Hormesis Podcast

Andrea, Sean, Nick, and Alison presented an unofficial episode of the Hormesis Podcast at AAPM/COMP 2020. You can access it at https://www.aapm.org/students/. Please share with any lost undergraduates that you know!

Andrea and Nick discuss the new ABR MOC format - the ABR OLA

ABR OLA Website: https://ola.theabr.org/

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.13136

Mackie, 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.13135

Mayo, 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.13114

McNutt, 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.12817

El 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.12811

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1). https://doi.org/10.1186/2047-2501-2-3

Spector-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.12707

Traverso, 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.12879

Vikram, 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.13109

Zou, 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

In this episode, Sean and Nick discuss IT in medicine, and are joined by Andrea and Alison for their experiences.

References:

Siochi, R. A., Balter, P., Bloch, C. D., Bushe, H. S., Mayo, C. S., Curran, B. H., ... & Stern, R. L. (2009). Information technology resource management in radiation oncology. Journal of applied clinical medical physics, 10(4), 16-35. https://doi.org/10.1120/jacmp.v10i4.3116

NEMA DICOM Standard: http://dicom.nema.org/medical/dicom/current/output/html/part01.html

Crash Course Computer Science: https://www.youtube.com/playlist?list=PLME-KWdxI8dcaHSzzRsNuOLXtM2Ep_C7a

Andrea and Sean discuss their experiences with ACR Site Reviews, the benefits of accreditation, and the role peer review plays in radiation oncology physics.

To learn more about ACR Accreditation, please visit https://www.acr.org/Clinical-Resources/Accreditation. For Radiation Oncology accreditation specifics, visit https://www.acraccreditation.org/Modalities/Radiation-Oncology-Practice. To be considered for as an ACR Site Surveyor, you can email [email protected]. 

For information on APEx Accreditation, please visit https://www.astro.org/Daily-Practice/Accreditation. To learn the requirements to become an APEx surveyor, visit https://www.astro.org/Daily-Practice/Accreditation/APEx-Surveyor. 

Please note that as of 11/26/2019, both accrediting bodies are not accepting new site surveyors. However, there may be further need in the future.

Welcome to the second Micro Dose or mini episode from the Hormesis Podcast! These mini episodes will be based on personal experiences rather than our usually researched full episodes and will feature only two of the four podcasters.

In this Micro Dose, join Sean and Alison in discuss his experience with the ABR part 3 exam. Have your own experiences that you want to share or more questions that you want answered? Join us at https://www.reddit.com/r/HormesisPodcast/comments/doqglb/micro_dose_%CE%B2_abr_part_3/

In this episode of the Hormesis Podcast, co-hosts Alison and Andrea discuss the Radium Girls by Kate Moore. For more listening enjoyment, focused on the history and social implications of the story, check out the podcast episode by Stuff You Missed in History Class.

Let us know what you think of the Radium Girls, the impact that they have had on our field, and your thoughts about this episode at https://www.reddit.com/r/HormesisPodcast/comments/d8ajde/episode_5_true_grit_a_recounting_of_the_radium/.

Join us in debriefing following the AAPM Annual Meeting 2019 at www.reddit.com/r/hormesispodcast/

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.

In this Episode, Andrea and Sean discuss the report from AAPM TG-218, “Tolerance limits and methodologies for IMRT measurement-based verification QA.” They review some of the TG Report, the report recommendations, and the background of IMRT QA. The hosts share their thoughts and experiences on the IMRT QA process and its impact on clinical outcomes.

Have any questions or comments? Talk to us at https://www.reddit.com/r/HormesisPodcast/comments/cgsys3/episode_3_the_definition_of_insanityand_tg218/

Miften et al. “Tolerance limits and methodologies for IMRT measurement-based verification QA: Recommendations of AAPM TG-218.” Med Phys. 45(4). 2018. [DOI: 10.1002/mp.12810]

Low et al. “Dosimetry tools and techniques for IMRT.” Med Phys. 38(3). 2011. [DOI: 10.1118/1.3514120]

Ezzel et al. “IMRT commissioning: Multiple institution planning and dosimetry comparison, a report of AAPM TG-119.” Med Phys. 36(11). 2009. [DOI: 10.1118/1.3238104]

Nelms et al. “Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors.” Med Phys. 38(2). 2011. [DOI: 10.1118/1.3544657]