Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

357
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...
357

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Role of vitamin D receptor and calcium sensing receptor in parathyroid cancer.

Brazilian journal of otorhinolaryngology·2026
Same author

Exosomal miR-27a-5p Helps Differentiate Parathyroid Carcinoma From Adenoma and Inhibits Apoptosis in Parathyroid Carcinoma Cells.

International journal of endocrinology·2026
Same author

[Coagulation function analysis and postoperative changes in patients with primary hyperparathyroidism].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2026
Same author

Discordant sestamibi uptake in synchronous parathyroid carcinoma and adenoma.

Endocrinology, diabetes & metabolism case reports·2026
Same author

Shear wave elastography can improve the preoperative diagnosis of parathyroid carcinoma or atypical parathyroid tumor: initial experience.

BMC medical imaging·2026
Same author

lnc-MPEG1-1 promotes papillary thyroid carcinoma progression through association with BRAF mutations.

Endokrynologia Polska·2025

相关实验视频

Updated: Jul 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.3K

基于机器学习的甲状腺癌预测模型使用了手术前的认知功能和临床特征.

Yuting Wang1, Bojun Wei2, Teng Zhao1

  • 1Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.

Scientific reports
|November 4, 2023
PubMed
概括

通过MMSE和MOCA分数评估的术前认知功能可以预测甲状腺癌 (PC). 机器学习模型,特别是XGBoost,在手术前识别PC方面表现有前途,改善了患者的治疗结果.

更多相关视频

A Personalized 3D-Printed Model for Preoperative Evaluation in Thyroid Surgery
04:42

A Personalized 3D-Printed Model for Preoperative Evaluation in Thyroid Surgery

Published on: February 17, 2023

1.4K
Establishment of a Simple and Effective Rat Model for Intraoperative Parathyroid Gland Imaging
07:12

Establishment of a Simple and Effective Rat Model for Intraoperative Parathyroid Gland Imaging

Published on: August 17, 2022

3.5K

相关实验视频

Last Updated: Jul 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.3K
A Personalized 3D-Printed Model for Preoperative Evaluation in Thyroid Surgery
04:42

A Personalized 3D-Printed Model for Preoperative Evaluation in Thyroid Surgery

Published on: February 17, 2023

1.4K
Establishment of a Simple and Effective Rat Model for Intraoperative Parathyroid Gland Imaging
07:12

Establishment of a Simple and Effective Rat Model for Intraoperative Parathyroid Gland Imaging

Published on: August 17, 2022

3.5K

科学领域:

  • 内分泌学 在内分泌学.
  • 在瘤学瘤学.
  • 医疗信息学 医疗信息学

背景情况:

  • 甲状腺癌 (PC) 诊断往往被推迟到手术后,导致患者的治疗结果不佳.
  • 早期检测PC对于改善手术成功和患者预后至关重要.

研究的目的:

  • 为了确定副甲状腺癌 (PC) 的手术前指标.
  • 开发基于机器学习的PC预测模型,使用手术前数据.

主要方法:

  • 评估了133名原发性甲状腺功能障碍患者,评估了手术前的神经心理功能和病理学.
  • 利用机器学习算法,包括极端梯度提升 (XGBoost) 和LASSO回归,用于模型开发.
  • 将机器学习模型与逻辑回归进行比较,以获得预测准确度.

主要成果:

  • 甲状腺腺激素升高和血清含量降低是PC患者的显著指标.
  • 在PC患者中观察到迷你心理状态检查 (MMSE) 和蒙特利尔认知评估 (MOCA) 的得分较低.
  • 与物流回归 (0.683) 和LASSO (0.607) 相比,XGBoost模型实现了更高的曲线下面面积 (AUC) 0.835.

结论:

  • 手术前的认知功能,特别是MMSE和MOCA得分,可以作为PC的预测指标.
  • 使用XGBoost的基于认知功能的预测模型超过了传统方法,为疑似PC病例提供了有价值的手术前决策支持.