Rationale and Objectives
To validate the educational value of a newly created learning application in enhancing
prostate MRI training of radiologists for detecting prostate cancer using an observer
study.
Materials and Methods
An interactive learning app, LearnRadiology, was developed using a web-based framework
to display multi-parametric prostate MRI images with whole-mount histology for 20
cases curated for unique pathology and teaching points.
Twenty new prostate MRI cases, different from the ones used in the web app, were uploaded
on 3D Slicer. Three radiologists (R1: radiologist; R2, R3: residents) blinded to pathology
results were asked to mark areas suspected of cancer and provide a confidence score
(1–5, with 5 being high confidence level). Then after a minimum memory washout period
of 1 month, the same radiologists used the learning app and then repeated the same
observer study. The diagnostic performance for detecting cancers before and after
accessing the learning app was measured by correlating MRI with whole-mount pathology
by an independent reviewer.
Results
The 20 subjects included in the observer study had 39 cancer lesions (13 Gleason 3 + 3,
17 Gleason 3 + 4, 7 Gleason 4 + 3, and 2 Gleason 4 + 5 lesions). The sensitivity (R1:
54% → 64%, P = 0.08; R2: 44% → 59%, P = 0.03; R3: 62% → 72%, P = 0.04) and positive predictive value (R1: 68% → 76%, P = 0.23; R2: 52% → 79%, P = 0.01; R3: 48% → 65%, P = 0.04) for all 3 radiologists improved after using the teaching app. The confidence
score for true positive cancer lesion also improved significantly (R1: 4.0 ± 1.0 → 4.3 ± 0.8;
R2: 3.1 ± 0.8 → 4.0 ± 1.1; R3: 2.8 ± 1.2 → 4.1 ± 1.1; P < 0.05).
Conclusion
The web-based and interactive LearnRadiology app learning resource can support medical
student and postgraduate education by improving diagnostic performance of trainees
for detecting prostate cancer.
Key Words
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References
- Cancer statistics, 2022.CA Cancer J Clin. 2022; 72: 7-33
- Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2.Eur Urol. 2019; 76: 340-351
- Prostate focal peripheral zone lesions: characterization at multiparametric MR imaging—influence of a computer-aided diagnosis system.Radiology. 2014; 271: 761-769
- Prostate MR: pitfalls and benign lesions.Abdom Radiol. 2020; 45: 2154-2164
- Computer-aided detection of prostate cancer in MRI.IEEE Trans Med Imaging. 2014; 33: 1083-1092
- Computer aided-diagnosis of prostate cancer on multiparametric MRI: a technical review of current research.Biomed Res Int. 2014; 789561: 1
- The vital role of radiology in the medical school curriculum.Am J Roentgenol. 2003; 180: 1239-1242
- Radiology medical student education: An Outcome-based Survey of PGY-1 Residents.Acad Radiol. 2013; 20: 284-289
- “Bought My Residents iPads… Now What?” The integration of mobile devices into radiology resident education.Am J Roentgenol. 2013; 201: 704-709
- How spatial abilities and dynamic visualizations interplay when learning functional anatomy with 3D anatomical models.Anat Sci Educ. 2015; 8: 452-462
- Learning theory in radiology education.Radiology. 2004; 233: 15-18
- Diagnosis of prostate cancer by use of MRI-derived quantitative risk maps: a feasibility study.Am J Roentgenol. 2019; 213: W66-W75
- Multiparametric MRI features and pathologic outcome of wedge-shaped lesions in the peripheral zone on T2-weighted images of the prostate.AJR Am J Roentgenol. 2019; 212: 124-129
- 3D slicer as an image computing platform for the quantitative imaging network.Magn Reson Imaging. 2012; 30: 1323-1341
- Effect of echo times on prostate cancer detection on T2-weighted images.Acad Radiol. 2020; 27: 1555-1563
- Performance of ultrafast DCE-MRI for diagnosis of prostate cancer.Acad Radiol. 2018; 25: 349-358
- Bridging the gap: interactive, case-based learning in radiology education.Abdom Radiol. 2021; 46: 5503-5508
- Undergraduate education in radiology. A white paper by the European Society of Radiology.Insights Imaging. 2011; 2: 363-374
- Interactive dedicated training curriculum improves accuracy in the interpretation of MR imaging of prostate cancer.Eur Radiol. 2010; 20: 995-1002
- A dedicated prostate MRI teaching course improves the ability of the urologist to interpret clinically significant prostate cancer on multiparametric MRI.Eur Urol. 2019; 75: 203-204
- The learning curve in prostate MRI interpretation: self-directed learning versus continual reader feedback.Am J Roentgenol. 2016; 208: W92-W100
- Improving prostate cancer detection with MRI: a multi-reader, multi-case study using computer-aided detection (CAD).Acad Radiol. 2022; https://doi.org/10.1016/j.acra.2022.09.009
- A cascaded deep learning-based artificial intelligence algorithm for automated lesion detection and classification on biparametric prostate magnetic resonance imaging.Acad Radiol. 2022; 29: 1159-1168
- Memory bias in observer-performance literature.J Med Imaging. 2018; 5: 24
- Artificial intelligence for precision education in radiology.Br J Radiol. 2019; 92: 20190389
Article info
Publication history
Published online: May 01, 2023
Accepted:
April 2,
2023
Received in revised form:
March 30,
2023
Received:
January 30,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
