相关实验视频

Updated: May 28, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

深度学习放射学用于CT图像对非小细胞肺癌患者的生存预测

Viet Huan Le¹, Tran Nguyen Tuan Minh^2,3, Quang Hien Kha^2,3

¹Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang City, 65000, Vietnam.

Journal of medical systems

|February 10, 2025

概括

这项研究使用深度学习和CT扫描来预测非小细胞肺癌 (NSCLC) 患者的生存率. 将深度放射学,传统放射学和临床数据与DeepSurv模型相结合,提供了最有效的生存预测.

科学领域:

放射学和医学成像学医学成像学
在瘤学瘤学.
人工智能在医学中的应用

背景情况:

非小细胞肺癌 (NSCLC) 的生存预测仍然具有挑战性.
基于CT的放射学为预后见解提供了潜在的潜力.
深度学习方法可以增强复杂的医学成像数据的分析.

研究的目的:

应用多模态深度学习方法来预测NSCLC患者的存活率,使用基于CT的放射学.
为了比较深度放射学,传统放射学和临床参数的不同组合对生存预测的有效性.
评估DeepSurv神经网络的性能与Cox比例危险 (Cox-PH) 模型相比.

主要方法:

利用两个公共NSCLC数据集 (TCIA) 进行培训 (肺部1,420名患者) 和测试 (肺部2,516名患者).
采用3D卷积神经网络 (CNN) 生存来从CT扫描中提取256个深度放射学特征.
将深度放射学,传统放射学和临床参数集成到DeepSurv神经网络中,用于生存预测.

主要成果:

传统放射学,深度放射学和临床参数的组合模型在训练 (0.733为DeepSurv) 和测试 (0.751为DeepSurv) 数据集中实现了最高的C指数值.
与Cox-PH模型相比,DeepSurv方法表现出优越的预测性能.
多参数模型显示,对NSCLC患者的生存预测效率提高.

结论:

基于CT的深度放射学与DeepSurv模型相结合,为NSCLC生存预测提供了一种有效的方法.
整合多模式数据 (深度放射学,传统放射学,临床参数) 可以显著提高预测准确度.
DeepSurv的深度放射学方法优于传统方法,如Cox-PH用于NSCLC的预后.

关键词:

深度放射力学是一种深度的放射力学.在DeepSurv上进行深度调查.手工制作的放射电学.非小细胞肺癌是非小细胞肺癌.总体生存率总体生存率

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

相关概念视频

Radiological Investigation I: X-ray and CT

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...