[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124632-en":3,"doc-seo-124632-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124632,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning and Quantitative Imaging for the Management of Brain Metastasis - Dissertation","A dissertation addresses the clinical burden of brain metastasis, where median survival drops sharply after intracranial spread and effective treatment depends on heterogeneous tumor response. The work targets two bottlenecks: stratifying patients by predicted therapy response and monitoring response with time-sensitive, longitudinal MRI follow-ups requiring labor-intensive tumor delineation. It introduces an attention-guided serial-MRI segmentation framework achieving strong Dice performance, an automated radiotherapy response assessment system aligned with expert clinicians, and machine-learning radiomics/deep models that predict outcomes before or early after treatment.","Machine Learning and Quantitative Imaging for the Management of Brain Metastasis  \nSeyed Ali Jalalifar  \nA Dissertation Submitted to  \nthe Faculty of Graduate Studies  \nin Partial Fulfillment of the Requirements  \nfor the Degree of  \nDoctor of Philosophy in Electrical Engineering and Computer Science  \nGraduate Program in  \nElectrical Engineering and Computer Science York University  \nToronto, Ontario  \nDecember 2022  \nAbstract  \nSignificantly affecting patients’ clinical course and quality of life, a growing number of cancer cases are diagnosed with brain metastasis annually. Although a considerable percentage ofcancer patients survive for several years if the disease is discovered at an early stage while it is still localized, when the tumour is metastasized to the brain, the median survival decreases considerably. Early detection followed by precise and effective treatment of brain metastasis may lead to improved patient survival and quality of life. A main challenge to prescribe an effective treatment regimen is the variability of tumour response to treatments, e.g., radiotherapy as a main treatment option for brain metastasis, despite similar cancer therapy, due to many patient-related factors. Stratifying patients based on their predicted response and consequently assessing their response to therapy are challenging yet crucial tasks. While risk assessment models with standard clinical attributes have been proposed for patient stratification, the imaging data acquired for these patients as a part of the standard-of-care are not computationally analyzed or directly incorporated in these models. Further, therapy response monitoring and assessment is a cumbersome task for patients with brain metastasis that requires longitudinal tumour delineation on MRI volumes before and at multiple follow-up sessions after treatment. This is aggravated by the time-sensitive nature of the disease. In an effort to address these challenges, a number of machine learning frameworks and computational techniques in areas of automatic tumour segmentation, radiotherapy outcome assessment, and therapy outcome prediction have been introduced and investigated in this dissertation. Powered by advanced machine learning algorithms, a complex attention-guided segmentation framework is introduced and investigated for segmenting brain tumours on serial MRI. The experimental results demonstrate that the proposed framework can achieve a dice score of 91.5% and 84.1% to 87.4% on the baseline and follow-up scans, respectively. This framework is then applied in a proposed system that follows  \nstandard clinical criteria based on changes in tumour size at post-treatment to assess tumour response to radiotherapy automatically. The system demonstrates a very good agreement with expert clinicians in detecting local response, with an accuracy of over 90% . Next, innovative machine-learning-based solutions are proposed and investigated for radiotherapy outcome prediction before or early after therapy, using MRI radiomic models and novel deep learning architectures that analyze treatment-planning MRI with and without standard clinical attributes. The developed models demonstrate an accuracy of up to 82.5% in predicting radiotherapy outcome before the treatment initiation. The ground-breaking machine learning platforms presented in this dissertation along with the promising results obtained in the conducted experiments are steps forward towards realizing important decision support tools for oncologistsand radiologists and, can eventually, pave the way towards the personalized therapeutics paradigm for cancer patients.  \nOriginality Statement  \nThis dissertation has been written by Seyed Ali Jalalifar under the supervision of Dr. Ali SadeghiNaini. Parts of the material presented in this thesis have been published in peer-reviewed journals or conference proceedings, as listed below. The research presented in each publication has been performed by the principal author, su","cbCaiuzgmeisIUSW","https://ap.wps.com/l/cbCaiuzgmeisIUSW","pdf",3553333,1,166,"English","en",105,"# Abstract\n# Originality Statement\n## Published Work (Chapters)","[{\"question\":\"What problem does the dissertation focus on in brain metastasis care?\",\"answer\":\"It focuses on improving early detection, precise treatment, and decision support by handling variability in tumor response and the difficulty of monitoring therapy using longitudinal MRI.\"},{\"question\":\"How does the dissertation approach tumor segmentation?\",\"answer\":\"It proposes an attention-guided machine learning framework for segmenting brain tumors on serial MRI, reporting Dice scores of 91.5% on baseline and 84.1% to 87.4% on follow-up scans.\"},{\"question\":\"How is radiotherapy response and outcome assessed or predicted?\",\"answer\":\"It presents an automated system that applies standard clinical criteria based on post-treatment tumor size changes to detect local response. It also develops MRI radiomic and deep learning models for predicting radiotherapy outcome before or early after therapy.\"}]","Machine Learning and Quantitative Imaging for the Management of Brain Metastasis - Dissertation | PDF",1785893419,418,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-quantitative-imaging-for-the-management-of-brain-metastasis-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-quantitative-imaging-for-the-management-of-brain-metastasis-dissertation/124632/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the dissertation focus on in brain metastasis care?","Question",{"text":75,"@type":76},"It focuses on improving early detection, precise treatment, and decision support by handling variability in tumor response and the difficulty of monitoring therapy using longitudinal MRI.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation approach tumor segmentation?",{"text":80,"@type":76},"It proposes an attention-guided machine learning framework for segmenting brain tumors on serial MRI, reporting Dice scores of 91.5% on baseline and 84.1% to 87.4% on follow-up scans.",{"name":82,"@type":73,"acceptedAnswer":83},"How is radiotherapy response and outcome assessed or predicted?",{"text":84,"@type":76},"It presents an automated system that applies standard clinical criteria based on post-treatment tumor size changes to detect local response. 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