Multiparametric MRI Radiomic Features Improve Patient Selection for Active Surveillance in Prostate Cancer
Motivation: Accurate selection of prostate cancer patients to undergo active surveillance (AS) is crucial to ensure suitable treatment. Goal(s): To develop an automated framework for mpMRI analysis to assist clinical decision making about whether a patient should remain on AS. Approach: We developed a progression risk stratification model using mpMRI data from an AS trial, and incorporating clinical biomarkers and radiomic features from lesions identified by a deep neural network. Results: The lesion segmentation network achieved a median DSC of 60.7%, and the progression prediction model an AUC of 81.1% in determining likelihood of progression within 12 months. Impact: We present a fully automated methodology to assess prostate cancer progression risk for AS patients within the timeframe between their follow-up visits, thereby providing essential data for clinicians that can prospectively improve AS patient selection.
Authors
- Oleksandr N. Kryvenko (ORCID: https://orcid.org/0000-0001-5731-1748)
- Radka Stoyanova (ORCID: https://orcid.org/0000-0002-1301-3228)
- Sanoj Punnen (ORCID: https://orcid.org/0000-0002-0383-7311)
- Ahmad Algohary (ORCID: https://orcid.org/0000-0003-1286-3164)
- Sandra Gaston
- Evangelia I. Zacharaki (ORCID: https://orcid.org/0000-0001-8228-0437)
- Alan Pollack (ORCID: https://orcid.org/0000-0002-1801-2865)
- Adrian Breto (ORCID: https://orcid.org/0000-0002-4958-2991)
- M. Alhusseini (ORCID: https://orcid.org/0000-0002-9239-470X)
- 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
- Rosa Castillo Acosta
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/10.58530/2024/0859
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00