ArticleComputer methods and programs in biomedicine2017
Multiview boosting digital pathology analysis of prostate cancer.
Article in Computer methods and programs in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep-learning-based artificial intelligence approaches for grading and progression prediction of clear cell renal cell carcinoma.iScience · 2026Article
- Improving the robustness and stability of a machine learning model for breast cancer prognosis through the use of multi-modal classifiers.Scientific reports · 2023Article
- Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology.BMC bioinformatics · 2023Article
- Biparametric MR signal characteristics can predict histopathological measures of prostate cancer.European radiology · 2022Article
- Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature.Journal of clinical medicine · 2021Review
- Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives.Diagnostics (Basel, Switzerland) · 2021Review
- Article
- [Research status and trend of artificial intelligence in the diagnosis of urinary diseases].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Review
- First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning.Entropy (Basel, Switzerland) · 2019Article
- Advances in the computational and molecular understanding of the prostate cancer cell nucleus.Journal of cellular biochemistry · 2018Review
- Development of a Whole Slide Imaging System on Smartphones and Evaluation With Frozen Section Samples.JMIR mHealth and uHealth · 2017Article
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2 authors.
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Abstract
BACKGROUND AND
objectiveVarious 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.
methodsDigitized 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.
resultsIn 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.
conclusionsThe 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.
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