Artificial Intelligence for Automated Cancer Detection on Prostate MRI: Opportunities and Ongoing Challenges, From the AJR Special Series on AI Applications

Impact of artificial intelligence assisted lesion detection on radiologists' interpretation at multiparametric prostate MRI

PURPOSE

To compare prostate cancer lesion detection using conventional and artificial intelligence (AI)-assisted image interpretation at multiparametric MRI (mpMRI).

MATERIALS AND METHODS

A retrospective study of 53 consecutive patients who underwent prostate mpMRI and subsequent prostate tissue sampling was performed. Two board-certified radiologists (with 4 and 12 years of experience) blinded to the clinical information interpreted anonymized exams using the PI-RADS v2.1 framework without and with an AI-assistance tool. The AI software tool provided radiologists with gland segmentation and automated lesion detection assigning a probability score for the likelihood of the presence of clinically significant prostate cancer (csPCa). The reference standard for all cases was the prostate pathology from systematic and targeted biopsies. Statistical analyses assessed interrater agreement and compared diagnostic performances with and without AI assistance.

RESULTS

Within the entire cohort, 42 patients (79 %) harbored Gleason-positive disease, with 25 patients (47 %) having csPCa. Radiologists' diagnostic performance for csPCa was significantly improved over conventional interpretation with AI assistance (reader A: AUC 0.82 vs. 0.72, p = 0.03; reader B: AUC 0.78 vs. 0.69, p = 0.03). Without AI assistance, 81 % (n = 36; 95 % CI: 0.89-0.91) of the lesions were scored similarly by radiologists for lesion-level characteristics, and with AI assistance, 59 % (26, 0.82-0.89) of the lesions were scored similarly. For reader A, there was a significant difference in PI-RADS scores (p = 0.02) between AI-assisted and non-assisted assessments. Signficant differences were not detected for reader B.

CONCLUSION

AI-assisted prostate mMRI interpretation improved radiologist diagnostic performance over conventional interpretation independent of reader experience.

Research-based clinical deployment of artificial intelligence algorithm for prostate MRI

PURPOSE

A critical limitation to deployment and utilization of Artificial Intelligence (AI) algorithms in radiology practice is the actual integration of algorithms directly into the clinical Picture Archiving and Communications Systems (PACS). Here, we sought to integrate an AI-based pipeline for prostate organ and intraprostatic lesion segmentation within a clinical PACS environment to enable point-of-care utilization under a prospective clinical trial scenario.

METHODS

A previously trained, publicly available AI model for segmentation of intra-prostatic findings on multiparametric Magnetic Resonance Imaging (mpMRI) was converted into a containerized environment compatible with MONAI Deploy Express. An inference server and dedicated clinical PACS workflow were established within our institution for evaluation of real-time use of the AI algorithm. PACS-based deployment was prospectively evaluated in two phases: first, a consecutive cohort of patients undergoing diagnostic imaging at our institution and second, a consecutive cohort of patients undergoing biopsy based on mpMRI findings. The AI pipeline was executed from within the PACS environment by the radiologist. AI findings were imported into clinical biopsy planning software for target definition. Metrics analyzing deployment success, timing, and detection performance were recorded and summarized.

RESULTS

In phase one, clinical PACS deployment was successfully executed in 57/58 cases and were obtained within one minute of activation (median 33 s [range 21-50 s]). Comparison with expert radiologist annotation demonstrated stable model performance compared to independent validation studies. In phase 2, 40/40 cases were successfully executed via PACS deployment and results were imported for biopsy targeting. Cancer detection rates for prostate cancer were 82.1% for ROI targets detected by both AI and radiologist, 47.8% in targets proposed by AI and accepted by radiologist, and 33.3% in targets identified by the radiologist alone.

CONCLUSIONS

Integration of novel AI algorithms requiring multi-parametric input into clinical PACS environment is feasible and model outputs can be used for downstream clinical tasks.

ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases

Magnetic Resonance Imaging (MRI) is increasingly being used to detect prostate cancer, yet its interpretation can be challenging due to subtle differences between benign and cancerous tissue. Recently, Denoising Diffusion Probabilistic Models (DDPMs) have shown great utility for medical image segmentation, modeling the process as noise removal in standard Gaussian distributions. In this study, we further enhance DDPMs by introducing the knowledge that the occurrence of cancer varies across the prostate (e.g., ∼70% of prostate cancers occur in the peripheral zone). We quantify such heterogeneity with a registration pipeline to calculate voxel-level cancer distribution mean and variances. Our proposed approach, ProstAtlasDiff, relies on DDPMs that use the cancer atlas to model noise removal and segment cancer on MRI. We trained and evaluated the performance of ProstAtlasDiff in detecting clinically significant cancer in a multi-institution multi-scanner dataset, and compared it with alternative models. In a lesion-level evaluation, ProstAtlasDiff achieved statistically significantly higher accuracy (0.91 vs. 0.85, p<0.001), specificity (0.91 vs. 0.84, p<0.001), positive predictive value (PPV, 0.50 vs. 0.35, p<0.001), compared to alternative models. ProstAtlasDiff also offers more accurate cancer outlines, achieving a higher Dice Coefficient (0.33 vs. 0.31, p<0.01). Furthermore, we evaluated ProstAtlasDiff in an independent cohort of 91 patients who underwent radical prostatectomy to compare its performance to that of radiologists, relative to whole-mount histopathology ground truth. ProstAtlasDiff detected 16% (15 lesions out of 93) more clinically significant cancers compared to radiologists (sensitivity: 0.90 vs. 0.75, p<0.01), and was comparable in terms of ROC-AUC, PR-AUC, PPV, accuracy, and Dice coefficient (p≥0.05). Furthermore, we evaluated ProstAtlasDiff in a second independent cohort of 537 subjects and observed that ProsAtlasDiff outperformed alternative approaches. These results suggest that ProstAltasDiff has the potential to assist in localizing cancer for biopsy guidance and treatment planning.

MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study

Background Although artificial intelligence is actively being developed for prostate MRI, few studies have prospectively validated these tools. Purpose To compare the diagnostic performance of a commercial deep learning algorithm (DLA) and radiologists' clinical reports for cancer detection in participants from two hospitals using histopathologic findings from biopsy specimens as the reference standard. Materials and Methods This prospective bicenter study enrolled participants suspected of having prostate cancer (PCa) who were scheduled for biopsy based on clinical information, including prostate MRI, from December 2022 to July 2023. Targeted prostate biopsies were performed for lesions with Prostate Imaging Reporting and Data System (PI-RADS) scores of 3 or higher, identified by either radiologists or the DLA. PI-RADS classifications by radiologists (using all imaging sequences), the DLA (using biparametric MRI), and the scenario in which radiologist-based PI-RADS 3 scores were modulated with DLA-based PI-RADS scores were compared using the area under the receiver operating characteristic curve (AUC) with DeLong and McNemar tests. Results A total of 259 lesions, including 117 clinically significant PCas (csPCas) (Gleason grade group ≥2), were evaluated in 205 men (median age, 68 years; age range, 47-90 years). At per-lesion analysis, the DLA had a lower sensitivity (94 of 117; 80%) and higher positive predictive value (PPV) (94 of 163; 58%) for detecting csPCa than did the radiologists (109 of 117 [93%] and 109 of 227 [48%]; P = .02 and P = .008, respectively). At per-participant analysis, incorporation of the DLA increased specificity from 23 of 108 (21%) to 48 of 108 (44%) (P = .001), with similar sensitivity (96 of 97 [99%] vs 93 of 97 [96%]; P = .74). There was no evidence of a difference in the AUC between radiologist-based and DLA-based PI-RADS score (0.77 [95% CI: 0.70, 0.82] vs 0.79 [95% CI: 0.73, 0.85]; P = .73). Conclusion The DLA demonstrated lower sensitivity but a greater PPV than radiologists for detecting csPCa in a biopsy setting. Using DLA results when radiologists' interpretations are indeterminate could improve specificity while maintaining sensitivity. International Clinical Trials Registry Platform registration no. KCT0006947 © RSNA, 2025 Supplemental material is available for this article.

Multi-Center Benchmarking of a Commercially Available Artificial Intelligence Algorithm for Prostate Imaging Reporting and Data System (PI-RADS) Score Assignment and Lesion Detection in Prostate MRI

BACKGROUND

The increase in multiparametric magnetic resonance imaging (mpMRI) examinations as a fundamental tool in prostate cancer (PCa) diagnostics raises the need for supportive computer-aided imaging analysis. Therefore, we evaluated the performance of a commercially available AI-based algorithm for prostate cancer detection and classification in a multi-center setting.

METHODS

Representative patients with 3T mpMRI between 2017 and 2022 at three different university hospitals were selected. Exams were read according to the PI-RADSv2.1 protocol and then assessed by an AI algorithm. Diagnostic accuracy for PCa of both human and AI readings were calculated using MR-guided ultrasound fusion biopsy as the gold standard.

RESULTS

Analysis of 91 patients resulted in 138 target lesions. Median patient age was 67 years (range: 49-82), median PSA at the time of the MRI exam was 8.4 ng/mL (range: 1.47-73.7). Sensitivity and specificity for clinically significant prostate cancer (csPCa, defined as ISUP ≥ 2) were 92%/64% for radiologists vs. 91%/57% for AI detection on patient level and 90%/70% vs. 81%/78% on lesion level, respectively (cut-off PI-RADS ≥ 4). Two cases of csPCa were missed by the AI on patient-level, resulting in a negative predictive value (NPV) of 0.88 at a cut-off of PI-RADS ≥ 3.

CONCLUSIONS

AI-augmented lesion detection and scoring proved to be a robust tool in a multi-center setting with sensitivity comparable to the radiologists, even outperforming human reader specificity on both patient and lesion levels at a threshold of PI-RADS ≥3 and a threshold of PI-RADS ≥ 4 on lesion level. In anticipation of refinements of the algorithm and upon further validation, AI-detection could be implemented in the clinical workflow prior to human reading to exclude PCa, thereby drastically improving reading efficiency.

Current applications and future perspectives of artificial intelligence in functional urology and neurourology: how far can we get?

In the last few years, the scientific community has seen an increasing interest towards the potential applications of artificial intelligence in medicine and healthcare. In this context, urology represents an area of rapid development, particularly in uro-oncology, where a wide range of applications has focused on prostate cancer diagnosis. Other urological branches are also starting to explore the potential advantages of AI in the diagnostic and therapeutic process, and functional urology and neurourology are among them. Although the experiences in this sense have been quite limited so far, some AI applications have already started to show potential benefits, especially for urodynamic and imaging interpretation, as well as for the development of AI-based predictive models for treatment response. A few experiences on the use of ChatGPT to answer questions on functional urology and neurourology topics have also been reported. Conversely, AI applications in functional urology surgery remain largely unexplored. This paper provides a critical overview of the current evidence on this topic, highlighting the potential benefits for the diagnostic workflow, therapeutic evaluation and surgical training, as well as the current limitations that need to be addressed to enable the integration of this tools in the clinical practice in the future.

Convergence of evolving artificial intelligence and machine learning techniques in precision oncology

The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor's biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.

Comparison of MRI artificial intelligence-guided cognitive fusion-targeted biopsy versus routine cognitive fusion-targeted prostate biopsy in prostate cancer diagnosis: a randomized controlled trial

BACKGROUND

Cognitive fusion MRI-guided targeted biopsy (cTB) has been widely used in the diagnosis of prostate cancer (PCa). However, cTB relies heavily on the operator's experience and confidence in MRI readings. Our objective was to compare the cancer detection rates of MRI artificial intelligence-guided cTB (AI-cTB) and routine cTB and explore the added value of using AI for the guidance of cTB.

METHODS

This was a prospective, single-institution randomized controlled trial (RCT) comparing clinically significant PCa (csPCa) and PCa detection rates between AI-cTB and cTB. A total of 380 eligible patients were randomized to the AI-cTB group (n = 191) or the cTB group (n = 189). The AI-cTB group underwent AI-cTB plus systematic biopsy (SB) and the cTB group underwent routine cTB plus SB. The primary outcome was the detection rate of csPCa. The reference standard was the pathological results of the combination of TB (AI-cTB/cTB) and SB. Comparisons of detection rates of csPCa and PCa between groups were performed using the chi-square test or Fisher's exact test.

RESULTS

The overall csPCa and PCa detection rates of the whole inclusion cohort were 58.8% and 61.3%, respectively. The csPCa detection rates of TB combined with SB in the AI-cTB group were significantly greater than those in the cTB group at both the patient level (58.64% vs. 46.56%, p = 0.018) and per-lesion level (61.47% vs. 47.79%, p = 0.004). Compared with cTB, the AI-cTB could detect a greater proportion of patients with csPCa at both the per-patient level (69.39% vs. 49.71%, p < 0.001) and per-lesion level (68.97% vs. 48.57%, p < 0.001). Multivariate logistic analysis indicated that compared with the cTB, the AI-cTB significantly improved the possibility of detecting csPCa (p < 0.001).

CONCLUSIONS

AI-cTB effectively improved the csPCa detection rate. This study successfully integrated AI with TB in the routine clinical workflow and provided a research paradigm for prospective AI-integrated clinical studies.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT06362291.

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification

RATIONALE AND OBJECTIVES

Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI.

MATERIAL AND METHODS

An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology.

RESULTS

A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth.

CONCLUSION

Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.

Part II: Effect of different evaluation methods to the application of a computer-aided prostate MRI detection/diagnosis (CADe/CADx) device on reader performance

INTRODUCTION

The construction and results of a multiple-reader multiple-case prostate MRI study are described and reported to illustrate recommendations for how to standardize artificial intelligence (AI) prostate studies per the review constituting Part I1.

METHODS

Our previously reported approach was applied to review and report an IRB approved, HIPAA compliant multiple-reader multiple-case clinical study of 150 bi-parametric prostate MRI studies across 9 readers, measuring physician performance both with and without the use of the recently FDA cleared CADe/CADx software ProstatID.

RESULTS

Unassisted reader AUC values ranged from 0.418 - 0.759, with AI assisted AUC values ranging from 0.507 - 0.787. This represented a statistically significant AUC improvement of 0.045 (α = 0.05). A free-response ROC (FROC) analysis similarly demonstrated a statistically significant increase in θ from 0.405 to 0.453 (α = 0.05). The standalone performance of ProstatID performed across all prostate tissues demonstrated an AUC of 0.929, while the standalone lesion level performance of ProstatID at all biopsied locations achieved an AUC of 0.710.

CONCLUSION

This study applies and illustrates suggested reporting and standardization methods for prostate AI studies that will make it easier to understand, evaluate and compare between AI studies. Providing radiologists with the ProstatID CADe/CADx software significantly increased diagnostic performance as assessed by both ROC and free-response ROC metrics. Such algorithms have the potential to improve radiologist performance in the detection and localization of clinically significant prostate cancer.

Deep Learning-Based T2-Weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates

BACKGROUND

Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.

PURPOSE

To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm.

STUDY TYPE

Retrospective.

SUBJECTS

615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy.

FIELD STRENGTH/SEQUENCE

3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced.

ASSESSMENTS

Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category.

STATISTICAL TESTS

Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant.

RESULTS

385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09).

DATA CONCLUSION

Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Contemporary Approach to Prostate Imaging and Data Reporting System Score 3 Lesions

The aim of this article is to review the technical and clinical considerations encountered with PI-RADS 3 lesions, which are equivocal for clinically significant Prostate Cancer (csPCa) with detection rates ranging between 10% and 35%. The number of PI-RADS 3 lesions reported vary according to several factors including MRI quality and radiologist training/expertise among the most influential. PI-RADS v.2.1 updated definitions for scores 2 and 3 in the PZ and scores 1 and 2 in the TZ is reviewed. The role of DWI role is highlighted in the assessment of the TZ with the possibility of upgrading score 2 lesions to score 3 based on DWI score. Given the increased utilization for prostate MRI, biparametric MRI can be considered as an alternative for low-risk patients where there is a need to rule out csPCa acknowledging this technique may increase the number of indeterminate cases going for biopsies. Management of patients with equivocal lesions at mpMRI and factors influencing biopsy decision process remain as an unmet need and additional studies using molecular/imaging markers as well as artificial intelligence tools are needed to further address their role in proper patient selection for biopsy.

The Present and Future of Artificial Intelligence in Urological Cancer

Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.

Artificial Intelligence in CT and MR Imaging for Oncological Applications

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.

Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers

Objective

To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers.

Materials and methods

A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group.

Results

Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively).

Conclusions

AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.