{"title":"早期肿瘤缩小和反应深度作为实体瘤治疗反应和预后的预测指标。","authors":"Peng Cao, Xiaojuan Yang","doi":"10.1002/cam4.71251","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Evaluation of tumor efficacy is central to cancer care. Progression-free survival (PFS) is widely used as an early surrogate for treatment effectiveness, but more timely and reliable biomarkers are needed. Early tumor shrinkage (ETS) and depth of response (DpR) have emerged as promising predictors: ETS reflects early treatment sensitivity at first radiologic assessment, whereas DpR quantifies the maximum tumor reduction and may capture the durability of benefit.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We conducted a narrative synthesis of clinical studies assessing ETS and/or DpR across solid tumors, focusing on their definitions, measurement under RECIST, and associations with PFS and overall suvival (OS). We also summarized advances in imaging and multidimensional assessment framworks that could improve the accuracy and clinical utility of these indicators, and highlighted sources of heterogeneity and current gaps.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Across multiple retrospective and post-hoc analyses, ETS provides an early signal that identifies patients more likely to benefit from therapy and can inform treatment adaptation. DpR shows consisten correlations with long-term outcomes and complements PFS by reflecting the magnitude of tumor control. Both ETS and DpR demonstrate predictive value for PFS and OS; however, variability in cut-offs (e.g., ETS 20%–30%), timing of assessments, tumor types, and treatment modalities limits comparability. Emerging imaging technologies and composite response frameworks offer opportunities to enhance measurement precision and reproducibility.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>ETS and DpR are promising, clinically interpretable markers for monitoring treatment effcacy and prognosis. Standardized definitons, prospective validation, and integration with molecular and imaging biomarkers (e.g., ctDNA, radiomics, and machine—learning—enhanced imaging) are needed to refine their application and solidify their role in routine cancer therapy monitoring.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"14 18","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12441742/pdf/","citationCount":"0","resultStr":"{\"title\":\"Early Tumor Shrinkage and Depth of Response as Predictive Markers of Treatment Response and Prognosis in Solid Tumors\",\"authors\":\"Peng Cao, Xiaojuan Yang\",\"doi\":\"10.1002/cam4.71251\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Evaluation of tumor efficacy is central to cancer care. Progression-free survival (PFS) is widely used as an early surrogate for treatment effectiveness, but more timely and reliable biomarkers are needed. Early tumor shrinkage (ETS) and depth of response (DpR) have emerged as promising predictors: ETS reflects early treatment sensitivity at first radiologic assessment, whereas DpR quantifies the maximum tumor reduction and may capture the durability of benefit.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We conducted a narrative synthesis of clinical studies assessing ETS and/or DpR across solid tumors, focusing on their definitions, measurement under RECIST, and associations with PFS and overall suvival (OS). We also summarized advances in imaging and multidimensional assessment framworks that could improve the accuracy and clinical utility of these indicators, and highlighted sources of heterogeneity and current gaps.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Across multiple retrospective and post-hoc analyses, ETS provides an early signal that identifies patients more likely to benefit from therapy and can inform treatment adaptation. DpR shows consisten correlations with long-term outcomes and complements PFS by reflecting the magnitude of tumor control. Both ETS and DpR demonstrate predictive value for PFS and OS; however, variability in cut-offs (e.g., ETS 20%–30%), timing of assessments, tumor types, and treatment modalities limits comparability. Emerging imaging technologies and composite response frameworks offer opportunities to enhance measurement precision and reproducibility.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>ETS and DpR are promising, clinically interpretable markers for monitoring treatment effcacy and prognosis. Standardized definitons, prospective validation, and integration with molecular and imaging biomarkers (e.g., ctDNA, radiomics, and machine—learning—enhanced imaging) are needed to refine their application and solidify their role in routine cancer therapy monitoring.</p>\\n </section>\\n </div>\",\"PeriodicalId\":139,\"journal\":{\"name\":\"Cancer Medicine\",\"volume\":\"14 18\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12441742/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cam4.71251\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cam4.71251","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Early Tumor Shrinkage and Depth of Response as Predictive Markers of Treatment Response and Prognosis in Solid Tumors
Background
Evaluation of tumor efficacy is central to cancer care. Progression-free survival (PFS) is widely used as an early surrogate for treatment effectiveness, but more timely and reliable biomarkers are needed. Early tumor shrinkage (ETS) and depth of response (DpR) have emerged as promising predictors: ETS reflects early treatment sensitivity at first radiologic assessment, whereas DpR quantifies the maximum tumor reduction and may capture the durability of benefit.
Methods
We conducted a narrative synthesis of clinical studies assessing ETS and/or DpR across solid tumors, focusing on their definitions, measurement under RECIST, and associations with PFS and overall suvival (OS). We also summarized advances in imaging and multidimensional assessment framworks that could improve the accuracy and clinical utility of these indicators, and highlighted sources of heterogeneity and current gaps.
Results
Across multiple retrospective and post-hoc analyses, ETS provides an early signal that identifies patients more likely to benefit from therapy and can inform treatment adaptation. DpR shows consisten correlations with long-term outcomes and complements PFS by reflecting the magnitude of tumor control. Both ETS and DpR demonstrate predictive value for PFS and OS; however, variability in cut-offs (e.g., ETS 20%–30%), timing of assessments, tumor types, and treatment modalities limits comparability. Emerging imaging technologies and composite response frameworks offer opportunities to enhance measurement precision and reproducibility.
Conclusions
ETS and DpR are promising, clinically interpretable markers for monitoring treatment effcacy and prognosis. Standardized definitons, prospective validation, and integration with molecular and imaging biomarkers (e.g., ctDNA, radiomics, and machine—learning—enhanced imaging) are needed to refine their application and solidify their role in routine cancer therapy monitoring.
期刊介绍:
Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas:
Clinical Cancer Research
Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations
Cancer Biology:
Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery.
Cancer Prevention:
Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach.
Bioinformatics:
Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers.
Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.