate its performance in recurrence monitoring.