bluuebunny/crossref_metadata_2025 huggingface dataset:
Table S2 from Associations between etiologic or prognostic tumor tissue markers and neighborhood contextual factors in male health professionals diagnosed with prostate cancer
Authors: Iyer Hari S. , Kensler Kevin H. , Vaselkiv Jane B. , Stopsack Konrad H. , Roscoe Charlotte , Bandera Elisa V. , Qin Bo , Jang Thomas L. , Lotan Tamara L. , James Peter , Hart Jaime E. , Mucci Lorelei A. , Laden Francine , Rebbeck Timothy R.
Published: September 2024

Odds Ratios and 95% Confidence Intervals for Associations Between Neighborhood Contextual Factors at Baseline (1988) and
Somatic Alterations in Prostate Tumor Tissue in US Male Health Professionals with Prostate Cancer
Supplementary Table 1 from Area Deprivation Index in patients with invasive lobular carcinoma of the breast: associations with tumor characteristics and outcomes
Authors: Kaur Mandeep , Patterson Anne , Molina-Vega Julissa , Rothschild Harriet , Clelland Elle , Ewing Cheryl A. , Mujir Firdows , Esserman Laura J. , Olopade Olufunmilayo I. , Mukhtar Rita A.
Published: September 2024

Supplementary Table 1. ILC patient and tumor characteristics, including cases with missing data.
Supplementary Table S1 from Pre-diagnostic 25-hydroxyvitamin D in association with cancer specific- and all-cause mortality among low income, non-Hispanic Black Americans with colorectal cancer
Authors: Lawler Thomas P. , Khankari Nikhil K. , Shu Xiao-Ou , Steinwandel Mark , Cai Qiuyin , Jurutka Peter W. , Zheng Wei , Warren Andersen Shaneda
Published: September 2024

Supplementary Table S1: Characteristics of participants by mortality status
Figure SF1 from A review of the application of spatial survival methods in cancer research: trends, modelling and visualization techniques
Authors: Bizuayehu Habtamu Mellie , Cameron Jessica K. , Dasgupta Paramita , Baade Peter D.
Published: September 2024

Supplementary Figure S1 shows summary of keywords extracted from articles
Supplemental Figure 4 from Elevated EVL methylation level in the normal colon mucosa is a potential risk biomarker for developing recurrent adenomas
Authors: Yu Ming , Carter Kelly T. , Baker Kelsey K. , Redman Mary W. , Wang Ting , Vickers Kathy , Li Christopher I. , Cohen Stacey A. , Krane Mukta , Ose Jennifer , Gigic Biljana , Figueiredo Jane C. , Toriola Adetunji T. , Siegel Erin M. , Shibata David , Schneider Martin , Ulrich Cornelia M. , Dzubinski Lynda Ann. , Schoen Robert E. , Grady William M.
Published: September 2024

Supplemental Figure 4: Methylation levels of normal colon for patients with no history of adenoma or cancer after initial amongst those at least 4 years of
follow-up (left group) and levels for patients with an adenoma or colorectal cancer detected after initial colonoscopy (middle group). Patients without an
adenoma or colorectal cancer and less than 4 years of follow-up (right group) were not included in the analyses for Comparison Set 3. %mEVL=percentage
of DNA that is methylated at the EVL locus assayed.
Supplemental Figure 1 from Elevated EVL methylation level in the normal colon mucosa is a potential risk biomarker for developing recurrent adenomas
Authors: Yu Ming , Carter Kelly T. , Baker Kelsey K. , Redman Mary W. , Wang Ting , Vickers Kathy , Li Christopher I. , Cohen Stacey A. , Krane Mukta , Ose Jennifer , Gigic Biljana , Figueiredo Jane C. , Toriola Adetunji T. , Siegel Erin M. , Shibata David , Schneider Martin , Ulrich Cornelia M. , Dzubinski Lynda Ann. , Schoen Robert E. , Grady William M.
Published: September 2024

Supplemental Figure 1. Flowchart of patient selection. * Subjects with hereditary conditions or inflammatory bowel disease (IBD)
were excluded. A flowchart showing the selection process for the subjects included in the studies is shown. The primary exclusion
criteria that affected subject selection were lack of serial colonoscopy data and lack of tissue from the normal colon
Supplementary Table S2 from Development of a breast cancer risk prediction model incorporating polygenic risk scores and non-genetic risk factors for Korean women
Authors: Choi Jihye , Ha Tae-woong , Choi Hye-Mi , Lee Han-Byoel , Shin Hee-Chul , Chung Woosung , Han Wonshik
Published: September 2024

Weights used for linear combinations of multiple PRS
Supplementary Figure S2 from Development of a breast cancer risk prediction model incorporating polygenic risk scores and non-genetic risk factors for Korean women
Authors: Choi Jihye , Ha Tae-woong , Choi Hye-Mi , Lee Han-Byoel , Shin Hee-Chul , Chung Woosung , Han Wonshik
Published: September 2024

The absolute risk of developing breast cancer was estimated using data from 20,434 Korean women. The absolute risk of breast cancer was estimated by PRS38_ASN+PRS190_EB for women in different percentiles and age categories. The dotted lines represent the average risks. (A) Lifetime absolute risk (B) 5-year absolute risk
Supplementary Table 2 from Sex-specific associations between body mass index and thyroid cancer incidence among Korean adults
Authors: Kim Kyoung-Nam , Kim Kyungsik , Lee Sangjun , Park Sue K.
Published: September 2024

Supplementary Table 2 shows the associations between body mass index (BMI) and thyroid cancer incidence in the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) using the Western BMI cutoff
Supplementary Table 1 from Sex-specific associations between body mass index and thyroid cancer incidence among Korean adults
Authors: Kim Kyoung-Nam , Kim Kyungsik , Lee Sangjun , Park Sue K.
Published: September 2024

Supplementary Table 1 shows the characteristics of the cohort participants and incident thyroid cancer cases in the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) and the Korean Multi-center Cancer Cohort (KMCC)
Supplemental Table 6 from Validating Wave 1 (2014) urinary cotinine and TNE-2 cut-points for differentiating Wave 4 (2017) cigarette use from non-use in the US using data from the PATH Study.
Authors: Edwards Kathryn C. , Khan Asia , Sharma Eva , Wang Lanqing , Feng June , Blount Benjamin C. , Sosnoff Connie S. , Smith Danielle M. , Goniewicz Maciej L. , Pearson Jennifer , Villanti Andrea C. , Delnevo Cristine D. , Bover Manderski Michelle T. , Hatsukami Dorothy K. , Niaura Raymond , Everard Colm , Kimmel Heather L. , Duffy Kara , Rostron Brian L. , Del Valle-Pinero Arseima Y. , van Bemmel Dana M. , Stanton Cassandra A. , Hyland Andrew
Published: September 2024

Supplemental Table 6 provides Wave 1 and Wave 4 TNE-2 cut-points for any tobacco use.
Supplemental Table 4 from Validating Wave 1 (2014) urinary cotinine and TNE-2 cut-points for differentiating Wave 4 (2017) cigarette use from non-use in the US using data from the PATH Study.
Authors: Edwards Kathryn C. , Khan Asia , Sharma Eva , Wang Lanqing , Feng June , Blount Benjamin C. , Sosnoff Connie S. , Smith Danielle M. , Goniewicz Maciej L. , Pearson Jennifer , Villanti Andrea C. , Delnevo Cristine D. , Bover Manderski Michelle T. , Hatsukami Dorothy K. , Niaura Raymond , Everard Colm , Kimmel Heather L. , Duffy Kara , Rostron Brian L. , Del Valle-Pinero Arseima Y. , van Bemmel Dana M. , Stanton Cassandra A. , Hyland Andrew
Published: September 2024

Supplemental Table 4 provides prevalence rates of any tobacco use using TNE-2 cut-points.
Supplemental Table 2 from Validating Wave 1 (2014) urinary cotinine and TNE-2 cut-points for differentiating Wave 4 (2017) cigarette use from non-use in the US using data from the PATH Study.
Authors: Edwards Kathryn C. , Khan Asia , Sharma Eva , Wang Lanqing , Feng June , Blount Benjamin C. , Sosnoff Connie S. , Smith Danielle M. , Goniewicz Maciej L. , Pearson Jennifer , Villanti Andrea C. , Delnevo Cristine D. , Bover Manderski Michelle T. , Hatsukami Dorothy K. , Niaura Raymond , Everard Colm , Kimmel Heather L. , Duffy Kara , Rostron Brian L. , Del Valle-Pinero Arseima Y. , van Bemmel Dana M. , Stanton Cassandra A. , Hyland Andrew
Published: September 2024

Supplemental Table 2 provides demographic and tobacco use characteristics of any tobacco users.
Supplemental Table 1 from Validating Wave 1 (2014) urinary cotinine and TNE-2 cut-points for differentiating Wave 4 (2017) cigarette use from non-use in the US using data from the PATH Study.
Authors: Edwards Kathryn C. , Khan Asia , Sharma Eva , Wang Lanqing , Feng June , Blount Benjamin C. , Sosnoff Connie S. , Smith Danielle M. , Goniewicz Maciej L. , Pearson Jennifer , Villanti Andrea C. , Delnevo Cristine D. , Bover Manderski Michelle T. , Hatsukami Dorothy K. , Niaura Raymond , Everard Colm , Kimmel Heather L. , Duffy Kara , Rostron Brian L. , Del Valle-Pinero Arseima Y. , van Bemmel Dana M. , Stanton Cassandra A. , Hyland Andrew
Published: September 2024

Supplemental Table 1 provides demographic and tobacco use characteristics of exclusive and polytobacco users.
Supplemental Figure 1 from Validating Wave 1 (2014) urinary cotinine and TNE-2 cut-points for differentiating Wave 4 (2017) cigarette use from non-use in the US using data from the PATH Study.
Authors: Edwards Kathryn C. , Khan Asia , Sharma Eva , Wang Lanqing , Feng June , Blount Benjamin C. , Sosnoff Connie S. , Smith Danielle M. , Goniewicz Maciej L. , Pearson Jennifer , Villanti Andrea C. , Delnevo Cristine D. , Bover Manderski Michelle T. , Hatsukami Dorothy K. , Niaura Raymond , Everard Colm , Kimmel Heather L. , Duffy Kara , Rostron Brian L. , Del Valle-Pinero Arseima Y. , van Bemmel Dana M. , Stanton Cassandra A. , Hyland Andrew
Published: September 2024

Supplemental Figure 1 shows the analytic sample flow chart.
Supplementary Table 3 from Relationship between ABO blood group alleles and pancreatic cancer is modulated by secretor (FUT2) genotype, but not Lewis antigen (FUT3) genotype
Authors: Kim Jihye , Yuan Chen , Amundadottir Laufey T. , Wolpin Brian M. , Klein Alison P. , Risch Harvey A. , Kraft Peter
Published: September 2024

Table S3 presents adjusted associations of ABO blood group alleles and pancreatic cancer risk, stratified by secretor status and cohorts, and interactions between ABO blood group alleles and secretor status.
Supplementary Table S3 from Urine pH and Risk of Bladder Cancer in Northern New England
Authors: Fischer Alexander H. , Wong Jason YY. , Baris Dalsu , Koutros Stella , Karagas Margaret R. , Schwenn Molly , Johnson Alison , Alguacil Juan , Silverman Debra T. , Rothman Nathaniel
Published: September 2024

Supplementary Table S3 shows spot urine pH categorized into ≤6.0 and >6.0 and bladder cancer risk, stratified by smoking status.
Supplementary Table 3 from Sources of disparities in surveillance mammography performance and risk-guided recommendations for supplemental breast imaging: A simulation study
Authors: Hubbard Rebecca A. , Pujol Toyya A. , Alhajjar Elie , Edoh Kossi , Martin Melissa L.
Published: September 2024

Supplementary Table 3. Sensitivity and cancer risk prediction logistic regression model coefficients
Supplementary Table 2 from Sources of disparities in surveillance mammography performance and risk-guided recommendations for supplemental breast imaging: A simulation study
Authors: Hubbard Rebecca A. , Pujol Toyya A. , Alhajjar Elie , Edoh Kossi , Martin Melissa L.
Published: September 2024

Supplementary Table 2. Additional descriptive statistics for the Breast Cancer Surveillance Consortium cohort of surveillance mammograms
Supplementary Table S3 from Genetic susceptibility to nonalcoholic fatty liver disease and risk for pancreatic cancer: Mendelian randomization
Authors: King Sontoria D. , Veliginti Swathi , Brouwers Martijn C G J. , Ren Zhewen , Zheng Wei , Setiawan Veronica Wendy. , Wilkens Lynne R. , Shu Xiao-Ou , Arslan Alan A. , Beane Freeman Laura E. , Bracci Paige M. , Canzian Federico , Du Mengmeng , Gallinger Steven J. , Giles Graham G. , Goodman Phyllis J. , Haiman Christopher A. , Kogevinas Manolis , Kooperberg Charles , Le Marchand Loic , Neale Rachel E. , Visvanathan Kala , White Emily , Albanes Demetrius , Andreotti Gabriella , Babic Ana , Berndt Sonja I. , Brais Lauren K. , Brennan Paul , Buring Julie E. , Rabe Kari G. , Bamlet William R. , Chanock Stephen J. , Fuchs Charles S. , Gaziano J. Michael , Giovannucci Edward L. , Hackert Thilo , Hassan Manal M. , Katzke Verena , Kurtz Robert C. , Lee I-Min , Malats Nuria , Murphy Neil , Oberg Ann L. , Orlow Irene , Porta Miquel , Real Francisco X. , Rothman Nathaniel , Sesso Howard D. , Silverman Debra T. , Thompson Ian M. , Wactawski-Wende Jean , Wang Xiaoliang , Wentzensen Nicolas , Yu Herbert , Zeleniuch-Jacquotte Anne , Yu Kai , Wolpin Brian M. , Duell Eric J. , Li Donghui , Hung Rayjean J. , Perdomo Sandra , McCullough Marjorie L. , Freedman Neal D. , Patel Alpa V. , Peters Ulrike , Riboli Elio , Sund Malin , Tjønneland Anne , Zhong Jun , Van Den Eeden Stephen K. , Kraft Peter , Risch Harvey A. , Amundadottir Laufey T. , Klein Alison P. , Stolzenberg-Solomon Rachael Z. , Antwi Samuel O.
Published: September 2024

Supplementary Table S3 shows results of univariate analysis for 36 biopsy-confirmed nonalcoholic fatty liver disease SNPs in the PanScan sample
Supplementary Table S2 from Genetic susceptibility to nonalcoholic fatty liver disease and risk for pancreatic cancer: Mendelian randomization
Authors: King Sontoria D. , Veliginti Swathi , Brouwers Martijn C G J. , Ren Zhewen , Zheng Wei , Setiawan Veronica Wendy. , Wilkens Lynne R. , Shu Xiao-Ou , Arslan Alan A. , Beane Freeman Laura E. , Bracci Paige M. , Canzian Federico , Du Mengmeng , Gallinger Steven J. , Giles Graham G. , Goodman Phyllis J. , Haiman Christopher A. , Kogevinas Manolis , Kooperberg Charles , Le Marchand Loic , Neale Rachel E. , Visvanathan Kala , White Emily , Albanes Demetrius , Andreotti Gabriella , Babic Ana , Berndt Sonja I. , Brais Lauren K. , Brennan Paul , Buring Julie E. , Rabe Kari G. , Bamlet William R. , Chanock Stephen J. , Fuchs Charles S. , Gaziano J. Michael , Giovannucci Edward L. , Hackert Thilo , Hassan Manal M. , Katzke Verena , Kurtz Robert C. , Lee I-Min , Malats Nuria , Murphy Neil , Oberg Ann L. , Orlow Irene , Porta Miquel , Real Francisco X. , Rothman Nathaniel , Sesso Howard D. , Silverman Debra T. , Thompson Ian M. , Wactawski-Wende Jean , Wang Xiaoliang , Wentzensen Nicolas , Yu Herbert , Zeleniuch-Jacquotte Anne , Yu Kai , Wolpin Brian M. , Duell Eric J. , Li Donghui , Hung Rayjean J. , Perdomo Sandra , McCullough Marjorie L. , Freedman Neal D. , Patel Alpa V. , Peters Ulrike , Riboli Elio , Sund Malin , Tjønneland Anne , Zhong Jun , Van Den Eeden Stephen K. , Kraft Peter , Risch Harvey A. , Amundadottir Laufey T. , Klein Alison P. , Stolzenberg-Solomon Rachael Z. , Antwi Samuel O.
Published: September 2024

Supplementary Table S2 shows results of univariate analysis for 22 imaging-defined nonalcoholic fatty liver disease SNPs in the PanScan sample
Supplementary Table S10 from Genetic susceptibility to nonalcoholic fatty liver disease and risk for pancreatic cancer: Mendelian randomization
Authors: King Sontoria D. , Veliginti Swathi , Brouwers Martijn C G J. , Ren Zhewen , Zheng Wei , Setiawan Veronica Wendy. , Wilkens Lynne R. , Shu Xiao-Ou , Arslan Alan A. , Beane Freeman Laura E. , Bracci Paige M. , Canzian Federico , Du Mengmeng , Gallinger Steven J. , Giles Graham G. , Goodman Phyllis J. , Haiman Christopher A. , Kogevinas Manolis , Kooperberg Charles , Le Marchand Loic , Neale Rachel E. , Visvanathan Kala , White Emily , Albanes Demetrius , Andreotti Gabriella , Babic Ana , Berndt Sonja I. , Brais Lauren K. , Brennan Paul , Buring Julie E. , Rabe Kari G. , Bamlet William R. , Chanock Stephen J. , Fuchs Charles S. , Gaziano J. Michael , Giovannucci Edward L. , Hackert Thilo , Hassan Manal M. , Katzke Verena , Kurtz Robert C. , Lee I-Min , Malats Nuria , Murphy Neil , Oberg Ann L. , Orlow Irene , Porta Miquel , Real Francisco X. , Rothman Nathaniel , Sesso Howard D. , Silverman Debra T. , Thompson Ian M. , Wactawski-Wende Jean , Wang Xiaoliang , Wentzensen Nicolas , Yu Herbert , Zeleniuch-Jacquotte Anne , Yu Kai , Wolpin Brian M. , Duell Eric J. , Li Donghui , Hung Rayjean J. , Perdomo Sandra , McCullough Marjorie L. , Freedman Neal D. , Patel Alpa V. , Peters Ulrike , Riboli Elio , Sund Malin , Tjønneland Anne , Zhong Jun , Van Den Eeden Stephen K. , Kraft Peter , Risch Harvey A. , Amundadottir Laufey T. , Klein Alison P. , Stolzenberg-Solomon Rachael Z. , Antwi Samuel O.
Published: September 2024

Supplementary Table S10 shows descriptive Statistics for PanC4 sample
Supplementary Table 4 from Intake of Proton-Pump Inhibitors and Gastric Cancer Within the Stomach cancer Pooling (StoP) Project
Authors: Sassano Michele , Mariani Marco , Pelucchi Claudio , Lunet Nuno , Morais Samantha , Martín Vicente , Moreno Victor , Curado Maria Paula. , Dias-Neto Emmanuel , Leja Marcis , Gašenko Evita , La Vecchia Carlo , Boccia Stefania , Pastorino Roberta
Published: September 2024

Supplementary Table 4: Pooled adjusted odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for the association between intake of proton-pump inhibitors and gastric cancer from the leave-one-out meta-analysis.
Supplementary Figure 2 from Intake of Proton-Pump Inhibitors and Gastric Cancer Within the Stomach cancer Pooling (StoP) Project
Authors: Sassano Michele , Mariani Marco , Pelucchi Claudio , Lunet Nuno , Morais Samantha , Martín Vicente , Moreno Victor , Curado Maria Paula. , Dias-Neto Emmanuel , Leja Marcis , Gašenko Evita , La Vecchia Carlo , Boccia Stefania , Pastorino Roberta
Published: September 2024

Supplementary Figure 2: Pooled adjusted odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for the association between intake of proton-pump inhibitors and gastric cancer, by smoking status and alcohol drinking status.
Table S9 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Table S9 shows the number of risk regions that are common to both CCFR and GECCO in stage 3 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.
Table S8 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Table S8 shows the number of risk regions that are common to both CCFR and GECCO in stage 2 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.
Table S6 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Table S6 shows the number of risk regions identified by first stage DEPTH analysis.
Table S5 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Table S5 shows the summary of known colorectal risk loci that are located in our CCFR and GECCO datasets.
Table S11 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Table S11 shows the ranking of SNPs at the HLA locus.
Table S1 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Table S1 shows the original number of cases and controls in both the CCFR and GECCO datasets.
Figure S2 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Figure S2 of a principle components analysis showing the CCFR and GECCO datasets cluster with European Caucasians.
Figure S1 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Figure S1 showing the flow chart of the analyses for identifying novel, unique, and common
risk regions from DEPTH and logistic regression analyses.
Supplementary Table 3 from Hospitalisations and Cost of Inpatient Care for Physical Diseases in Survivors of Childhood Cancer in Western Australia: a Longitudinal Matched Cohort Study.
Authors: Abdalla Tasnim , Walwyn Thomas , White Daniel , Choong Catherine S. , Bulsara Max , Preen David B. , Ohan Jeneva L.
Published: September 2024

Adjusted annual mean costs of privately funded hospitalisations per individual, by all-cause and cause-specific physical diseases.
Supplementary Table 1 from Hospitalisations and Cost of Inpatient Care for Physical Diseases in Survivors of Childhood Cancer in Western Australia: a Longitudinal Matched Cohort Study.
Authors: Abdalla Tasnim , Walwyn Thomas , White Daniel , Choong Catherine S. , Bulsara Max , Preen David B. , Ohan Jeneva L.
Published: September 2024

Major diagnostic groups of physical-health diseases based on the International Classification of Diseases (ICD), 9th (Clinical modification) and 10th (Australian Modification) revisions.
Supplementary Table S2 from Characteristics of Cancer Epidemiology Studies that Employ Metabolomics: A Scoping Review
Authors: Yu Catherine T. , Farhat Zeinab , Livinski Alicia A. , Loftfield Erikka , Zanetti Krista A.
Published: September 2024

Supplementary Table S2 shows additional study design and analysis characteristics of population-based cancer metabolomics studies.
Supplementary Table S1 from Socioeconomic status at birth and breast tissue composition in adolescence and adulthood
Authors: Kehm Rebecca D. , Lilge Lothar , Walter E Jane , White Melissa , Herbstman Julie B. , Perera Frederica P. , Miller Rachel L. , Terry Mary Beth , Tehranifar Parisa
Published: September 2024

Supplementary Table S1 shows the characteristics of adolescent girls by household income at birth in the Columbia Breast Cancer and the Environment Research Program (Columbia-BCERP) Study, n=165.
Supplementary Figure S1 from Socioeconomic status at birth and breast tissue composition in adolescence and adulthood
Authors: Kehm Rebecca D. , Lilge Lothar , Walter E Jane , White Melissa , Herbstman Julie B. , Perera Frederica P. , Miller Rachel L. , Terry Mary Beth , Tehranifar Parisa
Published: September 2024

Supplementary Figure S1 shows how adolescent girls in the study were classified into the composite socioeconomic status index based on their annual household income and maternal education at birth.
Supplementary Table S4 from Racial and ethnic variation in diagnostic mammography performance among women reporting a breast lump
Authors: Nyante Sarah J. , Abraham Linn , Aiello Bowles Erin J. , Lee Christoph I. , Kerlikowske Karla , Miglioretti Diana L. , Sprague Brian L. , Henderson Louise M.
Published: September 2024

Model-based comparisons of diagnostic mammography performance by race and ethnicity
Supplementary Table 5 from Body Composition, Relative Dose Intensity, and Adverse Events Among Patients with Colon Cancer
Authors: Cheng En , Caan Bette J. , Cawthon Peggy M. , Evans William J. , Hellerstein Marc K. , Shankaran Mahalakshmi , Nyangau Edna , Campbell Kristin L. , Lee Catherine , Binder Alexandra M. , Meyerhardt Jeffrey A. , Schmitz Kathryn H. , Cespedes Feliciano Elizabeth M.
Published: September 2024

Supplementary Table 5. The Adjusted Associations of Body Composition Measurements (Per SD Increase) with Reduced RDI Counting from the Initiation of First Chemotherapy
Supplementary Table 4 from Body Composition, Relative Dose Intensity, and Adverse Events Among Patients with Colon Cancer
Authors: Cheng En , Caan Bette J. , Cawthon Peggy M. , Evans William J. , Hellerstein Marc K. , Shankaran Mahalakshmi , Nyangau Edna , Campbell Kristin L. , Lee Catherine , Binder Alexandra M. , Meyerhardt Jeffrey A. , Schmitz Kathryn H. , Cespedes Feliciano Elizabeth M.
Published: September 2024

Supplementary Table 4. The Adjusted Associations of Body Composition Measurements (Per SD Increase) with Reduced RDI Counting for Each Chemotherapy Agent
Supplementary Table 3 from Body Composition, Relative Dose Intensity, and Adverse Events Among Patients with Colon Cancer
Authors: Cheng En , Caan Bette J. , Cawthon Peggy M. , Evans William J. , Hellerstein Marc K. , Shankaran Mahalakshmi , Nyangau Edna , Campbell Kristin L. , Lee Catherine , Binder Alexandra M. , Meyerhardt Jeffrey A. , Schmitz Kathryn H. , Cespedes Feliciano Elizabeth M.
Published: September 2024

Supplementary Table 3. The Adjusted Associations of Scaled Body Composition Measurements (Per SD Increase) with Reduced RDI and Relative Changes (%) in the Number of Moderate and Severe Adverse Events
Supplementary Table 2 from Body Composition, Relative Dose Intensity, and Adverse Events Among Patients with Colon Cancer
Authors: Cheng En , Caan Bette J. , Cawthon Peggy M. , Evans William J. , Hellerstein Marc K. , Shankaran Mahalakshmi , Nyangau Edna , Campbell Kristin L. , Lee Catherine , Binder Alexandra M. , Meyerhardt Jeffrey A. , Schmitz Kathryn H. , Cespedes Feliciano Elizabeth M.
Published: September 2024

Supplementary Table 2. Sex-Specific Standard Deviations of Body Composition Measurements (CT SMA, DXA ALM, D3Cr Muscle, CT TAT, and DXA TBF)
Table S2 from Associations between ambient air pollutants and clonal hematopoiesis of indeterminate potential (CHIP)
Authors: Leiser Claire L. , Whitsel Eric A. , Reiner Alexander , Rich Stephen S. , Rotter Jerome I. , Taylor Kent D. , Tracy Russel P. , Kooperberg Charles , Smith Albert Vernon , Mason JoAnn E. , Mychaleckyj Josyf C. , Bick Alexander G. , Szpiro Adam A. , Kaufman Joel D.
Published: September 2024

Supplementary Table 2 shows additional results for non smokers and never smokers in the MESA and WHI cohorts.
Supplementary Table S6 from Prediagnostic prescription antibiotics use and survival in colorectal cancer patients: A Swedish national register-based study
Authors: Lu Sai San Moon , Rutegård Martin , Ahmed Maghfoor , Häggström Christel , Gylfe Åsa , Harlid Sophia , Van Guelpen Bethany
Published: September 2024

Supplementary Table S6: Crosstabulation of antibiotics use and tumour stage I-IV at diagnosis
Supplementary Table S5 from Prediagnostic prescription antibiotics use and survival in colorectal cancer patients: A Swedish national register-based study
Authors: Lu Sai San Moon , Rutegård Martin , Ahmed Maghfoor , Häggström Christel , Gylfe Åsa , Harlid Sophia , Van Guelpen Bethany
Published: September 2024

Supplementary Table S5: Antibiotics use in relation to overall and cancer specific survival among stage IV colorectal cancer patients by sex
Supplementary Table S3 from Prediagnostic prescription antibiotics use and survival in colorectal cancer patients: A Swedish national register-based study
Authors: Lu Sai San Moon , Rutegård Martin , Ahmed Maghfoor , Häggström Christel , Gylfe Åsa , Harlid Sophia , Van Guelpen Bethany
Published: September 2024

Supplementary Table S3: Characteristics of stage I-III colorectal cancer patients by limiting the antibiotics use to a 4.5-year period before colorectal cancer diagnosis
Supplementary Table S2 from Prediagnostic prescription antibiotics use and survival in colorectal cancer patients: A Swedish national register-based study
Authors: Lu Sai San Moon , Rutegård Martin , Ahmed Maghfoor , Häggström Christel , Gylfe Åsa , Harlid Sophia , Van Guelpen Bethany
Published: September 2024

Supplementary Table S2: Reported number of defined daily doses (DDD) for specific antibiotics
Supplementary Table S1 from Prediagnostic prescription antibiotics use and survival in colorectal cancer patients: A Swedish national register-based study
Authors: Lu Sai San Moon , Rutegård Martin , Ahmed Maghfoor , Häggström Christel , Gylfe Åsa , Harlid Sophia , Van Guelpen Bethany
Published: September 2024

Supplementary Table S1: Classification of antibiotics types and indications
Supplementary Figure S7 from Prediagnostic prescription antibiotics use and survival in colorectal cancer patients: A Swedish national register-based study
Authors: Lu Sai San Moon , Rutegård Martin , Ahmed Maghfoor , Häggström Christel , Gylfe Åsa , Harlid Sophia , Van Guelpen Bethany
Published: September 2024

Supplementary Figure S7: Antibiotics use in relation to overall and cancer-specific survival in stage IV colorectal cancer by
antibiotics classes, typical indications and specific antibiotics drugs.
The hazards ratios (HR) are derived from the Cox proportional hazards models, stratified by tumour sites (proximal
colon, distal colon, rectum, unspecified), sex (male/female), age at diagnosis (</≥ 50 years), prediagnostic Charlson
comorbidity index (no/any comorbidity), income status (1st/2nd/3rd/4th quartile), and adjusted for age and calendar
year of diagnosis and prediagnostic Charlson comorbidity index as continuous variables, place of birth, county, marital
status, level of education, family history of colorectal cancer, palliative chemotherapy and surgical therapy as categorical
variables, defined as presented in Supplementary Table S4. Any use of specific antibiotics class was compared to no
reported use of specific antibiotics class (reference). Antibiotics use during one year before colorectal cancer diagnosis
was excluded to account for potential use due to undiagnosed but clinically manifest colorectal cancer. Antibiotics that
primarily or only affect aerobic bacteria were categorized as anti-aerobic antibiotics. Antibiotics with effect on both
2
anaerobic and aerobic bacteria, and metrodinazoles and/or tinidazoles (which only affect anaerobic bacteria) were categorized as anti-anaerobic antibiotics. Phenoxymethylpenicillin is presented as a specific antibiotic drug but was also the sole typical drug for the indication uncomplicated respiratory infections and some skin and soft tissue infections. Clindamycin is presented as a specific antibiotic drug but was also the sole typical drug for the indication complicated skin and soft tissue and deep-seated infections. Amoxicillin includes amoxicillin with beta-lactamase inhibitor. UTI = urinary tract infection, CI = confidence interval. Detailed antibiotics classification and typical indications are presented in Supplementary Table S1.
Figure S2 from A Genomic Analysis of Esophageal Squamous Cell Carcinoma in Eastern Africa
Authors: Van Loon Katherine , Mmbaga Elia J. , Mushi Beatrice P. , Selekwa Msiba , Mwanga Ally , Akoko Larry Onyango. , Mwaiselage Julius , Mosha Innocent , Ng Dianna L. , Wu Wei , Silverstein Jordyn , Mulima Gift , Kaimila Bongani , Gopal Satish , Snell Jeff M. , Benz Stephen C. , Vaske Charles , Sanborn Zack , Sedgewick Andrew J. , Radenbaugh Amie , Newton Yulia , Collisson Eric A.
Published: September 2024

Supplementary Figure S2. Expression of Mutsig CV genes in cohorts from Tanzania (Panel A) and Malawi (Panel B).
Supplementary Table 2 from Changes in geographic variation and socioeconomic inequalities in esophageal cancer mortality in China, 1973-2017
Authors: Ran Xianhui , Zheng Rongshou , Zeng Hongmei , Zhang Siwei , Sun Kexin , Han Bingfeng , Wang Shaoming , Chen Ru , Li Li , Wei Wenqiang , He Jie
Published: September 2024

Supplementary Table 2 shows the change in esophageal cancer mortality rates in counties with and without screening programs.
Supplementary Figure 2 from Changes in geographic variation and socioeconomic inequalities in esophageal cancer mortality in China, 1973-2017
Authors: Ran Xianhui , Zheng Rongshou , Zeng Hongmei , Zhang Siwei , Sun Kexin , Han Bingfeng , Wang Shaoming , Chen Ru , Li Li , Wei Wenqiang , He Jie
Published: September 2024

Supplementary Figure 2 was a scatter diagram of the age-standardized esophageal cancer mortality rate in 1973-1975 and the absolute change in mortality rates from 1973–1975 to 2015–2017.
Supplementary Table S5 from Genome-scale methylation analysis identifies immune profiles and age acceleration associations with bladder cancer outcomes
Authors: Chen Ji-Qing , Salas Lucas A. , Wiencke John K. , Koestler Devin C. , Molinaro Annette M. , Andrew Angeline S. , Seigne John D. , Karagas Margaret R. , Kelsey Karl T. , Christensen Brock C.
Published: September 2024

Supplementary Table S5: The information of optimal CpGs selected by SS-RPMM using the model controlling for Pheno and Hannum age acceleration respectively
Supplementary Table S3 from Genome-scale methylation analysis identifies immune profiles and age acceleration associations with bladder cancer outcomes
Authors: Chen Ji-Qing , Salas Lucas A. , Wiencke John K. , Koestler Devin C. , Molinaro Annette M. , Andrew Angeline S. , Seigne John D. , Karagas Margaret R. , Kelsey Karl T. , Christensen Brock C.
Published: September 2024

Supplementary Table S3: Cox proportional hazards models of immune cell proportions and NMIBC patient outcomes (For Hannum Age Acceleration)
Supplementary Table 5 from The Association of Kidney Function and Inflammatory Biomarkers with Epithelial Ovarian Cancer Risk
Authors: Hathaway Cassandra A. , Townsend Mary K. , Sklar Elliot M. , Thomas-Purcell Kamilah B. , Terry Kathryn L. , Trabert Britton , Tworoger Shelley S.
Published: September 2024

Hazard ratios (HR) and 95% confidence intervals (95%CI) of kidney related markers measured in urine and ovarian cancer risk by C Reactive Protein (CRP) in the UK Biobank.
Supplementary Table S2 from Sex-specific associations between adiponectin and leptin signaling and pancreatic cancer survival
Authors: Babic Ana , Wang Qiao-Li , Lee Alice A. , Yuan Chen , Rifai Nader , Luo Juhua , Tabung Fred K. , Shadyab Aladdin , Wactawski-Wende Jean , Saquib Nazmus , Kim Jihye , Kraft Peter , Sesso Howard D. , Buring Julie E. , Giovannucci Edward L. , Manson JoAnn E. , Stampfer Meir J. , Ng Kimmie , Fuchs Charles S. , Wolpin Brian M.
Published: September 2024

Association between SNPs in adiponectin receptor genes and pancreatic cancer mortality by sex
Supplementary Table S3 from Assessing a Polygenic Risk Score for Lung Cancer Susceptibility in Non-Hispanic White and Black Populations
Authors: Trendowski Matthew R. , Lusk Christine M. , Wenzlaff Angela S. , Neslund-Dudas Christine , Gadgeel Shirish M. , Soubani Ayman O. , Schwartz Ann G.
Published: September 2024

This supplementary table indicates the association between SNPs in the PRS and smoking intensity.
Data from Associations of race and ethnicity with hepatocellular carcinoma, decompensation, and mortality in US Veterans with cirrhosis
Authors: VoPham Trang , Cravero Anne , Feld Lauren D. , Green Pamela , Feng Ziding , Berry Kristin , Kim Nicole J. , Vutien Philip , Mendoza Jason A. , Ioannou George N.
Published: September 2024

Abstract
Background: Among cirrhosis patients, it remains unclear whether there are racial/ethnic differences in cirrhosis complications and mortality. We examined the associations between race/ethnicity and risk for hepatocellular carcinoma (HCC), cirrhosis decompensation, and all-cause mortality overall and by cirrhosis etiology.
Methods: US Veterans diagnosed with cirrhosis from 2001-2014 (n=120,992), due to hepatitis C virus (HCV) (n=55,814), alcohol-associated liver disease (ALD) (n=36,323), hepatitis B virus (HBV) (n=1,972), nonalcoholic fatty liver disease (NAFLD) (n=17,789), or other (n=9,094), were followed through 2020 for incident HCC (n=10,242), cirrhosis decompensation (n=27,887), and mortality (n=81,441). Multivariable Cox proportional hazards regression was used to estimate adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs).
Results: Compared to non-Hispanic White patients, Hispanic patients had higher risk for HCC overall (aHR 1.32, 95% CI 1.24-1.41) and by cirrhosis etiology, particularly for ALD- (aHR 1.63, 95% CI 1.42-1.87) and NAFLD-cirrhosis (aHR 1.76, 95% CI 1.41-2.20), while non-Hispanic Black patients had lower HCC risk in ALD- (aHR 0.79, 95% CI 0.63-0.98) and NAFLD-cirrhosis (aHR 0.54, 95% CI 0.33-0.89). Asian patients had higher HCC risk (aHR 1.70, 95% CI 1.29-2.23), driven by HCV- and HBV-cirrhosis. Non-Hispanic Black patients had lower risk for cirrhosis decompensation overall (aHR 0.71, 95% CI 0.68-0.74) and by cirrhosis etiology. There was lower risk for mortality among all other racial/ethnic groups compared to non-Hispanic White patients.
Conclusions: Race/ethnicity is an important predictor for risk of developing HCC, decompensation, and mortality.
Impact: Future research should examine factors underlying these racial/ethnic differences to inform prevention, screening, and treatment for cirrhosis patients.
Data from A review of the application of spatial survival methods in cancer research: trends, modelling and visualization techniques
Authors: Bizuayehu Habtamu Mellie , Cameron Jessica K. , Dasgupta Paramita , Baade Peter D.
Published: September 2024

Abstract
Spatial modelling of cancer survival is an important tool for identifying geographic disparities and providing an evidence base for resource allocation. Many different approaches have attempted to understand how survival varies geographically. This is the first scoping review to describe different methods and visualization techniques and to assess temporal trends in publications. The review was carried out using the PRISMA guideline using PubMed and Web of Science databases. Two authors independently screened articles. Articles were eligible for review if they measured cancer survival outcomes in small geographical areas by using spatial regression and/or mapping. Thirty-two articles were included, and the number increased over time. Most articles have been conducted in high-income countries using cancer registry databases. Eight different methods of modelling spatial survival were identified, and there were seven different ways of visualizing the results. Increasing the use of spatial modelling through enhanced data availability and knowledge sharing could help inform and motivate efforts to improve cancer outcomes and reduce excess deaths due to geographical inequalities. Efforts to improve the coverage and completeness of population-based cancer registries should continue to be a priority, in addition to encouraging the open sharing of relevant statistical programming syntax and international collaborations.
Data from Associations between etiologic or prognostic tumor tissue markers and neighborhood contextual factors in male health professionals diagnosed with prostate cancer
Authors: Iyer Hari S. , Kensler Kevin H. , Vaselkiv Jane B. , Stopsack Konrad H. , Roscoe Charlotte , Bandera Elisa V. , Qin Bo , Jang Thomas L. , Lotan Tamara L. , James Peter , Hart Jaime E. , Mucci Lorelei A. , Laden Francine , Rebbeck Timothy R.
Published: September 2024

Abstract
Background:
There is growing evidence that unfavorable neighborhood contexts may influence prostate cancer (CaP) progression. Whether these associations may be explained in part by differences in tumor-level somatic alterations remain unclear.
Methods:
Data on tumor markers (PTEN, p53, ERG, and SPINK1) were obtained from 1,157 participants with CaP in the Health Professionals Follow-up Study. Neighborhood greenness, socioeconomic status, and the income Index of Concentration at Extremes were obtained from satellite and Census data and linked to participants’ address at diagnosis and at study enrollment. Exposures were scaled to an interquartile range and modeled as tertiles. Bivariate associations between tertiles of neighborhood factors and tumor markers were assessed in covariate adjusted logistic regression models to estimate odds ratios and 95% confidence intervals.
Results:
There was no association between any of the neighborhood contextual factors and PTEN, p53, ERG, or SPINK1 in bivariate or multivariable adjusted models. Results were generally consistent when modeling exposure using exposure at diagnosis or at study enrollment.
Discussion:
In this multilevel study of men with CaP, we found no evidence of associations between neighborhood context and tumor tissue markers.
Impact:
Our results provide some of the first empirical data in support of the hypothesis that CaP risk conferred by tumor tissue markers may arise independently of underlying neighborhood context. Prospective studies in more diverse populations are needed to confirm these findings.
Data from Folate intake and ovarian cancer risk among women with endometriosis: a case-control study from the Ovarian Cancer Association Consortium
Authors: Gersekowski Kate , Ibiebele Torukiri I. , Doherty Jennifer A. , Harris Holly R. , Goodman Marc T. , Terry Kathryn L. , Wu Anna H. , Bandera Elisa V. , Qin Bo , Ong Jue-Sheng , Tyrer Jonathan P. , Dixon-Suen Suzanne C. , Modugno Francesmary , Risch Harvey A. , Webb Penelope M.
Published: September 2024

Abstract
Background
While folate intake has not been associated with an increased risk of ovarian cancer overall, studies of other cancer types have suggested that high folate intake may promote carcinogenesis in pre-cancerous lesions. Women with endometriosis (a potential pre-cancerous lesion) have an increased risk of developing ovarian cancer; however, whether high folate intake increases risk in this group is unknown.
Methods
We conducted a pooled analysis of six case-control studies from the Ovarian Cancer Association Consortium to investigate the association between folate intake and risk of ovarian cancer among women with and without self-reported endometriosis. We included 570 cases/558 controls with and 5,171/7,559 without endometriosis. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals for the association between folate intake (dietary, supplemental, total) and ovarian cancer risk. Finally, we used Mendelian randomization (MR) to evaluate our results using genetic markers as a proxy for folate status.
Results
Higher dietary folate intake was associated with an increased risk of ovarian cancer for women with endometriosis (OR 1.37[1.01-1.86]) but not for women without endometriosis. There was no association between supplemental folate intake and ovarian cancer risk for women with or without endometriosis. A similar pattern was seen using MR.
Conclusions
High dietary folate intake may be associated with an increased risk of ovarian cancer among women with endometriosis.
Impact
Women with endometriosis with high folate diets may be at increased risk of ovarian cancer. Further research is needed on the potential cancer-promoting effects of folate in this group.
Data from Sources of disparities in surveillance mammography performance and risk-guided recommendations for supplemental breast imaging: A simulation study
Authors: Hubbard Rebecca A. , Pujol Toyya A. , Alhajjar Elie , Edoh Kossi , Martin Melissa L.
Published: September 2024

Abstract
Background: Surveillance mammography is recommended for all women with a history of breast cancer.

Risk-guided surveillance incorporating advanced imaging modalities based on individual risk of a second cancer could improve cancer detection. However, personalized surveillance may also amplify disparities.

Methods: In simulated populations using inputs from the Breast Cancer Surveillance Consortium (BCSC), we investigated race- and ethnicity-based disparities. Disparities were decomposed into those due to primary breast cancer and treatment characteristics, social determinants of health (SDOH) and differential error in second cancer ascertainment by modeling populations with or without variation across race and ethnicity in the distribution of these characteristics. We estimated effects of disparities on mammography performance and supplemental imaging recommendations stratified by race and ethnicity.

Results: In simulated cohorts based on 65,446 BCSC surveillance mammograms, when only cancer characteristics varied by race and ethnicity, mammograms for Black women had lower sensitivity compared to the overall population (64.1% vs 71.1%). Differences between Black women and the overall population were larger when both cancer characteristics and SDOH varied by race and ethnicity (53.8% vs 71.1%). Basing supplemental imaging recommendations on high predicted second cancer risk resulted in less frequent recommendations for Hispanic (6.7%) and Asian/Pacific Islander women (6.4%) compared to the overall population (10.0%).

Conclusions: Variation in cancer characteristics and SDOH led to disparities in surveillance mammography performance and recommendations for supplemental imaging.

Impact: Risk-guided surveillance imaging may exacerbate disparities. Decision-makers should consider implications for equity in cancer outcomes resulting from implementing risk-guided screening programs.