{"title":"发行信息","authors":"","doi":"10.1002/msp2.37","DOIUrl":null,"url":null,"abstract":"<p><b>COVER</b>: By retrospectively evaluating 140 individuals with small cell lung cancer (SCLC) who received immunotherapy using neural networks, Li <i>et al</i>. developed an immune efficacy prediction model based on routine clinical data and deep learning neural networks to accurately predict the immunological efficacy in patients with SCLC, particularly in terms of the objective response rate (ORR). See pages 162–174 for details.</p>","PeriodicalId":100882,"journal":{"name":"Malignancy Spectrum","volume":"1 3","pages":"i-vi"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msp2.37","citationCount":"0","resultStr":"{\"title\":\"Issue Information\",\"authors\":\"\",\"doi\":\"10.1002/msp2.37\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>COVER</b>: By retrospectively evaluating 140 individuals with small cell lung cancer (SCLC) who received immunotherapy using neural networks, Li <i>et al</i>. developed an immune efficacy prediction model based on routine clinical data and deep learning neural networks to accurately predict the immunological efficacy in patients with SCLC, particularly in terms of the objective response rate (ORR). See pages 162–174 for details.</p>\",\"PeriodicalId\":100882,\"journal\":{\"name\":\"Malignancy Spectrum\",\"volume\":\"1 3\",\"pages\":\"i-vi\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msp2.37\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Malignancy Spectrum\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/msp2.37\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Malignancy Spectrum","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/msp2.37","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
COVER: By retrospectively evaluating 140 individuals with small cell lung cancer (SCLC) who received immunotherapy using neural networks, Li et al. developed an immune efficacy prediction model based on routine clinical data and deep learning neural networks to accurately predict the immunological efficacy in patients with SCLC, particularly in terms of the objective response rate (ORR). See pages 162–174 for details.
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