Multiparametric MRI for the detection of local recurrence of prostate cancer in the setting of biochemical recurrence after low dose rate brachytherapy

PURPOSE Prostate multiparametric magnetic resonance imaging (mpMRI) has utility in detecting post-ra-diotherapy local recurrence. We conducted a multireader study to evaluate the diagnostic performance of mpMRI for local recurrence after low dose rate (LDR) brachytherapy. METHODS A total of 19 patients with biochemical recurrence after LDR brachytherapy underwent 3T en-dorectal coil mpMRI with T2-weighted imaging, dynamic contrast-enhanced imaging (DCE) and diffusion-weighted imaging (DWI) with pathologic confirmation. Prospective reads by an experienced prostate radiologist were compared with reads from 4 radiologists of varying experience. Readers identified suspicious lesions and rated each MRI detection parameter. MRI-detected lesions were considered true-positive with ipsilateral pathologic confirmation. Inferences for sensitivity, specificity, positive predictive value (PPV), kappa, and index of specific agreement were made with the use of bootstrap resampling. RESULTS Pathologically confirmed recurrence was found in 15 of 19 patients. True positive recurrences identified by mpMRI were frequently located in the transition zone (46.7%) and seminal vesicles (30%). On patient-based analysis, average sensitivity of mpMRI was 88% (standard error [SE], 3.5%). For highly suspicious lesions, specificity of mpMRI was 75% (SE, 16.5%). On lesion-based analysis, the average PPV was 62% (SE, 6.7%) for all lesions and 78.7% (SE, 10.3%) for highly suspicious lesions. The average PPV for lesions invading the seminal vesicles was 88.8% (n=13). The average PPV was 66.6% (SE, 5.8%) for lesions identified with T2-weighted imaging, 64.9% (SE, 7.3%) for DCE, and 70% (SE, 7.3%) for DWI. CONCLUSION This series provides evidence that mpMRI after LDR brachytherapy is feasible with a high patient-based cancer detection rate. Radiologists of varying experience demonstrated moderate agreement in detecting recurrence.This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research (ZIA BC 011552). This research was also made possible in part through the NIH Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and generous contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, the Howard Hughes Medical Institute, the American Association for Dental Research, the Colgate-Palmolive Company, and other private donors

Operational Ontology for Oncology (O3) – A Professional Society Based, Multi-Stakeholder, Consensus Driven Informatics Standard Supporting Clinical and Research use of “Real -World” Data from Patients Treated for Cancer Operational Ontology for Radiation Oncology

PurposeThe ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), Radiation Oncology Information Systems (ROIS), treatment planning systems (TPSs), and other cancer care and outcomes databases. The effort sought to create a standardized ontology for clinical data, social determinants of health (SDOH), and other radiation oncology concepts and interrelationships.Methods and materialsThe American Association of Physicists in Medicine's (AAPM's) Big Data Science Committee (BDSC) was initiated July of 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra- institutional databases from EHRs. The BDSC adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community.ResultsWe developed the Operational Ontology for Oncology (O3) which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, or the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to four constituencies: device manufacturers, centers of clinical care, researchers, and professional societies.ConclusionsO3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used creating large, representative, findable, accessible, interoperable and reusable (FAIR) datasets supporting the scientific objectives of grant programs. The construction of comprehensive "real world" datasets and application of advanced analytic techniques, including artificial intelligence (AI), holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative datasets

Li-Fraumeni syndrome: cancer risk assessment and clinical management

Carriers of germline mutations in the TP53 gene, encoding the cell-cycle regulator and tumour suppressor p53, have a markedly increased risk of cancer-related morbidity and mortality during both childhood and adulthood, and thus require appropriate and effective cancer risk management. However, the predisposition of such patients to multiorgan tumorigenesis presents a specific challenge for cancer risk management programmes. Herein, we review the clinical implications of germline mutations in TP53 and the evidence for cancer screening and prevention strategies in individuals carrying such mutations, as well as examining the potential psychosocial implications of lifelong management for a ubiquitous cancer risk. In addition, we propose an evidence-based framework for the clinical management of TP53 mutation carriers and provide a platform for addressing the management of other cancer predisposition syndromes that can affect multiple organs