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Showing 1 to 12 of 15 entries
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Interactive modeling and evaluation of tumor growth.

Journal of digital imaging

Scharcanski J, da Silva LS, Koff D, Wong A.
PMID: 19768508
J Digit Imaging. 2010 Dec;23(6):755-68. doi: 10.1007/s10278-009-9234-4. Epub 2009 Sep 19.

This paper addresses the need to quantify tumor growth and detect changes as this information is relevant to manage the patient treatment and to aid biotechnological efforts to cure cancer (Silva et al. 2008). An interactive tumor segmentation technique...

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Scharcanski J, da Silva LS, Koff D, et al. Interactive modeling and evaluation of tumor growth. J Digit Imaging. 2009;23(6):755-68doi: 10.1007/s10278-009-9234-4.
Scharcanski, J., da Silva, L. S., Koff, D., & Wong, A. (2010). Interactive modeling and evaluation of tumor growth. Journal of digital imaging, 23(6), 755-68. https://doi.org/10.1007/s10278-009-9234-4
Scharcanski, Jacob, et al. "Interactive modeling and evaluation of tumor growth." Journal of digital imaging vol. 23,6 (2010): 755-68. doi: https://doi.org/10.1007/s10278-009-9234-4
Scharcanski J, da Silva LS, Koff D, Wong A. Interactive modeling and evaluation of tumor growth. J Digit Imaging. 2010 Dec;23(6):755-68. doi: 10.1007/s10278-009-9234-4. Epub 2009 Sep 19. PMID: 19768508; PMCID: PMC3046685.
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A novel method for calculating the volume of hemangiomas.

Pediatric dermatology

Berk DR, Berk EJ, Bruckner AL.
PMID: 21793894
Pediatr Dermatol. 2011 Jul-Aug;28(4):478-82. doi: 10.1111/j.1525-1470.2011.01498.x.

Accurately measuring the size of infantile hemangiomas is critical for objectively quantifying their growth, involution, and treatment response. Volumetric measurements are particularly appropriate because hemangiomas tend to mark out their territory early on and then grow and involute volumetrically....

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Berk DR, Berk EJ, Bruckner AL. A novel method for calculating the volume of hemangiomas. Pediatr Dermatol. 2011;28(4):478-82doi: 10.1111/j.1525-1470.2011.01498.x.
Berk, D. R., Berk, E. J., & Bruckner, A. L. (2011). A novel method for calculating the volume of hemangiomas. Pediatric dermatology, 28(4), 478-82. https://doi.org/10.1111/j.1525-1470.2011.01498.x
Berk, David R, et al. "A novel method for calculating the volume of hemangiomas." Pediatric dermatology vol. 28,4 (2011): 478-82. doi: https://doi.org/10.1111/j.1525-1470.2011.01498.x
Berk DR, Berk EJ, Bruckner AL. A novel method for calculating the volume of hemangiomas. Pediatr Dermatol. 2011 Jul-Aug;28(4):478-82. doi: 10.1111/j.1525-1470.2011.01498.x. PMID: 21793894.
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Will AI Improve Tumor Delineation Accuracy for Radiation Therapy?.

Radiology

Chang Z.
PMID: 30917296
Radiology. 2019 Jun;291(3):687-688. doi: 10.1148/radiol.2019190385. Epub 2019 Mar 26.

No abstract available.

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Chang Z. Will AI Improve Tumor Delineation Accuracy for Radiation Therapy?. Radiology. 2019;291(3):687-688doi: 10.1148/radiol.2019190385.
Chang, Z. (2019). Will AI Improve Tumor Delineation Accuracy for Radiation Therapy?. Radiology, 291(3), 687-688. https://doi.org/10.1148/radiol.2019190385
Chang, Zheng. "Will AI Improve Tumor Delineation Accuracy for Radiation Therapy?." Radiology vol. 291,3 (2019): 687-688. doi: https://doi.org/10.1148/radiol.2019190385
Chang Z. Will AI Improve Tumor Delineation Accuracy for Radiation Therapy?. Radiology. 2019 Jun;291(3):687-688. doi: 10.1148/radiol.2019190385. Epub 2019 Mar 26. PMID: 30917296.
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Microsatellite instability status determined by next-generation sequencing and compared with PD-L1 and tumor mutational burden in 11,348 patients.

Cancer medicine

Vanderwalde A, Spetzler D, Xiao N, Gatalica Z, Marshall J.
PMID: 29436178
Cancer Med. 2018 Mar;7(3):746-756. doi: 10.1002/cam4.1372. Epub 2018 Feb 13.

Microsatellite instability (MSI) testing identifies patients who may benefit from immune checkpoint inhibitors. We developed an MSI assay that uses data from a commercially available next-generation sequencing (NGS) panel to determine MSI status. The assay is applicable across cancer...

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Vanderwalde A, Spetzler D, Xiao N, et al. Microsatellite instability status determined by next-generation sequencing and compared with PD-L1 and tumor mutational burden in 11,348 patients. Cancer Med. 2018;7(3):746-756doi: 10.1002/cam4.1372.
Vanderwalde, A., Spetzler, D., Xiao, N., Gatalica, Z., & Marshall, J. (2018). Microsatellite instability status determined by next-generation sequencing and compared with PD-L1 and tumor mutational burden in 11,348 patients. Cancer medicine, 7(3), 746-756. https://doi.org/10.1002/cam4.1372
Vanderwalde, Ari, et al. "Microsatellite instability status determined by next-generation sequencing and compared with PD-L1 and tumor mutational burden in 11,348 patients." Cancer medicine vol. 7,3 (2018): 746-756. doi: https://doi.org/10.1002/cam4.1372
Vanderwalde A, Spetzler D, Xiao N, Gatalica Z, Marshall J. Microsatellite instability status determined by next-generation sequencing and compared with PD-L1 and tumor mutational burden in 11,348 patients. Cancer Med. 2018 Mar;7(3):746-756. doi: 10.1002/cam4.1372. Epub 2018 Feb 13. PMID: 29436178; PMCID: PMC5852359.
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The role of viral infectivity in oncolytic virotherapy outcomes: A mathematical study.

Mathematical biosciences

Pooladvand P, Yun CO, Yoon AR, Kim PS, Frascoli F.
PMID: 33290764
Math Biosci. 2021 Apr;334:108520. doi: 10.1016/j.mbs.2020.108520. Epub 2020 Dec 05.

A model capturing the dynamics between virus and tumour cells in the context of oncolytic virotherapy is presented and analysed. The ability of the virus to be internalised by uninfected cells is described by an infectivity parameter, which is...

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Pooladvand P, Yun CO, Yoon AR, et al. The role of viral infectivity in oncolytic virotherapy outcomes: A mathematical study. Math Biosci. 2020;334:108520doi: 10.1016/j.mbs.2020.108520.
Pooladvand, P., Yun, C. O., Yoon, A. R., Kim, P. S., & Frascoli, F. (2021). The role of viral infectivity in oncolytic virotherapy outcomes: A mathematical study. Mathematical biosciences, 334108520. https://doi.org/10.1016/j.mbs.2020.108520
Pooladvand, Pantea, et al. "The role of viral infectivity in oncolytic virotherapy outcomes: A mathematical study." Mathematical biosciences vol. 334 (2021): 108520. doi: https://doi.org/10.1016/j.mbs.2020.108520
Pooladvand P, Yun CO, Yoon AR, Kim PS, Frascoli F. The role of viral infectivity in oncolytic virotherapy outcomes: A mathematical study. Math Biosci. 2021 Apr;334:108520. doi: 10.1016/j.mbs.2020.108520. Epub 2020 Dec 05. PMID: 33290764.
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Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging.

EBioMedicine

Roy S, Whitehead TD, Quirk JD, Salter A, Ademuyiwa FO, Li S, An H, Shoghi KI.
PMID: 32891051
EBioMedicine. 2020 Sep;59:102963. doi: 10.1016/j.ebiom.2020.102963. Epub 2020 Sep 02.

BACKGROUND: Radiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and...

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Roy S, Whitehead TD, Quirk JD, et al. Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. EBioMedicine. 2020;59:102963doi: 10.1016/j.ebiom.2020.102963.
Roy, S., Whitehead, T. D., Quirk, J. D., Salter, A., Ademuyiwa, F. O., Li, S., An, H., & Shoghi, K. I. (2020). Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. EBioMedicine, 59102963. https://doi.org/10.1016/j.ebiom.2020.102963
Roy, Sudipta, et al. "Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging." EBioMedicine vol. 59 (2020): 102963. doi: https://doi.org/10.1016/j.ebiom.2020.102963
Roy S, Whitehead TD, Quirk JD, Salter A, Ademuyiwa FO, Li S, An H, Shoghi KI. Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. EBioMedicine. 2020 Sep;59:102963. doi: 10.1016/j.ebiom.2020.102963. Epub 2020 Sep 02. PMID: 32891051; PMCID: PMC7479492.
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Diagnostic accuracy of computed tomography colonography for tumor depth in colorectal cancer: A systematic review and meta-analysis.

Surgical oncology

Hoshino N, Sakamoto T, Hida K, Sakai Y.
PMID: 31500775
Surg Oncol. 2019 Sep;30:126-130. doi: 10.1016/j.suronc.2019.08.003. Epub 2019 Aug 06.

BACKGROUND: Accurate preoperative determination of tumor depth is a major concern in colorectal cancer. Assessment of tumor depth can influence the treatment strategy for colorectal cancer, but there is currently no universal diagnostic standard. This review sought to evaluate...

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Hoshino N, Sakamoto T, Hida K, et al. Diagnostic accuracy of computed tomography colonography for tumor depth in colorectal cancer: A systematic review and meta-analysis. Surg Oncol. 2019;30:126-130doi: 10.1016/j.suronc.2019.08.003.
Hoshino, N., Sakamoto, T., Hida, K., & Sakai, Y. (2019). Diagnostic accuracy of computed tomography colonography for tumor depth in colorectal cancer: A systematic review and meta-analysis. Surgical oncology, 30126-130. https://doi.org/10.1016/j.suronc.2019.08.003
Hoshino, Nobuaki, et al. "Diagnostic accuracy of computed tomography colonography for tumor depth in colorectal cancer: A systematic review and meta-analysis." Surgical oncology vol. 30 (2019): 126-130. doi: https://doi.org/10.1016/j.suronc.2019.08.003
Hoshino N, Sakamoto T, Hida K, Sakai Y. Diagnostic accuracy of computed tomography colonography for tumor depth in colorectal cancer: A systematic review and meta-analysis. Surg Oncol. 2019 Sep;30:126-130. doi: 10.1016/j.suronc.2019.08.003. Epub 2019 Aug 06. PMID: 31500775.
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Modelling treatment-response rates.

Nature biomedical engineering

Grassberger C, Ngwa W.
PMID: 33864036
Nat Biomed Eng. 2021 Apr;5(4):295-296. doi: 10.1038/s41551-021-00717-w.

No abstract available.

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Grassberger C, Ngwa W. Modelling treatment-response rates. Nat Biomed Eng. 2021;5(4):295-296doi: 10.1038/s41551-021-00717-w.
Grassberger, C., & Ngwa, W. (2021). Modelling treatment-response rates. Nature biomedical engineering, 5(4), 295-296. https://doi.org/10.1038/s41551-021-00717-w
Grassberger, Clemens, and Ngwa, Wilfred. "Modelling treatment-response rates." Nature biomedical engineering vol. 5,4 (2021): 295-296. doi: https://doi.org/10.1038/s41551-021-00717-w
Grassberger C, Ngwa W. Modelling treatment-response rates. Nat Biomed Eng. 2021 Apr;5(4):295-296. doi: 10.1038/s41551-021-00717-w. PMID: 33864036; PMCID: PMC8682812.
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Radiotherapy, tumor mutational burden, and immune checkpoint inhibitors: time to do the math.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]

Giordano FA, Veldwijk MR, Herskind C, Wenz F.
PMID: 30030581
Strahlenther Onkol. 2018 Oct;194(10):873-875. doi: 10.1007/s00066-018-1341-z. Epub 2018 Jul 20.

No abstract available.

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Giordano FA, Veldwijk MR, Herskind C, et al. Radiotherapy, tumor mutational burden, and immune checkpoint inhibitors: time to do the math. Strahlenther Onkol. 2018;194(10):873-875doi: 10.1007/s00066-018-1341-z.
Giordano, F. A., Veldwijk, M. R., Herskind, C., & Wenz, F. (2018). Radiotherapy, tumor mutational burden, and immune checkpoint inhibitors: time to do the math. Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 194(10), 873-875. https://doi.org/10.1007/s00066-018-1341-z
Giordano, Frank A, et al. "Radiotherapy, tumor mutational burden, and immune checkpoint inhibitors: time to do the math." Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] vol. 194,10 (2018): 873-875. doi: https://doi.org/10.1007/s00066-018-1341-z
Giordano FA, Veldwijk MR, Herskind C, Wenz F. Radiotherapy, tumor mutational burden, and immune checkpoint inhibitors: time to do the math. Strahlenther Onkol. 2018 Oct;194(10):873-875. doi: 10.1007/s00066-018-1341-z. Epub 2018 Jul 20. PMID: 30030581.
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ASO Author Reflections: Which Is a Better Way to Describe Tumor Size of Lung Cancer: Tumor Volume or Diameter?.

Annals of surgical oncology

Zhang X, Su XD.
PMID: 31620940
Ann Surg Oncol. 2019 Dec;26:790-791. doi: 10.1245/s10434-019-07938-z. Epub 2019 Oct 16.

No abstract available.

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Zhang X, Su XD. ASO Author Reflections: Which Is a Better Way to Describe Tumor Size of Lung Cancer: Tumor Volume or Diameter?. Ann Surg Oncol. 2019;26:790-791doi: 10.1245/s10434-019-07938-z.
Zhang, X., & Su, X. D. (2019). ASO Author Reflections: Which Is a Better Way to Describe Tumor Size of Lung Cancer: Tumor Volume or Diameter?. Annals of surgical oncology, 26790-791. https://doi.org/10.1245/s10434-019-07938-z
Zhang, Xu, and Su, Xiao-Dong. "ASO Author Reflections: Which Is a Better Way to Describe Tumor Size of Lung Cancer: Tumor Volume or Diameter?." Annals of surgical oncology vol. 26 (2019): 790-791. doi: https://doi.org/10.1245/s10434-019-07938-z
Zhang X, Su XD. ASO Author Reflections: Which Is a Better Way to Describe Tumor Size of Lung Cancer: Tumor Volume or Diameter?. Ann Surg Oncol. 2019 Dec;26:790-791. doi: 10.1245/s10434-019-07938-z. Epub 2019 Oct 16. PMID: 31620940.
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Modelling treatment-response rates.

Nature biomedical engineering

Grassberger C, Ngwa W.
PMID: 33864036
Nat Biomed Eng. 2021 Apr;5(4):295-296. doi: 10.1038/s41551-021-00717-w.

No abstract available.

Cite
Grassberger C, Ngwa W. Modelling treatment-response rates. Nat Biomed Eng. 2021;5(4):295-296doi: 10.1038/s41551-021-00717-w.
Grassberger, C., & Ngwa, W. (2021). Modelling treatment-response rates. Nature biomedical engineering, 5(4), 295-296. https://doi.org/10.1038/s41551-021-00717-w
Grassberger, Clemens, and Ngwa, Wilfred. "Modelling treatment-response rates." Nature biomedical engineering vol. 5,4 (2021): 295-296. doi: https://doi.org/10.1038/s41551-021-00717-w
Grassberger C, Ngwa W. Modelling treatment-response rates. Nat Biomed Eng. 2021 Apr;5(4):295-296. doi: 10.1038/s41551-021-00717-w. PMID: 33864036.
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Assessing the Optimal Cutpoint for Tumor Size in Patients with Lung Cancer Based on Linear Rank Statistics in a Competing Risks Framework.

Yonsei medical journal

Kim J, Ng HKT, Kim SW.
PMID: 31124334
Yonsei Med J. 2019 Jun;60(6):517-524. doi: 10.3349/ymj.2019.60.6.517.

PURPOSE: In clinical studies, patients may experience several types of events during follow up under the competing risks (CR) framework. Patients are often classified into low- and high-risk groups based on prognostic factors. We propose a method to determine...

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Kim J, Ng HKT, Kim SW. Assessing the Optimal Cutpoint for Tumor Size in Patients with Lung Cancer Based on Linear Rank Statistics in a Competing Risks Framework. Yonsei Med J. 2019;60(6):517-524doi: 10.3349/ymj.2019.60.6.517.
Kim, J., Ng, H. K. T., & Kim, S. W. (2019). Assessing the Optimal Cutpoint for Tumor Size in Patients with Lung Cancer Based on Linear Rank Statistics in a Competing Risks Framework. Yonsei medical journal, 60(6), 517-524. https://doi.org/10.3349/ymj.2019.60.6.517
Kim, Jinheum, et al. "Assessing the Optimal Cutpoint for Tumor Size in Patients with Lung Cancer Based on Linear Rank Statistics in a Competing Risks Framework." Yonsei medical journal vol. 60,6 (2019): 517-524. doi: https://doi.org/10.3349/ymj.2019.60.6.517
Kim J, Ng HKT, Kim SW. Assessing the Optimal Cutpoint for Tumor Size in Patients with Lung Cancer Based on Linear Rank Statistics in a Competing Risks Framework. Yonsei Med J. 2019 Jun;60(6):517-524. doi: 10.3349/ymj.2019.60.6.517. PMID: 31124334; PMCID: PMC6536389.
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Showing 1 to 12 of 15 entries