摘要
一位新闻记者兼机器人与机器学习每日新闻编辑每日新闻-药物和治疗的新研究-个性化医疗是一篇报道的主题。根据NewsRx记者从德克萨斯州休斯顿发回的消息，研究表明：“放射性引起的淋巴细胞减少症(RIL)在接受放射治疗的(RT)患者中很常见，严重的RIL与不良结果有关。RIL的严重程度和风险可以通过基线临床特征和剂量学参数来预测。”我们的新闻记者从Texas大学MD安德森癌症中心的研究中获得了一句话，“然而，”本文引入“复合剂量学评分”(CDS)作为反映免疫组织剂量分布的新指标，以研究RIL的剂量依赖性，并推导出一种改进的4级(G4)RIL风险的多元分类方案。根据这一新的RT剂量学特征,对734例经活检证实的食管癌患者进行了DV指数提取,并用非负矩阵分解法计算了肺、心、肺将所得CDS与基线临床因素和相互作用项合并，以便于对免疫毒性进行分类预测。采用五倍交叉验证评估模型性能。选定的处于风险中的免疫器官（OAR，即心脏、肺和脾）的CDS（1.791，95 CI[1.350，2）（1.791，95 CI[1.350，2.377]是G4 RIL的有统计学意义的危险决定因素,CDS与G4RIL个体IMU NE风险的Pearson相关系数大于任何单一DV指数,基于CDS和4个临床危险因素的G4RI L的个性化预测得到0.78.的曲线下面积,表明G4RIL风险随着CDS的增加而急剧增加。65. CDS可以预测接受食管癌化疗的患者的免疫毒性风险。CDS的概念可以扩展到其他癌症类型的免疫毒性和目前基于DV指数的剂量-反应模型。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news originating from Houston, Texas, by NewsRx correspondents, research stated, “Radiation-induc ed lymphopenia (RIL) is common among patients undergoing radiotherapy (RT), and severe RIL has been linked with adverse outcomes. The severity and risk of RIL c an be predicted from baseline clinical characteristics and dosimetric parameters .” Our news journalists obtained a quote from the research from the University of T exas MD Anderson Cancer Center, “However, dose-volume (DV) indices are highly co rrelated with one another and are only weakly associated with RIL. Here we intro duce the novel concept of ‘composite dosimetric score’ (CDS) as the index that c ondenses the dose distribution in immune tissues of interest to study the dosime tric dependence of RIL. We derived an improved multivariate classification schem e for risk of grade 4 (G4) RIL, based on this novel RT dosimetric feature, for p atients receiving chemoRT for esophageal cancer. DV indices were extracted for 7 34 patients who received chemoRT for biopsy-proven esophageal cancer. Non-negati ve matrix factorization was used to project the DV indices of lung, heart, and s pleen into a single CDS; XGBoost was employed to explore significant interaction s among predictors; and logistic regression was applied to combine the resultant CDS along with baseline clinical factors and interaction terms to facilitate in dividualized prediction of immunotoxicity. Five-fold cross-validation was applie d to evaluate the model performance. The CDS for selected immune organs at risk (OARs, i.e., heart, lung, and spleen) (1.791, 95 CI [1.350,2. 377]) was a statistically significant risk determinant for G4 RIL. Pearson correlation coefficients for CDS vs. G4RIL risk for individual immu ne OARs were greater than any single DV indices. Personalized prediction of G4RI L based on CDS and 4 clinical risk factors yielded an area under the curve value of 0.78. Interaction between age and CDS revealed that G4RIL risk increased mor e sharply with increasing CDS for patients 65. Risk of immunotoxicity for patien ts undergoing chemoRT for esophageal cancer can be predicted by CDS. The CDS con cept can be extended to immunotoxicity in other cancer types and in dose-respons e models currently based on DV indices.”
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