Childhood cancer data initiative: Status report.

In March 2023, over 800 researchers, clinicians, patients, survivors, and advocates from the pediatric oncology community met to discuss the progress of the National Cancer Institute's Childhood Cancer Data Initiative. We present here the status of the initiative's efforts in building its data ecosystem and updates on key programs, especially the Molecular Characterization Initiative and the planned Coordinated National Initiative for Rare Cancers in Children and Young Adults. These activities aim to improve access to childhood cancer data, foster collaborations, facilitate integrative data analysis, and expand access to molecular characterization, ultimately leading to the development of innovative therapeutic approaches.

A New Mixture Model With Cure Rate Applied to Breast Cancer Data.

We introduce a new modelling for long-term survival models, assuming that the number of competing causes follows a mixture of Poisson and the Birnbaum-Saunders distribution. In this context, we present some statistical properties of our model and demonstrate that the promotion time model emerges as a limiting case. We delve into detailed discussions of specific models within this class. Notably, we examine the expected number of competing causes, which depends on covariates. This allows for direct modeling of the cure rate as a function of covariates. We present an Expectation-Maximization (EM) algorithm for parameter estimation, to discuss the estimation via maximum likelihood (ML) and provide insights into parameter inference for this model. Additionally, we outline sufficient conditions for ensuring the consistency and asymptotic normal distribution of ML estimators. To evaluate the performance of our estimation method, we conduct a Monte Carlo simulation to provide asymptotic properties and a power study of LR test by contrasting our methodology against the promotion time model. To demonstrate the practical applicability of our model, we apply it to a real medical dataset from a population-based study of incidence of breast cancer in São Paulo, Brazil. Our results illustrate that the proposed model can outperform traditional approaches in terms of model fitting, highlighting its potential utility in real-world scenarios.

Semiparametric estimation and testing for panel count data with informative interval-censored failure event.

Panel count data and interval-censored data are two types of incomplete data that often occur in event history studies. Almost all existing statistical methods are developed for their separate analysis. In this paper, we investigate a more general situation where a recurrent event process and an interval-censored failure event occur together. To intuitively and clearly explain the relationship between the recurrent current process and failure event, we propose a failure time-dependent mean model through a completely unspecified link function. To overcome the challenges arising from the blending of nonparametric components and parametric regression coefficients, we develop a two-stage conditional expected likelihood-based estimation procedure. We establish the consistency, the convergence rate and the asymptotic normality of the proposed two-stage estimator. Furthermore, we construct a class of two-sample tests for comparison of mean functions from different groups. The proposed methods are evaluated by extensive simulation studies and are illustrated with the skin cancer data that motivated this study.

Breast cancer: A comparative review for breast cancer detection using machine learning techniques.

Breast cancer is the most common cancer among women globally and presents a significant challenge due to its rising incidence and fatality rates. Factors such as cultural, socioeconomic, and educational barriers contribute to inadequate awareness and access to healthcare services, often leading to delayed diagnoses and poor patient outcomes. Furthermore, fostering a collaborative approach among healthcare providers, policymakers, and community leaders is crucial in addressing this critical women's health issue, reducing mortality rates, alleviating, and the overall burden of breast cancer. The main goal of this review is to explore various techniques of machine learning algorithms to examine high accuracy and early detection of breast cancer for the safe health of women.

Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics.

We introduce binacox, a prognostic method to deal with the problem of detecting multiple cut-points per feature in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the binarsity penalty, which uses total-variation regularization together with an extra linear constraint, and enables feature selection. Original nonasymptotic oracle inequalities for prediction (in terms of Kullback-Leibler divergence) and estimation with a fast rate of convergence are established. The statistical performance of the method is examined in an extensive Monte Carlo simulation study, and then illustrated on three publicly available genetic cancer data sets. On these high-dimensional data sets, our proposed method outperforms state-of-the-art survival models regarding risk prediction in terms of the C-index, with a computing time orders of magnitude faster. In addition, it provides powerful interpretability from a clinical perspective by automatically pinpointing significant cut-points in relevant variables.

Clustering of cancer data based on Stiefel manifold for multiple views.

BACKGROUND: In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Nevertheless, clustering of omics data consists of many challenges. The primary challenges in omics data analysis come from high dimension of data and small size of sample. Therefore, it is difficult to find a suitable integration method for structural analysis of multiple datasets. RESULTS: In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. The MCSM method comprises three core steps. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, we solved the optimization problem by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. CONCLUSION: Particularly, our approach has better performance than compared approaches when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.

"Registries are not only a tool for data collection, they are for action": Cancer registration and gaps in data for health equity in six population-based registries in India.

In India, population-based cancer registries (PBCRs) cover less than 15% of the urban and 1% of the rural population. Our study examines practices of registration in PBCRs in India to understand efforts to include rural populations in registries and efforts to measure social inequalities in cancer incidence. We selected a purposive sample of six PBCRs in Maharashtra, Kerala, Punjab and Mizoram and conducted semistructured interviews with staff to understand approaches and challenges to cancer registration, and the sociodemographic information collected by PBCRs. We also conducted a review of peer-reviewed literature utilizing data from PBCRs in India. Findings show that in a context of poor access to cancer diagnosis and treatment and weak death registration, PBCRs have developed additional approaches to cancer registration, including conducting village and home visits to interview cancer patients in rural areas. Challenges included PBCR funding and staff retention, abstraction of data in medical records, address verification and responding to cancer stigma and patient migration. Most PBCRs published estimates of cancer outcomes disaggregated by age, sex and geography. Data on education, marital status, mother tongue and religion were collected, but rarely reported. Two PBCRs collected information on income and occupation and none collected information on caste. Most peer-reviewed studies using PBCR data did not publish estimates of social inequalities in cancer outcomes. Results indicate that collecting and reporting sociodemographic data collected by PBCRs is feasible. Improved PBCR coverage and data will enable India's cancer prevention and control programs to be guided by data on cancer inequities.

Racial and ethnic disparities in palliative care utilization among gynecological cancer patients.

BACKGROUND: Palliative care (PC) is recommended for gynecological cancer patients to improve survival and quality-of-life. Our objective was to evaluate racial/ethnic disparities in PC utilization among patients with metastatic gynecologic cancer. METHODS: We used data from the 2016 National Cancer Database (NCDB) and included patients between ages 18-90 years with metastatic (stage III-IV) gynecologic cancers including, ovarian, cervical and uterine cancer who were deceased at last contact or follow-up (n = 124,729). PC was defined by NCDB as non-curative treatment, and could include surgery, radiation, chemotherapy, and pain management or any combination. We used multivariable logistic regression to evaluate racial disparities in PC use. RESULTS: The study population was primarily NH-White (74%), ovarian cancer patients (74%), insured by Medicare (47%) or privately insured (36%), and had a Charlson-Deyo score of zero (77%). Over one-third of patients were treated at a comprehensive community cancer program. Overall, 7% of metastatic gynecologic deceased cancer patients based on last follow-up utilized palliative care: more specifically, 5% of ovarian, 11% of cervical, and 12% of uterine metastatic cancer patients. Palliative care utilization increased over time starting at 4% in 2004 to as high as 13% in 2015, although palliative care use decreased to 7% in 2016. Among metastatic ovarian cancer patients, NH-Black (aOR:0.87, 95% CI:0.78-0.97) and Hispanic patients (aOR:0.77, 95% CI:0.66-0.91) were less likely to utilize PC when compared to NH-White patients. Similarly, Hispanic cervical cancer patients were less likely (aOR:0.75, 95% CI:0.63-0.88) to utilize PC when compared to NH-White patients. CONCLUSIONS: PC is highly underutilized among metastatic gynecological cancer patients. Racial disparities exist in palliative care utilization among patients with metastatic gynecological cancer.

Improved survival in women versus men with merkel cell carcinoma.

BACKGROUND: Studies have observed that women have better outcomes than men in melanoma, but less is known about the influence of sex differences on outcomes for other aggressive cutaneous malignancies. OBJECTIVE: To investigate whether women and men have disparate outcomes in Merkel cell carcinoma (MCC). METHODS: Patients with nonmetastatic MCC undergoing surgery and lymph node evaluation were identified from the National Cancer Database (NCDB) and the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier analysis and Cox proportional hazards regression models were used for overall survival, and competing-risks analysis and Fine-Gray models were used for cause-specific and other-cause mortality. RESULTS: The NCDB cohort (n = 4178) included 1516 (36%) women. Women had a consistent survival advantage compared with men in propensity score-matched analysis (66.0% vs 56.8% at 5 years, P < .001) and multivariable Cox regression (hazard ratio, 0.68; 95% confidence interval, 0.61-0.75; P < .001). Similarly, women had a survival advantage in the SEER validation cohort (n = 1202) with 457 (38.0%) women, which was entirely due to differences in MCC-specific mortality (5-year cumulative incidence: 16.4% vs 26.7%, P = .002), with no difference in other-cause mortality (16.8% vs 17.8%, P = .43) observed in propensity score-matched patients. LIMITATIONS: Potential selection bias from a retrospective data set. CONCLUSION: In MCC, women have improved survival compared with men, driven by MCC-related mortality.

Survival outcomes in patients with gastric and gastroesophageal junction adenocarcinomas treated with perioperative chemotherapy with or without preoperative radiotherapy.

BACKGROUND: Perioperative chemotherapy (POC) is one standard approach for the treatment of resectable cancers of the stomach and gastroesophageal junction (GEJ), whereas there has been growing interest in preoperative therapies. The objective of the current study was to compare survival between patients treated with preoperative chemoradiotherapy and adjuvant chemotherapy (PCRT) with those receiving POC using a large database. METHODS: The National Cancer Data Base was queried for patients diagnosed between 2004 and 2013 with American Joint Committee on Cancer clinical group stage IB to stage IIIC (excluding T2N0 disease) adenocarcinoma of the stomach or GEJ. Patients treated with definitive surgery and POC with or without preoperative radiotherapy of 41 to 54 Gy were included. Overall survival (OS) was defined from the date of definitive surgery and estimated using the Kaplan-Meier method. A total of 14 patient and treatment variables were used for propensity score matching (PSM). RESULTS: A total of 1048 patients were analyzed: 53.2% received POC and 46.8% received PCRT. The primary tumor site was the GEJ in 69.1% of patients and stomach in 30.9% of patients. The median age of the patients was 60 years, and the median follow-up was 25.8 months. The use of PCRT was associated with a greater pathologic complete response rate of 13.1% versus 8.2% (P = .01). POC was associated with a decreased risk of death in unmatched groups (hazard ratio [HR], 0.83; P = .043). Using PSM cohorts, POC decreased the risk of death with a median OS of 45.1 months versus 31.4 months (HR, 0.70; P = .016). The 2-year OS rate was 72.9% versus 62.5% and the 5-year OS rate was 40.7% versus 33.1% for POC versus PCRT, respectively. Survival favored POC in PSM gastric (HR, 0.41; P = .07) and GEJ (HR, 0.77; P = .08) patient subgroups. CONCLUSIONS: The addition of preoperative radiotherapy to POC appears to be associated with an increased risk of death in patients with resectable gastric and GEJ cancers.

National treatment trends in human papillomavirus-positive oropharyngeal squamous cell carcinoma.

BACKGROUND: Human papillomavirus (HPV)-mediated oropharyngeal cancer (OPC) is associated with dramatically improved survival in comparison with HPV-negative OPC and can be successfully treated with surgical and nonsurgical approaches. National treatment trends for OPC were investigated with the National Cancer Data Base (NCDB). METHODS: The NCDB was reviewed for primary HPV-mediated OPC in 2010-2014. Multivariable regression was used to identify predictors of both nonsurgical therapy and receipt of adjuvant chemoradiation (CRT). RESULTS: There were 13,363 patients identified with a median age at diagnosis of 58 years. The incidence of triple-modality treatment (surgery with adjuvant chemotherapy) decreased from 23.7% in 2010 to 16.9% in 2014 (R2  = 0.96), whereas the incidence of nonsurgical treatment increased from 63.9% to 68.7% (R2  = 0.89). Hospitals in the top treatment volume quartile (quartile 1 [Q1]; n = 29) had a lower rate of positive margins (16.3%) than bottom-quartile centers (n = 741; rate of positive margins, 36.4%; P < .001); Q1 hospitals used surgical therapy significantly more. Independent predictors of nonsurgical therapy included older age, advanced disease, lower hospital volume, and living closer to the hospital or outside the Pacific United States. In surgically treated patients, younger age, lower hospital volume, nodal disease, positive surgical margins, and extranodal extension (ENE) also predicted more adjuvant CRT use. CONCLUSIONS: The use of upfront surgical treatment decreased from 2010 to 2014. Hospital volume shows a strong, inverse correlation with the rate of positive surgical margins. The upfront treatment strategy is predicted not only by staging but also by patient-, geographic-, and hospital-specific factors. Lower hospital volume remains independently associated with increased triple-modality therapy after adjustments for positive margins, ENE, and pathologic staging.

A Bayesian multi-risks survival (MRS) model in the presence of double censorings.

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.

Role of lymphadenectomy, adjuvant chemotherapy, and treatment at high-volume centers in patients with resected pancreatic cancer-a distinct view on lymph node yield.

PURPOSE: While the importance of lymphadenectomy is well-established for patients with resectable pancreatic cancer, its direct impact on survival in relation to other predictive factors is still ill-defined. METHODS: The National Cancer Data Base 2006-2015 was queried for patients with resected pancreatic adenocarcinoma (stage IA-IIB). Patients were dichotomized into the following two groups, those with 1-14 resected lymph nodes and those with ≥ 15. Optimal number of resected lymph nodes and the effect of lymphadenectomy on survival were assessed using various statistical modeling techniques. Mediation analysis was performed to differentiate the direct and indirect effect of lymph node resection on survival. RESULTS: A total of 21,912 patients were included; median age was 66 years (IQR 59-73), 48.9% were female. Median number of resected lymph nodes was 15 (IQR 10-22), 10,163 (46.4%) had 1-14 and 11,749 (53.6%) had ≥ 15 lymph nodes retrieved. Lymph node positivity increased by 4.1% per lymph node up to eight examined lymph nodes, and by 0.6% per lymph node above eight. Five-year overall survival was 17.9%. Overall survival was better in the ≥ 15 lymph node group (adjusted HR 0.91, CI 0.88-0.95, p < 0.001). On a continuous scale, survival improved with increasing LNs collected. Patients who underwent adjuvant chemotherapy and were treated at high-volume centers had improved overall survival compared with their counterparts (adjusted HR 0.59, CI 0.57-0.62, p < 0.001; adjusted HR 0.86, CI 0.83-0.89, p < 0.001, respectively). Mediation analysis revealed that lymphadenectomy had only 18% direct effect on improved overall survival, while 82% of its effect were mediated by other factors like treatment at high-volume hospitals and adjuvant chemotherapy. DISCUSSION: While higher number of resected lymph nodes increases lymph node positivity and is associated with better overall survival, most of the observed survival benefit is mediated by chemotherapy and treatment at high-volume centers.

ECCParaCorp: a cross-lingual parallel corpus towards cancer education, dissemination and application.

BACKGROUND: The increasing global cancer incidence corresponds to serious health impact in countries worldwide. Knowledge-powered health system in different languages would enhance clinicians' healthcare practice, patients' health management and public health literacy. High-quality corpus containing cancer information is the necessary foundation of cancer education. Massive non-structural information resources exist in clinical narratives, electronic health records (EHR) etc. They can only be used for training AI models after being transformed into structured corpus. However, the scarcity of multilingual cancer corpus limits the intelligent processing, such as machine translation in medical scenarios. Thus, we created the cancer specific cross-lingual corpus and open it to the public for academic use. METHODS: Aiming to build an English-Chinese cancer parallel corpus, we developed a workflow of seven steps including data retrieval, data parsing, data processing, corpus implementation, assessment verification, corpus release, and application. We applied the workflow to a cross-lingual, comprehensive and authoritative cancer information resource, PDQ (Physician Data Query). We constructed, validated and released the parallel corpus named as ECCParaCorp, made it openly accessible online. RESULTS: The proposed English-Chinese Cancer Parallel Corpus (ECCParaCorp) consists of 6685 aligned text pairs in Xml, Excel, Csv format, containing 5190 sentence pairs, 1083 phrase pairs and 412 word pairs, which involved information of 6 cancers including breast cancer, liver cancer, lung cancer, esophageal cancer, colorectal cancer, and stomach cancer, and 3 cancer themes containing cancer prevention, screening, and treatment. All data in the parallel corpus are online, available for users to browse and download ( http://www.phoc.org.cn/ECCParaCorp/ ). CONCLUSIONS: ECCParaCorp is a parallel corpus focused on cancer in a cross-lingual form, which is openly accessible. It would make up the imbalance of scarce multilingual corpus resources, bridge the gap between human readable information and machine understanding data resources, and would contribute to intelligent technology application as a preparatory data foundation e.g. cancer-related machine translation, cancer system development towards medical education, and disease-oriented knowledge extraction.

Prevalence of Cytoplasmic Actin Mutations in Diffuse Large B-Cell Lymphoma and Multiple Myeloma: A Functional Assessment Based on Actin Three-Dimensional Structures.

Mutations in actins have been linked to several developmental diseases. Their occurrence across different cancers has, however, not been investigated. Using the cBioPortal database we show that human actins are infrequently mutated in patient samples of various cancers types. Nevertheless, ranking these studies by mutational frequency suggest that some have a higher percentage of patients with ACTB and ACTG1 mutations. Within studies on hematological cancers, mutations in ACTB and ACTG1 are associated with lymphoid cancers since none have currently been reported in myeloid cancers. Within the different types of lymphoid cancers ACTB mutations are most frequent in diffuse large B-cell lymphoma (DLBCL) and ACTG1 mutations in multiple myeloma. We mapped the ACTB and ACTG1 mutations found in these two cancer types on the 3D-structure of actin showing they are in regions important for actin polymer formation or binding to myosin. The potential effects of the mutations on actin properties imply that mutations in cytoplasmic actins deserve dedicated research in DLBCL and multiple myeloma.

Recent trends in the age at diagnosis of colorectal cancer in the US National Cancer Data Base, 2004-2015.

BACKGROUND: The incidence of colorectal cancer (CRC) in adults younger than 50 years has increased in the United States over the past decades according to Surveillance, Epidemiology, and End Results data. National guidelines conflict over beginning screening at the age of 45 or 50 years. METHODS: This was a retrospective study of National Cancer Data Base data from 2004 to 2015. The Cochran-Armitage test for trend was used to assess changes in the proportion of cases diagnosed at an age younger than 50 years. RESULTS: This study identified 130,165 patients diagnosed at an age younger than 50 years and 1,055,598 patients diagnosed at the age of 50 years or older. The proportion of the total number of patients diagnosed with CRC at an age younger than 50 years rose (12.2% in 2015 vs 10.0% in 2004; P < .0001). Younger adults presented with more advanced disease (stage III/IV; 51.6% vs 40.0% of those 50 years old or older). Among men, diagnosis at ages younger than 50 years rose only in non-Hispanic whites (P < .0001), whereas among women, Hispanic and non-Hispanic whites had increases in younger diagnoses over time (P < .05). All income quartiles had a proportional increase in younger adults over time (P < .001), with the highest income quartile having the highest proportion of younger cases. The proportion of younger onset CRC cases rose in urban areas (P < .001) but did not rise in rural areas. CONCLUSIONS: The proportion of persons diagnosed with CRC at an age younger than 50 years in the United States has continued to increase over the past decade, and younger adults present with more advanced disease. These data should be considered in the ongoing discussion of screening guidelines.

Does the addition of chemotherapy to neoadjuvant radiotherapy impact survival in high-risk extremity/trunk soft-tissue sarcoma?

BACKGROUND: The role of chemotherapy in extremity/trunk soft-tissue sarcoma (ET-STS) is controversial, even for patients at high risk for distant recurrence and death (those with high-grade tumors ≥5 cm in size). This study examines the impact of integrating chemotherapy with neoadjuvant radiotherapy (RT) on overall survival (OS) for patients with high-risk ET-STS. METHODS: The National Cancer Data Base was queried for adult patients with high-risk ET-STS who received neoadjuvant RT and limb salvage surgery between 2006 and 2014. Patients were stratified into RT and chemoradiotherapy (CRT) cohorts. OS for the RT and CRT cohorts was analyzed with the Kaplan-Meier method, log-rank tests, and Cox proportional hazards models. Propensity score matching (PSM) analysis was used to account for a potential treatment selection bias between the cohorts. RESULTS: A total of 884 patients were identified: 639 (72.3%) in the RT cohort and 245 (27.7%) in the CRT cohort. The unadjusted 5-year Kaplan-Meier OS rate was significantly higher in the CRT cohort: 72.0% versus 56.1% (P < .001). Neoadjuvant chemotherapy was associated with improved OS in univariate and multivariable analyses (hazard ratio [HR], 0.57; 95% confidence interval [CI], 0.41-0.78; P < .001). PSM identified 2 evenly matched cohorts of 212 patients each. The 5-year matched Kaplan-Meier OS rates were 69.8% and 55.4% for the CRT and RT cohorts, respectively (P = .002). The addition of neoadjuvant chemotherapy remained prognostic for OS on PSM (HR, 0.56; 95% CI, 0.39-0.83; P = .003). CONCLUSIONS: The addition of chemotherapy to neoadjuvant RT was associated with improved OS for patients with high-risk ET-STS. In the absence of randomized data evaluating CRT versus RT, these findings warrant further investigation.

The opportunities and shortcomings of using big data and national databases for sarcoma research.

The rarity and heterogeneity of sarcomas make performing appropriately powered studies challenging and magnify the significance of large databases in sarcoma research. Established large tumor registries and population-based databases have become increasingly relevant for answering clinical questions regarding sarcoma incidence, treatment patterns, and outcomes. However, the validity of large databases has been questioned and scrutinized because of the inaccuracy and wide variability of coding practices and the absence of clinically relevant variables. In addition, the utilization of large databases for the study of rare cancers such as sarcoma may be particularly challenging because of the known limitations of administrative data and poor overall data quality. Currently, there are several large national cancer databases, including the Surveillance, Epidemiology, and End Results database, the National Cancer Data Base of the American College of Surgeons and the American Cancer Society, and the National Program of Cancer Registries of the Centers for Disease Control and Prevention. These databases are often used for sarcoma research, but they are limited by their dependence on administrative or billing data, the lack of agreement between chart abstractors on diagnosis codes, and the use of preexisting documented hospital diagnosis codes for tumor registries, which lead to a significant underestimation of sarcomas in large data sets. Current and future initiatives to improve databases and big data applications for sarcoma research include increasing the utilization of sarcoma-specific registries and encouraging national initiatives to expand on real-world, evidence-based data sets.

Characteristics of long-term survivors with multiple myeloma: A National Cancer Data Base analysis.

BACKGROUND: There exist insufficient data characterizing patients with multiple myeloma (MM) who experienced prolonged survival. A population-based analysis of long-term survivors was conducted to investigate the roles of sociodemographic factors and upfront stem cell transplantation (SCT). METHODS: The National Cancer Data Base is a US cancer database of approximately 34 million patients from >1500 cancer centers. Patients with MM were identified using the International Classification of Diseases for Oncology (ICD-O) code 9732 from January 2004 to December 2006 and were divided into 4 groups based on overall survival (OS). Sociodemographic characteristics, treatment facility, and use of SCT were recorded. The univariate and multivariate analyses were performed using multiple logistic regression and Pearson chi-square tests. RESULTS: A total of 26,986 patients with MM were identified. The median OS was 2.74 years. The majority of patients were male (54%), white (77%), insured (93%) and otherwise healthy (78%), lived in a metropolitan area (82%), were of high income (66%) and educational (58%) levels, and received treatment at nonacademic facilities (63%). Upfront SCT was used in 10% of patients. One in 6 patients (16%) were long-term survivors (group 4). When comparing group 4 (OS of ≥8.22 years) with the other groups (OS of <8.22 years), young age, female sex, high income and educational levels, residence in a rural area, insured status, no comorbidity, receipt of upfront SCT, and treatment at high-volume facilities were associated with long-term survival. CONCLUSIONS: Key differences in sociodemographic characteristics, patient volume at treatment facilities, and upfront SCT were associated with long-term survival. Improvements in health care access and health literacy, upfront SCT, and treatment at high-volume facilities might prolong patient survival.

Survival outcomes by high-risk human papillomavirus status in nonoropharyngeal head and neck squamous cell carcinomas: A propensity-scored analysis of the National Cancer Data Base.

BACKGROUND: The prognostic relevance of human papillomavirus (HPV) status in patients with nonoropharyngeal (OPX) squamous cell cancer (SCC) of the head and neck is controversial. In the current study, the authors evaluated the impact of high-risk HPV status on overall survival (OS) in patients with non-OPX SCC using a large database approach. METHODS: The National Cancer Data Base was queried to identify patients diagnosed from 2004 through 2014 with SCC of the OPX, hypopharynx (HPX), larynx, and oral cavity (OC) with known HPV status. Survival was estimated using Kaplan-Meier methods; distributions were compared using log-rank tests. Propensity score-matching and inverse probability of treatment weighing (IPTW) methods were used; cohorts were matched based on age, sex, Charlson-Deyo score, clinical American Joint Committee on Cancer (AJCC) group stage, treatments received, and anatomic subsite. Propensity analyses were stratified by group stage of disease. RESULTS: A total of 24,740 patients diagnosed from 2010 through 2013 were analyzed: 1085 patients with HPX, 4804 with laryngeal, 4,018 with OC, and 14,833 with OPX SCC. The percentages of HPV-positive cases by disease site were 17.7% for HPX, 11% for larynx, 10.6% for OC, and 62.9% for OPX. HPV status was found to be prognostic in multiple unadjusted and propensity-adjusted non-OPX populations. HPV positivity was associated with superior OS in patients with HPX SCC with a hazard ratio (HR) of 0.61 (P < .001 by IPTW), in patients with AJCC stage III to IVB laryngeal SCC (HR, 0.79; P = .019 by IPTW), and in patients with AJCC stage III to IVB OC SCC (HR, 0.78; P = .03 by IPTW). CONCLUSIONS: Positive high-risk HPV status appears to be associated with longer OS in multiple populations of patients with non-OPX head and neck disease (HPX, locally advanced larynx, and OC). If prospectively validated, these findings have implications for risk stratification.