Hormesis Podcast

Sean Tanny, Alison Roth, Andrea Herrick, & Nicholas Sperling

Discussions about medical physics with 4 medical physicists across a range of subjects. read less
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Episodes

Hormesis Podcast #7 - Big Data: Headbutt your way into medicine...
Feb 8 2020
Hormesis Podcast #7 - Big Data: Headbutt your way into medicine...
In this episode Sean and Nick lead our discussion of Big Data as it relates to Radiation Oncology medical physics. The discussion ranges from reasons to buy in, how we are starting to do it, and how we can proceed as a field in light of big data analytics.References:Matuszak, M. M., Fuller, C. D., Yock, T. I., Hess, C. B., McNutt, T., Jolly, S., Gabriel, P., Mayo, C. S., Thor, M., Caissie, A., Rao, A., Owen, D., Smith, W., Palta, J., Kapoor, R., Hayman, J., Waddle, M., Rosenstein, B., Miller, R., … Feng, M. (2018). Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology. Medical Physics, 45(10), e811–e819. https://doi.org/10.1002/mp.13136Mackie, T. R., Jackson, E. F., & Giger, M. (2018). Opportunities and challenges to utilization of quantitative imaging: Report of the AAPM practical big data workshop. Medical Physics, 45(10), e820–e828. https://doi.org/10.1002/mp.13135Mayo, C., Phillips, M., McNutt, T., Palta, J., Dekker, A., Miller, R., Xiao, Y., Moran, J., Matuszak, M., Gabriel, P., Ayan, A., Prisciandaro, J., Thor, M., Dixit, N., Popple, R., Killoran, J., Kaleba, E., Kantor, M., Ruan, D., … Lawrence, T. (2018). Treatment data and technical process challenges for practical big data efforts in radiation oncology. Medical Physics, 45(10), e793–e810. https://doi.org/10.1002/mp.13114McNutt, T. R., Bowers, M., Cheng, Z., Han, P., Hui, X., Moore, J., … Quon, H. (2018). Practical data collection and extraction for big data applications in radiotherapy. Medical Physics, 45(10), e863–e869. https://doi.org/10.1002/mp.12817El Naqa, I., Ruan, D., Valdes, G., Dekker, A., McNutt, T., Ge, Y., … Ten Haken, R. (2018). Machine learning and modeling: Data, validation, communication challenges. Medical Physics, 45(10), e834–e840. https://doi.org/10.1002/mp.12811Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1). https://doi.org/10.1186/2047-2501-2-3Spector-Bagdady, K., & Jagsi, R. (2018). Big data, ethics, and regulations: Implications for consent in the learning health system. Medical Physics, 45(10), e845–e847. https://doi.org/10.1002/mp.12707Traverso, A., van Soest, J., Wee, L., & Dekker, A. (2018). The radiation oncology ontology (ROO): Publishing linked data in radiation oncology using semantic web and ontology techniques. Medical Physics, 45(10), e854–e862. https://doi.org/10.1002/mp.12879Vikram, B. (2018). Perspectives on potential research benefits from big data efforts in Radiation Oncology. Medical Physics, 45(10), e848–e849. https://doi.org/10.1002/mp.13109Zou, W., Geng, H., Teo, B. K., Finlay, J., & Xiao, Y. (2018). NCTN clinical trial standardization for radiotherapy through IROC and CIRO. Medical Physics, 45(10), e850–e853. https://doi.org/10.1002/mp.12873
Hormesis Podcast #4 - Radiomics: How to (maybe) classify your future
Aug 20 2019
Hormesis Podcast #4 - Radiomics: How to (maybe) classify your future
Alison (radiomics skeptic) and Nick (radiomics hopeful) sit down to discuss the benefits, drawbacks, and potential of radiomics. A variety of papers were discussed and can be found below. We also briefly discussed (though we did try not to) deep learning and broader AI applications.Are you a radiomics optimist or pessimist? Tell us at https://www.reddit.com/r/HormesisPodcast/comments/ct6p1q/episode_4_radiomics_how_to_maybe_classify_your/.Listen and subscribe to our podcast at Apple Podcasts, Stitcher, Google Podcasts, or through the RSS Feed.References:[1] Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar, Sara Carvalho, Ruud G.P.M. van Stiphout, Patrick Granton, Catharina M.L. Zegers, Robert Gillies, Ronald Boellard, Andre ́ Dekker, and Hugo J.W.L. Aerts. “Radiomics: Extracting more information from medical images using advanced feature analysis.” European Journal of Cancer, vol. 48: 441-446. [DOI: 10.1016/j.ejca.2011.11.036].[2] Afsaneh Jalalian, Syamsiah Mashohor, Rozi Mahmud, Babak Karasfi, M. Iqbal B. Saripan, and Abdul Rahman B. Ramli. “Foundation and Methodologies in Computer-Aided Diagnosis Systems for Breast Cancer Diagnosis.” EXCLI Journal, vol. 16:113-137. [DOI: 10.17179/excli2016-701].[3] Virendra Kumar, Yuhua Gu, Satrajit Basu, Anders Berglund, Steven A. Eschrich, Matthew B. Schabath, Kenneth Forster, Hugo J.W.L. Aertsf, Andre Dekkerf, David Fenstermacher, Dmitry B. Goldgof, Lawrence O. Hall, Philippe Lambin, Yoganand Balagurunathan, Robert A. Gatenby, and Robert J. Gillies. “Radiomics: the process and the challenges.” Magnetic Resonance Imaging, vol. 30: 1234-1248. [DOI: 10.1016/j.mri.2012.06.010][4] Sunderland and Christian. “Quantitative PET/CT Scanner Performance Characterization Based Upon the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network Oncology Clinical Simulator Phantom.” Journal of Nuclear Medicine, vol. 56: 145-152. [DOI: 10.2967/jnumed.114.148056].[5] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. “Why Should I Trust You?: Explaining the Predictions of Any Classifier.” Association for Computing Machinery. [DOI: 10.1145/2939672.2939778].[6] Brijesh Verma, Peter McLeod, and Alan Klevansky. “Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer.” International Journal of Computer Applications, vol. 37: 3344-3351. [DOI: 10.1016/j.eswa.2009.10.016].[7] David L Raunig, Lisa M McShane, Gene Pennello, Constantine Gatsonis, Paul L Carson, James T Voyvodic, Richard L Wahl, Brenda F Kurland, Adam J Schwarz, Mithat Gönen, Gudrun Zahlmann, Marina Kondratovich, Kevin O'Donnell, Nicholas Petrick, Patricia E Cole, Brian Garra, Daniel C Sullivan and QIBA Technical Performance Working Group. “Quantitative Imaging Biomarkers: A Review of Statistical Methods for Technical Performance Assessment.” Stat Methods Med Res, vol. 0, 1-41. [DOI: 10.1177/0962280214537344].[8] Christie Lin, Stephanie Harmon, Tyler Bradshaw, Jens Eickhoff, Scott Perlman, Glenn Liu, and Robert Jeraj. “Response-to-repeatability of quantitative imaging features for longitudinal response assessment.” Physics in Medicine & Biology, 64. [DOI: 10.1088/1361-6560/aafa0a].[9] D. Karunanithi, Omar Alheyasat, Divya Thomas, and G. Kavitha. “Attacks on Artificial Intelligence Applications through Adversarial Image.” International Journal of Pure and Applied Mathematics, vol. 118: 4491-4495.