Quantitative Imaging Habitat Risk Score (HRS) Combined with PI-RADSv2 Improves Predictive Value of Prostate Lesion Identification on mpMRI
Motivation: Discrimination between true and false positive targets identified using PI-RADSv2 is needed to avoid unnecessary biopsies. Goal(s): To investigate how quantitative analysis of prostate mpMRI through Habitat Risk Scoring (HRS) combined with PI-RADS can improve prostate lesion identification compared to using PI-RADS alone. Approach: In prospective clinical trials lesions identified by PI-RADS and/or HRS were targeted through MRI/ultrasound fusion biopsies. Results: Using HRS yields 100% NPV in the PI-RADS 3 cohort and increases PPV by 7.4% in the PI-RADS 4-5 cohort with respect to clinically significant cancer. Overall, the NPV and PPV increased by 21.4% and 13.2% respectively. Impact: We present a quantitative imaging approach to complement the current standard for assessing prostate cancer risk in mpMRI data and demonstrate that the use of HRS strengthens fidelity in both positive and negative detections.
Authors
- Oleksandr N. Kryvenko (ORCID: https://orcid.org/0000-0001-5731-1748)
- Radka Stoyanova (ORCID: https://orcid.org/0000-0002-1301-3228)
- Ahmad Algohary (ORCID: https://orcid.org/0000-0003-1286-3164)
- Evangelia I. Zacharaki (ORCID: https://orcid.org/0000-0001-8228-0437)
- Sandra M. Gaston (ORCID: https://orcid.org/0000-0002-0564-0459)
- Adrian Breto (ORCID: https://orcid.org/0000-0002-4958-2991)
- Bruno Nahar (ORCID: https://orcid.org/0000-0002-3871-8742)
- Nachiketh Soodana‐Prakash (ORCID: https://orcid.org/0000-0002-0320-2914)
- Dipen J. Parekh (ORCID: https://orcid.org/0000-0002-8261-5185)
- Veronica Wallaengen
- Isabella Kimbel
- Sanoj Punnen
- Rosa Castillo Acosta
- Alan Pollack
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/4822
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00