Personalization of breast cancer chemotherapy using noninvasive imaging methods to detect tumor cell death responses
Bibliographic record
Abstract
Breast Cancer ManagementVol. 3, No. 1 CommentaryPersonalization of breast cancer chemotherapy using noninvasive imaging methods to detect tumor cell death responsesLakshmanan Sannachi, Hadi Tadayyon, Ali Sadeghi-Naini, Michael C Kolios & Gregory CzarnotaLakshmanan SannachiDepartment of Radiation Oncology & Physical Sciences, Sunnybrook Health Sciences Centre & Sunnybrook Research Institute, Toronto, ON, CanadaDepartments of Radiation Oncology & Medical Biophysics, University of Toronto, Toronto, ON, Canada, Hadi TadayyonDepartment of Radiation Oncology & Physical Sciences, Sunnybrook Health Sciences Centre & Sunnybrook Research Institute, Toronto, ON, CanadaDepartments of Radiation Oncology & Medical Biophysics, University of Toronto, Toronto, ON, Canada, Ali Sadeghi-NainiDepartment of Radiation Oncology & Physical Sciences, Sunnybrook Health Sciences Centre & Sunnybrook Research Institute, Toronto, ON, CanadaDepartments of Radiation Oncology & Medical Biophysics, University of Toronto, Toronto, ON, Canada, Michael C KoliosDepartment of Physics, Ryerson University, Toronto, ON, Canada & Gregory Czarnota* Author for correspondenceDepartment of Radiation Oncology & Physical Sciences, Sunnybrook Health Sciences Centre & Sunnybrook Research Institute, Toronto, ON, Canada. Departments of Radiation Oncology & Medical Biophysics, University of Toronto, Toronto, ON, CanadaPublished Online:11 Dec 2013https://doi.org/10.2217/bmt.13.58AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleReferences1 Feleppa EJ, Liu T, Kalisz A et al. Ultrasonic spectral-parameter imaging of the prostate. Int. J. Imag. Syst. Techn.8(1),11–25 (1997).Crossref, Google Scholar2 Yang M, Krueger TM, Miller JG, Holland MR. Characterization of anisotropic myocardial backscatter using spectral slope, intercept and midband fit parameters. Ultrason. Imaging29(2),122–134 (2007).Crossref, Medline, Google Scholar3 Guimond A, Teletin M, Garo E et al. Quantitative ultrasonic tissue characterization as a new tool for continuous monitoring of chronic liver remodelling in mice. 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POMA19,1–5 (2013).Google Scholar17 Sadeghi-Naini A, Falou O, Czarnota GJ. Quantitative ultrasound spectral parametric maps: early surrogates of cancer treatment response. Conf. Proc. IEEE Eng. Med. Biol. Soc.2012,2672–2675 (2012).Medline, Google Scholar18 Sadeghi-Naini A, Papanicolau N, Falou O et al. Quantitative ultrasound evaluation of tumour cell death response in locally advanced breast cancer patients receiving chemotherapy. Clin. Cancer Res.19(8),2163–2174 (2013).Crossref, Medline, CAS, Google Scholar19 Brindle K. New approaches f or imaging tumour responses to treatment. Nat. Rev. Cancer8(2),94–107 (2008).Crossref, Medline, CAS, Google ScholarFiguresReferencesRelatedDetailsCited ByTumor vascular conundrum: Hypoxia, ceramide, and biomechanical targeting of tumor vasculature17 March 2016Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast CancerIEEE Transactions on Medical Imaging, Vol. 35, No. 3 Vol. 3, No. 1 Follow us on social media for the latest updates Metrics Downloaded 37 times History Published online 11 December 2013 Published in print January 2014 Information© Future Medicine LtdAcknowledgementsThe authors wish to thank A Giles, A Al Mahrouki and A Worthington for many years of dedicated assistance with experiments.Financial & competing interests disclosureMC Kolios holds a Tier 2 Canada Research Chair in Biomedical Applications of Ultrasound. G Czarnota holds a Cancer Care Ontario Research Chair in Experimental Therapeutics and Imaging. The research here was supported by grants from the Natural Sciences and Engineering Council of Canada and the Canadian Institutes of Health Research to both G Czarnota and MC Kolios, and infrastructure grants from the Canadian Foundation of Innovation, Ontario Ministry of Research and Innovation and Ryerson University. MC Kolios and G Czarnota are authors on two issued patents 'Use of high frequency ultrasound imaging to detect and monitor the process of apoptosis in living tissues, ex vivo tissues and cell-culture' US patent #6511430 and 'Methods of monitoring cellular death using low frequency ultrasound' US patent #8192362 held by the Sunnybrook Health Sciences Centre (Toronto, ON, Canada). A Sadeghi-Naini holds a Banting Postdoctoral Fellowship, and held a Canadian Breast Cancer Foundation Postdoctoral Fellowship during the conduct of this research. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".