Survival paradox between stage IIB/C and stage IIIA colon cancer: is it time to revise the American Joint Committee on Cancer TNM system?

INTRODUCTION: A survival paradox between T4N0 (Stage IIB/IIC) and Stage IIIA colon cancer exists, even after adjusting for adequate lymph node (LN) retrieval and receipt of adjuvant chemotherapy (C). We conducted a large hospital-based study to re-evaluate this survival paradox based on the newest 8th edition staging system. METHODS: The National Cancer Data Base was queried to evaluate 35,606 patients diagnosed with Stage IIB, IIC, and IIIA colon cancer between 2010 and 2017. The Kaplan-Meier method and log-rank test were used to compare unadjusted overall survival (OS). Multivariable Cox proportional hazards model was used to determine the association of stage with hazard ratios adjusted for relevant demographic and clinical variables including ≥ 12 LNs retrieved and receipt of adjuvant chemotherapy. P value < 0.05 was considered statistically significant. RESULTS: The 5-year OS for optimally treated stage IIIA colon cancer (receipt of C) was 84.3%, which was significantly higher than stage IIB/C (≥ 12 LNs retrieved + C) (72.8%; P < 0.0001). Stage was an independent predictor of OS. Among optimally treated Stage IIIA patients, T1N1 had the best survival (90.6%) while stage T4bN0 (stage IIC) had the worst (70.9%) (P < 0.0001). Compared to stage IIB, stage IIC had a 17% increased risk of overall death while stage IIIA had a 21% reduction in death (P < 0.0001). CONCLUSION: Stage IIB/C and Stage IIIA survival paradox persists even after accounting for receipt of adjuvant chemotherapy and adequate lymph node retrieval. Future iteration of the TNM system should take this paradox into consideration.

Demographic and Clinical Factors Associated With Health-Related Quality-of-Life Profiles Among Prostate Cancer Survivors.

PURPOSE: Our purpose was to describe the prevalence and predictors of symptom and function clusters related to physical, emotional, and social components of general health-related quality of life (HRQOL) in a population-based sample of prostate cancer (PCa) survivors. METHODS: Participants (N = 1,162) completed a baseline survey at a median of 9 months after diagnosis to ascertain the co-occurrence of eight symptom and functional domains that are common across all cancers and not treatment-specific. We used latent profile analysis (LPA) to identify subgroup profiles of survivors with low, moderate, or high HRQOL levels. Multinomial logistic regression models were used to identify clinical and sociodemographic factors associated with survivors' membership in the low versus moderate or high HRQOL profile. RESULTS: The LPA identified 16% of survivors who were categorized in the low HRQOL profile at baseline, indicative of the highest symptom burden and lowest functioning. Factors related to survivors' membership in the low versus higher HRQOL profile groups included less than age 65 years at diagnosis, identifying as non-Hispanic Black race, not working, being a former versus never smoker, systemic therapy, less companionship, more comorbidities, lower health care financial well-being, or less spirituality. Several factors remained associated with remaining in the low versus higher HRQOL profiles on the follow-up survey (n = 699), including younger age, Black race, comorbidity, and lower financial and spiritual well-being. CONCLUSION: About one of six PCa survivors experienced elevated physical and psychosocial symptoms that were independent of local curative therapy, but with younger age, race, comorbidity, and lower financial and spiritual well-being as stable risk factors for poor HRQOL over time.

Association of Major Adverse Financial Events and Later-Stage Cancer Diagnosis in the United States.

PURPOSE: This study assessed the prevalence of specific major adverse financial events (AFEs)-bankruptcies, liens, and evictions-before a cancer diagnosis and their association with later-stage cancer at diagnosis. METHODS: Patients age 20-69 years diagnosed with cancer during 2014-2015 were identified from the Seattle, Louisiana, and Georgia SEER population-based cancer registries. Registry data were linked with LexisNexis consumer data to identify patients with a history of court-documented AFEs before cancer diagnosis. The association of AFEs and later-stage cancer diagnoses (stages III/IV) was assessed using separate sex-specific multivariable logistic regression. RESULTS: Among 101,649 patients with cancer linked to LexisNexis data, 36,791 (36.2%) had a major AFE reported before diagnosis. The mean and median timing of the AFE closest to diagnosis were 93 and 77 months, respectively. AFEs were most common among non-Hispanic Black, unmarried, and low-income patients. Individuals with previous AFEs were more likely to be diagnosed with later-stage cancer than individuals with no AFE (males-odds ratio [OR], 1.09 [95% CI, 1.03 to 1.14]; P < .001; females-OR, 1.18 [95% CI, 1.13 to 1.24]; P < .0001) after adjusting for age, race, marital status, income, registry, and cancer type. Associations between AFEs prediagnosis and later-stage disease did not vary by AFE timing. CONCLUSION: One third of newly diagnosed patients with cancer had a major AFE before their diagnosis. Patients with AFEs were more likely to have later-stage diagnosis, even accounting for traditional measures of socioeconomic status that influence the stage at diagnosis. The prevalence of prediagnosis AFEs underscores financial vulnerability of patients with cancer before their diagnosis, before any subsequent financial burden associated with cancer treatment.

Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports.

PURPOSE: Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS: We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an off-the-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro F1 scores. RESULTS: We found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the site and laterality tasks. Versions of the Path-BigBird model performed best on the two most difficult tasks: subsite (micro F1 score of 72.53, macro F1 score of 35.76) and histology (micro F1 score of 80.96, macro F1 score of 37.94). The largest performance gains over the HiSAN model were for histology, for which a Path-BigBird model increased the micro F1 score by 1.44 points and the macro F1 score by 3.55 points. Overall, the results suggest that a Path-BigBird model with a vocabulary derived from well-curated and deidentified data is the best-performing model. CONCLUSION: The Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.

Medicaid Expansion Increases Access for Rural and Impoverished Patients with Pancreatic Ductal Adenocarcinoma in Southern States.

INTRODUCTION: Medicaid expansion (ME) impacted patients when assessed at a national level. However, of the 32 states in which Medicaid expansion occurred, only 3 were Southern states. Whether results apply to Southern states that share similar geopolitical perspectives remains elusive. We aimed to assess the impact of ME on pancreatic ductal adenocarcinoma (PDAC) treatment in eight Southern states in the USA. PATIENTS AND METHODS: We identified uninsured or Medicaid patients (age 40-64 years) diagnosed with PDAC between 2011 and 2018 in Southern states from the North American Association of Central Cancer Registries-Cancer in North America (NAACCR-CiNA) research dataset. Medicaid-expanded states (MES; Louisiana, Kentucky, and Arkansas) were compared with non-MES (NMES; Tennessee, Alabama, Mississippi, Texas, and Oklahoma) using multivariate logistic regression. P < 0.05 was considered statistically significant. RESULTS: Among 3036 patients, MES significantly increased odds of Medicaid insurance by 36%, and increased proportions of insured Black patients by 3.7%, rural patients by 3.8%, and impoverished patients by 18.4%. After adjusting for age, race, rural-urban status, poverty status, and summary stage, the odds of receiving radiation therapy decreased by 26% for each year of expansion in expanded states (P = 0.01). Last, ME did not result in a significant difference between MES and NMES in diagnosing early stage disease (P = 0.98) nor in receipt of chemotherapy or surgery (P = 0.23 and P = 0.63, respectively). CONCLUSIONS: ME in Southern states increased insurance access to traditionally underserved groups. Interestingly, ME decreased the odds of receiving radiation therapy yearly and had no significant impact on receipt of chemotherapy or surgery.

Adherence Rate to Alliance for Clinical Trials in Oncology Z0011 Trial Based on Breast Cancer Subtype.

BACKGROUND: The American College of Surgeons Oncology Group Z0011 (ACOSOG Z0011 or Z11) trial demonstrated no survival advantage with completion axillary lymph node dissection (ALND) for patients with T1-2 breast cancer, 1 to 2 positive SLNs who received adjuvant chemoradiation therapy. More than 70% of the cohort had estrogen receptor (ER)+ tumors. There is paucity of data on the adherence rate to Z11, as well as a dearth of data on the applicability of Z11 for the different subtypes. We conducted a large hospital-based study to evaluate the adherence rate to Z11 based on subtypes. STUDY DESIGN: The National Cancer Database was queried to evaluate 33,859 patients diagnosed with T1-2, N1, and M0 breast cancer treated with lumpectomy with negative margins, and adjuvant chemoradiation therapy between 2012 and 2018. Patients were classified into 3 groups: (1) ER+/HER2-, (2) ER-/HER2-, and (3) HER2+ regardless of ER status. The revised Scope of the Regional Lymph Node Surgery 2012 was used to classify patients into those who underwent an SLN or ALND. Differences in use of ALND by subtypes were compared. The Kaplan-Meier method and log-rank test were used to compare overall survival (OS). A p value of <0.05 was considered statistically significant. RESULTS: For ER+/human epidermal growth factor receptor 2 (HER2)-, ER-/HER2-, and HER2+ tumors, the rate of ALND was 43.6%, 50.2%, and 47.8%, respectively. The 5-year OS for SLN and ALND for the entire cohort was 94.0% and 93.1% (p = 0.0004); for ER+/HER2-, it was 95.4% and 94.7% (p = 0.04); for ER-/HER2-, it was 84.1% and 84.3% (p = 0.41); for HER2+, it was 94.2% and 93.2% (p = 0.20). Multivariable cox proportional hazard regression analysis demonstrated no significant survival differences between SLN and ALND (p = 0.776). CONCLUSIONS: Z11 is applicable for women with early N1 disease, regardless of subtypes. ALND did not confer a survival advantage over SLN. Despite this, up to 50% of patients who fit Z11 criteria continue to undergo ALND.

Functional Outcomes After Localized Prostate Cancer Treatment.

Importance: Adverse outcomes associated with treatments for localized prostate cancer remain unclear. Objective: To compare rates of adverse functional outcomes between specific treatments for localized prostate cancer. Design, Setting, and Participants: An observational cohort study using data from 5 US Surveillance, Epidemiology, and End Results Program registries. Participants were treated for localized prostate cancer between 2011 and 2012. At baseline, 1877 had favorable-prognosis prostate cancer (defined as cT1-cT2bN0M0, prostate-specific antigen level <20 ng/mL, and grade group 1-2) and 568 had unfavorable-prognosis prostate cancer (defined as cT2cN0M0, prostate-specific antigen level of 20-50 ng/mL, or grade group 3-5). Follow-up data were collected by questionnaire through February 1, 2022. Exposures: Radical prostatectomy (n = 1043), external beam radiotherapy (n = 359), brachytherapy (n = 96), or active surveillance (n = 379) for favorable-prognosis disease and radical prostatectomy (n = 362) or external beam radiotherapy with androgen deprivation therapy (n = 206) for unfavorable-prognosis disease. Main Outcomes and Measures: Outcomes were patient-reported sexual, urinary, bowel, and hormone function measured using the 26-item Expanded Prostate Cancer Index Composite (range, 0-100; 100 = best). Associations of specific therapies with each outcome were estimated and compared at 10 years after treatment, adjusting for corresponding baseline scores, and patient and tumor characteristics. Minimum clinically important differences were 10 to 12 for sexual function, 6 to 9 for urinary incontinence, 5 to 7 for urinary irritation, and 4 to 6 for bowel and hormone function. Results: A total of 2445 patients with localized prostate cancer (median age, 64 years; 14% Black, 8% Hispanic) were included and followed up for a median of 9.5 years. Among 1877 patients with favorable prognosis, radical prostatectomy was associated with worse urinary incontinence (adjusted mean difference, -12.1 [95% CI, -16.2 to -8.0]), but not worse sexual function (adjusted mean difference, -7.2 [95% CI, -12.3 to -2.0]), compared with active surveillance. Among 568 patients with unfavorable prognosis, radical prostatectomy was associated with worse urinary incontinence (adjusted mean difference, -26.6 [95% CI, -35.0 to -18.2]), but not worse sexual function (adjusted mean difference, -1.4 [95% CI, -11.1 to 8.3), compared with external beam radiotherapy with androgen deprivation therapy. Among patients with unfavorable prognosis, external beam radiotherapy with androgen deprivation therapy was associated with worse bowel (adjusted mean difference, -4.9 [95% CI, -9.2 to -0.7]) and hormone (adjusted mean difference, -4.9 [95% CI, -9.5 to -0.3]) function compared with radical prostatectomy. Conclusions and Relevance: Among patients treated for localized prostate cancer, radical prostatectomy was associated with worse urinary incontinence but not worse sexual function at 10-year follow-up compared with radiotherapy or surveillance among people with more favorable prognosis and compared with radiotherapy for those with unfavorable prognosis. Among men with unfavorable-prognosis disease, external beam radiotherapy with androgen deprivation therapy was associated with worse bowel and hormone function at 10-year follow-up compared with radical prostatectomy.

Deep learning uncertainty quantification for clinical text classification.

INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated. METHOD: In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount-that is, the number of electronic pathology reports for which the model's predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. RESULTS: Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. CONCLUSIONS: We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining-thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction.

PURPOSE: Manual extraction of case details from patient records for cancer surveillance is a resource-intensive task. Natural Language Processing (NLP) techniques have been proposed for automating the identification of key details in clinical notes. Our goal was to develop NLP application programming interfaces (APIs) for integration into cancer registry data abstraction tools in a computer-assisted abstraction setting. METHODS: We used cancer registry manual abstraction processes to guide the design of DeepPhe-CR, a web-based NLP service API. The coding of key variables was performed through NLP methods validated using established workflows. A container-based implementation of the NLP methods and the supporting infrastructure was developed. Existing registry data abstraction software was modified to include results from DeepPhe-CR. An initial usability study with data registrars provided early validation of the feasibility of the DeepPhe-CR tools. RESULTS: API calls support submission of single documents and summarization of cases across one or more documents. The container-based implementation uses a REST router to handle requests and support a graph database for storing results. NLP modules extract topography, histology, behavior, laterality, and grade at 0.79-1.00 F1 across multiple cancer types (breast, prostate, lung, colorectal, ovary, and pediatric brain) from data of two population-based cancer registries. Usability study participants were able to use the tool effectively and expressed interest in the tool. CONCLUSION: The DeepPhe-CR system provides an architecture for building cancer-specific NLP tools directly into registrar workflows in a computer-assisted abstraction setting. Improved user interactions in client tools may be needed to realize the potential of these approaches.

Rural versus urban commuting patients with stage III colon cancer: is there a difference in treatment and outcome?

BACKGROUND: To evaluate if there are differences in outcomes for patients with stage III colon cancer in those from urban vs. rural commuting areas. METHODS: Data were evaluated on patients diagnosed with stage III colon cancer between 2012 and2018 from the Louisiana Tumor Registry. Patients were classified into rural and urban groups. Data on overall survival, time from diagnosis to surgery and time from surgery to chemotherapy, and sociodemographic factors (including race, age, and poverty level) were recorded. RESULTS: Of 2652 patients identified, 2159 were urban (81.4%) and 493 rural (18.6%). No age difference between rural and urban patients (p = 0.56). Stage IIIB accounted for 66.7%, followed by IIIC (21.6%) and IIIA (11%), with a significant difference between rural and urban patients based on stage (p = 0.02). There was no difference in the extent of surgery (p = 0.34) or tumor size (p = 0.72) between urban and rural settings. No difference in undergoing chemotherapy (p = 0.12). There was a statistically significant difference in receiving timely treatment for hospital volume (p < 0.0001) and poverty level (p < 0.0001), but no difference in time from diagnosis to surgery (p = 0.48), and time from surgery to chemotherapy (p = 0.27). Non-Hispanic Blacks were less likely to receive timely treatment when compared with non-Hispanic Whites for both surgery and adjuvant chemotherapy, (aHR 0.91, 95% CI 0.83-0.99) and (aHR 0.86, 95% CI 0.77-0.97), respectively. There was no difference in Kaplan-Meier overall survival curves comparing rural vs. urban patients (p = 0.77). CONCLUSIONS: There was no statistical difference in overall survival, time to surgery, and time to adjuvant chemotherapy between rural and urban patients with Stage III colon cancer.

Effect of chronic disease on racial difference in COVID-19-associated hospitalization among cancer patients.

BACKGROUND: Research indicates that Black cancer patients have higher rates of COVID-19 hospitalization than their White counterparts. However, the extent to which chronic diseases contribute to racial disparities remains uncertain. We aimed to quantify the effect of chronic diseases on racial disparity in COVID-19-associated hospitalization among cancer patients. METHODS: We linked Louisiana Tumor Registry's data with statewide COVID-19 data and hospital in-patient discharge data to identify patients diagnosed with cancer in 2015-2019 who tested positive for COVID-19 in 2020 and those with COVID-19-associated hospitalization. Multivariable logistic regression and mediation methods based on linear structural equations were employed to assess the effects of the number of chronic diseases (0, 1-2, ≥3) and individual chronic diseases. RESULTS: Of 6381 cancer patients who tested positive for COVID-19, 31.6% were non-Hispanic Black cancer patients. Compared with non-Hispanic White cancer patients, non-Hispanic Black cancer patients had a higher prevalence of chronic diseases (79.5% vs 66.0%) and higher COVID-19-associated hospitalization (27.2% vs 17.2%). The odds of COVID-19-associated hospitalization were 80% higher for non-Hispanic Black cancer patients than non-Hispanic White cancer patients (odds ratio = 1.80, 95% confidence interval = 1.59 to 2.04). After adjusting for age, sex, insurance, poverty, obesity, and cancer type, number of chronic diseases explained 37.8% of the racial disparity in COVID-19-associated hospitalization, and hypertension, diabetes, and chronic renal disease were the top 3 chronic diseases explaining 9.6%, 8.9%, and 7.3% of the racial disparity, respectively. CONCLUSION: Chronic diseases played a substantial role in the racial disparity in COVID-19-associated hospitalization among cancer patients, especially hypertension, diabetes, and renal disease. Understanding and addressing the root causes are crucial for targeted interventions, policies, and health-care strategies to reduce racial disparity.

Use of the Decipher genomic classifier among men with prostate cancer in the United States.

BACKGROUND: Management of localized or recurrent prostate cancer since the 1990s has been based on risk stratification using clinicopathological variables, including Gleason score, T stage (based on digital rectal exam), and prostate-specific antigen (PSA). In this study a novel prognostic test, the Decipher Prostate Genomic Classifier (GC), was used to stratify risk of prostate cancer progression in a US national database of men with prostate cancer. METHODS: Records of prostate cancer cases from participating SEER (Surveillance, Epidemiology, and End Results) program registries, diagnosed during the period from 2010 through 2018, were linked to records of testing with the GC prognostic test. Multivariable analysis was used to quantify the association between GC scores or risk groups and use of definitive local therapy after diagnosis in the GC biopsy-tested cohort and postoperative radiotherapy in the GC-tested cohort as well as adverse pathological findings after prostatectomy. RESULTS: A total of 572 545 patients were included in the analysis, of whom 8927 patients underwent GC testing. GC biopsy-tested patients were more likely to undergo active active surveillance or watchful waiting than untested patients (odds ratio [OR] =2.21, 95% confidence interval [CI] = 2.04 to 2.38, P < .001). The highest use of active surveillance or watchful waiting was for patients with a low-risk GC classification (41%) compared with those with an intermediate- (27%) or high-risk (11%) GC classification (P < .001). Among National Comprehensive Cancer Network patients with low and favorable-intermediate risk, higher GC risk class was associated with greater use of local therapy (OR = 4.79, 95% CI = 3.51 to 6.55, P < .001). Within this subset of patients who were subsequently treated with prostatectomy, high GC risk was associated with harboring adverse pathological findings (OR = 2.94, 95% CI = 1.38 to 6.27, P = .005). Use of radiation after prostatectomy was statistically significantly associated with higher GC risk groups (OR = 2.69, 95% CI = 1.89 to 3.84). CONCLUSIONS: There is a strong association between use of the biopsy GC test and likelihood of conservative management. Higher genomic classifier scores are associated with higher rates of adverse pathology at time of surgery and greater use of postoperative radiotherapy.In this study the Decipher Prostate Genomic Classifier (GC) was used to analyze a US national database of men with prostate cancer. Use of the GC was associated with conservative management (ie, active surveillance). Among men who had high-risk GC scores and then had surgery, there was a 3-fold higher chance of having worrisome findings in surgical specimens.

Impact on the Volume of Pathology Reports Before and During the COVID-19 Pandemic in SEER Cancer Registries.

INTRODUCTION: Health care procedures including cancer screening and diagnosis were interrupted due to the COVID-19 pandemic. The extent of this impact on cancer care in the United States is not fully understood. We investigated pathology report volume as a reflection of trends in oncology services pre-pandemic and during the pandemic. METHODS: Electronic pathology reports were obtained from 11 U.S. central cancer registries from NCI's SEER Program. The reports were sorted by cancer site and document type using a validated algorithm. Joinpoint regression was used to model temporal trends from January 2018 to February 2020, project expected counts from March 2020 to February 2021 and calculate observed-to-expected ratios. Results were stratified by sex, age, cancer site, and report type. RESULTS: During the first 3 months of the pandemic, pathology report volume decreased by 25.5% and 17.4% for biopsy and surgery reports, respectively. The 12-month O/E ratio (March 2020-February 2021) was lowest for women (O/E 0.90) and patients 65 years and older (O/E 0.91) and lower for cancers with screening (melanoma skin, O/E 0.86; breast, O/E 0.88; lung O/E 0.89, prostate, O/E 0.90; colorectal, O/E 0.91) when compared with all other cancers combined. CONCLUSIONS: These findings indicate a decrease in cancer diagnosis, likely due to the COVID-19 pandemic. This decrease in the number of pathology reports may result in a stage shift causing a subsequent longer-term impact on survival patterns. IMPACT: Investigation on the longer-term impact of the pandemic on pathology services is vital to understand if cancer care delivery levels continue to be affected.

Is There Really a Difference in Outcomes between Men and Women with Hepatocellular Cancer?

Hepatocellular carcinoma (HCC) is a male-dominated disease. Currently, gender differences remain incompletely defined. Data from the state tumor registry were used to investigate differences in demographics, comorbidities, treatment patterns, and cancer-specific survival (HSS) among HCC patients according to gender. Additional analyses were performed to evaluate racial differences among women with HCC. 2627 patients with HCC were included; 498 (19%) were women. Women were mostly white (58%) or African American (39%)-only 3.8% were of another or unknown race. Women were older (65.1 vs. 61.3 years), more obese (33.7% vs. 24.2%), and diagnosed at an earlier stage (31.7% vs. 28.4%) than men. Women had a lower incidence of liver associated comorbidities (36.1% vs. 43%), and more often underwent liver-directed surgery (LDS; 27.5% vs. 22%). When controlling for LDS, no survival differences were observed between genders. African American women had similar HSS rates compared to white women (HR 1.14 (0.91,1.41), p = 0.239) despite having different residential and treatment geographical distributions. African American race and age >65 were predictive for worse HSS in men, but not in women. Overall, women with HCC undergo more treatment options-likely because of the earlier stage of the cancer and/or less severe underlying liver disease. However, when controlling for similar stages and treatments, HCC treatment outcomes were similar between men and women. African American race did not appear to influence outcomes among women with HCC as it did in men.

31-Gene Expression Profile Testing in Cutaneous Melanoma and Survival Outcomes in a Population-Based Analysis: A SEER Collaboration.

PURPOSE: The DecisionDx-Melanoma 31-gene expression profile (31-GEP) test is validated to classify cutaneous malignant melanoma (CM) patient risk of recurrence, metastasis, or death as low (class 1A), intermediate (class 1B/2A), or high (class 2B). This study aimed to examine the effect of 31-GEP testing on survival outcomes and confirm the prognostic ability of the 31-GEP at the population level. METHODS: Patients with stage I-III CM with a clinical 31-GEP result between 2016 and 2018 were linked to data from 17 SEER registries (n = 4,687) following registries' operation procedures for linkages. Melanoma-specific survival (MSS) and overall survival (OS) differences by 31-GEP risk category were examined using Kaplan-Meier analysis and the log-rank test. Crude and adjusted hazard ratios (HRs) were calculated using Cox regression model to evaluate variables associated with survival. 31-GEP tested patients were propensity score-matched to a cohort of non-31-GEP tested patients from the SEER database. Robustness of the effect of 31-GEP testing was assessed using resampling. RESULTS: Patients with a 31-GEP class 1A result had higher 3-year MSS and OS than patients with a class 1B/2A or class 2B result (MSS: 99.7% v 97.1% v 89.6%, P < .001; OS: 96.6% v 90.2% v 79.4%, P < .001). A class 2B result was an independent predictor of MSS (HR, 7.00; 95% CI, 2.70 to 18.00) and OS (HR, 2.39; 95% CI, 1.54 to 3.70). 31-GEP testing was associated with a 29% lower MSS mortality (HR, 0.71; 95% CI, 0.53 to 0.94) and 17% lower overall mortality (HR, 0.83; 95% CI, 0.70 to 0.99) relative to untested patients. CONCLUSION: In a population-based, clinically tested melanoma cohort, the 31-GEP stratified patients by their risk of dying from melanoma.

Breast and colorectal cancer recurrence-free survival estimates in the US: Modeling versus active data collection.

BACKGROUND: A modeling method was developed to estimate recurrence-free survival using cancer registry survival data. This study aims to validate the modeled recurrence-free survival against "gold-standard" estimates from data collected by the National Program of Cancer Registries (NPCR) Patient-Centered Outcomes Research (PCOR) project. METHODS: We compared 5-year metastatic recurrence-free survival using modeling and empirical estimates from the PCOR project that collected disease-free status, tumor progression and recurrence for colorectal and female breast cancer cases diagnosed in 2011 in 5 U.S. state registries. To estimate empirical recurrence-free survival, we developed an algorithm that combined disease-free, recurrence, progression, and date information from NPCR-PCOR data. We applied the modeling method to relative survival for patients diagnosed with female breast and colorectal cancer in 2000-2015 in the SEER-18 areas. RESULTS: When grouping patients with stages I-III, the 5-year metastatic recurrence-free modeled and NPCR-PCOR estimates are very similar being respectively, 90.2 % and 88.6 % for female breast cancer, 74.6 % and 75.3 % for colon cancer, and 68.8 % and 68.5 % for rectum cancer. In general, the 5-year recurrence-free NPCR-PCOR and modeled estimates are still similar when controlling by stage. The modeled estimates, however, are not as accurate for recurrence-free survival in years 1-3 from diagnosis. CONCLUSIONS: The alignment between NPCR-PCOR and modeled estimates supports their validity and provides robust population-based estimates of 5-year metastatic recurrence-free survival for female breast, colon, and rectum cancers. The modeling approach can in principle be extended to other cancer sites to provide provisional population-based estimates of 5-year recurrence free survival.

DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction.

Objective: The manual extraction of case details from patient records for cancer surveillance efforts is a resource-intensive task. Natural Language Processing (NLP) techniques have been proposed for automating the identification of key details in clinical notes. Our goal was to develop NLP application programming interfaces (APIs) for integration into cancer registry data abstraction tools in a computer-assisted abstraction setting. Methods: We used cancer registry manual abstraction processes to guide the design of DeepPhe-CR, a web-based NLP service API. The coding of key variables was done through NLP methods validated using established workflows. A container-based implementation including the NLP wasdeveloped. Existing registry data abstraction software was modified to include results from DeepPhe-CR. An initial usability study with data registrars provided early validation of the feasibility of the DeepPhe-CR tools. Results: API calls support submission of single documents and summarization of cases across multiple documents. The container-based implementation uses a REST router to handle requests and support a graph database for storing results. NLP modules extract topography, histology, behavior, laterality, and grade at 0.79-1.00 F1 across common and rare cancer types (breast, prostate, lung, colorectal, ovary and pediatric brain) on data from two cancer registries. Usability study participants were able to use the tool effectively and expressed interest in adopting the tool. Discussion: Our DeepPhe-CR system provides a flexible architecture for building cancer-specific NLP tools directly into registrar workflows in a computer-assisted abstraction setting. Improving user interactions in client tools, may be needed to realize the potential of these approaches. DeepPhe-CR: https://deepphe.github.io/.

Impact of Medicaid Expansion on Breast Cancer Diagnosis and Treatment in Southern States.

BACKGROUND: Medicaid expansion impacted patients when assessed at a national level. However, of the 32 states that expanded Medicaid, only three were Southern states. Whether results apply to Southern states that share similar geopolitical perspectives remains elusive. We aimed to assess the impact of Medicaid expansion on breast cancer diagnosis and treatment in 8 Southern states in the US. STUDY DESIGN: We identified uninsured or Medicaid patients (age 40 to 64 years) diagnosed with invasive breast cancer from 2011 to 2018 in Southern states from the North American Association of Central Cancer Registries-Cancer in North America Research Dataset. Medicaid-expanded states ([MES], Louisiana, Kentucky, Arkansas) were compared with non-MES ([NMES], Tennessee, Alabama, Mississippi, Texas, Oklahoma) using multivariate logistic regression and differences-in-differences analyses during pre- and postexpansion periods; p < 0.05 was considered statistically significant. RESULTS: Among 21,974 patients, patients in MES had increased odds of Medicaid insurance by 43% (odds ratio 1.43, p < 0.01) and decreased odds of distant-stage disease by 7% (odds ratio 0.93, p = 0.03). After Medicaid expansion, Medicaid patients increased by 10.6% in MES (Arkansas, Kentucky), in contrast to a 1.3% decrease in NMES (differences-in-differences 11.9%, p < 0. 0001, adjusting for age, race/ethnicity, rural-urban status, and poverty status). MES (Arkansas, Kentucky) had 2.3% fewer patients diagnosed with distant-stage disease compared with a 0.5% increase in NMES (differences-in-differences 2.8%, p = 0.01, after adjustment). Patients diagnosed in MES had higher odds of receiving treatment (odds ratio 2.27, p = 0.03). CONCLUSIONS: Unlike NMES, MES experienced increased Medicaid insured, increased treatment, and decreased distant-stage disease at diagnosis. Medicaid expansion in the South leads to earlier and more comprehensive treatment of breast cancer.