東華大學圖書館 |

Advancing Machine Learning for Precision Medicine and AI-Driven Clinical Decision Support for Bladder Cancer Treatment Response Assessment and Survival Prediction /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancing Machine Learning for Precision Medicine and AI-Driven Clinical Decision Support for Bladder Cancer Treatment Response Assessment and Survival Prediction // Di Sun.
Author:	Sun, Di,
Description:	1 electronic resource (168 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
Contained By:	Dissertations Abstracts International87-02B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32271917
ISBN:	9798291567494

Advancing Machine Learning for Precision Medicine and AI-Driven Clinical Decision Support for Bladder Cancer Treatment Response Assessment and Survival Prediction /
Sun, Di,

Advancing Machine Learning for Precision Medicine and AI-Driven Clinical Decision Support for Bladder Cancer Treatment Response Assessment and Survival Prediction /Di Sun. - 1 electronic resource (168 pages)

Source: Dissertations Abstracts International, Volume: 87-02, Section: B.

Bladder cancer is a common and clinically challenging malignancy with a substantial impact on global health, making it crucial to develop advanced methodologies for diagnosis, prognosis, and treatment planning. This dissertation explores the application of advanced machine learning (ML) and artificial intelligence (AI) techniques to improve clinical decision-making, enhance assessment of treatment response, and predict patient outcomes for bladder cancer. The research developed and evaluated several AI-driven models, focusing on integrating multi-modal data sources, including imaging, clinical, and genomic information, to build predictive tools that can guide personalized treatment strategies.A key contribution of this work was the development of a deep-learning convolutional neural network model combined with radiomics features from CT urography, demonstrating the potential of AI in assessing bladder cancer treatment response to neoadjuvant chemotherapy. This model achieved an area under the receiver operating characteristic curve (AUC) of 0.77-0.80, highlighting its feasibility and potential for clinical implementation.Additionally, the dissertation introduced a multi-modal model (termed CRD) for five-year survival prediction of bladder cancer patients post-cystectomy. This model, which integrated clinical, radiomics, and deep-learning features, achieved an AUC of 0.87, demonstrating the importance of combining diverse data sources for more accurate survival predictions. An enhanced CRD model was proposed by incorporating large language models (LLMs) into the modeling to improve efficiency by automating clinical information extraction from unstructured electronic medical records. The results showed the LLM-assisted model achieved survival prediction AUCs comparable to those based on manually curated data, emphasizing the potential of LLMs to automate clinical data extraction. LLM reliability and its impact on downstream prediction model were further investigated. The results showed GPT-4 and Llama held outstanding consistency in information retrieval for predictive modeling.Furthermore, an observer study was conducted and its result demonstrated that AI-based decision support systems (CDSS-T) significantly improved diagnostic accuracy of 17 clinicians when assessing bladder cancer treatment response. The use of AI tools to assist in clinical decision-making resulted in a notable increase in diagnostic performance, with AUC improving from 0.73 to 0.77 (p = 0.002). These findings highlight the practical benefits of integrating AI into clinical practice, suggesting that AI can complement and enhance clinician expertise, particularly in complex diagnostic scenarios.The dissertation also explored methods for confidence estimation of AI outputs and examined the influence of case complexity and physician characteristics on AI-assisted assessments. This is crucial for providing the user with an estimate of the trustworthiness of AI output, allowing the user to properly weight the AI recommendation with their own assessment, thus maximizing the benefits of AI-assisted decision-making.In conclusion, this work advances the integration of AI and ML in bladder cancer prognosis and treatment response assessment. By developing robust predictive models, evaluating AI in a pre-clinical multi-institutional observer study, and investigating the reliability and trustworthiness of ML models, this research underscores the transformative potential of AI in improving patient care. Future research will focus on integrating multi-institutional data to enhance model generalizability, developing longitudinal models to track disease progression, and conducting prospective study of the impact of AI tools in clinical settings. Ethical considerations and human-AI collaboration will be essential to ensure AI supports, rather than replaces, clinician judgment. This dissertation contributes to integrating AI into clinical workflows for more personalized, accurate, and timely interventions to improve outcomes for bladder cancer patients worldwide.

English

ISBN: 9798291567494Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Bladder cancer

Advancing Machine Learning for Precision Medicine and AI-Driven Clinical Decision Support for Bladder Cancer Treatment Response Assessment and Survival Prediction /
LDR:05789nmm a22004693i 4500 001 2438023
005 20251121100757.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798291567494
035 $a (MiAaPQD)AAI32271917
035 $a (MiAaPQD)umichrackham006268
035 $a AAI32271917
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Sun, Di, $e author. $3 3829200
245 1 0 $a Advancing Machine Learning for Precision Medicine and AI-Driven Clinical Decision Support for Bladder Cancer Treatment Response Assessment and Survival Prediction / $c Di Sun.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (168 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
500 $a Advisors: Hadjiyski, Lubomir M.; Matuszak, Martha M. Committee members: Shen, Liyue; Chan, Heang-Ping; Palmbos, Phillip L.; Pozzi, Sara A.
502 $b Ph.D. $c University of Michigan $d 2025.
520 $a Bladder cancer is a common and clinically challenging malignancy with a substantial impact on global health, making it crucial to develop advanced methodologies for diagnosis, prognosis, and treatment planning. This dissertation explores the application of advanced machine learning (ML) and artificial intelligence (AI) techniques to improve clinical decision-making, enhance assessment of treatment response, and predict patient outcomes for bladder cancer. The research developed and evaluated several AI-driven models, focusing on integrating multi-modal data sources, including imaging, clinical, and genomic information, to build predictive tools that can guide personalized treatment strategies.A key contribution of this work was the development of a deep-learning convolutional neural network model combined with radiomics features from CT urography, demonstrating the potential of AI in assessing bladder cancer treatment response to neoadjuvant chemotherapy. This model achieved an area under the receiver operating characteristic curve (AUC) of 0.77-0.80, highlighting its feasibility and potential for clinical implementation.Additionally, the dissertation introduced a multi-modal model (termed CRD) for five-year survival prediction of bladder cancer patients post-cystectomy. This model, which integrated clinical, radiomics, and deep-learning features, achieved an AUC of 0.87, demonstrating the importance of combining diverse data sources for more accurate survival predictions. An enhanced CRD model was proposed by incorporating large language models (LLMs) into the modeling to improve efficiency by automating clinical information extraction from unstructured electronic medical records. The results showed the LLM-assisted model achieved survival prediction AUCs comparable to those based on manually curated data, emphasizing the potential of LLMs to automate clinical data extraction. LLM reliability and its impact on downstream prediction model were further investigated. The results showed GPT-4 and Llama held outstanding consistency in information retrieval for predictive modeling.Furthermore, an observer study was conducted and its result demonstrated that AI-based decision support systems (CDSS-T) significantly improved diagnostic accuracy of 17 clinicians when assessing bladder cancer treatment response. The use of AI tools to assist in clinical decision-making resulted in a notable increase in diagnostic performance, with AUC improving from 0.73 to 0.77 (p = 0.002). These findings highlight the practical benefits of integrating AI into clinical practice, suggesting that AI can complement and enhance clinician expertise, particularly in complex diagnostic scenarios.The dissertation also explored methods for confidence estimation of AI outputs and examined the influence of case complexity and physician characteristics on AI-assisted assessments. This is crucial for providing the user with an estimate of the trustworthiness of AI output, allowing the user to properly weight the AI recommendation with their own assessment, thus maximizing the benefits of AI-assisted decision-making.In conclusion, this work advances the integration of AI and ML in bladder cancer prognosis and treatment response assessment. By developing robust predictive models, evaluating AI in a pre-clinical multi-institutional observer study, and investigating the reliability and trustworthiness of ML models, this research underscores the transformative potential of AI in improving patient care. Future research will focus on integrating multi-institutional data to enhance model generalizability, developing longitudinal models to track disease progression, and conducting prospective study of the impact of AI tools in clinical settings. Ethical considerations and human-AI collaboration will be essential to ensure AI supports, rather than replaces, clinician judgment. This dissertation contributes to integrating AI into clinical workflows for more personalized, accurate, and timely interventions to improve outcomes for bladder cancer patients worldwide.
546 $a English
590 $a School code: 0127
650 4 $a Computer science. $3 523869
650 4 $a Medical imaging. $3 3172799
650 4 $a Oncology. $3 751006
650 4 $a Biomedical engineering. $3 535387
653 $a Bladder cancer
653 $a Radiology
653 $a Computer vision
653 $a Artificial intelligence
653 $a Large language models
690 $a 0574
690 $a 0984
690 $a 0992
690 $a 0541
710 2 $a University of Michigan. $b Nuclear Engineering & Radiological Sciences. $e degree granting institution. $3 3821726
720 1 $a Hadjiyski, Lubomir M. $e degree supervisor.
720 1 $a Matuszak, Martha M. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 87-02B.
790 $a 0127
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32271917