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Stoyanova R, Zavala-Romero O, Kwon D, Breto AL, Xu IR, Algohary A, Alhusseini M, Gaston SM, Castillo P, Kryvenko ON, Davicioni E, Nahar B, Spieler B, Abramowitz MC, Dal Pra A, Parekh DJ, Punnen S, Pollack A. Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI. Cancers (Basel) 2023; 15:5240. [PMID: 37958414 PMCID: PMC10647832 DOI: 10.3390/cancers15215240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/12/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients' risk using combined clinical-genomic classification.
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Affiliation(s)
- Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Olmo Zavala-Romero
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Deukwoo Kwon
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Adrian L. Breto
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Isaac R. Xu
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Ahmad Algohary
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Mohammad Alhusseini
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sandra M. Gaston
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Patricia Castillo
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Oleksandr N. Kryvenko
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Elai Davicioni
- Research and Development, Veracyte Inc., San Francisco, CA 94080, USA
| | - Bruno Nahar
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Benjamin Spieler
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Matthew C. Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Dipen J. Parekh
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sanoj Punnen
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
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