Introduction:Pediatric cancer patients suffer mental health deficits. Patients who are vulnerable with respect to socioeconomic or other sociodemographic factors may be at heightened risk for worse mental health outcomes during cancer for many reasons including entering treatment with an existing mental health disorder. The purpose of this study was to describe the prevalence and trajectory of mental health before and after a childhood cancer diagnosis in a socio-demographically at-risk sample. Methods:Data from the Kentucky Cancer Registry was utilized to identify patients aged 19 or under with a first primary childhood cancer diagnosis during 2001-2017. Linking KCR data with Medicaid claims, we included patients with continuous Medicaid enrollment 12 months before and after their cancer diagnosis. MHDs were identified using both International Classification of Diseases (ICD)-9 and ICD-10 diagnosis codes in Medicaid claims. Results:Of the 978 patients, 54% were male, and 39% were from Appalachian counties. The most common cancers diagnosed were leukemias (n = 238), brain and central nervous system (n = 220), and lymphomas (n = 147). For the 12-month pre-cancer diagnosis period, 32% (n = 310) of the patients had a MHD, increasing to 55% (n = 540) in the 12 months post-diagnosis period. The most frequent MHDs were mood disorder (before n = 120; after n = 311) and neuropsychiatric/developmental disorders (before n = 228; after n = 267). Mood disorders increased from 12% pre-cancer diagnosis to 32% post-cancer diagnosis, from 10 to 37% for lymphoma patients, and from 15 to 64% for bone cancer patients. Conclusion:Over half of the Medicaid-enrolled childhood cancer patients in Kentucky diagnosed with MHDs within a year of their cancer diagnosis, with a notable increase from pre-diagnosis levels. This increased prevalence post-diagnosis may result from the identification of pre-existing MHDs during cancer treatment, or the emergence of new MHD because of the cancer diagnosis and treatment. Our study highlights the psychosocial needs that extend beyond standard cancer treatment and underscores the importance of psychosocial services during and after the cancer treatment period.

Introduction:Oncology is a data-rich environment reflecting the increasing incidence of cancer in the US aging population, transformation of cancer into a chronic disease due to advances in treatment, and the emergence of new data categories such as genomics. Concomitantly, healthcare systems are challenged to meet regulatory and voluntary reporting of cancer data to support government, quality certification, research and strategic partnership needs. Materials and Methods:The National Academies of Science, Engineering, and Medicine organized a workshop entitled "Enabling 21st-century applications for cancer surveillance through enhanced registries and beyond" with representation from NCI Comprehensive Cancer Centers, oncology and medical informatics professional societies, industry, CDC, NCI, and patient advocacy groups. Results:The proliferation of cancer registries has resulted in heterogeneity in data vocabularies and data transport standards. Federal policy is complementing private initiatives to modernize cancer data architecture that would support a Learning Health System. However, business models are needed to provide sustained investments in data infrastructure. Conclusion:A computational approach to cancer registries would set the stage for interoperability and data sharing within a learning health ecosystem. Healthcare systems need to invest in their data infrastructure to improve data quality and adaptation of new data sources such as genomics and wearables. The ecosystem must evolve business models to sustain these investments.

This study examined rural and urban parents’ reports of their children’s receipt of general follow-up care and psychological services, ratings of the acceptability of common sources of follow-up care, and ratings of local/nearby follow-up care access and effectiveness. A cross-sectional, quantitative survey was conducted among 238 (139 urban, 46 rural adjacent, and 53 rural non-adjacent) parents of childhood cancer survivors (ages 2–17 and at least 2 years since cancer diagnosis) recruited through the Kentucky Cancer Registry. Bivariate analyses and multivariable logistic regressions adjusting for predisposing, enabling, and need factors tested for differences across residence in urban counties, rural counties adjacent to an urban county, and rural counties non-adjacent to an urban county. Children in rural adjacent counties had higher adjusted odds of receiving psychological services (OR 4.37; 95

We present a global explainability method to characterize sources of errors in a real-world multitask deep abstaining classifier (DAC), in the context of cancer histology prediction. Our multitask classifier, currently deployed for automated annotation of cancer pathology reports from NCI-SEER registries, was trained and evaluated on 1.04 million hand-annotated samples and makes simultaneous predictions of cancer site, subsite, histology, laterality, and behavior for each report. The DAC framework enables the model to abstain on ambiguous reports and confusing classes to achieve the target accuracy on the retained (non-abstained) samples, but at the cost of decreased coverage. Requiring 97% accuracy on the histology task caused our model to retain only 22% of all samples, mostly the less ambiguous and common classes. Local explainability with the GradInp technique provided a computationally efficient way of obtaining contextual reasoning for hundreds of thousands of individual predictions. Our method, involving dimensionality reduction of approximately 13000 aggregated local explanations (ALE), offers a tractable path to true global explainability. It enabled identification of sources of errors in histology classification, globally, as hierarchical complexity among classes, label noise, insufficient information, and conflicting evidence. This suggests several strategies for iterative improvement of our DAC, including well-designed exclusion criteria, focused annotation, and reduced penalties for errors involving hierarchically related classes.

Childhood cancers are a heterogeneous group of rare diseases, accounting for less than 2% of all cancers diagnosed worldwide. Most countries, therefore, do not have enough cases to provide robust information on epidemiology, treatment, and late effects, especially for rarer types of cancer. Thus, only through a concerted effort to share data internationally will we be able to answer research questions that could not otherwise be answered. With this goal in mind, the US National Cancer Institute and the French National Cancer Institute co-sponsored the Paris Conference for an International Childhood Cancer Data Partnership in November 2023. This meeting convened more than 200 participants from 17 countries to address complex challenges in pediatric cancer research and data sharing. This Commentary delves into some key topics discussed during the Paris Conference and describes pilots that will help move this international effort forward. Main topics presented include: (1) the wide variation in interpreting the European Union's General Data Protection Regulation among Member States; (2) obstacles with transferring personal health data outside of the European Union; (3) standardization and harmonization, including common data models; and (4) novel approaches to data sharing such as federated querying and federated learning. We finally provide a brief description of 3 ongoing pilot projects. The International Childhood Cancer Data Partnership is the first step in developing a process to better support pediatric cancer research internationally through combining data from multiple countries.

We present a global explainability method to characterize sources of errors in the histology prediction task of our real-world multitask convolutional neural network (MTCNN)-based deep abstaining classifier (DAC), for automated annotation of cancer pathology reports from NCI-SEER registries. Our classifier was trained and evaluated on 1.04 million hand-annotated samples and makes simultaneous predictions of cancer site, subsite, histology, laterality, and behavior for each report. The DAC framework enables the model to abstain on ambiguous reports and/or confusing classes to achieve a target accuracy on the retained (non-abstained) samples, but at the cost of decreased coverage. Requiring 97 22 explainability with the GradInp technique provided a computationally efficient way of obtaining contextual reasoning for thousands of individual predictions. Our method, involving dimensionality reduction of approximately 13000 aggregated local explanations, enabled global identification of sources of errors as hierarchical complexity among classes, label noise, insufficient information, and conflicting evidence. This suggests several strategies such as exclusion criteria, focused annotation, and reduced penalties for errors involving hierarchically related classes to iteratively improve our DAC in this complex real-world implementation.

BACKGROUND:Kentucky is within the top five leading states for breast cancer mortality nationwide. This study investigates the association between neighborhood socioeconomic disadvantage and breast cancer outcomes, including surgical treatment, radiotherapy, chemotherapy, and survival, and how associations vary by race and ethnicity in Kentucky. METHODS:We conducted a retrospective cohort analysis using data from the Kentucky Cancer Registry for patients with breast cancer diagnosed between 2010 and 2017, with follow-up through December 31, 2022. We linked Kentucky Cancer Registry data with census tract data to examine the relationship between area deprivation index (ADI) and breast cancer outcomes. Logistic regression and Cox proportional hazards models analyzed binary outcomes and time-to-event data, respectively. RESULTS:Women in the most disadvantaged (ADI fourth quartile) neighborhoods were more likely to be diagnosed at later stages (OR, 1.26; 95% confidence interval, 1.12-1.41) and 34% more likely to die from breast cancer (HR, 1.34; 95% confidence interval, 1.14-1.57) after adjusting for age, race, tobacco use, tobacco pack-years, marital status, insurance status, family history, stage at diagnosis, breast cancer subtype, and residence in Appalachia when compared with women living in the least disadvantaged neighborhoods (ADI first quartile). CONCLUSIONS:Women in disadvantaged neighborhoods had significantly higher odds of late-stage diagnosis and breast cancer death, regardless of race, indicating that neighborhood factors contribute to breast cancer disparities. IMPACT:Socioeconomic and neighborhood factors may contribute to breast cancer outcomes, suggesting the necessity for targeted interventions. Future research should explore the effectiveness of such interventions and investigate additional social determinants contributing to disparities.

The National Cancer Policy Forum workshop Enabling 21st Century Applications for Cancer Surveillance Through Enhanced Registries and Beyond examined the current state of cancer registries and how they might evolve to extend registry missions to national health priorities related to improving patient and health economic outcomes, equitable access to care, and improvement in quality of health care and health system operational efficiencies. Session 3 of the workshop focused on medical informatics as a driver of improvement in cancer registry data quality and interoperability. Data quality begins with precision in data definitions as codified in controlled vocabularies and ontologies. Oncology data dictionaries that have been established or are evolving are described. Harmonization of various data dictionaries through representation in Systematized Nomenclature of Medicine-Clinical Terms and hierarchical classification systems within Common Data Models are outlined. Interoperability requires transmission standards that facilitate exchange of data between data sources, registries, and data consumers. While highly structured data capture and representation support semantically appropriate data use, the high degree of effort related to data capture and the accompanying rigidity in the data structure are challenges to implementation. Artificial intelligence may provide alternative paths for the extraction and representation of cancer registry data. Higher-fidelity cancer data and greater interoperability of data combined with data governance will help realize a Learning Health System for oncology, but economic benefits need to be shared to support the infrastructure costs incurred by health care systems.

Background Cancer incidence decreased during the COVID-19 pandemic; this study describes cancer incidence changes for children, adolescents, and young adults during this period. We used US Cancer Statistics (USCS) to describe potential impacts of the pandemic on cancer incidence for persons 0-39 years.Methods We used data from USCS, covering 98% of the US population, to evaluate cancer case counts, incidence rates, and monthly counts; incidence rate ratios were calculated comparing year 2019 (baseline) to 2020 and 2021. We calculated trends using joinpoint regression for the period 2003-2021.Results Comparing 2019 with 2020, cancer incidence decreased 5% (95% confidence interval [CI], 3%-7%) for ages 0-19 years and 7% (95% CI, 6%-8%) for ages 20-39. For ages 0-19, decreases were seen for thyroid cancer, melanoma, and nonmalignant central nervous system (CNS) neoplasms. For ages 20-39, decreases were seen for leukemias, lymphomas, CNS neoplasms, sarcomas, melanoma, and some carcinoma types. Decreases in 2020 were least pronounced for the distant stage at diagnosis. For ages 0-39, new diagnoses were lowest during March or April 2020 but returned to prepandemic levels during the second half of 2020. Decreases in 2021 were consistent with previously decreasing trends for many cancer types.Conclusions Cancer rate decreases were largest for young adults (vs. children) and were significant for some cancer types. These findings might help assess the impacts of the COVID-19 pandemic, inform investigations into potential causes of these decreases, and guide responses to future public health crises.

Abstract Wilms tumor is the most common solid renal malignancy in children. To better understand the biology of Wilms tumors and develop drugs especially against tumors that relapse, we sought to identify mutations in the exome, differential gene expression patterns, and tumor suppressor expression status of Wilms tumors in Kentucky. The Kentucky Cancer Registry (KCR) Virtual Tissue Repository identified 29 cases of Wilms tumor diagnosed between 2015 and 2021 at hospitals across the Commonwealth of Kentucky. The Markey Cancer Center Biospecimen Procurement and Translational Pathology Facility prepared tumor microarrays (TMAs) using Wilms tumor and matching normal tissue cores for immunohistochemistry (IHC) analysis. RNA-Seq analysis performed at the Markey Cancer Center Oncogenomics Facility identified a gene signature that was further validated by IHC and reverse-transcription quantitative-PCR (RT-qPCR). Outcome data provided by KCR indicated that increased expression of an anti-apoptotic protein in the tumor counter-intuitively provided better prognosis and outcome to treatment, indicating that the treatments either directly or indirectly targeted this protein. Loss of function of the tumor suppressor proteins in Par-4/PAWR, WT1 and p53 did not explain the higher expression of the cell survival protein. Our multidisciplinary team approach involves basic science/translational researchers, population scientists, biomedical informaticists, and clinicians and has resulted in well-characterized, Wilms tumor tissue microarrays. The next phase of the study is generating patient derived xenografts (PDXs) for a better understanding of Wilms tumor biology in children. Citation Format: Jieyun Jiang, Derek B. Allison, Ravshan Burikhanov, Saptadwipa Ganguly, Ryan A. Goettl, Jinpeng Liu, Chi Wang, Amanda F. Saltzman, Thomas Tucker, Peter H. Spielmann, Eric B. Durbin, John A. D’Orazio, Vivek M. Rangnekar. Molecular signature for treatment-responsive and recurrent Wilms tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1103.

Abstract Background Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.

PURPOSE Database linkage between cancer registries and clinical trial consortia has the potential to elucidate referral patterns of children and adolescents with newly diagnosed cancer, including enrollment into cancer clinical trials. This study's primary objective was to assess the feasibility of this linkage approach. METHODS Patients younger than 20 years diagnosed with incident cancer during 2012-2017 in the Kentucky Cancer Registry (KCR) were linked with patients enrolled in a Children's Oncology Group (COG) study. Matched patients between databases were described by sex, age, race and ethnicity, geographical location when diagnosed, and cancer type. Logistic regression modeling identified factors associated with COG study enrollment. Timeliness of patient identification by KCR was reported through the Centers for Disease Control and Prevention's Early Case Capture (ECC) program. RESULTS Of 1,357 patients reported to KCR, 47% were determined by matching to be enrolled in a COG study. Patients had greater odds of enrollment if they were age 0-4 years ( v 15-19 years), reported from a COG-affiliated institution, and had renal cancer, neuroblastoma, or leukemia. Patients had lower odds of enrollment if Hispanic ( v non-Hispanic White) or had epithelial (eg, thyroid, melanoma) cancer. Most (59%) patients were reported to KCR within 10 days of pathologic diagnosis. CONCLUSION Linkage of clinical trial data with cancer registries is a feasible approach for tracking patient referral and clinical trial enrollment patterns. Adolescents had lower enrollment compared with younger age groups, independent of cancer type. Population-based early case capture could guide interventions designed to increase cancer clinical trial enrollment.

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.

Background: This retrospective cohort study investigates the prognostic significance of genetic mutations in Chronic Myelomonocytic Leukemia (CMML) and their association with treatment responses among patients treated at a single institution, juxtaposed with a statewide dataset from Kentucky. Methods: The study includes 51 patients diagnosed with CMML under the World Health Organization criteria from January 2005 to December 2023. It examines their genomic profiles and subsequent survival outcomes. The analysis also categorizes patients into CMML-1 and CMML-2 subtypes and assesses survival differences between transformed and non-transformed cases. Results: Mutations in TET2, ASXL1, and SRSF2 were found to significantly influence survival, establishing their roles as critical prognostic markers. Additionally, the cohort from the University of Kentucky exhibited distinct survival patterns compared to the broader Kentucky state population, suggesting that demographic and treatment-related factors could underlie these variances. Conclusions: This research underscores the pivotal role of targeted genetic profiling in deciphering the progression of CMML and refining therapeutic strategies. The findings emphasize the necessity for advanced genetic screening in managing CMML to better understand individual prognoses and optimize treatment efficacy, thereby offering insights that could lead to personalized treatment approaches.

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.

e20089 Background: Lung adenocarcinoma in young adults (≤50) has an aggressive course even in the presence of targetable mutations. Prior US studies were conducted predominantly in the Northeast where smoking prevalence is low compared to Kentucky. To assess the mutation spectrum in this high-risk population we performed a descriptive analysis of mutations in patients 50 years old and younger (YA), compared the survival of YA patients with and without NGS, and compared the survival of YA to patients older than 50 seen at an NCI designated Comprehensive Cancer Center situated in the tobacco belt. Methods: Using the Kentucky Cancer Registry (KCR) we identified 139 YA patients with lung adenocarcinoma seen at our institution between 01/2015 to 11/2023, 54 had NGS testing. 933 patients over age 50 were identified for comparison. Adenocarcinoma specimens were analyzed using next-generation sequencing of DNA (DNA-592-gene panel or whole exome) or RNA (whole transcriptome or +1,500-gene panel) at Caris Life Sciences or Foundation Medicine. Hazard ratios (HR) for survival were calculated using the Cox proportional hazards model. The cutoff for statistical significance was a p-value < 0.05 as calculated by the Chi-square test. Results: Of the 54 YA patients with NGS, 28 (52%) were female, 48 were white (88%), 3 Black (6%), and 3 Asian (6%). Only 18% were never smokers (median 28 pack years), and 44% had a family history of lung cancer. Targetable mutations were noted in 27 (50%). EGFR was mutated in 9 patients (17%), 2 with exon 19 deletion, 2 with exon 20 insertion, and 5 with exon 21 L858R. ALK rearrangement was seen in 3 patients (5%), while 3 (5%) had ERBB2 mutations. There was a mutation of KRAS in 17 patients (31%), and 16 of the 17 were transversion mutations. (13/17 G12C, 1/17 G12R, 1/17 G12V and 1/17 G13C). TP53 was mutated in 44 patients (81%), and 16 patients (30%) had mutations in homologous recombination repair (HHR) genes. The tumor mutation burden was high in 28 (52%) of patients. PD-L1 expression was > 50% in 22/54, 1-49% in 18/54, and 0% in 10/54 (4/54 NA). Survival was not significantly different between YA patients with and without NGS nor between YA and older patients. Conclusions: We identified a high prevalence of KRAS G12C, previously associated with increasing age and environmental insults, and EGFR exon 21 L858R in a YA population. Alterations in HHR genes and TP53 were present at high rates. Acquisition of aggressive forms of KRAS and EGFR mutation, as well as the high prevalence of TP53 and HHR defects, may contribute to early disease development. This highlights the importance of early tobacco cessation efforts and the need to assess germline mutations. Lung cancer remains a therapeutic challenge even in young patients with actionable mutations, as outcomes remain poor, equivalent to older patients.

Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.

INTRODUCTION:Pediatric and young adult brain tumors (PYBT) account for a large share of cancer-related morbidity and mortality among children in the United States, but their etiology is not well understood. Previous research suggests the Appalachian region of Kentucky has high rates of PYBT. This study explored PYBT incidence over 25 years in Kentucky to identify geographic and temporal trends and generate hypotheses for future research.METHODS:The Kentucky Cancer Registry contributed data on all PYBT diagnosed among those aged 0-29 during years 1995-2019. Age- and sex-adjusted spatio-temporal scan statistics-one for each type of PYBT, and one for all types-comprised the primary analysis. These results were mapped along with environmental and occupational data.RESULTS:Findings indicated that north-central Kentucky and the Appalachian region experienced higher rates of some PYBT. High rates of astrocytomas were clustered in a north-south strip of central Kentucky toward the end of the study period, while high rates of other specified types of intracranial and intraspinal neoplasms were significantly clustered in eastern Kentucky. The area where these clusters overlapped, in north-central Kentucky, had significantly higher rates of PYBT generally.DISCUSSION:This study demonstrates north-central Kentucky and the Appalachian region experienced higher PYBT risk than the rest of the state. These regions are home to some of Kentucky's signature industries, which should be examined in further research. Future population-based and individual-level studies of genetic factors are needed to explore how the occupations of parents, as well as prenatal and childhood exposures to pesticides and air pollutants, impact PYBT incidence.

BACKGROUND Cancer is a leading cause of death by disease among children and adolescents in the United States. This study updates cancer incidence rates and trends using the most recent and comprehensive US cancer registry data available. METHODS We used data from US Cancer Statistics to evaluate counts, age-adjusted incidence rates, and trends among children and adolescents aged <20 years diagnosed with malignant tumors during 2003-2019. We calculated average annual percent change and annual percent change (APC) using joinpoint regression. Rates and trends were stratified by demographic and geographic characteristics and by cancer type. RESULTS With 248,749 cases reported during 2003-2019, the overall cancer incidence rate was 178.3 per 1 million; incidence rates were highest for leukemia (46.6), central nervous system (CNS) neoplasms (30.8), and lymphoma (27.3). Rates were highest for males, children aged 0-4 years, Non-Hispanic White children and adolescents, those in the Northeast census region, top 25% of counties by economic status, and metropolitan counties with population ≥1 million. While the overall incidence rate of pediatric cancer increased 0.5% per year on average during 2003-2019, the rate increased during 2003-2016 (APC = 1.1%) and then decreased during 2016-2019 (APC = -2.1%). During 2003-2019, rates of leukemia, lymphoma, hepatic tumors, bone tumors, and thyroid carcinomas increased, while melanoma rates decreased. CNS neoplasms rates increased until 2017 and then decreased. Other cancer types remained stable. CONCLUSIONS Incidence of pediatric cancer increased overall, although increases were limited to certain cancer types. These findings may guide future public health and research priorities.