{"id":281647,"date":"2020-11-05T15:17:09","date_gmt":"2020-11-05T20:17:09","guid":{"rendered":"https:\/\/yfiledev.uit.yorku.ca\/?p=281647"},"modified":"2025-04-03T11:14:10","modified_gmt":"2025-04-03T15:14:10","slug":"phd-student-develops-tool-for-improved-symptom-management-in-oncology-patients","status":"publish","type":"post","link":"https:\/\/www.yorku.ca\/yfile\/2020\/11\/05\/phd-student-develops-tool-for-improved-symptom-management-in-oncology-patients\/","title":{"rendered":"PhD student develops tool for improved symptom management in oncology patients"},"content":{"rendered":"

New research from York University represents a remarkable step forward in personalized breast cancer treatment. Lassonde School of Engineering PhD student Khadijeh Saednia<\/strong>, in collaboration with Sunnybrook Health Sciences Centre, the University of Toronto and others, investigated a novel application of machine learning to detect skin toxicity (or damage) from breast radiotherapy much earlier than was previously possible.<\/p>\n

This study proved the feasibility of artificial intelligence (AI)-assisted image-guided approaches \u2013 specifically, Quantitative Thermal Imaging (QTI) \u2013 as a new clinical decision support tool for symptom management in the breast radiation oncology clinic.<\/p>\n

This was possible through earlier detection using machine learning methodologies: \u201cPatients undergoing radiation therapy, or RT, would benefit from earlier detection of skin damage or toxicity because symptom management could be introduced sooner than is the existing practice. These individuals could experience an improved quality of life during and beyond treatment,\u201d Saednia emphasizes.<\/p>\n

\"A

A significant patient population could benefit from early detection and early intervention for symptom management<\/p><\/div>\n

This original research was supervised by Lassonde Professor Ali Sadeghi-Naini<\/strong>, York Research Chair in Quantitative Imaging and Smart Biomarkers, and Sunnybrook Scientist Dr. William Tran, funded by the Terry Fox Foundation and published in the International Journal of Radiation Oncology*Biology*Physics<\/em><\/a> (2020).<\/p>\n

Radiation therapy, a key part of post-operative management, often has side effects on the skin<\/h3>\n

This research fills an important void. Breast cancer is the most common cancer among Canadian women (excluding non-melanoma skin cancers). It is the second leading cause of death from cancer in Canadian women. It is estimated that, in 2020, 27,400 women will be diagnosed with breast cancer \u2013 that\u2019s 25 per cent of all new cancer cases in women in 2020. (Canadian Cancer Society)<\/p>\n

RT, which uses ionizing radiation to target residual cancer cells of the breast, is a crucial component in the postoperative management of breast cancer. But the side effects from this treatment may affect patients\u2019 quality of life since the skin is susceptible to radiation damage and toxicity. This can mean pain and discomfort for these patients. That\u2019s one of the reasons why patients undergoing RT are carefully monitored.<\/p>\n

\"From

From the left: Ali Sadeghi-Naini and Khadijeh Saednia<\/p><\/div>\n

Saednia, who specializes in AI and machine learning for cancer management, turned her attention to one common side effect of RT in breast cancer patients: dermatitis. She suspected that thermal imaging in conjunction with machine learning could help because it could detect the damage earlier than previously possible.<\/p>\n

She explains how this would work: \u201cPhysiological changes associated with radiation-induced dermatitis, such as inflammation, may also increase body-surface temperature, which can be detected by thermal imaging.\u201d Quantitative imaging techniques coupled with machine learning can potentially be adapted to detect such alterations earlier after the start of RT.<\/p>\n

So, she investigated the use of QTI biomarkers and machine learning for early detection of radiation-induced skin toxicity in breast cancer.<\/p>\n

Ninety patients recruited from Sunnybrook<\/h3>\n
\"Saednia\u2019s

Saednia\u2019s study took place at the Odette Cancer Centre at Sunnybrook Health Sciences Centre, Toronto. Image reproduced with permission of Sunnybrook.<\/p><\/div>\n

The research team recruited 90 patients who were being treated for RT. The study took place in the Department of Radiation Oncology at the Odette Cancer Centre at Sunnybrook Health Sciences Centre in Toronto.<\/p>\n

Thermal images of the treated areas of these patients were acquired at various intervals: before RT, then weekly. Parametric thermograms, which measure heat, were applied and their findings analyzed. The thermograms were used to derive quantitative thermal-based features that included surface temperature and texture parameters. Skin toxicity or damage was evaluated at the end of RT using the Common Terminology Criteria for Adverse Events guidelines.<\/p>\n

\"<Caption>

This diagram illustrates how the QTI measured the heat and detected the damage or toxicity resulting from the RT at various different points in time. (\u201cFraction\u201d refers to the session.)<\/p><\/div>\n

Results: Researchers were able to predict skin toxicity much earlier<\/h3>\n

Thirty-seven patients, of the 90 in the study, exhibited adverse skin effects, and had significantly higher local increases in skin temperature, reaching above 36 C.<\/p>\n

The timing<\/em> of this key finding is what\u2019s important; the researchers\u2019 ability to measure skin toxicity earlier than previously possible is key. Skin toxicity is typically observed after the 10th RT session (or fraction) or after 10 or 14 days of initiating RT. Instead, Saednia and her team, using QTI with machine learning, obtained this information at the fifth RT session, demonstrating early prediction capabilities to severe skin toxicity.<\/p>\n

\u201cMachine learning models demonstrated early thermal signals associated with skin-toxicity after the fifth radiotherapy fraction with high prediction accuracy,\u201d she explains.<\/p>\n

\u201cOur study concluded that QTI can be used to detect changes associated with radiation-induced dermatitis and can be integrated with machine learning frameworks to develop a predictive tool for skin-toxicity assessment at early treatment times,\u201d adds Sadeghi-Naini.<\/p>\n

Saednia is confident that a significant patient population would potentially benefit from early detection and early intervention for symptom management. She emphasizes the direct application of this research: \u201cWe propose that \u2018smart\u2019 QTI be used as a clinical tool in radiation oncology.\u201d<\/p>\n

To read the article, \u201cQuantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity from Breast Radiotherapy Using Supervised Machine Learning,\u201d visit the publisher\u2019s website<\/a>. To learn more about Ali Sadeghi-Naini, visit his Faculty profile page.<\/a><\/p>\n

To learn more about Research & Innovation at York, follow us at @YUResearch<\/a>; watch our new video<\/a>, which profiles current research strengths and areas of opportunity, such as Artificial Intelligence and Indigenous futurities; and see the snapshot infographic,<\/u> a glimpse of the year\u2019s successes.<\/p>\n

By Megan Mueller, senior manager, Research Communications, Office of the Vice-President Research & Innovation, York University, muellerm@yorku.ca<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Collaborating with a team from Sunnybrook and U of T, a grad student in the Lassonde School of Engineering leverages artificial intelligence - machine learning - to develop a new tool for symptom management in the breast radiation oncology clinic. It could have wide application in cancer treatment.<\/p>\n","protected":false},"author":5,"featured_media":281686,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","footnotes":""},"categories":[1],"tags":[],"yfileauthor":[204],"qualifier":[],"yfile-author":[],"tags-to-show":[173],"workflow":[],"class_list":["post-281647","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","yfileauthor-yfilestaff","tags-to-show-lassonde"],"acf":{"internal_publish_date":null,"original_image":null},"yoast_head":"\nPhD student develops tool for improved symptom management in oncology patients - YFile<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.yorku.ca\/yfile\/2020\/11\/05\/phd-student-develops-tool-for-improved-symptom-management-in-oncology-patients\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PhD student develops tool for improved symptom management in oncology patients - YFile\" \/>\n<meta property=\"og:description\" content=\"Collaborating with a team from Sunnybrook and U of T, a grad student in the Lassonde School of Engineering leverages artificial intelligence - machine learning - to develop a new tool for symptom management in the breast radiation oncology clinic. 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