Robotics & Machine Learning Daily News2024,Issue(Jun.24) :52-53.

Shengjing Hospital of China Medical University Reports Findings in Neoplasms (De velopment and Validation of a Novel Machine Learning Model to Predict the Surviv al of Patients with Gastrointestinal Neuroendocrine Neoplasms)

中国医科大学盛京医院报告肿瘤发现(预测胃肠神经内分泌肿瘤患者生存的机器学习模型的开发与验证)

Robotics & Machine Learning Daily News2024,Issue(Jun.24) :52-53.

Shengjing Hospital of China Medical University Reports Findings in Neoplasms (De velopment and Validation of a Novel Machine Learning Model to Predict the Surviv al of Patients with Gastrointestinal Neuroendocrine Neoplasms)

中国医科大学盛京医院报告肿瘤发现(预测胃肠神经内分泌肿瘤患者生存的机器学习模型的开发与验证)

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摘要

由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-肿瘤的新研究是一份报告的子标题。根据NewsRx编辑在中国沈阳的新闻报道,研究表明:“胃肠神经内分泌肿瘤患者(GINENs)的人格预测模型是有限的。本研究旨在开发和验证一种机器学习模型来预测吉宁患者的生存。”我们的新闻记者引用了中国医科大学盛京医院的研究,“斜随机生存森林(ORSF)模型,C ox比例风险模型,最小绝对收缩和S选择算子惩罚的Cox模型,CoxBoost,生存梯度提升机,Ex treme梯度提升生存回归,DeepHit,DeepSurv,DNNSurv,Logisti C-风险模型,”对43444例GINES患者的Med Ian(Interquartile Range)生存期53(19-102)个月,以年龄、组织学、M分期、肿瘤大小、原发部位、性别、肿瘤数目、手术切除、淋巴结切除为最佳模型。ORSF模型的总体C指数为0.86(95%可信区间为0.85~0.87),1、3、5、10年受检者手术曲线下面积分别为0.91、0.89、0.87、0.80.曲线分析显示ORSF模型比美国癌症分期联合委员会具有更好的临床应用价值,并提供了列线图和在线工具。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Neoplasms is the subje ct of a report. According to news reporting out of Shenyang, People's Republic o f China, by NewsRx editors, research stated, "Well-calibrated models for persona lized prognostication of patients with gastrointestinal neuroendocrine neoplasms (GINENs) are limited. This study aimed to develop and validate a machine-learni ng model to predict the survival of patients with GINENs." Our news journalists obtained a quote from the research from the Shengjing Hospi tal of China Medical University, "Oblique random survival forest (ORSF) model, C ox proportional hazard risk model, Cox model with least absolute shrinkage and s election operator penalization, CoxBoost, Survival Gradient Boosting Machine, Ex treme Gradient Boosting survival regression, DeepHit, DeepSurv, DNNSurv, logisti c-hazard model, and PC-hazard model were compared. We further tuned hyperparamet ers and selected variables for the best-performing ORSF. Then, the final ORSF mo del was validated. A total of 43,444 patients with GINENs were included. The med ian (interquartile range) survival time was 53 (19-102) months. The ORSF model p erformed best, in which age, histology, M stage, tumor size, primary tumor site, sex, tumor number, surgery, lymph nodes removed, N stage, race, and grade were ranked as important variables. However, chemotherapy and radiotherapy were not n ecessary for the ORSF model. The ORSF model had an overall C index of 0.86 (95% confidence interval, 0.85-0.87). The area under the receiver operation curves at 1, 3, 5, and 10 years were 0.91, 0.89, 0.87, and 0.80, respectively. The decisi on curve analysis showed superior clinical usefulness of the ORSF model than the American Joint Committee on Cancer Stage. A nomogram and an online tool were gi ven."

Key words

Shenyang/People's Republic of China/As ia/Cyborgs/Emerging Technologies/Gastroenterology/Health and Medicine/Machi ne Learning/Neoplasms

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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