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Purpose: Cancer registries are often asked to present cancer data for small geographic areas to inform and facilitate targeted interventions and prevention programs. However, it is challenging to compute and visualize reliable cancer estimates for areas with small case counts and populations to support cancer control planning., Methods: We used a Bayesian hierarchical model that borrows strength from neighboring areas and over time to produce cancer estimates for small areas. We developed a visual analytics platform to present these estimates in interactive graphics that demonstrate risk in small areas. In a user-centered design process, development of the tool was informed by cancer registry and public health professionals through focus groups and surveys., Results: The Cancer Analytics and Maps for Small Areas tool (CAMSA) provides age-adjusted cancer incidence and mortality rates and risk probabilities for eight cancers at the county and ZIP-code tabulation area (ZCTA) levels. It allows the user to identify cancer hotpots, including among sub-groups defined by sex and race/ethnicity. Potential end users were enthusiastic about the opportunity to implement CAMSA within their practice, emphasizing the tool's potential for increasing collaborative opportunities at local and state levels. Suggestions for improvement included adding map overlays such as additional cancer risk variables and incorporating functionalities like exportable data tables., Conclusions: CAMSA presents cancer rate and risk estimates for small geographic areas where they may have previously been suppressed. Through our user-informed design process, we developed statistical models and data visualizations to support the needs of an array of potential end users., Competing Interests: Competing Interests The authors have no relevant financial or non-financial interests to disclose.

Hit screening, which involves the identification of compounds or targets capable of modulating disease-relevant processes, is an important step in drug discovery. Some assays, such as image-based high-content screenings, produce complex multivariate readouts. To fully exploit the richness of such data, advanced analytical methods that go beyond the conventional univariate approaches should be employed. In this work, we tackle the problem of hit identification in multivariate assays. As with univariate assays, a hit from a multivariate assay can be defined as a candidate that yields an assay value sufficiently far away in distance from the mean or central value of inactives. Viewed another way, a hit is an outlier from the distribution of inactives. A method was developed for identifying multivariate hit in high-dimensional data sets based on principal components and robust Mahalanobis distance (the multivariate analogue to the Z- or T-statistic). The proposed method, termed mROUT (multivariate robust outlier detection), demonstrates superior performance over other techniques in the literature in terms of maintaining Type I error, false discovery rate and true discovery rate in simulation studies. The performance of mROUT is also illustrated on a CRISPR knockout data set from in-house phenotypic screening programme., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Leong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The National Cancer Institute's (NCI) Health Information National Trends Survey (HINTS) is a nationally representative survey of U.S. adults in which 12-17% of respondents report a cancer history. To increase representation from adult cancer survivors, in 2021, NCI sampled survivors from three Surveillance, Epidemiology, and End Results (SEER) program cancer registries: Iowa, New Mexico, and the Greater Bay Area. Sampling frames were stratified by time since diagnosis and race/ethnicity, with nonmalignant tumors and non-melanoma skin cancers excluded. Participants completed a self-administered postal questionnaire. The overall response rate for HINTS-SEER ( N  = 1,234) was 12.6%; a non-response bias analysis indicated few demographic differences between respondents and the pool of sampled patients in each registry. Most of the sample was 10+ years since diagnosis ( n  = 722; 60.2%); 392 respondents were 5 to < 10 years since diagnosis (29.6%); and 120 were < 5 years since diagnosis (10.2%). Common cancers included male reproductive ( n  = 304; 24.6%), female breast ( n  = 284; 23.0%), melanoma ( n  = 119; 9.6%), and gastrointestinal ( n  = 106; 8.6%). Tumors were mostly localized (67.8%; n  = 833), with 22.4% ( n  = 282) regional, 6.2% ( n  = 72) distant, and 3.7% ( n  = 47) unknown. HINTS-SEER data are available by request and may be used for secondary analyses to examine a range of social, behavioral, and healthcare outcomes among cancer survivors.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)