Deep learning AI and Restriction Spectrum Imaging for patient-level detection of clinically significant prostate cancer on MRI

Abstract: Background: The Prostate Imaging Reporting & Data System (PI-RADS), based on multiparametric MRI (mpMRI), is widely used for the detection of clinically significant prostate cancer (csPCa, Gleason Grade Group (GG≥2)). However, its diagnostic accuracy can be impacted by variability in interpretation. Restriction Spectrum Imaging (RSI), an advanced diffusion-weighted technique, offers a standardized, quantitative approach for detecting csPCa, potentially enhancing diagnostic consistency and performing comparably to expert-level assessments. Purpose: To evaluate whether combining maximum RSI-derived restriction scores (RSIrs-max) with deep learning (DL) models can enhance patient-level detection of csPCa compared to using PI-RADS or RSIrs-max alone. Materials and Methods: Data from 1,892 patients across seven institutions were analyzed, selected based on MRI results and biopsy-confirmed diagnoses. Two deep learning architectures, 3D-DenseNet and 3D-DenseNet+RSI (incorporating RSIrs-max), were developed and trained using biparametric MRI (bpMRI) and RSI data across two data splits. Model performance was compared using the area under the receiver operating characteristic curve (AUC) for patient-level csPCa detection, using PI-RADS performance for clinical reference. Results: Neither RSIrs-max nor the best DL model combined with RSIrs-max significantly outperformed PI-RADS interpretation by expert radiologists. However, when combined with PI-RADS, both approaches significantly improved patient-level csPCa detection, with AUCs of 0.79 (95% CI: 0.74-0.83;P=.005) for combination of RSIrs-max with PI-RADS and 0.81 (95% CI: 0.76-0.85;P<.001) for combination of best DL model with PI-RADS, compared to 0.73 (95% CI: 0.68-0.78) for PI-RADS alone. Conclusion: Both RSIrs-max and DL models demonstrate comparable performance to PI-RADS alone. Integrating either model with PI-RADS significantly enhances patient-level detection of csPCa compared to using PI-RADS alone. Summary Statement: RSIrs-max and deep learning models match the performance of expert PI-RADS in patient-level csPCa detection and combining either with PI-RADS yields a significant improvement over PI-RADS alone. Key Points: In a study of 1,892 patients from seven institutions undergoing MRI and biopsy for prostate cancer, RSIrs-max and the DL model (AUC, 0.75 (P=.59) and 0.78 (P=.09)) performed comparably to expert-level PI-RADS scores (AUC, 0.73). Including prostate auto-segmentation improved the DL model (AUC, 0.68 (P=.01) vs 0.72 (P=.60)). Combining RSIrs-max or the DL model (AUC, 0.79 (P=.005) and 0.81 (P<.001)) with PI-RADS statistically significantly outperformed PI-RADS alone (AUC, 0.73)

Robustness of a Restriction Spectrum Imaging (RSI) quantitative MRI biomarker for prostate cancer: assessing for systematic bias due to age, race, ethnicity, prostate volume, medication use, or imaging acquisition parameters

IntroductionProstate multiparametric magnetic resonance imaging (mpMRI) has greatly improved the detection of clinically significant prostate cancer (csPCa). However, the limited number of expert sub-specialist radiologists capable of interpreting conventional prostate mpMRI is a bottleneck for universal access to this healthcare advance. A reliable and reproducible quantitative imaging biomarker could facilitate implementation of accurate prostate MRI at clinical sites with limited experience, thus ensuring more equitable patient care. Restriction Spectrum Imaging restriction score (RSIrs) is an MRI biomarker that has shown the ability to enhance the qualitative and quantitative interpretation of prostate MRI. However, patient-level factors (age, race, ethnicity, prostate volume, and 5-alpha-reductase inhibitor (5-ARI) use) and acquisition-level factors (scanner manufacturer/model and protocol parameters) can affect prostate mpMRI, and their impact on quantitative RSIrs is unknown.MethodsRSI data from patients with known or suspected csPCa were collected from seven centers. We estimated effects of patient and acquisition factors on prostate voxels overall (Method 1: benign patients only) and on only the maximum RSIrs within each prostate (RSIrsmax; Method 2: benign and csPCa patients) using linear models. We then tested whether adjusting for any estimated systematic biases would improve performance of RSIrs for patient-level detection of csPCa, as measured by area under the ROC curve (AUC).ResultsUsing both Method 1 and Method 2, we observed statistically significant effects on RSIrs of age and acquisition group (p < 0.05). Prostate volume had significant effects using only Method 2. All of these effects were small, and adjusting for them did not improve csPCa detection performance (p ≥ 0.05). AUC of RSIrsmaxfor patient-level csPCa detection was 0.77 (95% CI: 0.75, 0.79) unadjusted, compared to 0.77 (0.76, 0.79) and 0.74 (0.72, 0.76) after adjustment using Method 1 and 2 respectively.ConclusionAge, prostate volume, and imaging acquisition factors may lead to systematic differences in RSIrs, but these effects are small and have minimal impact on performance of RSIrs for detection of csPCa. RSIrs can be used as a reliable biomarker across a wide range of patients, centers, scanners, and acquisition factors

Restriction Spectrum Imaging as a quantitative biomarker for prostate cancer with reliable positive predictive value

Abstract: Background and Objective: Positive predictive value of PI-RADS for clinically significant prostate cancer (csPCa, grade group [GG]≥2) varies widely between institutions and radiologists. The Restriction Spectrum Imaging restriction score (RSIrs) is a metric derived from diffusion MRI that could be an objectively interpretable biomarker for csPCa. Methods: In patients scanned for suspected or known csPCa at 7 centers, we calculated patient-level csPCa probability based on maximum RSIrs in the prostate, without relying on subjectively defined lesions. We used area under the ROC curve (AUC) to compare patient-level csPCa detection for RSIrs, ADC, and PI-RADS. Finally, we combined RSIrs with clinical risk factors via multivariable regression, training in a single-center cohort and testing in an independent, multi-center dataset. Key Findings and Limitations: Among all patients (n=1892), probability of csPCa increased with higher RSIrs . GG≥4 csPCa was most common in patients with very high RSIrs. Among biopsy-naïve patients (n=877), AUCs for GG≥2 vs. non-csPCa were 0.73 (0.69-0.76), 0.54 (0.50-0.57), and 0.75 (0.71-0.78) for RSIrs, ADC, and PI-RADS, respectively. RSIrs significantly outperformed ADC (p<0.01) and was comparable to PI-RADS (p=0.31). The combination of RSIrs and PI-RADS outperformed either alone. Combining RSIrs with PI-RADS, age, and PSA density in a multivariable model achieved the best discrimination of csPCa. Conclusions and Clinical Implications: RSIrs is an accurate and reliable quantitative biomarker that performs better than conventional ADC and comparably to expert-defined PI-RADS for patient-level detection of csPCa. RSIrs provides objective estimates of probability of csPCa that do not require radiology expertise

Prospective multicenter assessment of patient preferences for properties of gadolinium-based contrast media and their potential socioeconomic impact in a screening breast MRI setting

Objective: It is unknown how patients prioritize gadolinium-based contrast media (GBCM) benefits (detection sensitivity) and risks (reactions, gadolinium retention, cost). The purpose of this study is to measure preferences for properties of GBCM in women at intermediate or high risk of breast cancer undergoing annual screening MRI. Methods: An institutional reviewed board-approved prospective discrete choice conjoint survey was administered to patients at intermediate or high risk for breast cancer undergoing screening MRI at 4 institutions (July 2018-March 2020). Participants were given 15 tasks and asked to choose which of two hypothetical GBCM they would prefer. GBCMs varied by the following attributes: sensitivity for cancer detection (80-95%), intracranial gadolinium retention (1-100 molecules per 100 million administered), severe allergic-like reaction rate (1-19 per 100,000 administrations), mild allergic-like reaction rate (10-1000 per 100,000 administrations), out-of-pocket cost ($25-$100). Attribute levels were based on published values of existing GBCMs. Hierarchical Bayesian analysis was used to derive attribute "importance." Preference shares were determined by simulation. Results: Response (87% [247/284]) and completion (96% [236/247]) rates were excellent. Sensitivity (importance = 44.3%, 95% confidence interval = 42.0-46.7%) was valued more than GBCM-related risks (mild allergic-like reaction risk (19.5%, 17.9-21.1%), severe allergic-like reaction risk (17.0%, 15.8-18.1%), intracranial gadolinium retention (11.6%, 10.5-12.7%), out-of-pocket expense (7.5%, 6.8-8.3%)). Lower income participants placed more importance on cost and less on sensitivity (p < 0.01). A simulator is provided that models GBCM preference shares by GBCM attributes and competition. Conclusions: Patients at intermediate or high risk for breast cancer undergoing MRI screening prioritize cancer detection over GBCM-related risks, and prioritize reaction risks over gadolinium retention. Key points: • Among women undergoing annual breast MRI screening, cancer detection sensitivity (attribute "importance," 44.3%) was valued more than GBCM-related risks (mild allergic reaction risk 19.5%, severe allergic reaction risk 17.0%, intracranial gadolinium retention 11.6%, out-of-pocket expense 7.5%). • Prospective four-center patient preference data have been incorporated into a GBCM choice simulator that allows users to input GBCM properties and calculate patient preference shares for competitor GBCMs. • Lower-income women placed more importance on out-of-pocket cost and less importance on cancer detection (p < 0.01) when prioritizing GBCM properties