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Predicting prostate cancer biochemical recurrence and metastasis with computer extracted features from tumor nuclei and benign regions
Predicting prostate cancer biochemical recurrence and metastasis with computer extracted features from tumor nuclei and benign regions
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机译：用计算机提取的肿瘤核和良性区域特征预测前列腺癌的生化复发和转移
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摘要
Embodiments predict biochemical recurrence (BCR) or metastasis by accessing a set of images of a region of tissue demonstrating cancerous pathology, including a tumor region and a tumor adjacent benign (TAB) region, the set of images including a first stain type image, and a second stain type image; segmenting cellular nuclei represented in the first and second image; generating a combined feature set by extracting at least one feature from each of a tumor region and TAB region represented in the first image, and a tumor region and TAB region represented in the second image, providing the combined feature set to a machine learning classifier; receiving, from the classifier, a probability that the region of tissue will experience BCR or metastasis; and generating a classification of the region of tissue as likely to experience BCR or metastasis, or unlikely to experience BCR or metastasis.
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