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Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance.

Cancers

Chandramouli S, Leo P, Lee G, Elliott R, Davis C, Zhu G, Fu P, Epstein JI, Veltri R, Madabhushi A.
PMID: 32967377
Cancers (Basel). 2020 Sep 21;12(9). doi: 10.3390/cancers12092708.

In this work, we assessed the ability of computerized features of nuclear morphology from diagnostic biopsy images to predict prostate cancer (CaP) progression in active surveillance (AS) patients. Improved risk characterization of AS patients could reduce over-testing of low-risk...

Cite
Chandramouli S, Leo P, Lee G, et al. Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance. Cancers (Basel). 2020;12(9)doi: 10.3390/cancers12092708.
Chandramouli, S., Leo, P., Lee, G., Elliott, R., Davis, C., Zhu, G., Fu, P., Epstein, J. I., Veltri, R., & Madabhushi, A. (2020). Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance. Cancers, 12(9), . https://doi.org/10.3390/cancers12092708
Chandramouli, Sacheth, et al. "Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance." Cancers vol. 12,9 (2020). doi: https://doi.org/10.3390/cancers12092708
Chandramouli S, Leo P, Lee G, Elliott R, Davis C, Zhu G, Fu P, Epstein JI, Veltri R, Madabhushi A. Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance. Cancers (Basel). 2020 Sep 21;12(9). doi: 10.3390/cancers12092708. PMID: 32967377; PMCID: PMC7563653.
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Quick Annotator: an open-source digital pathology based rapid image annotation tool.

The journal of pathology. Clinical research

Miao R, Toth R, Zhou Y, Madabhushi A, Janowczyk A.
PMID: 34288586
J Pathol Clin Res. 2021 Nov;7(6):542-547. doi: 10.1002/cjp2.229. Epub 2021 Jul 19.

Image-based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized...

Cite
Miao R, Toth R, Zhou Y, et al. Quick Annotator: an open-source digital pathology based rapid image annotation tool. J Pathol Clin Res. 2021;7(6):542-547doi: 10.1002/cjp2.229.
Miao, R., Toth, R., Zhou, Y., Madabhushi, A., & Janowczyk, A. (2021). Quick Annotator: an open-source digital pathology based rapid image annotation tool. The journal of pathology. Clinical research, 7(6), 542-547. https://doi.org/10.1002/cjp2.229
Miao, Runtian, et al. "Quick Annotator: an open-source digital pathology based rapid image annotation tool." The journal of pathology. Clinical research vol. 7,6 (2021): 542-547. doi: https://doi.org/10.1002/cjp2.229
Miao R, Toth R, Zhou Y, Madabhushi A, Janowczyk A. Quick Annotator: an open-source digital pathology based rapid image annotation tool. J Pathol Clin Res. 2021 Nov;7(6):542-547. doi: 10.1002/cjp2.229. Epub 2021 Jul 19. PMID: 34288586; PMCID: PMC8503896.
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