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PurposeCurrently, rural residents in the United States (US) experience a greater cancer burden for tobacco-related cancers and cancers that can be prevented by screening. We aim to characterize geographic determinants of colorectal cancer (CRC) incidence in Louisiana due to rural residence and other known geographic risk factors, area socioeconomic status (SES), and cultural region (Acadian or French-speaking).MethodsPrimary colorectal cancer diagnosed among adults 30 years and older in 2008–2017 were obtained from the Louisiana Tumor Registry. Population and social and economic data were obtained from US Census American Community Survey. Rural areas were defined using US Department of Agriculture 2010 rural–urban commuting area codes. Estimates of relative risk (RR) were obtained from multilevel binomial regression models of incidence.ResultsThe study population was 16.1% rural, 18.4% low SES, and 17.9% Acadian. Risk of CRC was greater among rural white residents (RR Women: 1.09(1.02–1.16), RR Men: 1.11(1.04–1.18)). Low SES was associated with increased CRC for all demographic groups, with excess risk ranging from 8% in Black men (RR: 1.08(1.01–1.16)) to 16% in white men (RR: 1.16(1.08–1.24)). Increased risk in the Acadian region was greatest for Black men (RR: 1.21(1.10–1.33)) and women (RR: 1.21(1.09–1.33)). Rural–urban disparities in CRC were no longer significant after controlling for SES and Acadian region.ConclusionSES remains a significant determinant of CRC disparities in Louisiana and may contribute to observed rural–urban disparities in the state. While the intersectionality of CRC risk factors is complex, we have confirmed a robust regional disparity for the Acadian region of Louisiana.

Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.

Early-onset colorectal cancer incidence rates among patients aged 45 to 49 years have been considered much lower compared with the rates among patients aged 50 to 54 years, prompting debate about earlier screening benefits at 45 years. However, the observed incidence rates in the Surveillance, Epidemiology, and End Results (SEER) registries may underestimate colorectal cancer case burdens in those younger than 50 years compared with those older than 50 years because average-risk screening is generally not performed to detect preclinical cases of colorectal cancer. Finding steep incidence increases of invasive stage (beyond in situ) cases of colorectal cancer from age 49 to 50 years would be consistent with high rates of preexisting, undetected cancers in younger patients ultimately receiving a diagnosis of colorectal cancer after undergoing screening at 50 years. To assess the preclinical burden of colorectal cancer by analyzing its incidence in 1-year age increments, focusing on the transition between ages 49 and 50 years. Data from the SEER 18 registries, representing 28% of the US population, were used to conduct a cross-sectional study of colorectal cancer incidence rates from January 1, 2000, to December 31, 2015, in 1-year age increments (ages 30-60 years) stratified by US region (South, West, Northeast, and Midwest), sex, race, disease stage, and tumor location. Statistical analysis was conducted from November 1, 2018, to December 15, 2019. Incidence rates of colorectal cancer. A total of 170 434 cases of colorectal cancer were analyzed among 165 160 patients (92 247 men [55.9%]; mean [SD] age, 51.6 [6.7] years). Steep increases in the incidence of colorectal cancer in the SEER 18 registries were found from 49 to 50 years of age (46.1% increase: 34.9 [95% CI, 34.1-35.8] to 51.0 [95% CI, 50.0-52.1] per 100 000 population). Steep rate increases from 49 to 50 years of age were also seen in all US regions, men and women, white and black populations, and in colon and rectal cancers. The rate ratio incidence increase in the SEER 18 registries from 49 to 50 years of age (1.46 [95% CI, 1.43-1.51]) was significantly higher than earlier 1-year age transitions. Steep rate increases in the SEER 18 registries were found from 49 to 50 years of age in localized-stage (75.9% increase: 11.2 [95% CI, 10.7-11.7] to 19.7 [95% CI, 19.0-20.3] per 100 000) and regional-stage (30.3% increase: 13.2 [95% CI, 12.7-13.8] to 17.2 [95% CI, 16.7-17.8] per 100 000) colorectal cancers. A total of 8799 of the 9474 cases (92.9%) of colorectal cancer in the SEER 18 registries from 2000 to 2015 that were diagnosed among individuals aged 50 years were invasive. Steep incidence increases between 49 and 50 years of age are consistent with previously undetected colorectal cancers diagnosed via screening uptake at 50 years. These cancers are not reflected in observed rates of colorectal cancer in the SEER registries among individuals younger than 50 years. Hence, using observed incidence rates from 45 to 49 years of age alone to assess potential outcomes of earlier screening may underestimate cancer prevention benefits.

Purpose To examine (1) the trend and associated factors of Oncotype DX (ODX) use among hormone receptor-positive (HR+) breast cancer (BC) patients in 2004–2015; (2) the trend of reported chemotherapy by Recurrence Score (RS); and (3) the survival differences associated with ODX use. Methods ODX data from Genomic Health Inc. were linked with 17 SEER registries data. HR + BC cases with lymph node negative (N0) or 1–3 positive LNs (N1) from 2004–2015 were analyzed. The Cochrane-Armitage trend test, logistic regression, Kaplan–Meier survival curve, and stratified Cox model were performed. Survival analysis was restricted to HR+/HER2− patients from 2010 to 2014, matched on propensity score. Results ODX use increased substantially from 2004 to 2015 (N0: 2.0% to 42.7%; N1: 0.3% to 27.9%). Non-Hispanic black and Medicaid insured patients had lower odds of receiving ODX. N0 patients with moderately differentiated or 2.1–5.0 cm tumor and N1 patients with well-differentiated or < 2.0 cm tumor had higher odds of using ODX. The reported chemotherapy use decreased significantly with low and intermediate RS, and increased for high RS among N0 patients. ODX use was associated with better breast cancer-specific survival [hazard ratio (95% CI) N0 1.96 (1.60–2.41), N1 1.90 (1.42–2.54)] and overall survival [N0 2.06 (1.83–2.31), N1 1.72 (1.42–2.09)], especially in the first 36 months. Conclusion ODX use has increased significantly since 2004, nonetheless disparities remain, especially for racial/ethnic minorities and Medicaid insured patients. Administering chemotherapy based on ODX results has been improved among N0 patients. Patients receiving ODX had better survival than those not.

PurposeTo investigate the impact of chemotherapy relative dose intensity (RDI) on cause-specific and overall survival for stage I–III breast cancer: estrogen receptor or progesterone receptor positive, human epidermal-growth factor receptor negative (ER+/PR+ and HER2-) vs. triple-negative (TNBC) and to identify the optimal RDI cut-off points in these two patient populations.MethodsData were collected by the Louisiana Tumor Registry for two CDC-funded projects. Women diagnosed with stage I–III ER+/PR+, HER2- breast cancer, or TNBC in 2011 with complete information on RDI were included. Five RDI cut-off points (95, 90, 85, 80, and 75%) were evaluated on cause-specific and overall survival, adjusting for multiple demographic variables, tumor characteristics, comorbidity, use of granulocyte-growth factor/cytokines, chemotherapy delay, chemotherapy regimens, and use of hormone therapy. Cox proportional hazards models and Kaplan–Meier survival curves were estimated and adjusted by stabilized inverse probability treatment weighting (IPTW) of propensity score.ResultsOf 494 ER+/PR+, HER2- patients and 180 TNBC patients, RDI < 85% accounted for 30.4 and 27.8%, respectively. Among ER+/PR+, HER2- patients, 85% was the only cut-off point at which the low RDI was significantly associated with worse overall survival (HR = 1.93; 95% CI 1.09–3.40). Among TNBC patients, 75% was the cut-off point at which the high RDI was associated with better cause-specific (HR = 2.64; 95% CI 1.09, 6.38) and overall survival (HR = 2.39; 95% CI 1.04–5.51).ConclusionsHigher RDI of chemotherapy is associated with better survival for ER+/PR+, HER2- patients and TNBC patients. To optimize survival benefits, RDI should be maintained ≥ 85% in ER+/PR+, HER2- patients, and ≥ 75% in TNBC patients.

Objective This research describes the clinical pathway and characteristics of two cohorts of patients. The first cohort consists of patients with a confirmed diagnosis of lung cancer while the second consists of patients with a solitary pulmonary nodule (SPN) and no evidence of lung cancer. Linked data from an electronic medical record and the Louisiana Tumor Registry were used in this investigation. Materials and methods REACHnet is one of 9 clinical research networks (CRNs) in PCORnet®, the National Patient-Centered Clinical Research Network and includes electronic health records for over 8 million patients from multiple partner health systems. Data from Ochsner Health System and Tulane Medical Center were linked to Louisiana Tumor Registry (LTR), a statewide population-based cancer registry, for analysis of patient’s clinical pathways between July 2013 and 2017. Patient characteristics and health services utilization rates by cancer stage were reported as frequency distributions. The Kaplan-Meier product limit method was used to estimate the time from index date to diagnosis by stage in lung cancer cohort. Results A total of 30,559 potentially eligible patients were identified and 2929 (9.58%) had primary lung cancer. Of these, 1496 (51.1%) were documented in LTR and their clinical pathway to diagnosis was further studied. Time to diagnosis varied significantly by cancer stage. A total of 24,140 patients with an SPN were identified in REACHnet and 15,978 (66.6%) had documented follow up care for 1 year. 1612 (10%) had no evidence of any work up for their SPN. The remaining 14,366 had some evidence of follow up, primarily office visits and additional chest imaging. Conclusion In both cohorts multiple biopsies were evident in the clinical pathway. Despite clinical workup, 70% of patients in the lung cancer cohort had stage III or IV disease. In the SPN cohort, only 66% were identified as receiving a diagnostic work-up.

Previous research demonstrated Non-Hispanic Black populations experience higher COVID-19 mortality rates than Non-Hispanic White individuals. Additionally, cancer status is a known risk factor for COVID-19 death. While prior studies investigated comorbidities as exploratory variables in differences in COVID-19 hospitalization, none have explored their role in COVID-19-related deaths. This study aimed to evaluate whether Charlson Comorbidity Index (CCI) and subsequently, individual diseases are potential explanatory variables for this relationship. The analysis focused on Non-Hispanic Black and Non-Hispanic White cancer patients aged 20 or older, diagnosed between 2011 and 2019, who tested positive for COVID-19 from the start of pandemic through June 30, 2021 from Louisiana Tumor Registry. Two separate mediation analyses were conducted. First checked whether overall comorbidity, measured by CCI, could explain the difference in COVID-19 mortality. If so, further checked which individual comorbidities contributed to this difference. The hazard rate for Non-Hispanic Black cancer patients dying from COVID-19 was 6.46 times than that of Non-Hispanic White patients. The CCI accounted for 12.7% of the differences observed in COVID-19 mortality, with renal disease as the top contributor, explaining 4.9%. These findings could help develop interventions to reduce COVID-19 mortality and address the disproportionate impact, especially by managing chronic conditions like renal disease.

Background: Breast cancer outcomes are worse among Black women in the U.S. compared to White women. While extensive research has focused on risk factors contributing to breast cancer; the role of genomic elements in health disparities between these racial groups remains unclear. This study aims to identify genomic variants and socioeconomic status (SES) determinants influencing racial disparities in breast cancer survival through multiple mediation analyses. Methods: Our investigation is based on the NIH-supported All of Us (AoU) program and analyzes 7452 female participants with malignant tumors of breast, including 5073 with genomic data. A log-rank test reveals significant racial differences in overall survival time between Black and White participants (p-value = 0.04). Multiple mediation analysis examines the effects of 9481 genetic variables across 23 chromosomes in explaining the racial disparity in survival, adjusting for SES variables. Results: 15 gene mutations, in addition to age, general health, and general quality of life, have significant effects (p-values < 0.001) in explaining the observed racial disparity. Mutations in TMEM132B, NARFL, SALL1, PAD12, RIPK1, ASB14, DCX, GNB1L, ARHGAP32, AL135787.1, WBP11, SLC16A12AS1, AP000345.1, IKBKB, and SUPT20H have significantly different distributions between Black and White participants. The disparity is completely explained by the included variables as the direct effect is insignificant (p-value = 0.73). Conclusions: The combined impact of SES determinants and genetic mutations can explain the observed differences in breast cancer survival among Black and White participants. Future studies will explore pathways and design in vivo and in vitro experiments to validate the functions of these genes

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks-site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

Background A survival paradox between Stage IIB/C and Stage IIIA colon cancers exists. It is unclear how adequate lymph nodes dissection (LN) and post-surgery chemotherapy contribute to the survival paradox. We intended to assess the impact of these two factors on the survival paradox. Results We evaluated 34,999 patients diagnosed with stage IIIA or stage IIB/C colon cancer in 2003–2012 from the National Cancer Data Base. The 5-year overall survival (OS) was 73.5 % for stage IIIA and 51.1 % for stage IIB/C ( P  < 0.0001). The 5-year OS was 84.1 % for stage IIIA with post-surgery chemotherapy, 70.8 % for stage IIB/C with ≥ 12 LNs retrieved with chemotherapy, 53.9 % for stage IIB/C < 12 LNs with chemotherapy, 49.5 % for stage IIIA without chemotherapy, 43.7 % for stage IIB/C ≥ 12 LNs retrieved without chemotherapy, to 27.7 % for stage IIB/C < 12 LNs without chemotherapy. Even among stage IIB/C who had optimal treatment (≥12 LNs retrieved, received chemotherapy), OS remains lower than stage IIIA with chemotherapy. After adjusting LN dissection and chemotherapy in addition to the adjustment of other clinical factors, the survival paradox was reduced from HR = 1.76 (95 % CI: 1.68–1.85) to HR 1.51 (95 % CI: 1.44–1.59). Conclusions LN dissection and post-surgery chemotherapy partially explained the survival paradox. More research is warranted to identify other factors that contribute to this paradox. Future iteration of TNM staging system should take this into consideration.