Abstract LB-285: Computational pathology for predicting prostate cancer recurrence
Authors: Amit Sethi, Lingdao Sha, Ryan J. Deaton, Virgilia Macias, Andrew H. Beck, Peter H. Gann
Journal: Cancer Research (2015)

Abstract Background: Conventional methods for predicting prostate cancer (PCa) aggressiveness, which rely heavily on the Gleason score of architectural pattern, remain imperfect. We have developed an approach that combines machine vision and machine learning analysis of routine H&amp;E stained tumors to discriminate recurrent from non-recurrent patients after prostatectomy, based on architectural, color, and nuclear patterns. Methods: We used a set of H&amp;E-stained TMAs containing quadruplicate cores from 176 recurrent cases of PCa and 154 non-recurrent controls, frequency matched on age, Gleason, pathological stage and race. Using software specifically adaptable for computational pathology with hierarchical object recognition (Definiens Developer XD®), we processed images of each tumor at two spatial scale levels: a larger scale for recognition of tissue compartments (epithelium, stroma, lumen and inflammation), and a smaller scale for recognition of normal and atypical nuclei in each of the compartments. Magnification of 20x was used for both levels. At the tissue level, we used multi-resolution segmentation to segment homogeneous superpixels, which were then classified using machine learning into epithelium, stroma. or ambiguous tissue classified as intermediate between epithelium and stroma. We used relative darkness in a spatial neighborhood, size, and shape criteria to segment typical and atypical nuclei. Relational features among objects within and between the tissue and nuclear levels were also created. The resultant feature library contained 884 base features and 3,551 total variables. We used several stages of iterative L1 penalized logistic regression and 5-fold cross-validation to reduce variables and obtain AUC estimates. Variable stability criteria applied across the folds further minimized model overfitting. Results: PSA, Gleason and CAPRA-s achieved AUCs &amp;lt; 0.60 for recurrence in this matched dataset. A fifth stage L1 model containing 18 histometric features had cross-validation AUC = 0.81 (95% CI: 0.76-0.85). A more permissive 74 feature model had AUC = 0.95 (95% CI: 0.93-0.97). Subgroup analysis restricted to Gleason 7 cases produced AUCs ranging from 0.79-0.93 with varying model sparseness. Including pre-surgical PSA, Gleason grade or CAPRA-s score did not change model performance. Notably, leading features were frequently derived from stromal or ambiguously stromal regions and involved nuclear shape or texture. Their independence from tumor grading reflected subject matching and lack of explicit accounting for nuclear or stromal characteristics in the Gleason criteria. Conclusion: A computational pathology approach, applied to discrimination of recurrent and non-recurrent PCa in routine H&amp;E stains, shows considerable promise and appears capable of adding to the predictive power of Gleason and CAPRA scoring. We will conduct additional validation studies and extend this work to biopsy samples. Citation Format: Amit Sethi, Lingdao Sha, Ryan J. Deaton, Virgilia Macias, Andrew H. Beck, Peter H. Gann. Computational pathology for predicting prostate cancer recurrence. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-285. doi:10.1158/1538-7445.AM2015-LB-285