{"title":"RE: Potential role of cannabis in ameliorating observed racialized disparities in cancer pain management.","authors":"R. Giusti, Giampiero Porzio","doi":"10.1093/jnci/djae090","DOIUrl":"https://doi.org/10.1093/jnci/djae090","url":null,"abstract":"","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arjun Gupta, Christopher J O'Callaghan, Liting Zhu, Derek J Jonker, Ralph P W Wong, Bruce Colwell, Malcolm J Moore, Christos S Karapetis, Niall C Tebbutt, Jeremy D Shapiro, Dongsheng Tu, Christopher M Booth
Introduction While contact days—days with healthcare contact outside home—are increasingly adopted as a measure of time toxicity and treatment burden, they could also serve as a surrogate of treatment-related harm. We sought to assess the association between contact days and patient-reported outcomes, and the prognostic ability of contact days. Methods We conducted a secondary analysis of CO.17 that evaluated cetuximab vs supportive care in patients with advanced colorectal cancer. CO.17 collected EORTC-QLQ-C30 instrument data. We assessed the association between number of contact days in a window and changes in physical function and global health status, and the association between number of contact days in the first 4 weeks with overall survival (OS). Results There was a negative association between the number of contact days and change in physical function (per each additional contact day at 4 weeks, 1.50 point decrease; and 8 weeks, 1.06 point decrease, p < .0001 for both), but not with global health status. This negative association was seen in patients receiving cetuximab, but not supportive care. More contact days in the first 4 weeks was associated with worse OS for all comers and patients receiving cetuximab (per each additional contact day; all comers, aHR 1.07, 95% CI, 1.05- 1.10; and cetuximab, aHR 1.08, 95%CI 1.05- 1.11, p < .0001 for both). Conclusions In this secondary analysis of a clinical trial, more contact days early in the course was associated with declines in physical function and worse survival in all-comers and in participants receiving cancer-directed treatment. Trial registration ClinicalTrials.gov number, NCT00079066
{"title":"The association of healthcare contact days with physical function and survival in CCTG/AGITG CO.17","authors":"Arjun Gupta, Christopher J O'Callaghan, Liting Zhu, Derek J Jonker, Ralph P W Wong, Bruce Colwell, Malcolm J Moore, Christos S Karapetis, Niall C Tebbutt, Jeremy D Shapiro, Dongsheng Tu, Christopher M Booth","doi":"10.1093/jnci/djae077","DOIUrl":"https://doi.org/10.1093/jnci/djae077","url":null,"abstract":"Introduction While contact days—days with healthcare contact outside home—are increasingly adopted as a measure of time toxicity and treatment burden, they could also serve as a surrogate of treatment-related harm. We sought to assess the association between contact days and patient-reported outcomes, and the prognostic ability of contact days. Methods We conducted a secondary analysis of CO.17 that evaluated cetuximab vs supportive care in patients with advanced colorectal cancer. CO.17 collected EORTC-QLQ-C30 instrument data. We assessed the association between number of contact days in a window and changes in physical function and global health status, and the association between number of contact days in the first 4 weeks with overall survival (OS). Results There was a negative association between the number of contact days and change in physical function (per each additional contact day at 4 weeks, 1.50 point decrease; and 8 weeks, 1.06 point decrease, p &lt; .0001 for both), but not with global health status. This negative association was seen in patients receiving cetuximab, but not supportive care. More contact days in the first 4 weeks was associated with worse OS for all comers and patients receiving cetuximab (per each additional contact day; all comers, aHR 1.07, 95% CI, 1.05- 1.10; and cetuximab, aHR 1.08, 95%CI 1.05- 1.11, p &lt; .0001 for both). Conclusions In this secondary analysis of a clinical trial, more contact days early in the course was associated with declines in physical function and worse survival in all-comers and in participants receiving cancer-directed treatment. Trial registration ClinicalTrials.gov number, NCT00079066","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"164 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140642699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The complexities of PM2.5, greenspace, and childhood cancer.","authors":"Rena R Jones","doi":"10.1093/jnci/djae069","DOIUrl":"https://doi.org/10.1093/jnci/djae069","url":null,"abstract":"","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140684750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Da-Feng Lin, Hai-Lin Li, Ting Liu, Xiao-Fei Lv, Chuan-Miao Xie, Xiao-Min Ou, Jian Guan, Ye Zhang, Wen-Bin Yan, Mei-Lin He, Meng-Yuan Mao, Xun Zhao, Lian-Zhen Zhong, Wen-Hui Chen, Qiu-Yan Chen, Hai-Qiang Mai, Rou-Jun Peng, Jie Tian, Lin-Quan Tang, Di Dong
Background The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. Methods This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. Results The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. Conclusions An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.
{"title":"Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma","authors":"Da-Feng Lin, Hai-Lin Li, Ting Liu, Xiao-Fei Lv, Chuan-Miao Xie, Xiao-Min Ou, Jian Guan, Ye Zhang, Wen-Bin Yan, Mei-Lin He, Meng-Yuan Mao, Xun Zhao, Lian-Zhen Zhong, Wen-Hui Chen, Qiu-Yan Chen, Hai-Qiang Mai, Rou-Jun Peng, Jie Tian, Lin-Quan Tang, Di Dong","doi":"10.1093/jnci/djae081","DOIUrl":"https://doi.org/10.1093/jnci/djae081","url":null,"abstract":"Background The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. Methods This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. Results The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. Conclusions An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"280 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jing Sun, Jianhui Zhao, Siyun Zhou, Xinxuan Li, Tengfei Li, Lijuan Wang, Shuai Yuan, Dong Chen, Philip J Law, Susanna C Larsson, Susan M Farrington, Richard S Houlston, Malcolm G Dunlop, Evropi Theodoratou, Xue Li
Background We aimed to identify plasma and urinary metabolites related to colorectal cancer (CRC) risk and elucidate their mediator role in the associations between modifiable risk factors and CRC. Methods Metabolite quantitative trait loci were derived from two published metabolomics genome-wide association studies (GWASs), and summary-level data were extracted for 651 plasma metabolites and 208 urinary metabolites. Genetic associations with CRC were obtained from a large-scale GWAS meta-analysis (100,204 cases; 154,587 controls) and the FinnGen cohort (4,957 cases; 304,197 controls). Mendelian randomization (MR) and colocalization analyses were performed to evaluate the causal roles of metabolites in CRC. Druggability evaluation was employed to prioritize potential therapeutic targets. Multivariable MR and mediation estimation were conducted to elucidate the mediating effects of metabolites on the associations between modifiable risk factors and CRC. Results The study identified 30 plasma metabolites and four urinary metabolites for CRC. Plasma sphingomyelin and urinary lactose, which were positively associated with CRC risk, could be modulated by drug interventions (ie, Olipudase alfa, Tilactase). Thirteen modifiable risk factors were associated with nine metabolites and eight of these modifiable risk factors were associated with CRC risk. These nine metabolites mediated the effect of modifiable risk factors (Actinobacteria, BMI, waist-hip ratio, fasting insulin, smoking initiation) on CRC. Conclusion This study identified key metabolite biomarkers associated with CRC and elucidated their mediator roles in the associations between modifiable risk factors and CRC. These findings provide new insights into the etiology and potential therapeutic targets for CRC and the etiological pathways of modifiable environmental factors with CRC.
{"title":"Systematic investigation of genetically determined plasma and urinary metabolites to discover potential interventional targets for colorectal cancer","authors":"Jing Sun, Jianhui Zhao, Siyun Zhou, Xinxuan Li, Tengfei Li, Lijuan Wang, Shuai Yuan, Dong Chen, Philip J Law, Susanna C Larsson, Susan M Farrington, Richard S Houlston, Malcolm G Dunlop, Evropi Theodoratou, Xue Li","doi":"10.1093/jnci/djae089","DOIUrl":"https://doi.org/10.1093/jnci/djae089","url":null,"abstract":"Background We aimed to identify plasma and urinary metabolites related to colorectal cancer (CRC) risk and elucidate their mediator role in the associations between modifiable risk factors and CRC. Methods Metabolite quantitative trait loci were derived from two published metabolomics genome-wide association studies (GWASs), and summary-level data were extracted for 651 plasma metabolites and 208 urinary metabolites. Genetic associations with CRC were obtained from a large-scale GWAS meta-analysis (100,204 cases; 154,587 controls) and the FinnGen cohort (4,957 cases; 304,197 controls). Mendelian randomization (MR) and colocalization analyses were performed to evaluate the causal roles of metabolites in CRC. Druggability evaluation was employed to prioritize potential therapeutic targets. Multivariable MR and mediation estimation were conducted to elucidate the mediating effects of metabolites on the associations between modifiable risk factors and CRC. Results The study identified 30 plasma metabolites and four urinary metabolites for CRC. Plasma sphingomyelin and urinary lactose, which were positively associated with CRC risk, could be modulated by drug interventions (ie, Olipudase alfa, Tilactase). Thirteen modifiable risk factors were associated with nine metabolites and eight of these modifiable risk factors were associated with CRC risk. These nine metabolites mediated the effect of modifiable risk factors (Actinobacteria, BMI, waist-hip ratio, fasting insulin, smoking initiation) on CRC. Conclusion This study identified key metabolite biomarkers associated with CRC and elucidated their mediator roles in the associations between modifiable risk factors and CRC. These findings provide new insights into the etiology and potential therapeutic targets for CRC and the etiological pathways of modifiable environmental factors with CRC.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140637709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Measures of physical function clarify the prognostic blur of cancer survivorship.","authors":"Justin C Brown","doi":"10.1093/jnci/djae076","DOIUrl":"https://doi.org/10.1093/jnci/djae076","url":null,"abstract":"","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"17 S3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julie A Wolfson, Elizabeth S Davis, Aniket Saha, Isaac Martinez, David McCall, Prachi Kothari, Julienne Brackett, David S Dickens, Alissa R. Kahn, Carla Schwalm, Archana Sharma, Joshua Richman, Branko Cuglievan, Smita Bhatia, C. Dai, Jennifer M Levine, Emily E. Johnston
Adolescents and Young Adults (AYAs: 15-39 y) with cancer face unique vulnerabilities, yet remain under-represented on clinical trials, including adult registries of COVID-19 in cancer (AYAs: 8-12%). Thus, we leveraged the Pediatric Oncology COVID-19 Case Report (POCC) to examine the clinical course of COVID-19 among AYAs with cancer. POCC collects de-identified clinical and sociodemographic data regarding 0-39yo with cancer (AYAs = 37%) and COVID-19 from >100 institutions. Between 04/01/2020-11/28/2023, 191 older AYAs [22-39y] and 640 younger AYAs [15-21y] were captured. Older AYAs were less often hospitalized (p < .001), admitted to the intensive care unit (ICU, p = .02), and/or required respiratory support (p = .057). In multivariable analyses, older AYAs faced 80% lower odds of ICU admission but 2.3-times greater odds of changes to cancer-directed therapy. Unvaccinated patients had 5.4-times higher odds of ICU admission. Among AYAs with cancer, the COVID-19 course varies by age. These findings can inform pediatric/adult oncology teams surrounding COVID-19 management and prevention.
{"title":"Adolescents and young adults (AYA) with cancer: the clinical course of COVID-19 infections.","authors":"Julie A Wolfson, Elizabeth S Davis, Aniket Saha, Isaac Martinez, David McCall, Prachi Kothari, Julienne Brackett, David S Dickens, Alissa R. Kahn, Carla Schwalm, Archana Sharma, Joshua Richman, Branko Cuglievan, Smita Bhatia, C. Dai, Jennifer M Levine, Emily E. Johnston","doi":"10.1093/jnci/djae085","DOIUrl":"https://doi.org/10.1093/jnci/djae085","url":null,"abstract":"Adolescents and Young Adults (AYAs: 15-39 y) with cancer face unique vulnerabilities, yet remain under-represented on clinical trials, including adult registries of COVID-19 in cancer (AYAs: 8-12%). Thus, we leveraged the Pediatric Oncology COVID-19 Case Report (POCC) to examine the clinical course of COVID-19 among AYAs with cancer. POCC collects de-identified clinical and sociodemographic data regarding 0-39yo with cancer (AYAs = 37%) and COVID-19 from >100 institutions. Between 04/01/2020-11/28/2023, 191 older AYAs [22-39y] and 640 younger AYAs [15-21y] were captured. Older AYAs were less often hospitalized (p < .001), admitted to the intensive care unit (ICU, p = .02), and/or required respiratory support (p = .057). In multivariable analyses, older AYAs faced 80% lower odds of ICU admission but 2.3-times greater odds of changes to cancer-directed therapy. Unvaccinated patients had 5.4-times higher odds of ICU admission. Among AYAs with cancer, the COVID-19 course varies by age. These findings can inform pediatric/adult oncology teams surrounding COVID-19 management and prevention.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"18 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kyle A Mani, Xue Wu, Daniel E Spratt, Ming Wang, Nicholas G Zaorsky
Background In this study, we provide the largest analysis to date of a US-based cancer cohort to characterize death from COVID-19. Methods A total of 4,020,669 patients across 15 subtypes living with cancer in 2020 and included in the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database were abstracted. We investigated prognostic factors for death due to COVID-19 using a cox proportional hazards model and calculated hazard ratios (HRs). Standardized mortality ratios (SMRs) were calculated using observed mortality counts from SEER and expected mortality based on U.S. mortality rates. Results 291,323 patients died, with 14,821 (5.1%) deaths attributed to COVID-19 infection. The COVID-19 disease-specific mortality rate was 11.81/10,000-persons years, and SMR of COVID-19 was 2.30 (95% CI: 2.26-2.34, p < .0001). COVID-19 ranked as the second leading cause of death following ischemic heart disease (5.2%) among 26 non-cancer causes of death. Patients who are older (80+ vs < =49 years old: HR 21.47, 95% CI: 19.34-23.83), male (vs female: HR 1.46, 95% CI: 1.40-1.51), unmarried (vs married: HR 1.47, 95% CI: 1.42-1.53), and Hispanic or Non-Hispanic African American (vs Non-Hispanic White: HR 2.04, 95% CI: 1.94-2.14 and HR 2.03, 95% CI: 1.94-2.14, respectively) were at greatest risk of COVID-19 mortality. Conclusions and Relevance We observed that people living with cancer are at two times greater risk of dying from COVID-19 compared to the general US population. This work may be used by physicians and public health officials in the creation of survivorship programs that mitigate the risk of COVID-19 mortality.
{"title":"A population-based study of COVID-19 mortality risk in US cancer patients","authors":"Kyle A Mani, Xue Wu, Daniel E Spratt, Ming Wang, Nicholas G Zaorsky","doi":"10.1093/jnci/djae086","DOIUrl":"https://doi.org/10.1093/jnci/djae086","url":null,"abstract":"Background In this study, we provide the largest analysis to date of a US-based cancer cohort to characterize death from COVID-19. Methods A total of 4,020,669 patients across 15 subtypes living with cancer in 2020 and included in the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database were abstracted. We investigated prognostic factors for death due to COVID-19 using a cox proportional hazards model and calculated hazard ratios (HRs). Standardized mortality ratios (SMRs) were calculated using observed mortality counts from SEER and expected mortality based on U.S. mortality rates. Results 291,323 patients died, with 14,821 (5.1%) deaths attributed to COVID-19 infection. The COVID-19 disease-specific mortality rate was 11.81/10,000-persons years, and SMR of COVID-19 was 2.30 (95% CI: 2.26-2.34, p &lt; .0001). COVID-19 ranked as the second leading cause of death following ischemic heart disease (5.2%) among 26 non-cancer causes of death. Patients who are older (80+ vs &lt; =49 years old: HR 21.47, 95% CI: 19.34-23.83), male (vs female: HR 1.46, 95% CI: 1.40-1.51), unmarried (vs married: HR 1.47, 95% CI: 1.42-1.53), and Hispanic or Non-Hispanic African American (vs Non-Hispanic White: HR 2.04, 95% CI: 1.94-2.14 and HR 2.03, 95% CI: 1.94-2.14, respectively) were at greatest risk of COVID-19 mortality. Conclusions and Relevance We observed that people living with cancer are at two times greater risk of dying from COVID-19 compared to the general US population. This work may be used by physicians and public health officials in the creation of survivorship programs that mitigate the risk of COVID-19 mortality.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"114 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140556467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cathy J. Bradley, K. R. Yabroff, Ya‐Chen Tina Shih
{"title":"Clinic-based interventions for improving access to care: a good start.","authors":"Cathy J. Bradley, K. R. Yabroff, Ya‐Chen Tina Shih","doi":"10.1093/jnci/djae068","DOIUrl":"https://doi.org/10.1093/jnci/djae068","url":null,"abstract":"","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"21 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140714494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hormuzd A Katki, Philip C Prorok, Philip E Castle, Lori M Minasian, Paul F Pinsky
Background Cancer screening trials have required large sample-sizes and long time-horizons to demonstrate cancer mortality reductions, the primary goal of cancer screening. We examine assumptions and potential power gains from exploiting information from testing control-arm specimens, which we call the “Intended Effect” (IE) analysis that we explain in detail herein. The IE analysis is particularly suited to tests that can be conducted on stored specimens in the control-arm, such as stored blood for multicancer detection (MCD) tests. Methods We simulated hypothetical MCD screening trials to compare power and sample-size for the standard vs IE analysis. Under two assumptions that we detail herein, we projected the IE analysis for 3 existing screening trials (National Lung Screening Trial (NLST), Minnesota Colon Cancer Control Study (MINN-FOBT-A), and Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial—colorectal component (PLCO-CRC)). Results Compared to the standard analysis for the 3 existing trials, the IE design could have reduced cancer-specific mortality p-values 5-fold (NLST), 33-fold (MINN-FOBT-A), or 14,160-fold (PLCO-CRC), or alternately, reduced sample-size (90% power) by 26% (NLST), 48% (MINN-FOBT-A), or 59% (PLCO-CRC). For potential MCD trial designs requiring 100,000 subjects per-arm to achieve 90% power for multi-cancer mortality for the standard analysis, the IE analysis achieves 90% power for only 37,500-50,000 per arm, depending on assumptions concerning control-arm test-positives. Conclusions Testing stored specimens in the control arm of screening trials to conduct the IE analysis could substantially increase power to reduce sample-size or accelerate trials, and provide particularly strong power gains for MCD tests.
{"title":"Increasing power in screening trials by testing control-arm specimens: Application to multicancer detection screening","authors":"Hormuzd A Katki, Philip C Prorok, Philip E Castle, Lori M Minasian, Paul F Pinsky","doi":"10.1093/jnci/djae083","DOIUrl":"https://doi.org/10.1093/jnci/djae083","url":null,"abstract":"Background Cancer screening trials have required large sample-sizes and long time-horizons to demonstrate cancer mortality reductions, the primary goal of cancer screening. We examine assumptions and potential power gains from exploiting information from testing control-arm specimens, which we call the “Intended Effect” (IE) analysis that we explain in detail herein. The IE analysis is particularly suited to tests that can be conducted on stored specimens in the control-arm, such as stored blood for multicancer detection (MCD) tests. Methods We simulated hypothetical MCD screening trials to compare power and sample-size for the standard vs IE analysis. Under two assumptions that we detail herein, we projected the IE analysis for 3 existing screening trials (National Lung Screening Trial (NLST), Minnesota Colon Cancer Control Study (MINN-FOBT-A), and Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial—colorectal component (PLCO-CRC)). Results Compared to the standard analysis for the 3 existing trials, the IE design could have reduced cancer-specific mortality p-values 5-fold (NLST), 33-fold (MINN-FOBT-A), or 14,160-fold (PLCO-CRC), or alternately, reduced sample-size (90% power) by 26% (NLST), 48% (MINN-FOBT-A), or 59% (PLCO-CRC). For potential MCD trial designs requiring 100,000 subjects per-arm to achieve 90% power for multi-cancer mortality for the standard analysis, the IE analysis achieves 90% power for only 37,500-50,000 per arm, depending on assumptions concerning control-arm test-positives. Conclusions Testing stored specimens in the control arm of screening trials to conduct the IE analysis could substantially increase power to reduce sample-size or accelerate trials, and provide particularly strong power gains for MCD tests.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140547562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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