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Generating evidence on the use, effectiveness, and safety of new cancer therapies is a priority for researchers, health care providers, payers, and regulators given the rapid pace of change in cancer diagnosis and treatments. The use of real-world data (RWD) is integral to understanding the utilization patterns and outcomes of these new treatments among patients with cancer who are treated in clinical practice and community settings. An initial step in the use of RWD is careful study design to assess the suitability of an RWD source. This pivotal process can be guided by using a conceptual model that encourages predesign conceptualization. The primary types of RWD included are electronic health records, administrative claims data, cancer registries, and specialty data providers and networks. Careful consideration of each data type is necessary because they are collected for a specific purpose, capturing a set of data elements within a certain population for that purpose, and they vary by population coverage and longitudinality. In this review, the authors provide a high-level assessment of the strengths and limitations of each data category to inform data source selection appropriate to the study question. Overall, the development and accessibility of RWD sources for cancer research are rapidly increasing, and the use of these data requires careful consideration of composition and utility to assess important questions in understanding the use and effectiveness of new therapies.

Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.

This retrospective observational study aimed to gain a better understanding of the protective duration of prior SARS-CoV-2 infection against reinfection. The objectives were two-fold: to assess the durability of immunity to SARS-CoV-2 reinfection among initially unvaccinated individuals with previous SARS-CoV-2 infection, and to evaluate the crude SARS-CoV-2 reinfection rate and associated risk factors. During the pandemic era time period from February 29, 2020, through April 30, 2021, 144,678,382 individuals with SARS-CoV-2 molecular diagnostic or antibody test results were studied. Rates of reinfection among index-positive individuals were compared to rates of infection among index-negative individuals. Factors associated with reinfection were evaluated using multivariable logistic regression. For both objectives, the outcome was a subsequent positive molecular diagnostic test result. Consistent with prior findings, the risk of reinfection among index-positive individuals was 87% lower than the risk of infection among index-negative individuals. The duration of protection against reinfection was stable over the median 5 months and up to 1-year follow-up interval. Factors associated with an increased reinfection risk included older age, comorbid immunologic conditions, and living in congregate care settings; healthcare workers had a decreased reinfection risk. This large US population-based study suggests that infection induced immunity is durable for variants circulating pre-Delta predominance.

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks-site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

Population-based cancer registries (PBCRs) generate measures of cancer incidence and survival that are essential for cancer surveillance, research, and cancer control strategies. In 2014, the Toronto Paediatric Cancer Stage Guidelines were developed to standardise how PBCRs collect data on the stage at diagnosis for childhood cancer cases. These guidelines have been implemented in multiple jurisdictions worldwide to facilitate international comparative studies of incidence and outcome. Robust stratification by risk also requires data on key non-stage prognosticators (NSPs). Key experts and stakeholders used a modified Delphi approach to establish principles guiding paediatric cancer NSP data collection. With the use of these principles, recommendations were made on which NSPs should be collected for the major malignancies in children. The 2014 Toronto Stage Guidelines were also reviewed and updated where necessary. Wide adoption of the resultant Paediatric NSP Guidelines and updated Toronto Stage Guidelines will enhance the harmonisation and use of childhood cancer data provided by PBCRs.

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.

The Surveillance, Epidemiology, and End Results (SEER) cancer registry program is currently evaluating the use of archival, diagnostic, formalin-fixed, paraffin-embedded (FFPE) tissue obtained through SEER cancer registries, functioning as honest brokers for deidentified tissue and associated data. To determine the feasibility of this potential program, laboratory policies for sharing tissue for research needed to be assessed. To understand the willingness of pathology laboratories to share archival diagnostic tissue for cancer research and related policies. Seven SEER registries administered a 27-item questionnaire to pathology laboratories within their respective registry catchment areas. Only laboratories that processed diagnostic FFPE specimens and completed the questionnaire were included in the analysis. Of the 153 responding laboratories, 127 (83%) responded that they process FFPE specimens. Most (n = 88; 69%) were willing to share tissue specimens for research, which was not associated with the number of blocks processed per year by the laboratories. Most laboratories retained the specimens for at least 10 years. Institutional regulatory policies on sharing deidentified tissue varied considerably, ranging from requiring a full Institutional Review Board review to considering such use exempt from Institutional Review Board review, and 43% (55 of 127) of the laboratories did not know their terms for sharing tissue for research. This project indicated a general willingness of pathology laboratories to participate in research by sharing FFPE tissue. Given the variability of research policies across laboratories, it is critical for each SEER registry to work with laboratories in their catchment area to understand such policies and state legislation regulating tissue retention and guardianship.

The Surveillance, Epidemiology, and End Results (SEER) cancer registry program is currently evaluating the use of archival, diagnostic, formalin-fixed, paraffin-embedded (FFPE) tissue obtained through SEER cancer registries, functioning as honest brokers for deidentified tissue and associated data. To determine the feasibility of this potential program, laboratory policies for sharing tissue for research needed to be assessed. To understand the willingness of pathology laboratories to share archival diagnostic tissue for cancer research and related policies. Seven SEER registries administered a 27-item questionnaire to pathology laboratories within their respective registry catchment areas. Only laboratories that processed diagnostic FFPE specimens and completed the questionnaire were included in the analysis. Of the 153 responding laboratories, 127 (83%) responded that they process FFPE specimens. Most (n = 88; 69%) were willing to share tissue specimens for research, which was not associated with the number of blocks processed per year by the laboratories. Most laboratories retained the specimens for at least 10 years. Institutional regulatory policies on sharing deidentified tissue varied considerably, ranging from requiring a full Institutional Review Board review to considering such use exempt from Institutional Review Board review, and 43% (55 of 127) of the laboratories did not know their terms for sharing tissue for research. This project indicated a general willingness of pathology laboratories to participate in research by sharing FFPE tissue. Given the variability of research policies across laboratories, it is critical for each SEER registry to work with laboratories in their catchment area to understand such policies and state legislation regulating tissue retention and guardianship.

Until recent decades, sickle cell disease (SCD) was associated with recurrent, disabling pain, organ failure and death in childhood or early adulthood. SCD treatment advances have now decreased pain and prolonged survival, but episodic or chronic pain may still require substantial analgesic use and frequent hospitalization for pain episodes. This pain is poorly characterized and often poorly treated. Adult patients may face barriers to comprehensive SCD care, stigmatization of their care-seeking behavior by providers and lack of family support, forcing them into maladaptive coping strategies. The Pain in Sickle Cell Epidemiology Study (PiSCES) attempts to develop and validate a biopsychosocial model of SCD pain, pain response and healthcare utilization in a large, multisite adult cohort. PiSCES participants complete a baseline survey and six months of daily pain diaries in which they record levels of SCD-related pain and related disability and distress as well as responses to pain (e.g., medication use, hospital visits). PiSCES will advance methods of measuring pain and pain response in SCD by better describing home-managed as well as provider-managed pain. PiSCES will assess the relative contributions of biological (disease-related), psychosocial and environmental (readiness to utilize) factors to overall pain and pain response in SCD, suggesting targets for biobehavioral interventions over time. Importantly, PiSCES will also identify \"triggers\" of SCD pain episodes and healthcare utilization in the moment of pain, suggesting targets for timely care that mutes pain episodes.

Purpose This analysis describes the impact of hysterectomy on incidence rates and trends in endometrioid endometrial cancer in the United States among women of reproductive age. Methods Hysterectomy prevalence for states containing Surveillance, Epidemiology, and End Results (SEER) registry was estimated using data from the Behavioral Risk Factor Surveillance System (BRFSS) between 1992 and 2010. The population was adjusted for age, race, and calendar year strata. Age-adjusted incidence rates and trends of endometrial cancer among women age 20–49 corrected for hysterectomy were estimated. Results Hysterectomy prevalence varied by age, race, and ethnicity. Increasing incidence trends were observed, and were attenuated after correcting for hysterectomy. Among all women, the incidence was increasing 1.6% annually (95% CI 0.9, 2.3) and this increase was no longer significant after correction for hysterectomy (+ 0.7; 95% CI − 0.1, 1.5). Stage at diagnosis was similar with and without correction for hysterectomy. The largest increase in incidence over time was among Hispanic women; even after correction for hysterectomy, incidence was increasing (1.8%; 95% CI 0.2, 3.4) annually. Conclusion Overall, endometrioid endometrial cancer incidence rates in the US remain stable among women of reproductive age. Routine reporting of endometrial cancer incidence does not accurately measure incidence among racial and ethnic minorities.