[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128787-en":3,"doc-seo-128787-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128787,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Cancer Recurrence and Survival Prediction and Evaluation using Machine Learning - Master of Science Thesis","Cancer remains the leading global cause of death, and accurately predicting individual progression is essential for personalized treatment planning. This thesis develops and evaluates PathCLR, a semi-supervised approach that uses prostate tissue images with clinicopathological features to predict five-year biochemical cancer recurrence after radical prostatectomy. It evaluates models on CPCTR and JHU, improving beyond clinicopathological-only baselines. It also studies survival prediction goals and links them to evaluation metrics using SEER data for nine solid tumor types.","Cancer Recurrence and Survival Prediction and Evaluation using  \nMachine Learning  \nby  \nMahtab Farrokh  \nA thesis submitted in partial fulfillment of the requirements for the degree of  \nMaster of Science  \nDepartment of Computing Science  \nUniversity of Alberta  \n© Mahtab Farrokh, 2024  \nAbstract  \nAs cancer is the leading global cause of death, an ongoing challenge is predicting an individual’s cancer progression accurately, to facilitate personalized treatment planning. Individuals diagnosed with cancer may succumb to the illness or face cancer recurrence post-treatment. The first part of this thesis focuses on predicting prostate cancer recurrence using tissue images. Roughly 30% of men with prostate cancer who undergo radical prostatectomy (RP) will suffer biochemical cancer recurrence (BCR) . Unfortunately, no current method can effectively predict which patients will experience BCR after RP. We develop and evaluate PathCLR, a novel semi-supervised method that learns a model that can use tissue images along with clinicopathological features to predict prostate cancer recurrence within five years after RP. We built and evaluated models using two prostate cancer datasets: CPCTR and JHU. PathCLR’s (10-fold cross-validation) F1 score was 0.61 for CPCTR and 0.85 for JHU, which were statistically superior to the best-learned model that relied solely on clinicopathological features. This finding suggests that there is essential predictive information in tissue images at the time of surgery that goes beyond the knowledge obtained from reported clinicopathological features, helping predict the patient’s five-year outcome.  \nThe second part of this dissertation focuses on effective survival prediction and evaluation for cancer patients. In the context of deploying individual survival prediction models, a pivotal question emerges: Are we striving to compare survival durations between patients (i.e.,‘Who survives longer between patients A and B?’) or are we endeavoring to estimate a specific patient’s survival time (i.e.,‘How long will patient A survive?’), among other scenarios. We address this fundamental inquiry and con-  \nduct a comprehensive evaluation of such predictive models. We consider 9 common solid tumors (breast, lung, prostate, etc.) using data from the Surveillance, Epidemiology, and End Results (SEER) Program. We consider several different possible goals of a survival prediction model and connect each goal to a specific evaluation metric. We propose modified versions of the Mean Absolute Error (MAE) measure tailored to address a query about a patient’s expected survival duration. Here, we trained multiple models (including both conventional and advanced machine learning models) on various cancer types and rigorously evaluated those models using the proposed metrics. We demonstrate that a model might be effective for one goal but ineffective for another, and show that we can determine this based on the measure used. Our findings underscore the importance of selecting the evaluation measure that is aligned with the primary objective of a study. This research sets a path for future research that seeks to further refine predictive models for oncological prognostication.  \nPreface  \nThis thesis is an original work by ‘Mahtab Farrokh’. Chapter 2 reproduces a paper titled “Learning to Predict Prostate Cancer Recurrence from Tissue Images” [1] accepted in the Journal of Pathology Informatics written jointly with Neeraj Kumar, Peter Gann, and Russell Greiner. My contributions were to design, develop, and evaluate the proposed method, and write the paper under the supervision of Russell Greiner and Peter Gann. This research was an international research collaboration with Professor Peter Gann from the Department of Pathology, College of Medicine, University of Illinois at Chicago, United States. Professor Gann provided access to the clinical dataset and shared his expertise in the clinical field.  \nChapter 3 reproduces a pape","cbCaijiT2tCABFth","https://ap.wps.com/l/cbCaijiT2tCABFth","pdf",4939231,1,83,"English","en",105,"# Abstract\n## Prostate cancer recurrence prediction\n## Survival prediction goals and evaluation\n# Preface\n## Chapter 2: Tissue image recurrence model\n## Chapter 3: Survival prediction framework\n# Acknowledgements","[{\"question\":\"What problem does the thesis address in predicting cancer outcomes?\",\"answer\":\"It addresses accurate prediction of cancer progression and post-treatment recurrence, and it also tackles effective survival prediction and evaluation for cancer patients.\"},{\"question\":\"How does PathCLR predict prostate cancer recurrence?\",\"answer\":\"PathCLR is a novel semi-supervised method that learns from tissue images together with clinicopathological features to predict biochemical cancer recurrence within five years after radical prostatectomy.\"},{\"question\":\"Why is evaluation metric selection important for survival prediction models?\",\"answer\":\"The thesis shows that a model can perform well for one survival-prediction goal but fail for another, and the correct evaluation measure is necessary to match the study’s primary objective.\"}]","Cancer Recurrence and Survival Prediction and Evaluation using Machine Learning - 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