Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 5 of 5 entries
Sorted by: Best Match Show Resources per page
A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans.

Cancers

Alilou M, Prasanna P, Bera K, Gupta A, Rajiah P, Yang M, Jacono F, Velcheti V, Gilkeson R, Linden P, Madabhushi A.
PMID: 34205005
Cancers (Basel). 2021 Jun 03;13(11). doi: 10.3390/cancers13112781.

The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious....

Cite
Alilou M, Prasanna P, Bera K, et al. A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers (Basel). 2021;13(11)doi: 10.3390/cancers13112781.
Alilou, M., Prasanna, P., Bera, K., Gupta, A., Rajiah, P., Yang, M., Jacono, F., Velcheti, V., Gilkeson, R., Linden, P., & Madabhushi, A. (2021). A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers, 13(11), . https://doi.org/10.3390/cancers13112781
Alilou, Mehdi, et al. "A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans." Cancers vol. 13,11 (2021). doi: https://doi.org/10.3390/cancers13112781
Alilou M, Prasanna P, Bera K, Gupta A, Rajiah P, Yang M, Jacono F, Velcheti V, Gilkeson R, Linden P, Madabhushi A. A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers (Basel). 2021 Jun 03;13(11). doi: 10.3390/cancers13112781. PMID: 34205005; PMCID: PMC8199879.
Copy Download .nbib Format: NLM
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.
Copy Download .nbib Format: NLM
Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study.

Cancers

Alvarez-Jimenez C, Antunes JT, Talasila N, Bera K, Brady JT, Gollamudi J, Marderstein E, Kalady MF, Purysko A, Willis JE, Stein S, Friedman K, Paspulati R, Delaney CP, Romero E, Madabhushi A, Viswanath SE.
PMID: 32722082
Cancers (Basel). 2020 Jul 24;12(8). doi: 10.3390/cancers12082027.

(1)

Cite
Alvarez-Jimenez C, Antunes JT, Talasila N, et al. Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study. Cancers (Basel). 2020;12(8)doi: 10.3390/cancers12082027.
Alvarez-Jimenez, C., Antunes, J. T., Talasila, N., Bera, K., Brady, J. T., Gollamudi, J., Marderstein, E., Kalady, M. F., Purysko, A., Willis, J. E., Stein, S., Friedman, K., Paspulati, R., Delaney, C. P., Romero, E., Madabhushi, A., & Viswanath, S. E. (2020). Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study. Cancers, 12(8), . https://doi.org/10.3390/cancers12082027
Alvarez-Jimenez, Charlems, et al. "Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study." Cancers vol. 12,8 (2020). doi: https://doi.org/10.3390/cancers12082027
Alvarez-Jimenez C, Antunes JT, Talasila N, Bera K, Brady JT, Gollamudi J, Marderstein E, Kalady MF, Purysko A, Willis JE, Stein S, Friedman K, Paspulati R, Delaney CP, Romero E, Madabhushi A, Viswanath SE. Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study. Cancers (Basel). 2020 Jul 24;12(8). doi: 10.3390/cancers12082027. PMID: 32722082; PMCID: PMC7463898.
Copy Download .nbib Format: NLM
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.
Copy Download .nbib Format: NLM
A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.

BMC medical imaging

Atta-Fosu T, LaBarbera M, Ghose S, Schoenhagen P, Saliba W, Tchou PJ, Lindsay BD, Desai MY, Kwon D, Chung MK, Madabhushi A.
PMID: 33750343
BMC Med Imaging. 2021 Mar 09;21(1):45. doi: 10.1186/s12880-021-00578-4.

OBJECTIVE: To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.METHODS: This retrospective study included 68 AF patients who had...

Cite
Atta-Fosu T, LaBarbera M, Ghose S, et al. A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Med Imaging. 2021;21(1):45doi: 10.1186/s12880-021-00578-4.
Atta-Fosu, T., LaBarbera, M., Ghose, S., Schoenhagen, P., Saliba, W., Tchou, P. J., Lindsay, B. D., Desai, M. Y., Kwon, D., Chung, M. K., & Madabhushi, A. (2021). A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC medical imaging, 21(1), 45. https://doi.org/10.1186/s12880-021-00578-4
Atta-Fosu, Thomas, et al. "A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT." BMC medical imaging vol. 21,1 (2021): 45. doi: https://doi.org/10.1186/s12880-021-00578-4
Atta-Fosu T, LaBarbera M, Ghose S, Schoenhagen P, Saliba W, Tchou PJ, Lindsay BD, Desai MY, Kwon D, Chung MK, Madabhushi A. A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Med Imaging. 2021 Mar 09;21(1):45. doi: 10.1186/s12880-021-00578-4. PMID: 33750343; PMCID: PMC7941998.
Copy Download .nbib Format: NLM
Showing 1 to 5 of 5 entries