| Title | Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Bhargava, HK, Leo P, Elliott R, Janowczyk A, Whitney J, Gupta S, Fu P, Yamoah K, Khani F, Robinson BD, Rebbeck TR, Feldman M, Lal P, Madabhushi A |
| Journal | Clinical cancer research : an official journal of the American Association for Cancer Research |
| Volume | 26 |
| Issue | 8 |
| Pagination | 1915-1923 |
| Date Published | 2020 04 15 |
| ISSN | 1557-3265 |
| Keywords | African Americans, Biomarkers, Tumor, Disease Progression, Humans, Image Processing, Computer-Assisted, Machine Learning, Male, Middle Aged, Neoplasm Recurrence, Local, Nomograms, Predictive Value of Tests, Prognosis, Prostate-Specific Antigen, Prostatectomy, Prostatic Neoplasms, Risk Assessment, ROC Curve, Stromal Cells, Survival Rate |
| Abstract | Between 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, despite recent evidence suggesting the presence of a unique, more aggressive prostate cancer phenotype in African American (AA) patients. We investigated the capacity of digitally measured, population-specific phenotypes of the intratumoral stroma to create improved models for prediction of recurrence following radical prostatectomy. |
| DOI | 10.1158/1078-0432.CCR-19-2659 |
| PDF Link | |
| Alternate Journal | Clin Cancer Res |
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