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Purpose

Prostate cancer (PCa) represents a highly heterogeneous disease that requires tools to assess oncologic risk and guide patient management and treatment planning. Current models are based on various clinical and pathologic parameters including Gleason grading, which suffers from a high interobserver variability. In this study, we determine whether objective machine learning (ML)–driven histopathology image analysis would aid us in better risk stratification of PCa.

Materials and Methods

We propose a deep learning, histopathology image–based risk-stratification model that combines clinicopathologic data along with hematoxylin and eosin– and Ki-67–stained histopathology images. We train and test our model, using a five-fold cross-validation strategy, on a data set from 502 treatment-naïve PCa patients who underwent radical prostatectomy (RP) between 2000 and 2012.

Results

We used the concordance index as a measure to evaluate the performance of various risk-stratification models. Our risk-stratification model on the basis of convolutional neural networks demonstrated superior performance compared with Gleason grading and the Cancer of the Prostate Risk Assessment Post-Surgical risk stratification models. Using our model, 3.9% of the low-risk patients were correctly reclassified to be high-risk and 21.3% of the high-risk patients were correctly reclassified as low-risk.

Conclusion

These findings highlight the importance of ML as an objective tool for histopathology image assessment and patient risk-stratification. With further validation on large cohorts, the digital pathology risk-classification we propose may be helpful in guiding administration of adjuvant therapy including radiotherapy after RP.

Read an interview about the study here.