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Showing 25 to 32 of 32 entries
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Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer.

Frontiers in oncology

Chen BT, Jin T, Ye N, Mambetsariev I, Wang T, Wong CW, Chen Z, Rockne RC, Colen RR, Holodny AI, Sampath S, Salgia R.
PMID: 33747933
Front Oncol. 2021 Mar 05;11:621088. doi: 10.3389/fonc.2021.621088. eCollection 2021.

No abstract available.

Cite
Chen BT, Jin T, Ye N, et al. Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer. Front Oncol. 2021;11:621088doi: 10.3389/fonc.2021.621088.
Chen, B. T., Jin, T., Ye, N., Mambetsariev, I., Wang, T., Wong, C. W., Chen, Z., Rockne, R. C., Colen, R. R., Holodny, A. I., Sampath, S., & Salgia, R. (2021). Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer. Frontiers in oncology, 11621088. https://doi.org/10.3389/fonc.2021.621088
Chen, Bihong T, et al. "Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer." Frontiers in oncology vol. 11 (2021): 621088. doi: https://doi.org/10.3389/fonc.2021.621088
Chen BT, Jin T, Ye N, Mambetsariev I, Wang T, Wong CW, Chen Z, Rockne RC, Colen RR, Holodny AI, Sampath S, Salgia R. Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer. Front Oncol. 2021 Mar 05;11:621088. doi: 10.3389/fonc.2021.621088. eCollection 2021. PMID: 33747933; PMCID: PMC7973105.
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Phase 1 Clinical Trial of Trametinib and Ponatinib in Patients With NSCLC Harboring .

JTO clinical and research reports

Arbour KC, Manchado E, Bott MJ, Ahn L, Tobi Y, Ni AA, Yu HA, Shannon A, Ladanyi M, Perron V, Ginsberg MS, Johnson A, Holodny A, Kris MG, Rudin CM, Lito P, Rosen N, Lowe S, Riely GJ.
PMID: 34984405
JTO Clin Res Rep. 2021 Nov 23;3(1):100256. doi: 10.1016/j.jtocrr.2021.100256. eCollection 2022 Jan.

INTRODUCTION: Somatic METHODS: A phase 1 dose escalation study of trametinib and ponatinib was conducted in patients with advanced NSCLC with RESULTS: A total of 12 patients with KRAS-mutant NSCLC were treated (seven at trametinib 2 mg and ponatinib...

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Arbour KC, Manchado E, Bott MJ, et al. Phase 1 Clinical Trial of Trametinib and Ponatinib in Patients With NSCLC Harboring . JTO Clin Res Rep. 2021;3(1):100256doi: 10.1016/j.jtocrr.2021.100256.
Arbour, K. C., Manchado, E., Bott, M. J., Ahn, L., Tobi, Y., Ni, A. A., Yu, H. A., Shannon, A., Ladanyi, M., Perron, V., Ginsberg, M. S., Johnson, A., Holodny, A., Kris, M. G., Rudin, C. M., Lito, P., Rosen, N., Lowe, S., & Riely, G. J. (2022). Phase 1 Clinical Trial of Trametinib and Ponatinib in Patients With NSCLC Harboring . JTO clinical and research reports, 3(1), 100256. https://doi.org/10.1016/j.jtocrr.2021.100256
Arbour, Kathryn C, et al. "Phase 1 Clinical Trial of Trametinib and Ponatinib in Patients With NSCLC Harboring ." JTO clinical and research reports vol. 3,1 (2022): 100256. doi: https://doi.org/10.1016/j.jtocrr.2021.100256
Arbour KC, Manchado E, Bott MJ, Ahn L, Tobi Y, Ni AA, Yu HA, Shannon A, Ladanyi M, Perron V, Ginsberg MS, Johnson A, Holodny A, Kris MG, Rudin CM, Lito P, Rosen N, Lowe S, Riely GJ. Phase 1 Clinical Trial of Trametinib and Ponatinib in Patients With NSCLC Harboring . JTO Clin Res Rep. 2021 Nov 23;3(1):100256. doi: 10.1016/j.jtocrr.2021.100256. eCollection 2022 Jan. PMID: 34984405; PMCID: PMC8693267.
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Core language brain network for fMRI language task used in clinical applications.

Network neuroscience (Cambridge, Mass.)

Li Q, Del Ferraro G, Pasquini L, Peck KK, Makse HA, Holodny AI.
PMID: 32043047
Netw Neurosci. 2020 Feb 01;4(1):134-154. doi: 10.1162/netn_a_00112. eCollection 2020.

Functional magnetic resonance imaging (fMRI) is widely used in clinical applications to highlight brain areas involved in specific cognitive processes. Brain impairments, such as tumors, suppress the fMRI activation of the anatomical areas they invade and, thus, brain-damaged functional...

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Li Q, Del Ferraro G, Pasquini L, et al. Core language brain network for fMRI language task used in clinical applications. Netw Neurosci. 2020;4(1):134-154doi: 10.1162/netn_a_00112.
Li, Q., Del Ferraro, G., Pasquini, L., Peck, K. K., Makse, H. A., & Holodny, A. I. (2020). Core language brain network for fMRI language task used in clinical applications. Network neuroscience (Cambridge, Mass.), 4(1), 134-154. https://doi.org/10.1162/netn_a_00112
Li, Qiongge, et al. "Core language brain network for fMRI language task used in clinical applications." Network neuroscience (Cambridge, Mass.) vol. 4,1 (2020): 134-154. doi: https://doi.org/10.1162/netn_a_00112
Li Q, Del Ferraro G, Pasquini L, Peck KK, Makse HA, Holodny AI. Core language brain network for fMRI language task used in clinical applications. Netw Neurosci. 2020 Feb 01;4(1):134-154. doi: 10.1162/netn_a_00112. eCollection 2020. PMID: 32043047; PMCID: PMC7006870.
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Correction to: Paradoxical Activation in the Cerebellum During Language fMRI in Patients with Brain Tumors: Possible Explanations Based on Neurovascular Uncoupling and Functional Reorganization.

Cerebellum (London, England)

Cho NS, Peck KK, Zhang Z, Holodny AI.
PMID: 31583516
Cerebellum. 2019 Dec;18(6):1154. doi: 10.1007/s12311-019-01077-7.

The original version of this article unfortunately contained mistake in Funding information section.

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Cho NS, Peck KK, Zhang Z, et al. Correction to: Paradoxical Activation in the Cerebellum During Language fMRI in Patients with Brain Tumors: Possible Explanations Based on Neurovascular Uncoupling and Functional Reorganization. Cerebellum. 2019;18(6):1154doi: 10.1007/s12311-019-01077-7.
Cho, N. S., Peck, K. K., Zhang, Z., & Holodny, A. I. (2019). Correction to: Paradoxical Activation in the Cerebellum During Language fMRI in Patients with Brain Tumors: Possible Explanations Based on Neurovascular Uncoupling and Functional Reorganization. Cerebellum (London, England), 18(6), 1154. https://doi.org/10.1007/s12311-019-01077-7
Cho, Nicholas S, et al. "Correction to: Paradoxical Activation in the Cerebellum During Language fMRI in Patients with Brain Tumors: Possible Explanations Based on Neurovascular Uncoupling and Functional Reorganization." Cerebellum (London, England) vol. 18,6 (2019): 1154. doi: https://doi.org/10.1007/s12311-019-01077-7
Cho NS, Peck KK, Zhang Z, Holodny AI. Correction to: Paradoxical Activation in the Cerebellum During Language fMRI in Patients with Brain Tumors: Possible Explanations Based on Neurovascular Uncoupling and Functional Reorganization. Cerebellum. 2019 Dec;18(6):1154. doi: 10.1007/s12311-019-01077-7. PMID: 31583516.
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Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

Radiology

Swinburne NC, Yadav V, Kim J, Choi YR, Gutman DC, Yang JT, Moss N, Stone J, Tisnado J, Hatzoglou V, Haque SS, Karimi S, Lyo J, Juluru K, Pichotta K, Gao J, Shah SP, Holodny AI, Young RJ.
PMID: 35040676
Radiology. 2022 Jan 18;210817. doi: 10.1148/radiol.210817. Epub 2022 Jan 18.

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and performing dedicated post hoc image labeling is burdensome...

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Swinburne NC, Yadav V, Kim J, et al. Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations. Radiology. 2022;210817doi: 10.1148/radiol.210817.
Swinburne, N. C., Yadav, V., Kim, J., Choi, Y. R., Gutman, D. C., Yang, J. T., Moss, N., Stone, J., Tisnado, J., Hatzoglou, V., Haque, S. S., Karimi, S., Lyo, J., Juluru, K., Pichotta, K., Gao, J., Shah, S. P., Holodny, A. I., Young, R. J. (2022). Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations. Radiology, 210817. https://doi.org/10.1148/radiol.210817
Swinburne, Nathaniel C, et al. "Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations." Radiology vol. (2022): 210817. doi: https://doi.org/10.1148/radiol.210817
Swinburne NC, Yadav V, Kim J, Choi YR, Gutman DC, Yang JT, Moss N, Stone J, Tisnado J, Hatzoglou V, Haque SS, Karimi S, Lyo J, Juluru K, Pichotta K, Gao J, Shah SP, Holodny AI, Young RJ. Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations. Radiology. 2022 Jan 18;210817. doi: 10.1148/radiol.210817. Epub 2022 Jan 18. PMID: 35040676.
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Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach.

Frontiers in oncology

Chen BT, Chen Z, Ye N, Mambetsariev I, Fricke J, Daniel E, Wang G, Wong CW, Rockne RC, Colen RR, Nasser MW, Batra SK, Holodny AI, Sampath S, Salgia R.
PMID: 32391274
Front Oncol. 2020 Apr 22;10:593. doi: 10.3389/fonc.2020.00593. eCollection 2020.

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar...

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Chen BT, Chen Z, Ye N, et al. Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach. Front Oncol. 2020;10:593doi: 10.3389/fonc.2020.00593.
Chen, B. T., Chen, Z., Ye, N., Mambetsariev, I., Fricke, J., Daniel, E., Wang, G., Wong, C. W., Rockne, R. C., Colen, R. R., Nasser, M. W., Batra, S. K., Holodny, A. I., Sampath, S., & Salgia, R. (2020). Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach. Frontiers in oncology, 10593. https://doi.org/10.3389/fonc.2020.00593
Chen, Bihong T, et al. "Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach." Frontiers in oncology vol. 10 (2020): 593. doi: https://doi.org/10.3389/fonc.2020.00593
Chen BT, Chen Z, Ye N, Mambetsariev I, Fricke J, Daniel E, Wang G, Wong CW, Rockne RC, Colen RR, Nasser MW, Batra SK, Holodny AI, Sampath S, Salgia R. Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach. Front Oncol. 2020 Apr 22;10:593. doi: 10.3389/fonc.2020.00593. eCollection 2020. PMID: 32391274; PMCID: PMC7188953.
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Chronic Pain Patients Are Not Immune to Opioid Harms.

Journal of pain & palliative care pharmacotherapy

Kolodny A.
PMID: 27779435
J Pain Palliat Care Pharmacother. 2016 Dec;30(4):330-331. doi: 10.1080/15360288.2016.1231736. Epub 2016 Oct 25.

No abstract available.

Cite
Kolodny A. Chronic Pain Patients Are Not Immune to Opioid Harms. J Pain Palliat Care Pharmacother. 2016;30(4):330-331doi: 10.1080/15360288.2016.1231736.
Kolodny, A. (2016). Chronic Pain Patients Are Not Immune to Opioid Harms. Journal of pain & palliative care pharmacotherapy, 30(4), 330-331. https://doi.org/10.1080/15360288.2016.1231736
Kolodny, Andrew. "Chronic Pain Patients Are Not Immune to Opioid Harms." Journal of pain & palliative care pharmacotherapy vol. 30,4 (2016): 330-331. doi: https://doi.org/10.1080/15360288.2016.1231736
Kolodny A. Chronic Pain Patients Are Not Immune to Opioid Harms. J Pain Palliat Care Pharmacother. 2016 Dec;30(4):330-331. doi: 10.1080/15360288.2016.1231736. Epub 2016 Oct 25. PMID: 27779435.
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Clarification of Reporting of Potential Conflicts of Interest in JAMA Articles.

JAMA

Kolodny A.
PMID: 31483441
JAMA. 2019 Sep 24;322(12):1214-1215. doi: 10.1001/jama.2019.12743.

No abstract available.

Cite
Kolodny A. Clarification of Reporting of Potential Conflicts of Interest in JAMA Articles. JAMA. 2019;322(12):1214-1215doi: 10.1001/jama.2019.12743.
Kolodny, A. (2019). Clarification of Reporting of Potential Conflicts of Interest in JAMA Articles. JAMA, 322(12), 1214-1215. https://doi.org/10.1001/jama.2019.12743
Kolodny, Andrew. "Clarification of Reporting of Potential Conflicts of Interest in JAMA Articles." JAMA vol. 322,12 (2019): 1214-1215. doi: https://doi.org/10.1001/jama.2019.12743
Kolodny A. Clarification of Reporting of Potential Conflicts of Interest in JAMA Articles. JAMA. 2019 Sep 24;322(12):1214-1215. doi: 10.1001/jama.2019.12743. PMID: 31483441.
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Showing 25 to 32 of 32 entries