Applying Deep Learning to Derive Noninvasive Imaging Biomarkers for High-Risk Phenotypes of Prostate Cancer
Applying Deep Learning to Derive Noninvasive Imaging Biomarkers for High-Risk Phenotypes of Prostate Cancer
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151113
- ISBN
- 9798382321813
- DDC
- 610
- 서명/저자
- Applying Deep Learning to Derive Noninvasive Imaging Biomarkers for High-Risk Phenotypes of Prostate Cancer
- 발행사항
- [Sl] : Yale University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 98 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Aneja, Sanjay.
- 학위논문주기
- Thesis (D.Med.)--Yale University, 2024.
- 초록/해제
- 요약Novel biomarkers can help guide management of Prostate Cancer (PCa) through the identification of high-risk phenotypes among similar patients in traditional National Comprehensive Cancer Network (NCCN) risk groups. We hypothesized that deep learning (DL) models which identified Extraprostatic Extension (EPE) and Seminal Vesicle Invasion (SVI), both pathologies associated with treatment failure, on Magnetic Resonance Imaging (MRI) could provide imaging biomarkers of PCa prognosis. In this study, two deep learning models were trained on axial T2-weighted (T2W) prostate MRI images (n=612) to derive imaging biomarkers of EPE and SVI. Area Under the Receiver Operating Characteristic Curve (AUC) was used to measure the discriminatory ability of each model on three test sets. Unsupervised hierarchal clustering of deeply learned features and GradCAM images were generated to promote interpretability. Clinical utility of EPE and SVI biomarkers was assessed with Kaplan-Meier analysis, log-rank tests were used to evaluate biochemical recurrence free survival (BrFS) for patients stratified by each biomarker, and c-indexes were calculated. Biochemical failure was defined as a post-treatment Prostate Specific Antigen (PSA) 0.1ng/mL for patients who underwent radical prostatectomy (RP) or PSA 2ng/ml above nadir for patients who received radiation treatment (RT). Within our cohort of 820 patients treated at Yale, the median age was 66.1 with a median follow up of 3.1 years. 48.4% (n=397) underwent RP and 51.6% (n=423) received RT. DL models for EPE and SVI showed good discriminatory ability, both with AUCs of 0.72. Each biomarker showed good prognostic ability to identify high risk prostate phenotypes. Patients deemed high risk based on our EPE classifier had worse 5-year BrFS (59% vs 80%, p.001). Similarly, patients classified as high risk based on SVI also had worse 5-year BrFS (47% vs 76%, p.001). In conclusion, deep learning classifiers of prostate MRIs demonstrated the ability to stratify high-risk prostate cancer phenotypes beyond traditional risk paradigms and imaging biomarkers represent a non-invasive method to help aid in the personalization of treatment for patients with localized prostate cancer.
- 일반주제명
- Medicine
- 일반주제명
- Medical imaging
- 일반주제명
- Biomedical engineering
- 일반주제명
- Oncology
- 키워드
- Deep learning
- 키워드
- Prostate cancer
- 기타저자
- Yale University Yale School of Medicine
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aHossain, Sajid M.
■24510▼aApplying Deep Learning to Derive Noninvasive Imaging Biomarkers for High-Risk Phenotypes of Prostate Cancer
■260 ▼a[Sl]▼bYale University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a98 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Aneja, Sanjay.
■5021 ▼aThesis (D.Med.)--Yale University, 2024.
■520 ▼aNovel biomarkers can help guide management of Prostate Cancer (PCa) through the identification of high-risk phenotypes among similar patients in traditional National Comprehensive Cancer Network (NCCN) risk groups. We hypothesized that deep learning (DL) models which identified Extraprostatic Extension (EPE) and Seminal Vesicle Invasion (SVI), both pathologies associated with treatment failure, on Magnetic Resonance Imaging (MRI) could provide imaging biomarkers of PCa prognosis. In this study, two deep learning models were trained on axial T2-weighted (T2W) prostate MRI images (n=612) to derive imaging biomarkers of EPE and SVI. Area Under the Receiver Operating Characteristic Curve (AUC) was used to measure the discriminatory ability of each model on three test sets. Unsupervised hierarchal clustering of deeply learned features and GradCAM images were generated to promote interpretability. Clinical utility of EPE and SVI biomarkers was assessed with Kaplan-Meier analysis, log-rank tests were used to evaluate biochemical recurrence free survival (BrFS) for patients stratified by each biomarker, and c-indexes were calculated. Biochemical failure was defined as a post-treatment Prostate Specific Antigen (PSA) 0.1ng/mL for patients who underwent radical prostatectomy (RP) or PSA 2ng/ml above nadir for patients who received radiation treatment (RT). Within our cohort of 820 patients treated at Yale, the median age was 66.1 with a median follow up of 3.1 years. 48.4% (n=397) underwent RP and 51.6% (n=423) received RT. DL models for EPE and SVI showed good discriminatory ability, both with AUCs of 0.72. Each biomarker showed good prognostic ability to identify high risk prostate phenotypes. Patients deemed high risk based on our EPE classifier had worse 5-year BrFS (59% vs 80%, p.001). Similarly, patients classified as high risk based on SVI also had worse 5-year BrFS (47% vs 76%, p.001). In conclusion, deep learning classifiers of prostate MRIs demonstrated the ability to stratify high-risk prostate cancer phenotypes beyond traditional risk paradigms and imaging biomarkers represent a non-invasive method to help aid in the personalization of treatment for patients with localized prostate cancer.
■590 ▼aSchool code: 0265.
■650 4▼aMedicine
■650 4▼aMedical imaging
■650 4▼aBiomedical engineering
■650 4▼aOncology
■653 ▼aDeep learning
■653 ▼aImaging biomarkers
■653 ▼aProstate cancer
■653 ▼aMagnetic Resonance Imaging
■690 ▼a0564
■690 ▼a0574
■690 ▼a0541
■690 ▼a0992
■71020▼aYale University▼bYale School of Medicine.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0265
■791 ▼aD.Med.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160762▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
