Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 18 entries
Sorted by: Best Match Show Resources per page
The revolving door for AI and pathologists-docendo discimus?.

Journal of medical artificial intelligence

Van Es SL, Madabhushi A.
PMID: 31372599
J Med Artif Intell. 2019 Jun;2. doi: 10.21037/jmai.2019.05.02. Epub 2019 Jun 11.

No abstract available.

Cite
Van Es SL, Madabhushi A. The revolving door for AI and pathologists-docendo discimus?. J Med Artif Intell. 2019;2doi: 10.21037/jmai.2019.05.02.
Van Es, S. L., & Madabhushi, A. (2019). The revolving door for AI and pathologists-docendo discimus?. Journal of medical artificial intelligence, 2. https://doi.org/10.21037/jmai.2019.05.02
Van Es, Simone L, and Madabhushi, Anant. "The revolving door for AI and pathologists-docendo discimus?." Journal of medical artificial intelligence vol. 2 (2019). doi: https://doi.org/10.21037/jmai.2019.05.02
Van Es SL, Madabhushi A. The revolving door for AI and pathologists-docendo discimus?. J Med Artif Intell. 2019 Jun;2. doi: 10.21037/jmai.2019.05.02. Epub 2019 Jun 11. PMID: 31372599; PMCID: PMC6675021.
Copy Download .nbib Format: NLM
Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.

Journal of medical imaging (Bellingham, Wash.)

Prasanna P, Karnawat A, Ismail M, Madabhushi A, Tiwari P.
PMID: 31093517
J Med Imaging (Bellingham). 2019 Apr;6(2):024005. doi: 10.1117/1.JMI.6.2.024005. Epub 2019 May 07.

Accurate segmentation of gliomas on routine magnetic resonance image (MRI) scans plays an important role in disease diagnosis, prognosis, and patient treatment planning. We present a fully automated approach, radiomics-based convolutional neural network (RadCNN), for segmenting both high- and...

Cite
Prasanna P, Karnawat A, Ismail M, et al. Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. J Med Imaging (Bellingham). 2019;6(2):024005doi: 10.1117/1.JMI.6.2.024005.
Prasanna, P., Karnawat, A., Ismail, M., Madabhushi, A., & Tiwari, P. (2019). Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. Journal of medical imaging (Bellingham, Wash.), 6(2), 024005. https://doi.org/10.1117/1.JMI.6.2.024005
Prasanna, Prateek, et al. "Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging." Journal of medical imaging (Bellingham, Wash.) vol. 6,2 (2019): 024005. doi: https://doi.org/10.1117/1.JMI.6.2.024005
Prasanna P, Karnawat A, Ismail M, Madabhushi A, Tiwari P. Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. J Med Imaging (Bellingham). 2019 Apr;6(2):024005. doi: 10.1117/1.JMI.6.2.024005. Epub 2019 May 07. PMID: 31093517; PMCID: PMC6503346.
Copy Download .nbib Format: NLM
Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation.

NPJ breast cancer

Li H, Bera K, Toro P, Fu P, Zhang Z, Lu C, Feldman M, Ganesan S, Goldstein LJ, Davidson NE, Glasgow A, Harbhajanka A, Gilmore H, Madabhushi A.
PMID: 34362928
NPJ Breast Cancer. 2021 Aug 06;7(1):104. doi: 10.1038/s41523-021-00310-z.

Collagen fiber organization has been found to be implicated in breast cancer prognosis. In this study, we evaluated whether computerized features of Collagen Fiber Orientation Disorder in Tumor-associated Stroma (CFOD-TS) on Hematoxylin & Eosin (H&E) slide images were prognostic...

Cite
Li H, Bera K, Toro P, et al. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ Breast Cancer. 2021;7(1):104doi: 10.1038/s41523-021-00310-z.
Li, H., Bera, K., Toro, P., Fu, P., Zhang, Z., Lu, C., Feldman, M., Ganesan, S., Goldstein, L. J., Davidson, N. E., Glasgow, A., Harbhajanka, A., Gilmore, H., & Madabhushi, A. (2021). Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ breast cancer, 7(1), 104. https://doi.org/10.1038/s41523-021-00310-z
Li, Haojia, et al. "Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation." NPJ breast cancer vol. 7,1 (2021): 104. doi: https://doi.org/10.1038/s41523-021-00310-z
Li H, Bera K, Toro P, Fu P, Zhang Z, Lu C, Feldman M, Ganesan S, Goldstein LJ, Davidson NE, Glasgow A, Harbhajanka A, Gilmore H, Madabhushi A. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ Breast Cancer. 2021 Aug 06;7(1):104. doi: 10.1038/s41523-021-00310-z. PMID: 34362928; PMCID: PMC8346522.
Copy Download .nbib Format: NLM
Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR.

IEEE transactions on radiation and plasma medical sciences

Ropella-Panagis KM, Seiberlich N, Gulani V.
PMID: 32864537
IEEE Trans Radiat Plasma Med Sci. 2019 Jul;3(4):388-399. doi: 10.1109/trpms.2019.2897425. Epub 2019 Feb 04.

Magnetic Resonance Imaging (MRI) can be used to assess anatomical structure, and its sensitivity to a variety of tissue properties enables superb contrast between tissues as well as the ability to characterize these tissues. However, despite vast potential for...

Cite
Ropella-Panagis KM, Seiberlich N, Gulani V. Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR. IEEE Trans Radiat Plasma Med Sci. 2019;3(4):388-399doi: 10.1109/trpms.2019.2897425.
Ropella-Panagis, K. M., Seiberlich, N., & Gulani, V. (2019). Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR. IEEE transactions on radiation and plasma medical sciences, 3(4), 388-399. https://doi.org/10.1109/trpms.2019.2897425
Ropella-Panagis, Kathleen M, et al. "Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR." IEEE transactions on radiation and plasma medical sciences vol. 3,4 (2019): 388-399. doi: https://doi.org/10.1109/trpms.2019.2897425
Ropella-Panagis KM, Seiberlich N, Gulani V. Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR. IEEE Trans Radiat Plasma Med Sci. 2019 Jul;3(4):388-399. doi: 10.1109/trpms.2019.2897425. Epub 2019 Feb 04. PMID: 32864537; PMCID: PMC7454032.
Copy Download .nbib Format: NLM
Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study.

Cancers

Algohary A, Shiradkar R, Pahwa S, Purysko A, Verma S, Moses D, Shnier R, Haynes AM, Delprado W, Thompson J, Tirumani S, Mahran A, Rastinehad AR, Ponsky L, Stricker PD, Madabhushi A.
PMID: 32781640
Cancers (Basel). 2020 Aug 06;12(8). doi: 10.3390/cancers12082200.

No abstract available.

Cite
Algohary A, Shiradkar R, Pahwa S, et al. Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers (Basel). 2020;12(8)doi: 10.3390/cancers12082200.
Algohary, A., Shiradkar, R., Pahwa, S., Purysko, A., Verma, S., Moses, D., Shnier, R., Haynes, A. M., Delprado, W., Thompson, J., Tirumani, S., Mahran, A., Rastinehad, A. R., Ponsky, L., Stricker, P. D., & Madabhushi, A. (2020). Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers, 12(8), . https://doi.org/10.3390/cancers12082200
Algohary, Ahmad, et al. "Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study." Cancers vol. 12,8 (2020). doi: https://doi.org/10.3390/cancers12082200
Algohary A, Shiradkar R, Pahwa S, Purysko A, Verma S, Moses D, Shnier R, Haynes AM, Delprado W, Thompson J, Tirumani S, Mahran A, Rastinehad AR, Ponsky L, Stricker PD, Madabhushi A. Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers (Basel). 2020 Aug 06;12(8). doi: 10.3390/cancers12082200. PMID: 32781640; PMCID: PMC7465024.
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
Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2.

European urology focus

Leo P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Shih NNC, Feldman M, Gupta S, McKenney J, Lal P, Madabhushi A.
PMID: 33941504
Eur Urol Focus. 2021 Jul;7(4):722-732. doi: 10.1016/j.euf.2021.04.016. Epub 2021 Apr 30.

BACKGROUND: The presence of invasive cribriform adenocarcinoma (ICC), an expanse of cells containing punched-out lumina uninterrupted by stroma, in radical prostatectomy (RP) specimens has been associated with biochemical recurrence (BCR). However, ICC identification has only moderate inter-reviewer agreement.OBJECTIVE: To...

Cite
Leo P, Chandramouli S, Farré X, et al. Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. Eur Urol Focus. 2021;7(4):722-732doi: 10.1016/j.euf.2021.04.016.
Leo, P., Chandramouli, S., Farré, X., Elliott, R., Janowczyk, A., Bera, K., Fu, P., Janaki, N., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T. R., Khani, F., Robinson, B. D., Shih, N. N. C., Feldman, M., Gupta, S., McKenney, J., Lal, P., & Madabhushi, A. (2021). Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. European urology focus, 7(4), 722-732. https://doi.org/10.1016/j.euf.2021.04.016
Leo, Patrick, et al. "Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2." European urology focus vol. 7,4 (2021): 722-732. doi: https://doi.org/10.1016/j.euf.2021.04.016
Leo P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Shih NNC, Feldman M, Gupta S, McKenney J, Lal P, Madabhushi A. Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. Eur Urol Focus. 2021 Jul;7(4):722-732. doi: 10.1016/j.euf.2021.04.016. Epub 2021 Apr 30. PMID: 33941504; PMCID: PMC8419103.
Copy Download .nbib Format: NLM
Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography.

Journal of medical imaging (Bellingham, Wash.)

Orooji M, Alilou M, Rakshit S, Beig N, Khorrami MH, Rajiah P, Thawani R, Ginsberg J, Donatelli C, Yang M, Jacono F, Gilkeson R, Velcheti V, Linden P, Madabhushi A.
PMID: 29721515
J Med Imaging (Bellingham). 2018 Apr;5(2):024501. doi: 10.1117/1.JMI.5.2.024501. Epub 2018 Apr 18.

Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers....

Cite
Orooji M, Alilou M, Rakshit S, et al. Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J Med Imaging (Bellingham). 2018;5(2):024501doi: 10.1117/1.JMI.5.2.024501.
Orooji, M., Alilou, M., Rakshit, S., Beig, N., Khorrami, M. H., Rajiah, P., Thawani, R., Ginsberg, J., Donatelli, C., Yang, M., Jacono, F., Gilkeson, R., Velcheti, V., Linden, P., & Madabhushi, A. (2018). Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. Journal of medical imaging (Bellingham, Wash.), 5(2), 024501. https://doi.org/10.1117/1.JMI.5.2.024501
Orooji, Mahdi, et al. "Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography." Journal of medical imaging (Bellingham, Wash.) vol. 5,2 (2018): 024501. doi: https://doi.org/10.1117/1.JMI.5.2.024501
Orooji M, Alilou M, Rakshit S, Beig N, Khorrami MH, Rajiah P, Thawani R, Ginsberg J, Donatelli C, Yang M, Jacono F, Gilkeson R, Velcheti V, Linden P, Madabhushi A. Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J Med Imaging (Bellingham). 2018 Apr;5(2):024501. doi: 10.1117/1.JMI.5.2.024501. Epub 2018 Apr 18. PMID: 29721515; PMCID: PMC5904542.
Copy Download .nbib Format: NLM
Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.

Journal of magnetic resonance imaging : JMRI

Algohary A, Viswanath S, Shiradkar R, Ghose S, Pahwa S, Moses D, Jambor I, Shnier R, Böhm M, Haynes AM, Brenner P, Delprado W, Thompson J, Pulbrock M, Purysko AS, Verma S, Ponsky L, Stricker P, Madabhushi A.
PMID: 29469937
J Magn Reson Imaging. 2018 Feb 22; doi: 10.1002/jmri.25983. Epub 2018 Feb 22.

BACKGROUND: Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance...

Cite
Algohary A, Viswanath S, Shiradkar R, et al. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. J Magn Reson Imaging. 2018;doi: 10.1002/jmri.25983.
Algohary, A., Viswanath, S., Shiradkar, R., Ghose, S., Pahwa, S., Moses, D., Jambor, I., Shnier, R., Böhm, M., Haynes, A. M., Brenner, P., Delprado, W., Thompson, J., Pulbrock, M., Purysko, A. S., Verma, S., Ponsky, L., Stricker, P., & Madabhushi, A. (2018). Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. Journal of magnetic resonance imaging : JMRI, . https://doi.org/10.1002/jmri.25983
Algohary, Ahmad, et al. "Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings." Journal of magnetic resonance imaging : JMRI vol. (2018). doi: https://doi.org/10.1002/jmri.25983
Algohary A, Viswanath S, Shiradkar R, Ghose S, Pahwa S, Moses D, Jambor I, Shnier R, Böhm M, Haynes AM, Brenner P, Delprado W, Thompson J, Pulbrock M, Purysko AS, Verma S, Ponsky L, Stricker P, Madabhushi A. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. J Magn Reson Imaging. 2018 Feb 22; doi: 10.1002/jmri.25983. Epub 2018 Feb 22. PMID: 29469937; PMCID: PMC6105554.
Copy Download .nbib Format: NLM
Magnetic Resonance Fingerprinting-An Overview.

Current opinion in biomedical engineering

Panda A, Mehta BB, Coppo S, Jiang Y, Ma D, Seiberlich N, Griswold MA, Gulani V.
PMID: 29868647
Curr Opin Biomed Eng. 2017 Sep;3:56-66. doi: 10.1016/j.cobme.2017.11.001.

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition. The ability to reproducibly and quantitatively measure tissue properties could enable more objective...

Cite
Panda A, Mehta BB, Coppo S, et al. Magnetic Resonance Fingerprinting-An Overview. Curr Opin Biomed Eng. 2017;3:56-66doi: 10.1016/j.cobme.2017.11.001.
Panda, A., Mehta, B. B., Coppo, S., Jiang, Y., Ma, D., Seiberlich, N., Griswold, M. A., & Gulani, V. (2017). Magnetic Resonance Fingerprinting-An Overview. Current opinion in biomedical engineering, 356-66. https://doi.org/10.1016/j.cobme.2017.11.001
Panda, Ananya, et al. "Magnetic Resonance Fingerprinting-An Overview." Current opinion in biomedical engineering vol. 3 (2017): 56-66. doi: https://doi.org/10.1016/j.cobme.2017.11.001
Panda A, Mehta BB, Coppo S, Jiang Y, Ma D, Seiberlich N, Griswold MA, Gulani V. Magnetic Resonance Fingerprinting-An Overview. Curr Opin Biomed Eng. 2017 Sep;3:56-66. doi: 10.1016/j.cobme.2017.11.001. PMID: 29868647; PMCID: PMC5984038.
Copy Download .nbib Format: NLM
Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools.

Journal of pathology informatics

Hipp JD, Johann DJ, Chen Y, Madabhushi A, Monaco J, Cheng J, Rodriguez-Canales J, Stumpe MC, Riedlinger G, Rosenberg AZ, Hanson JC, Kunju LP, Emmert-Buck MR, Balis UJ, Tangrea MA.
PMID: 30622835
J Pathol Inform. 2018 Dec 11;9:45. doi: 10.4103/jpi.jpi_60_18. eCollection 2018.

INTRODUCTION: The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on...

Cite
Hipp JD, Johann DJ, Chen Y, et al. Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools. J Pathol Inform. 2018;9:45doi: 10.4103/jpi.jpi_60_18.
Hipp, J. D., Johann, D. J., Chen, Y., Madabhushi, A., Monaco, J., Cheng, J., Rodriguez-Canales, J., Stumpe, M. C., Riedlinger, G., Rosenberg, A. Z., Hanson, J. C., Kunju, L. P., Emmert-Buck, M. R., Balis, U. J., & Tangrea, M. A. (2018). Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools. Journal of pathology informatics, 945. https://doi.org/10.4103/jpi.jpi_60_18
Hipp, Jason D, et al. "Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools." Journal of pathology informatics vol. 9 (2018): 45. doi: https://doi.org/10.4103/jpi.jpi_60_18
Hipp JD, Johann DJ, Chen Y, Madabhushi A, Monaco J, Cheng J, Rodriguez-Canales J, Stumpe MC, Riedlinger G, Rosenberg AZ, Hanson JC, Kunju LP, Emmert-Buck MR, Balis UJ, Tangrea MA. Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools. J Pathol Inform. 2018 Dec 11;9:45. doi: 10.4103/jpi.jpi_60_18. eCollection 2018. PMID: 30622835; PMCID: PMC6298131.
Copy Download .nbib Format: NLM
Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.

Journal of medical imaging (Bellingham, Wash.)

Chirra P, Leo P, Yim M, Bloch BN, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE.
PMID: 31259199
J Med Imaging (Bellingham). 2019 Apr;6(2):024502. doi: 10.1117/1.JMI.6.2.024502. Epub 2019 Jun 14.

Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple...

Cite
Chirra P, Leo P, Yim M, et al. Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. J Med Imaging (Bellingham). 2019;6(2):024502doi: 10.1117/1.JMI.6.2.024502.
Chirra, P., Leo, P., Yim, M., Bloch, B. N., Rastinehad, A. R., Purysko, A., Rosen, M., Madabhushi, A., & Viswanath, S. E. (2019). Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. Journal of medical imaging (Bellingham, Wash.), 6(2), 024502. https://doi.org/10.1117/1.JMI.6.2.024502
Chirra, Prathyush, et al. "Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI." Journal of medical imaging (Bellingham, Wash.) vol. 6,2 (2019): 024502. doi: https://doi.org/10.1117/1.JMI.6.2.024502
Chirra P, Leo P, Yim M, Bloch BN, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE. Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. J Med Imaging (Bellingham). 2019 Apr;6(2):024502. doi: 10.1117/1.JMI.6.2.024502. Epub 2019 Jun 14. PMID: 31259199; PMCID: PMC6566001.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 18 entries