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| 1 | 帕博利珠单抗单用或与放疗联用治疗转移性非小细胞肺癌:两个随机试验的汇总分析显示文摘背景放疗可以提高整个机体对免疫治疗的应答。在Ⅱ期PEMBRO-RT研究和Ⅰ/Ⅱ期MDACC研究中,患有转移性非小细胞肺癌(NSCLC)的患者被随机分配入组,接受免疫治疗(帕博利珠单抗)+放疗联合疗法,或免疫治疗单一疗法。当上述2个研究单独分析时,联合疗法组显示出潜在获益。由于每个研究的样本量较小,缓解率和结局并未显示出统计学意义,然而却有显著的临床获益。因此,本研究进行汇总分析,来判断放疗是否会改善转移性NSCLC患者的免疫治疗应答。方法PEMBRO-RT和MDACC研究纳入标准:患者年龄≥18岁,患有转移性NSCLC,且有≥1处未经放疗照射的病灶,以便进行射野外应答监测。PEMBRO-RT研究纳入曾接受过化疗患者,MDACC研究纳入曾接受过治疗或新诊断患者。2个研究中的患者均未接受过免疫治疗。在PEMBRO-RT研究中患者被等比例随机分配入组,并根据吸烟状态进行分层(分为<10年组和≥10年组)。MDACC研究的患者根据放疗计划可行性被等比例随机分配入2个受试组。由于联合治疗组的干预本质,每个研究中的放疗均不适用盲法。在2个研究中,不论是否进行放疗,均静脉滴入帕博利珠单抗(每3周200 mg)。在PEMBRO-RT研究中,在放疗(24 Gy 3次分割照射)结束后1周给予第1剂帕博利珠单抗。在MDACC研究中,在第1次放疗(50 Gy 4次分割照射或45 Gy 15次分割照射)同时给予帕博利珠单抗。仅检测未经照射病灶的应答。本研究的终点为最佳射野外(远隔)应答率(ARR)、最佳射野外疾病控制率(ACR)、12周时ARR、12周时ACR、无进展生存期(PFS)和总生存期(OS)。2个研究的意向治疗(ITT)人群均纳入分析。PEMBRO-RT研究(NCT02492568)和MDACC研究(NCT02444741)均在ClinicalTrials.gov上注册。发现纳入148例患者,76例接受帕博利珠单抗治疗,72例接受帕博利珠单抗+放疗治疗。所有患者随访时间中位数为33个月[四分位距(IQR):32.4~33.6]。148例患者中124例(84%)组织学特征为非鳞癌,111例(75%)患者曾经接受过化疗。组间没有基线特征差异,包括PD-L1表达状态和转移灶体积。最常见的照射部位为肺转移灶(39%,28/72)、胸腔内淋巴结(21%,15/72)和非原发灶(17%,12/72)。帕博利珠单抗组和联合治疗组的最佳ARR分别为19.7%(15/76)和41.7%(30/72),OR=2.96,95%CI:1.42~6.20,P=0.0039;最佳ACR分别为43.4%(33/76)和65.3%(47/72),OR=2.51,95%CI:1.28~4.91,P=0.0071;PFS中位数分别为4.4(IQR:2.9~5.9)和9.0个月(IQR:6.8~11.2),HR=0.67,95%CI:0.45~0.99,P=0.045;OS中位数分别为8.7(IQR:6.4~11.0)和19.2个月(IQR:14.6~23.8),OR=0.67,95%CI:0.54~0.84,P=0.0004。在汇总分析中没有发现新的安全问题。解读帕博利珠单抗免疫疗法+放疗显著提高转移性NSCLC患者的应答和改善治疗结局。这些结果需要在三期临床试验中进行验证。 | 陈大卫(翻译) 于金明(校对) Willemijn S M E Theelen Vivek Verma Brian P Hobbs Heike M U Peulen Joachim G J V Aerts Idris Bahce Anna Larissa N Niemeijer Joe Y Chang Patricia M de Groot Quynh-Nhu Nguyen Nathan I Comeaux George R Simon Ferdinandos Skoulidis Steven H Lin Kewen He Roshal Patel John Heymach Paul Baas James W Welsh | 2021 | 中华肿瘤防治杂志2021，28，24： | 49 |
| 2 | Dissolution of Feather Keratin in Ionic Liquids显示文摘 | Idris A Vijayaraghavan R Rana U A | 2013 | Green Chemistry2013，15，2： | 1 |
| 3 | Diversity and succession of the intestinal bacterial community of the maturing broiler chicken 显示文摘 | Lu J R Idris U Harmon B | 2003 | Applied and Environmental Microbiology2003，69，11： | 1 |
| 4 | Diversity and succession of the intestinal bacterial community of the maturing broiler chick- en 显示文摘 | Lu J Idris U Harmon B | 2003 | Applied and Environmental Microbiology2003，69，11： | 1 |
| 5 | Diversity and succession of the intestinal bacterial community of the maturing broiler chicken显示文摘 | LU J IDRIS U HARMON B | 2003 | Appl Environ Microbiol2003，69，： | 1 |
| 6 | Eco-friendly asbes- tos free brake-pad: Using banana peels显示文摘 | Idris U D Aigbodion V S Abubakar I J | 2015 | J King Saud University: Eng Sci2015，27，2： | 1 |
| 7 | Diversity and succession of the intestinal bacterial community of the maturing broiler chicken显示文摘 | Lu J Idris U Hamaon B G | 2003 | Applied and Environ- mental Microbiology2003，69，： | 1 |
| 8 | Dissolution and regeneration of wool keratin in ionic liquids显示文摘 | IDRIS A VIJAYARAGHAVAN R RANA U A | 2014 | Green Chemistry2014，16，5： | 1 |
| 9 | Drift DetectionMethod Using DistanceMeasures and Windowing Schemes for Sentiment Classification显示文摘Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products.Due to changes in data distribution,commonly referred to as concept drift,mining this data stream is a challenging problem for researchers.The majority of the existing drift detection techniques are based on classification errors,which have higher probabilities of false-positive or missed detections.To improve classification accuracy,there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams.This paper presents an adaptive unsupervised learning technique,an ensemble classifier based on drift detection for opinion mining and sentiment classification.To improve classification performance,this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data stream.Whenever a drift is detected,the proposed method builds and adds a new classifier to the ensemble.To add a new classifier,the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the ensemble.To this end,a weighting mechanism is used to calculate the weight of each classifier,which decides the contribution of each classifier in the final classification results.Several experiments were conducted on real-world datasets and the resultswere evaluated on the false positive rate,miss detection rate,and accuracy measures.The proposed method is also compared with the state-of-the-art methods,which include DDM,EDDM,and PageHinkley with support vector machine(SVM)and Naive Bayes classifiers that are frequently used in concept drift detection studies.In all cases,the results show the efficiency of our proposed method. | Idris Rabiu Naomie Salim Maged Nasser Aminu Da’u Taiseer Abdalla Elfadil Eisa Mhassen Elnour Elneel Dalam | 2023 | Computers, Materials & Continua2023，，3： | 0 |
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