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/.

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[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, StephanieHarmon, Tyler Bradshaw, Jens Eickhoff, Scott Perlman, Glenn Liu, and RobertJeraj. “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.