NDLI: Time-dependent personalized prognostic analysis by machine learning in biochemical recurrence after radical prostatectomy: a retrospective cohort study

Content Provider	Springer Nature : BioMed Central
Author	Sato, Kodai Sakamoto, Shinichi Saito, Shinpei Shibata, Hiroki Yamada, Yasutaka Takeuchi, Nobuyoshi Goto, Yusuke Tomokazu, Sazuka Imamura, Yusuke Ichikawa, Tomohiko Kawakami, Eiryo
Abstract	Background For biochemical recurrence following radical prostatectomy for prostate cancer, treatments such as radiation therapy and androgen deprivation therapy are administered. To diagnose postoperative recurrence as early as possible and to intervene with treatment at the appropriate time, it is essential to accurately predict recurrence after radical prostatectomy. However, postoperative recurrence involves numerous patient-related factors, making its prediction challenging. The purpose of this study is to accurately predict the timing of biochemical recurrence after radical prostatectomy and to analyze the risk factors for follow-up of high-risk patients and early detection of recurrence. Methods We utilized the machine learning survival analysis model called the Random Survival Forest utilizing the 58 clinical factors from 548 patients who underwent radical prostatectomy at Chiba University Hospital. To visualize prognostic factors and assess accuracy of the time course probability, we employed SurvSHAP(t) and time-dependent Area Under Cureve(AUC). Results The time-dependent AUC of RSF was 0.785, which outperformed the Cox proportional hazards model (0.704), the Cancer of the Prostate Risk Assessment (CAPRA) score (0.710), and the D’Amico score (0.658). The key prognostic factors for early recurrence were Gleason score(GS), Seminal vesicle invasion(SV), and PSA. The contribution of PSA to recurrence decreases after the first year, while SV and GS increase over time. Conclusion Our prognostic model analyzed the time-dependent relationship between the timing of recurrence and prognostic factors. Our study achieved personalized prognosis analysis and its rationale after radical prostatectomy by employing machine learning prognostic model. This prognostic model contributes to the early detection of recurrence by enabling clinicians to conduct appropriate follow-ups for high-risk patients.
Related Links	https://bmccancer.biomedcentral.com/counter/pdf/10.1186/s12885-024-13203-8.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14712407
DOI	10.1186/s12885-024-13203-8
Journal	BMC Cancer
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-11-25
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Surgical Oncology Health Promotion and Disease Prevention Biomedicine Medicine Public Health Prostate cancer Prostatectomy Recurrence Machine learning Prognostic model Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Cancer Research Oncology Genetics
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.8/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

High miR-449b expression in prostate cancer is associated with biochemical recurrence after radical prostatectomy

Performance of prostate cancer recurrence nomograms by obesity status: a retrospective analysis of a radical prostatectomy cohort

Risk of biochemical recurrence based on extent and location of positive surgical margins after robot-assisted laparoscopic radical prostatectomy

Robot-assisted radical prostatectomy significantly reduced biochemical recurrence compared to retro pubic radical prostatectomy

Efficacy of PSMA ligand PET-based radiotherapy for recurrent prostate cancer after radical prostatectomy and salvage radiotherapy

Periprostatic fat magnetic resonance imaging based radiomics nomogram for predicting biochemical recurrence-free survival in patients with non-metastatic prostate cancer after radical prostatectomy

HP1γ expression is elevated in prostate cancer and is superior to Gleason score as a predictor of biochemical recurrence after radical prostatectomy

Nomogram predicting early urinary incontinence after radical prostatectomy

Clinical significance and risk factors of International Society of Urological Pathology (ISUP) grade upgrading in prostate cancer patients undergoing robot-assisted radical prostatectomy

Time-dependent personalized prognostic analysis by machine learning in biochemical recurrence after radical prostatectomy: a retrospective cohort study

Similar Documents

High miR-449b expression in prostate cancer is associated with biochemical recurrence after radical prostatectomy

Performance of prostate cancer recurrence nomograms by obesity status: a retrospective analysis of a radical prostatectomy cohort

Risk of biochemical recurrence based on extent and location of positive surgical margins after robot-assisted laparoscopic radical prostatectomy

Robot-assisted radical prostatectomy significantly reduced biochemical recurrence compared to retro pubic radical prostatectomy

Efficacy of PSMA ligand PET-based radiotherapy for recurrent prostate cancer after radical prostatectomy and salvage radiotherapy

Periprostatic fat magnetic resonance imaging based radiomics nomogram for predicting biochemical recurrence-free survival in patients with non-metastatic prostate cancer after radical prostatectomy

HP1γ expression is elevated in prostate cancer and is superior to Gleason score as a predictor of biochemical recurrence after radical prostatectomy

Nomogram predicting early urinary incontinence after radical prostatectomy

Clinical significance and risk factors of International Society of Urological Pathology (ISUP) grade upgrading in prostate cancer patients undergoing robot-assisted radical prostatectomy

Time-dependent personalized prognostic analysis by machine learning in biochemical recurrence after radical prostatectomy: a retrospective cohort study