Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI

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. Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article

Cite

@article{stoyanova2023clinical,
  title = {Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI},
  author = {Stoyanova, Radka and Zavala-Romero, Olmo and Kwon, Deukwoo and Breto, Adrian L. and Xu, Isaac R. and Algohary, Ahmad and Alhusseini, Mohammad and Gaston, Sandra M. and Castillo, Patricia and Kryvenko, Oleksandr N. and Davicioni, Elai and Nahar, Bruno and Spieler, Benjamin},
  year = {2023},
  doi = {10.3390/cancers15215240},
  url = {https://doi.org/10.3390/cancers15215240},
}

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Topics

• Deep Learning
• Health
• SciML