Multiview boosting digital pathology analysis of prostate cancer. (April 2017) - Kwak, Jin Tae; Hewitt, Stephen M.
Highlights: We propose a multiview boosting method to detect prostate cancer. Morphological tissue characteristics are extracted at differing resolutions. Information from multiple resolutions are cooperatively combined and used to determine disease status. A large set of tissue microarrays are employed to systematically evaluate the proposed method. The proposed method achieves accurate and robust classifications and outperforms standard machine learning algorithms. Abstract: Background and Objective: Various digital pathology tools have been developed to aid in analyzing tissues and improving cancer pathology. The multi-resolution nature of cancer pathology, however, has not been fully analyzed and utilized. Here, we develop an automated, cooperative, and multi-resolution method for improving prostate cancer diagnosis. Methods: Digitized tissue specimen images are obtained from 5 tissue microarrays (TMAs). The TMAs include 70 benign and 135 cancer samples (TMA1), 74 benign and 89 cancer samples (TMA2), 70 benign and 115 cancer samples (TMA3), 79 benign and 82 cancer samples (TMA4), and 72 benign and 86 cancer samples (TMA5). The tissue specimen images are segmented using intensity- and texture-based features. Using the segmentation results, a number of morphological features from lumens and epithelial nuclei are computed to characterize tissues at different resolutions. Applying a multiview boosting algorithm, tissue characteristics, obtained from differing resolutions, are cooperatively combined to achieve accurate cancer detection. Results: In segmenting prostate tissues, the multiview boosting method achieved ≥ 0.97 AUC using TMA1. For detecting cancers, the multiview boosting method achieved an AUC of 0.98 (95% CI: 0.97–0.99) as trained on TMA2 and tested on TMA3, TMA4, and TMA5. The proposed method was superior to single-view approaches, utilizing features from a single resolution or merging features from all the resolutions. Moreover, the performance of the proposed method was insensitive to the choice of the training dataset. Trained on TMA3, TMA4, and TMA5, the proposed method obtained an AUC of 0.97 (95% CI: 0.96–0.98), 0.98 (95% CI: 0.96–0.99), and 0.97 (95% CI: 0.96–0.98), respectively. Conclusions: The multiview boosting method is capable of integrating information from multiple resolutions in an effective and efficient fashion and identifying cancers with high accuracy. The multiview boosting method holds a great potential for improving digital pathology tools and research.