Personalization of breast cancer chemotherapy using noninvasive imaging methods to detect tumor cell death responses
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Résumé
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. Liver Int.27(6),854–864 (2007).Crossref, Medline, Google Scholar4 Oelze ML, O'Brien WD, Zachary JF. Quantitative ultrasound assessment of breast cancer using a multiparameter approach. IEEE Ultrason. S. Proc.981–984 (2007).Google Scholar5 Czarnota GJ, Kolios MC, Abraham J et al. Ultrasound imaging of apoptosis: high-resolution non-invasive monitoring of programmed cell death in vitro, in situ and in vivo. Br. J. Cancer81(3),520–527 (1999).Crossref, Medline, CAS, Google Scholar6 Czarnota GJ, Karshafian R, Burns PN et al. Tumour radiation response enhancement by acoustical stimulation of the vasculature. Proc. Natl Acad. Sci. USA109(30),E2033–E2041 (2012).Crossref, Medline, CAS, Google Scholar7 Tunis AS, Czarnota GJ, Giles A, Sherar MD, Hunt JW, Kolios MC. Monitoring structural changes in cells with high-frequency ultrasound signal statistics. Ultrasound Med. Biol.31(8),1041–1049 (2005).Crossref, Medline, CAS, Google Scholar8 Vlad RM, Alajez NM, Giles A, Kolios MC, Czarnota GJ. Quantitative ultrasound characterization of cancer radiotherapy effects in vitro. Int. J. Radiation Oncology Biol. Phys.72(4),1236–1243 (2008).Crossref, Medline, Google Scholar9 Yao LX, Zagzebski JA, Madsen EL. Backscatter coefficient measurements using a reference phantom to extract depth-dependent instrumentation factors. Ultrason. Imaging12(1),58–70 (1990).Crossref, Medline, CAS, Google Scholar10 Lizzi FL, Ostromogilsky M, Feleppa EJ, Rorke MC, Yaremko MM. Relationship of ultrasonic spectral parameters to features of tissue microstructure. IEEE Trans. Ultrason. Ferroelectr. Freq. Control34(3),319–329 (1987).Crossref, Medline, CAS, Google Scholar11 Insana MF, Hall TJ. Parametric ultrasound imaging from backscatter coefficient measurements: image formation and interpretation. Ultrason. Imaging12(4),245–267 (1990).Crossref, Medline, CAS, Google Scholar12 Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. Syst.3(6),610–621 (1973).Crossref, Google Scholar13 Sadeghi-Naini A, Falou O, Tadayyon H et al. Conventional frequency ultrasonic biomarkers of cancer treatment response in vivo. Transl. Oncol.6(3),234–243 (2013).Crossref, Medline, Google Scholar14 Tadayyon H, Sannachi L, Sadeghi-Naini A, Falou O, Oelze ML, Czarnota GJ. Quantitative ultrasound monitoring of breast cancer cell death in vivo using tissue-scattering models-preclinical study. Presented at: Ultrasonic Imaging and Tissue Characterization. Rosslyn, VA, USA, 10–12 June 2013.Google Scholar15 Sadeghi-Naini A, Papanicolau N, Falou O et al. Low-frequency quantitative ultrasound imaging of cell death in vivo. Med. Phys.40(8),082901 (2013).Crossref, Medline, Google Scholar16 Sannachi L, Tadayyon H, Sadeghi-Naini A et al. Evaluation of tumour cell death response in locally-advanced breast cancer patients to chemotherapy treatment by scattering property estimates using ultrasound backscatter. 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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».