Assessing sociodemographic and regional disparities in Oncotype DX Genomic Prostate Score uptake

BACKGROUND

The Oncotype DX Genomic Prostate Score (ODX-GPS) is a gene expression assay that predicts disease aggressiveness. The objective of this study was to identify sociodemographic and regional factors associated with ODX-GPS uptake.

METHODS

Data from Surveillance Epidemiology and End Results registries on men with localized prostate cancer with a Gleason score of 3 + 3 or 3 + 4, PSA ≤20 ng/mL, and stage T1c to T2c disease from 2013 through 2017 were linked with ODX-GPS data. Census-tract level neighborhood socioeconomic status (nSES) quintiles were constructed using a composite socioeconomic score. Multivariable logistic regression was used to estimate the associations of ODX-GPS uptake with age at diagnosis, race and ethnicity, nSES, geographic region, insurance type, and marital status, accounting for National Comprehensive Cancer Network risk group, year of diagnosis, and clustering by census tract.

RESULTS

Among 111,434 eligible men, 5.5% had ODX-GPS test uptake. Of these, 78.3% were non-Hispanic White, 9.6% were Black, 6.7% were Hispanic, and 3.6% were Asian American. Black men had the lowest odds of ODX-GPS uptake (odds ratio, 0.70; 95% confidence interval [CI], 0.63-0.76). Those in the highest versus lowest quintile of nSES were 1.64 times more likely (95% CI, 1.38-2.94) to have ODX-GPS uptake. The odds of ODX-GPS uptake were statistically significantly higher among men residing in the Northeast, West, and Midwest compared to the South.

CONCLUSIONS

Disparities in ODX-GPS uptake by race, ethnicity, nSES, and geographical region were identified. Concerted efforts should be made to ensure that this clinical test is equitably available.

Reporting tumor genomic test results to SEER registries via linkages

BACKGROUND

Precision medicine has become a mainstay of cancer care in recent years. The National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program has been an authoritative source of cancer statistics and data since 1973. However, tumor genomic information has not been adequately captured in the cancer surveillance data, which impedes population-based research on molecular subtypes. To address this, the SEER Program has developed and implemented a centralized process to link SEER registries' tumor cases with genomic test results that are provided by molecular laboratories to the registries.

METHODS

Data linkages were carried out following operating procedures for centralized linkages established by the SEER Program. The linkages used Match*Pro, a probabilistic linkage software, and were facilitated by the registries' trusted third party (an honest broker). The SEER registries provide to NCI limited datasets that undergo preliminary evaluation prior to their release to the research community.

RESULTS

Recently conducted genomic linkages included OncotypeDX Breast Recurrence Score, OncotypeDX Breast Ductal Carcinoma in Situ, OncotypeDX Genomic Prostate Score, Decipher Prostate Genomic Classifier, DecisionDX Uveal Melanoma, DecisionDX Preferentially Expressed Antigen in Melanoma, DecisionDX Melanoma, and germline tests results in Georgia and California SEER registries.

CONCLUSIONS

The linkages of cancer cases from SEER registries with genomic test results obtained from molecular laboratories offer an effective approach for data collection in cancer surveillance. By providing de-identified data to the research community, the NCI's SEER Program enables scientists to investigate numerous research inquiries.

NCI SEER-Linked Virtual Tissue Repository Pilot

BACKGROUND

The Surveillance, Epidemiology, and End Results (SEER) Program with the National Cancer Institute tested whether population-based cancer registries can serve as honest brokers to acquire tissue and data in the SEER-Linked Virtual Tissue Repository (VTR) Pilot.

METHODS

We collected formalin-fixed, paraffin-embedded tissue and clinical data from patients with pancreatic ductal adenocarcinoma (PDAC) and breast cancer (BC) for two studies comparing cancer cases with highly unusual survival (≥5 years for PDAC and ≤30 months for BC) to pair-matched controls with usual survival (≤2 years for PDAC and ≥5 years for BC). Success was defined as the ability for registries to acquire tissue and data on cancer cases with highly unusual outcomes.

RESULTS

Of 98 PDAC and 103 BC matched cases eligible for tissue collection, sources of attrition for tissue collection were tissue being unavailable, control paired with failed case, second control that was not requested, tumor necrosis ≥20%, and low tumor cellularity. In total, tissue meeting the study criteria was obtained for 70 (71%) PDAC and 74 (72%) BC matched cases. For patients with tissue received, clinical data completeness ranged from 59% for CA-19-9 after treatment to >95% for margin status, whether radiation therapy and chemotherapy were administered, and comorbidities.

CONCLUSIONS

The VTR Pilot demonstrated the feasibility of using SEER cancer registries as honest brokers to provide tissue and clinical data for secondary use in research. Studies using this program should oversample by 45% to 50% to obtain sufficient sample size and targeted population representation and involve subspecialty matter expert pathologists for tissue selection.

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.

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.

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.

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.

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.

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.

[Clinical research of multisystem inflammatory syndrome in children]

Objective: To analyze the clinical characteristics of children with multisystem inflammatory syndrome (MIS-C) associated with SARS-CoV-2 in China, and to improve the understanding of MIS-C among pediatricians. Methods: Case series study.Collect the clinical characteristics, auxiliary examinations, treatment decisions, and prognosis of 64 patients with MIS-C from 9 hospitals in China from December 2022 to June 2023. Results: Among the 64 MIS-C patients, 36 were boys and 28 were girls, with an onset age being 2.8 (0.3, 14.0) years. All patients suffered from fever, elevated inflammatory indicators, and multiple system involvement. Forty-three patients (67%) were involved in more than 3 systems simultaneously, including skin mucosa 60 cases (94%), blood system 52 cases (89%), circulatory system 54 cases (84%), digestive system 48 cases (75%), and nervous system 24 cases (37%). Common mucocutaneous lesions included rash 54 cases (84%) and conjunctival congestion and (or) lip flushing 45 cases (70%). Hematological abnormalities consisted of coagulation dysfunction 48 cases (75%), thrombocytopenia 9 cases (14%), and lymphopenia 8 cases (13%). Cardiovascular lesions mainly affected cardiac function, of which 11 patients (17%) were accompanied by hypotension or shock, and 7 patients (12%) had coronary artery dilatation.Thirty-six patients (56%) had gastrointestinal symptoms, 23 patients (36%) had neurological symptoms. Forty-five patients (70%) received the initial treatment of intravenous immunoglobulin in combination with glucocorticoids, 5 patients (8%) received the methylprednisolone pulse therapy and 2 patients (3%) treated with biological agents, 7 patients with coronary artery dilation all returned to normal within 6 months. Conclusions: MIS-C patients are mainly characterized by fever, high inflammatory response, and multiple organ damage. The preferred initial treatment is intravenous immunoglobulin combined with glucocorticoids. All patients have a good prognosis.

[Clinical analysis of 10 cases of multi-center tumor necrosis factor receptor-associated periodic syndrome]

Objective: To summarize the clinical characteristics of tumour necrosis factor receptor-associated periodic syndrome (TRAPS) in children. Methods: The clinical manifestations, laboratory tests, genetic testing and follow-up of 10 children with TRAPS from May 2011 to May 2021 in 6 hospitals in China were retrospectively analyzed. Results: Among the 10 patients with TRAPS, including 8 boys and 2 girls. The age of onset was 2 (1, 5) years, the age of diagnosis was (8±4) years, and the time from onset to diagnosis was 3 (1, 7) years. A total of 7 types of TNFRSF1A gene variants were detected, including 5 paternal variations, 1 maternal variation and 4 de novo variations. Six children had a family history of related diseases. Clinical manifestations included recurrent fever in 10 cases, rash in 4 cases, abdominal pain in 6 cases, joint involvement in 6 cases, periorbital edema in 1 case, and myalgia in 4 cases. Two patients had hematological system involvement. The erythrocyte sedimentation rate and C-reactive protein were significantly increased in 10 cases. All patients were negative for autoantibodies. In the course of treatment, 5 cases were treated with glucocorticoids, 7 cases with immunosuppressants, and 7 cases with biological agents. Conclusions: TRAPS is clinically characterized by recurrent fever accompanied by joint, gastrointestinal, skin, and muscle involvement. Inflammatory markers are elevated, and autoantibodies are mostly negative. Treatment mainly involves glucocorticoids, immunosuppressants, and biological agents.

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.

Prostate volume, baseline urinary function, and their association with treatment choice and post-treatment urinary function in men treated for localized prostate cancer

BACKGROUND

Benign prostatic hyperplasia, lower urinary tract symptoms, and prostate cancer often co-occur. Their effect on urinary function is an important consideration regarding prostate cancer treatment choices. While prostate volume (PV) and urinary symptoms are commonly used in treatment choice decision making, their association with post-treatment urinary function is unknown. We evaluated the associations between PV and baseline urinary function with treatment choice and post-treatment urinary function among men with localized prostate cancer.

METHODS

We identified 1647 patients from CEASAR, a multicenter population-based, prospective cohort study of men with localized prostate cancer, for analysis. Primary outcomes were treatment choice and health-related quality of life (HRQOL) assessed by the 26-item Expanded Prostate Index Composite (EPIC-26) at pre-specified intervals up to 5 years. Multivariable analysis was performed, controlling for demographic and clinicopathologic features.

RESULTS

Median baseline PV was 36 mL (IQR 27-48), and baseline urinary irritative/obstructive domain score was 87 (IQR 75-100). There was no observed clinically meaningful association between PV and treatment choice or post-treatment urinary function. Among patients with poor baseline urinary function, treatment with radiation or surgery was associated with statistically and clinically significant improvement in urinary function at 6 months which was durable through 5 years (improvement from baseline at 5 years: radiation 20.4 points, surgery 24.5 points).

CONCLUSIONS

PV was not found to be associated with treatment modality or post-treatment urinary irritative/obstructive function among men treated for localized prostate cancer. Men with poor baseline urinary irritative/obstructive function improve after treatment with surgery or radiation therapy.

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.

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/.

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.

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.

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.

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.

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.

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.

Racial/ethnic and socioeconomic disparities in COVID-19 infections among working-age women with precancerous cervical lesion in Louisiana: analysis of more than two years of COVID-19 data

Background

Precancerous cervical lesion (PCL) is common in working-age and minority women. In Louisiana, 98% of PCL cases were diagnosed at age 18-65 with over 90% of them being human papillomavirus (HPV)-related. PCL women represent those who may be immunocompromised from the precancerous condition and thus more vulnerable to SARS-CoV-2. Most studies evaluating racial disparities for COVID-19 infection have only used data prior to vaccine availability. This study assessed disparities by race/ethnicity and socioeconomic status (SES) in COVID-19 infections among working-age PCL women for pre- and post-COVID-19 vaccine availability.

Methods

Louisiana women aged 18-65 with PCL diagnosed in 2009-2021 were linked with the Louisiana statewide COVID-19 database to identify those with positive COVID-19 test. Race/ethnicity was categorized as non-Hispanic white (NHW), non-Hispanic black (NHB), Hispanic, and others. The census tract SES quintiles were created based on American Community Survey estimates. Logistic regression was employed to assess the racial/ethnic and SES differences in COVID-19 infections.

Results

Of 14,669 eligible PCL women, 30% were tested COVID-19 positive. NHB had the highest percentage of COVID-19 infection (34.6%), followed by NHW (27.7%). The infection percentage was inversely proportional to SES, with 32.9% for women having the lowest SES and 26.8% for those with the highest SES. NHB women and those with lower SES had higher COVID-19 infection than their counterparts with an aOR of 1.37 (95% CI 1.25-1.49) and 1.21 (95% CI 1.07-1.37), respectively. In the pre-vaccine period, NHB and Hispanic women had higher odds of infection than NHW women. However, after the vaccine was implemented, the significant racial/ethnic and SES differences in COVID-19 infections still existed in PCL women residing in non-Greater New Orleans area.

Conclusions

There are substantial variations in racial/ethnic and SES disparities in COVID-19 infections among working-age women with PCL, even after vaccine implementation. It is imperative to provide public health interventions and resources to reduce this unequal burden for this vulnerable population.