{"id":2342,"date":"2024-02-25T16:11:50","date_gmt":"2024-02-25T16:11:50","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/mark-asselin\/"},"modified":"2024-02-25T16:11:50","modified_gmt":"2024-02-25T16:11:50","slug":"mark-asselin","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/mark-asselin\/","title":{"rendered":"Mark Asselin"},"content":{"rendered":"
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Mark Asselin<\/h1>\n<\/div>\n
Masters Student<\/div>\n
School of Computing<\/div>\n
Queen’s University<\/div>\n
Member from May 2017<\/em> to present<\/em><\/div>\n
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\n\tMark is a masters student in biomedical computing with previous experience in electrical engineering. Please see his website at https:\/\/markasselin.github.io\/<\/a> for more information about him.
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Kaufmann, Martin; Jamzad, Amoon; Ungi, Tamas; Rodgers, Jessica R; Koster, Teaghan; Yeung, Chris; Ehrlich, Josh; Santilli, Alice; Asselin, Mark; Janssen, Natasja; McMullen, Julie; Solberg, Kathryn; Cheesman, Joanna; Carlo, Alessia Di; Ren, Kevin Yi Mi; Varma, Sonal; Merchant, Shaila; Engel, Cecil Jay; Walker, G Ross; Gallo, Andrea; Jabs, Doris; Mousavi, Parvin; Fichtinger, Gabor; Rudan, John F<\/p>

Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery<\/a> Journal Article<\/span> <\/p>

In: <\/span>Cancer Research, <\/span>vol. 84, <\/span>iss. 9_Supplement, <\/span>pp. PO2-23-07-PO2-23-07, <\/span>2024<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{fichtinger2024c,
\r\ntitle = {Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery},
\r\nauthor = {Martin Kaufmann and Amoon Jamzad and Tamas Ungi and Jessica R Rodgers and Teaghan Koster and Chris Yeung and Josh Ehrlich and Alice Santilli and Mark Asselin and Natasja Janssen and Julie McMullen and Kathryn Solberg and Joanna Cheesman and Alessia Di Carlo and Kevin Yi Mi Ren and Sonal Varma and Shaila Merchant and Cecil Jay Engel and G Ross Walker and Andrea Gallo and Doris Jabs and Parvin Mousavi and Gabor Fichtinger and John F Rudan},
\r\nurl = {https:\/\/aacrjournals.org\/cancerres\/article\/84\/9_Supplement\/PO2-23-07\/743683},
\r\nyear  = {2024},
\r\ndate = {2024-01-01},
\r\njournal = {Cancer Research},
\r\nvolume = {84},
\r\nissue = {9_Supplement},
\r\npages = {PO2-23-07-PO2-23-07},
\r\npublisher = {The American Association for Cancer Research},
\r\nabstract = {Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional \u2026},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
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Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional \u2026<\/div>
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