An Interactive App with Multi-parametric MRI - Whole-Mount Histology Correlation for Enhanced Prostate MRI Training of Radiology Residents

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.

Qresp, a tool for curating, discovering and exploring reproducible scientific papers

We propose a strategy and present a simple tool to facilitate scientific data reproducibility by making available, in a distributed manner, all data and procedures presented in scientific papers, together with metadata to render them searchable and discoverable. In particular, we describe a graphical user interface (GUI), Qresp, to curate papers (i.e. generate metadata) and to explore curated papers and automatically access the data presented in scientific publications.