A FULLY AUTOMATED MULTI-TASK MACHINE LEARNING PROGNOSTIC MODEL INTEGRATING RADIOMICS AND CLINICAL DATA TO PREDICT OUTCOMES IN HIGH-GRADE PROSTATE CANCER

被引:0
|
作者
Touma, Nawar
Larose, Maxence
Brodeur, Raphael
Desroches, Felix
Bedard-Tremblay, Daphnee
Raymond, Nicolas
Leblanc, Danahe
Rasekh, Fatemeh
Hovington, Helene
Neveu, Bertrand
Vallieres, Martin
Archambault, Louis
Pouliot, Frederic
机构
来源
JOURNAL OF UROLOGY | 2024年 / 211卷 / 05期
关键词
D O I
10.1097/01.JU.0001008728.41882.d7.08
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
MP07-08
引用
收藏
页码:E107 / E107
页数:1
相关论文
共 10 条
  • [1] RADIOMICS-BASED PROGNOSTIC MODEL GUIDED BY ARTIFICIAL INTELLIGENCE FOR PREDICTING CLINICAL OUTCOMES IN INDIVIDUALS WITH HIGH-GRADE PROSTATE CANCER
    Touma, Nawar
    Larose, Maxence
    Brodeur, Raphael
    Desroches, Felix
    Raymond, Nicolas
    Bedard-Tremblay, Daphnee
    Leblanc, Danahe
    Rasekh, Fatemeh
    Hovington, Helene
    Neveu, Bertrand
    Vallieres, Martin
    Archambault, Louis
    Pouliot, Frederic
    JOURNAL OF UROLOGY, 2024, 211 (05): : E107 - E107
  • [2] Multi-task Bayesian model combining FDG-PET/CT imaging and clinical data for interpretable high-grade prostate cancer prognosis
    Larose, Maxence
    Archambault, Louis
    Touma, Nawar
    Brodeur, Raphael
    Desroches, Felix
    Raymond, Nicolas
    Bedard-Tremblay, Daphnee
    Leblanc, Danahe
    Rasekh, Fatemeh
    Hovington, Helene
    Neveu, Bertrand
    Vallieres, Martin
    Pouliot, Frederic
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [3] Radiomics-based prognostic model guided by artificial intelligence to predict lymph node invasion and biochemical recurrence in patients with high-grade prostate cancer
    Touma, N.
    Larose, M.
    Brodeur, R.
    Desroches, F.
    Raymond, N.
    Bedard-Tremblay, D.
    Leblanc, D.
    Rasekh, F.
    Hovington, H.
    Neveu, B.
    Vallieres, M.
    Archambault, L.
    Pouliot, F.
    EUROPEAN UROLOGY, 2024, 85 : S358 - S358
  • [4] Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features
    Zhao, Wenjun
    Hou, Mengyan
    Wang, Juan
    Song, Dan
    Niu, Yongchao
    BMC MEDICAL IMAGING, 2024, 24 (01):
  • [5] A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer
    Feng Zhan
    Lidan He
    Yuanlin Yu
    Qian Chen
    Yina Guo
    Lili Wang
    Scientific Reports, 13
  • [6] A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer
    Zhan, Feng
    He, Lidan
    Yu, Yuanlin
    Chen, Qian
    Guo, Yina
    Wang, Lili
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [7] ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data
    Yao, Yuhua
    Lv, Yaping
    Tong, Ling
    Liang, Yuebin
    Xi, Shuxue
    Ji, Binbin
    Zhang, Guanglu
    Li, Ling
    Tian, Geng
    Tang, Min
    Hu, Xiyue
    Li, Shijun
    Yang, Jialiang
    BRIEFINGS IN BIOINFORMATICS, 2022, 23 (06)
  • [8] Predicting Tumor Perineural Invasion Status in High-Grade Prostate Cancer Based on a Clinical-Radiomics Model Incorporating T2-Weighted and Diffusion-Weighted Magnetic Resonance Images
    Zhang, Wei
    Zhang, Weiting
    Li, Xiang
    Cao, Xiaoming
    Yang, Guoqiang
    Zhang, Hui
    CANCERS, 2023, 15 (01)
  • [9] Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice
    Zhou, Jie
    JOURNAL OF TRANSLATIONAL MEDICINE, 2025, 23 (01)
  • [10] Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study
    Zou, Congying
    Chen, Ruiyuan
    Wang, Baodong
    Fei, Qi
    Song, Hongxing
    Zang, Lei
    BIOMEDICAL ENGINEERING ONLINE, 2025, 24 (01)