Relevant bibliographies by topics / Oncology Knowledge Base

Academic literature on the topic 'Oncology Knowledge Base'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Oncology Knowledge Base"

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Chakravarty, Debyani, Jianjiong Gao, Sarah Phillips, Ritika Kundra, Hongxin Zhang, Jiaojiao Wang, Julia E. Rudolph, et al. "OncoKB: A Precision Oncology Knowledge Base." JCO Precision Oncology, no. 1 (November 2017): 1–16. http://dx.doi.org/10.1200/po.17.00011.

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Purpose With prospective clinical sequencing of tumors emerging as a mainstay in cancer care, an urgent need exists for a clinical support tool that distills the clinical implications associated with specific mutation events into a standardized and easily interpretable format. To this end, we developed OncoKB, an expert-guided precision oncology knowledge base. Methods OncoKB annotates the biologic and oncogenic effects and prognostic and predictive significance of somatic molecular alterations. Potential treatment implications are stratified by the level of evidence that a specific molecular alteration is predictive of drug response on the basis of US Food and Drug Administration labeling, National Comprehensive Cancer Network guidelines, disease-focused expert group recommendations, and scientific literature. Results To date, > 3,000 unique mutations, fusions, and copy number alterations in 418 cancer-associated genes have been annotated. To test the utility of OncoKB, we annotated all genomic events in 5,983 primary tumor samples in 19 cancer types. Forty-one percent of samples harbored at least one potentially actionable alteration, of which 7.5% were predictive of clinical benefit from a standard treatment. OncoKB annotations are available through a public Web resource ( http://oncokb.org ) and are incorporated into the cBioPortal for Cancer Genomics to facilitate the interpretation of genomic alterations by physicians and researchers. Conclusion OncoKB, a comprehensive and curated precision oncology knowledge base, offers oncologists detailed, evidence-based information about individual somatic mutations and structural alterations present in patient tumors with the goal of supporting optimal treatment decisions.
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Suehnholz, Sarah P., Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Benjamin Xu, Maria E. Arcila, et al. "Abstract 1189: OncoKB, MSK’s precision oncology knowledge base." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1189. http://dx.doi.org/10.1158/1538-7445.am2022-1189.

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Abstract OncoKB, Memorial Sloan Kettering Cancer Center’s (MSK) precision oncology knowledge base (www.oncokb.org), is a comprehensive database that annotates the oncogenic effects and clinical actionability of somatic alterations in cancer. OncoKB supports variant interpretation by the cBioPortal for Cancer Genomics and is used to annotate >12,000 MSK patient sequencing reports annually. Since its introduction in 2016, OncoKB has expanded to include 5685 alterations in 682 genes, and in October 2021, it became the first somatic knowledge base to be partially recognized by the FDA. The scope of the OncoKB FDA recognition includes clinically actionable variants that map to an FDA level of evidence, the processes of variant curation, and policies regarding database oversight, personnel training and transparency of data sources and operations. This recognition credentials OncoKB as providing accurate, reliable and clinically meaningful information to the medical and scientific communities. The OncoKB Therapeutic (Tx) Levels of Evidence categorize variants based on their tumor type-specific predictive value of sensitivity or resistance to matched standard care or investigational targeted therapies. To date, OncoKB includes 43 Level 1 genes (included in the FDA drug label), 23 Level 2 genes (included in professional guidelines), 25 Level 3A genes (predictive of drug response in well-powered clinical studies), 23 Level 4 genes (predictive of drug response based on compelling biological evidence), and 11 R1 or R2 resistance genes. Initially focused on solid tumors, OncoKB was expanded to include hematologic disease annotation in 2019 and introduced Diagnostic (Dx) and Prognostic (Px) levels of evidence. All three level of evidence systems (Tx, Dx and Px) are consistent with the guidelines for evidence-based categorization of somatic variants published as a joint consensus recommendation by AMP/ASCO/CAP. OncoKB is governed by a Clinical Genomics Annotation Committee, composed of MSK physicians and scientists who ensure that the information captured is accurate and current, and an external advisory board composed of leaders in the clinical oncology and genomics communities who oversee OncoKB updates and progress. OncoKB curation rules and processes are transparent and documented in the OncoKB Curation Standard Operating Procedure, which is publicly available via the website. User feedback to OncoKB content is encouraged via the website and through cBioPortal. Queries or suggestions by OncoKB users are addressed by the OncoKB team within 72 hours. OncoKB offers licenses for academic, commercial and hospital use, with which users can programmatically access the web API. Future work includes coverage of additional cancer-associated genes, annotation of germline alterations that are predictive of drug response and/or associated with increased heritable cancer risk and the development of a clinical trial matching system. Citation Format: Sarah P. Suehnholz, Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Benjamin Xu, Maria E. Arcila, Marc Ladanyi, Michael F. Berger, Ahmet Zehir, Aijaz Syed, Julia E. Rudolph, Ross L. Levine, Ahmet Dogan, Jianjiong Gao, David B. Solit, Nikolaus Schultz, Debyani Chakravarty. OncoKB, MSK’s precision oncology knowledge base [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1189.
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Akabe, Koichi, Toshiki Takeuchi, Takashi Aoki, and Kunihiro Nishimura. "Information retrieval on oncology knowledge base using recursive paraphrase lattice." Journal of Biomedical Informatics 116 (April 2021): 103705. http://dx.doi.org/10.1016/j.jbi.2021.103705.

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Rubinstein, Samuel M., Tarsheen Sethi, Neeta K. Venepalli, Bishal Gyawali, Candice Schwartz, Donna R. Rivera, Peter C. Yang, and Jeremy L. Warner. "Chemotherapy Knowledge Base Management in the Era of Precision Oncology." JCO Clinical Cancer Informatics, no. 5 (January 2021): 30–35. http://dx.doi.org/10.1200/cci.20.00076.

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Cancer medicine has grown increasingly complex in recent years with the advent of precision oncology and wide utilization of multidrug regimens. Representing this increasingly granular knowledge is a significant challenge. As users and managers of a freely available chemotherapy drug and regimen database, we describe the changes we have made to accommodate these challenges. These include the development of a domain ontology and increased granularity in the classification of cancer types on the website.
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Takeuchi, Shiho, and Shujiro Okuda. "Knowledge base toward understanding actionable alterations and realizing precision oncology." International Journal of Clinical Oncology 24, no. 2 (December 12, 2018): 123–30. http://dx.doi.org/10.1007/s10147-018-1378-0.

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Rogers, Leland, Igor Barani, Marc Chamberlain, Thomas J. Kaley, Michael McDermott, Jeffrey Raizer, David Schiff, Damien C. Weber, Patrick Y. Wen, and Michael A. Vogelbaum. "Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review." Journal of Neurosurgery 122, no. 1 (January 2015): 4–23. http://dx.doi.org/10.3171/2014.7.jns131644.

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Evolving interest in meningioma, the most common primary brain tumor, has refined contemporary management of these tumors. Problematic, however, is the paucity of prospective clinical trials that provide an evidence-based algorithm for managing meningioma. This review summarizes the published literature regarding the treatment of newly diagnosed and recurrent meningioma, with an emphasis on outcomes stratified by WHO tumor grade. Specifically, this review focuses on patient outcomes following treatment (either adjuvant or at recurrence) with surgery or radiation therapy inclusive of radiosurgery and fractionated radiation therapy. Phase II trials for patients with meningioma have recently completed accrual within the Radiation Therapy Oncology Group and the European Organisation for Research and Treatment of Cancer consortia, and Phase III studies are being developed. However, at present, there are no completed prospective, randomized trials assessing the role of either surgery or radiation therapy. Successful completion of future studies will require a multidisciplinary effort, dissemination of the current knowledge base, improved implementation of WHO grading criteria, standardization of response criteria and other outcome end points, and concerted efforts to address weaknesses in present treatment paradigms, particularly for patients with progressive or recurrent low-grade meningioma or with high-grade meningioma. In parallel efforts, Response Assessment in Neuro-Oncology (RANO) subcommittees are developing a paper on systemic therapies for meningioma and a separate article proposing standardized end point and response criteria for meningioma.
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Pallarz, Steffen, Manuela Benary, Mario Lamping, Damian Rieke, Johannes Starlinger, Christine Sers, David Luis Wiegandt, et al. "Comparative Analysis of Public Knowledge Bases for Precision Oncology." JCO Precision Oncology, no. 3 (December 2019): 1–8. http://dx.doi.org/10.1200/po.18.00371.

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PURPOSE Precision oncology depends on the availability of up-to-date, comprehensive, and accurate information about associations between genetic variants and therapeutic options. Recently, a number of knowledge bases (KBs) have been developed that gather such information on the basis of expert curation of the scientific literature. We performed a quantitative and qualitative comparison of Clinical Interpretations of Variants in Cancer, OncoKB, Cancer Gene Census, Database of Curated Mutations, CGI Biomarkers (the cancer genome interpreter biomarker database), Tumor Alterations Relevant for Genomics-Driven Therapy, and the Precision Medicine Knowledge Base. METHODS We downloaded each KB and restructured their content to describe variants, genes, drugs, and gene-drug associations in a common format. We normalized gene names to Entrez Gene IDs and drug names to ChEMBL and DrugBank IDs. For the analysis of clinically relevant gene-drug associations, we obtained lists of genes affected by genetic alterations and putative drug therapies for 113 patients with cancer whose cases were presented at the Molecular Tumor Board (MTB) of the Charité Comprehensive Cancer Center. RESULTS Our analysis revealed that the KBs are largely overlapping but also that each source harbors a notable amount of unique information. Although some KBs cover more genes, others contain more data about gene-drug associations. Retrospective comparisons with findings of the Charitè MTB at the gene level showed that use of multiple KBs may considerably improve retrieval results. The relative importance of a KB in terms of cancer genes was assessed in more detail by logistic regression, which revealed that all but one source had a notable impact on result quality. We confirmed these findings using a second data set obtained from an independent MTB. CONCLUSION To date, none of the existing publicly available KBs on gene-drug associations in precision oncology fully subsumes the others, but all of them exhibit specific strengths and weaknesses. Consideration of multiple KBs, therefore, is essential to obtain comprehensive results.
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Reardon, Brendan, Nathanael D. Moore, Nicholas S. Moore, Eric Kofman, Saud H. AlDubayan, Alexander T. M. Cheung, Jake Conway, et al. "Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology." Nature Cancer 2, no. 10 (September 30, 2021): 1102–12. http://dx.doi.org/10.1038/s43018-021-00243-3.

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AbstractTumor molecular profiling of single gene-variant (‘first-order’) genomic alterations informs potential therapeutic approaches. Interactions between such first-order events and global molecular features (for example, mutational signatures) are increasingly associated with clinical outcomes, but these ‘second-order’ alterations are not yet accounted for in clinical interpretation algorithms and knowledge bases. We introduce the Molecular Oncology Almanac (MOAlmanac), a paired clinical interpretation algorithm and knowledge base to enable integrative interpretation of multimodal genomic data for point-of-care decision making and translational-hypothesis generation. We benchmarked MOAlmanac to a first-order interpretation method across multiple retrospective cohorts and observed an increased number of clinical hypotheses from evaluation of molecular features and profile-to-cell line matchmaking. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 47% of patients. Overall, we present an open-source computational method for integrative clinical interpretation of individualized molecular profiles.
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Maggiore, Ronald J., William Dale, Arti Hurria, Heidi D. Klepin, Andrew Chapman, Efrat Dotan, Supriya G. Mohile, Arash Naeim, Ajeet Gajra, and Mary K. Buss. "Hematology-Oncology Fellows’ Training in Geriatrics and Geriatric Oncology: Findings From an American Society of Clinical Oncology–Sponsored National Survey." Journal of Oncology Practice 13, no. 11 (November 2017): e900-e908. http://dx.doi.org/10.1200/jop.2017.022111.

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Purpose: Older adults compose the majority of patients with cancer in the United States; however, it is unclear how well geriatrics or geriatric oncology training is being incorporated into hematology-oncology (hem-onc) fellowships. Methods: A convenience sample of hem-onc fellows completed a (written or electronic) survey assessing their education, clinical experiences, and perceived proficiency in geriatric oncology during training; knowledge base in geriatric oncology; confidence in managing older adults with cancer; and general attitudes toward geriatric oncology principles. Results: Forty-five percent of respondents (N = 138) were female, 67% were based in the United States, and most (60%) were past their first year of training. Most fellows rated geriatric oncology as important or very important (84%); however, only 25% reported having access to a geriatric oncology clinic and more than one half (53%) reported no lectures in geriatric oncology. Fellows reported fewer educational experiences in geriatric oncology than in nongeriatric oncology. For example, among procedure-based activities, 12% learned how to perform a geriatric assessment but 78% learned how to perform a bone marrow biopsy ( P < .05). Of those completing the knowledge-based items, 41% were able to identify correctly the predictors of chemotherapy toxicity in older adults with cancer. Conclusion: Despite the prevalence of cancer in older adults, hem-onc fellows report limited education in or exposure to geriatric oncology. The high value fellows place on geriatric oncology suggests that they would be receptive to additional training in this area.
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Mai, Yun, Kyeryoung Lee, Zongzhi Liu, Meng Ma, Christopher Gilman, Minghao Li, Mingwei Zhang, et al. "Phenotyping of clinical trial eligibility text from cancer studies into computable criteria in electronic health records." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 6592. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.6592.

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6592 Background: Clinical trial phenotyping is the process of extracting clinical features and patient characteristics from eligibility criteria. Phenotyping is a crucial step that precedes automated cohort identification from patient electronic health records (EHRs) against trial criteria. We establish a clinical trial phenotyping pipeline to transform clinical trial eligibility criteria into computable criteria and enable high throughput cohort selection in EHRs. Methods: Formalized clinical trial criteria attributes were acquired from a natural-language processing (NLP)-assisted approach. We implemented a clinical trial phenotyping pipeline that included three components: First, a rule-based knowledge engineering component was introduced to annotate the trial attributes into a computable and customizable granularity from EHRs. The second component involved normalizing annotated attributes using standard terminologies and pre-defined reference tables. Third, a knowledge base of computable criteria attributes was built to match patients to clinical trials. We evaluated the pipeline performance by independent manual review. The inter-rater agreement of the annotation was measured on a random sample of the knowledge base. The accuracy of the pipeline was evaluated on a subset of randomly selected matched patients for a subset of randomly selected attributes. Results: Our pipeline phenotyped 2954 clinical trials from five cancer types including Non-Small Cell Lung Cancer, Small Cell Lung Cancer, Prostate Cancer, Breast Cancer, and Multiple Myeloma. We built a knowledge base of 256 computable attributes that included comorbidities, comorbidity-related treatment, previous lines of therapy, laboratory tests, and performance such as ECOG and Karnofsky score. Among 256 attributes, 132 attributes were encoded using standard terminologies and 124 attributes were normalized to customized concepts. The inter-rater agreement of the annotation measured by Cohen’s Kappa coefficient was 0.83. We applied the knowledge base to our EHRs and efficiently identified 33258 potential subjects for cancer clinical trials. Our evaluation on the patient matching indicated the F1 score was 0.94. Conclusions: We established a clinical trial phenotyping pipeline and built a knowledge base of computable criteria attributes that enabled efficient screening of EHRs for patients meeting clinical trial eligibility criteria, providing an automated way to efficiently and accurately identify clinical trial cohorts. The application of this knowledge base to patient matching from EHR data across different institutes demonstrates its generalization capability. Taken together, this knowledge base will be particularly valuable in computer-assisted clinical trial subject selection and clinical trial protocol design in cancer studies based on real-world evidence.
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Dissertations / Theses on the topic "Oncology Knowledge Base"

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Müller, Robert, M. Sergl, U. Nauerth, Schoppe D, K. Pommerening, and H. M. Dittrich. "TheMPO: A knowledge-based system for therapy planning in pediatric oncology." 1997. https://ul.qucosa.de/id/qucosa%3A32001.

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This article describes the knowledge-based system THEMPO (Therapy Management in Pediatric Oncology), which supports protocol-directed therapy planning and configuration in pediatric oncology. THEMPO provides a semantic network controlled by graph grammars to cover the different types of knowledge relevant in the domain, and offers a suite of acquisition tools for knowledge base authoring. Medical problem solvers, operating on the oncological network, reason about adequate therapeutic and diagnostic timetables for a patient. Furthermore, a corresponding patient record, also based on semantic networks and graph grammars, has been implemented to represent the course of therapy of an oncological patient.
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Books on the topic "Oncology Knowledge Base"

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Rhoten, Bethany. Theoretical Foundations of Body Image. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190655617.003.0002.

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Abstract: The purpose of this chapter is to review the theoretical foundations of body image. Neurological, psychoanalytic, psychological, nursing, contextual, fear-avoidance, information-processing, feminist, evolutionary, genetic, and positivist viewpoints have all influenced the conceptualization of body image. Body image in the context of oncology is a multidimensional experience influenced by a variety of factors. By understanding the origin and history of body image conceptualization, researchers and clinicians in cancer care can build upon the existing knowledge base to develop appropriate and timely assessments of body image, train oncology healthcare providers to include body image in holistic survivorship care, and design interventions that appropriately address the body image needs of this population.
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Breitbart, William, Phyllis Butow, Paul Jacobsen, Wendy Lam, Mark Lazenby, and Matthew Loscalzo, eds. Psycho-Oncology. 4th ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780190097653.001.0001.

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Psycho-Oncology, 4th edition, follows the publication of Psycho-Oncology, 3rd edition in 2015. This is the latest in the series of textbooks which have defined the field of psycho-oncology. William Breitbart, MD, serves as the new senior editor along with associate editors Phyllis N. Butow, PhD, MPH, of the University of Sydney; Paul B. Jacobsen, PhD, of the U.S. National Cancer Institute; Wendy W. T. Lam, RN, PhD, of the University of Hong Kong; Mark Lazenby, APRN, PhD, of the University of Connecticut School of Nursing; and Matthew J. Loscalzo, MSW, of the City of Hope. In this 4th edition of Psycho-Oncology, we feel we have accomplished the delicate task of having this “Official Textbook of our Field” serve both as the source textbook providing the broadest and most multidisciplinary essential science and practice of the field of psycho-oncology, as well as the newest and latest innovations and cutting-edge research and clinical practice that would equip our readers with the knowledge and resources to participate in the “new frontiers of psycho-oncology.” Several new sections and areas of update include: 1. Evidence-Based Interventions; 2. Digital Health Intervention; 3. Biobehavioral Psycho-Oncology; 4. Geriatric Oncology; 5. Pediatric Psycho-Oncology; 6. Survivorship; 7. Palliative Care and Advanced Planning; 8. Diversities in the Experience of Cancer; 9. Behavioral and Psychological Factors in Cancer Risk; Screening for Cancer in Normal and At-Risk Populations; 10. Screening and Testing for Germ Line and Somatic Mutations; 11. Screening and Assessment in Psychosocial Oncology; 12. Building Supportive Care Teams; 13. Psycho-Oncology in Health Policy.
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Gardiner, Matthew D., and Neil R. Borley. Breast and endocrine surgery. Oxford University Press, 2012. http://dx.doi.org/10.1093/med/9780199204755.003.0003.

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This chapter begins by discussing the basic principles of oncology, cancer diagnosis and classification, and cancer treatment, before focusing on the key areas of knowledge, namely disorders of breast development and involution, breast cancer assessment and management, goitre, altered thyroid state, thyroid cancer, parathyroid conditions, adrenal conditions, and multiple endocrine neoplasia. The chapter concludes with relevant case-based discussions.
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Arulkumaran, Sabaratnam, William Ledger, Lynette Denny, and Stergios Doumouchtsis, eds. Oxford Textbook of Obstetrics and Gynaecology. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198766360.001.0001.

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The Oxford Textbook of Obstetrics and Gynaecology is an up-to-date, objective, and readable text that covers the full speciality of obstetrics and gynaecology. This comprehensive and rigorously referenced textbook will be a vital resource in print and online for all practising clinicians. Larger sections on the basics in obstetrics and gynaecology, fetomaternal medicine, management of labour, gynaecological problems, and gynaecological oncology are complemented by specialist sections on areas such as neonatal care and neonatal problems, reproductive medicine, and urogynaecology and pelvic floor disorders, to name a few. The evidence-based presentation of current diagnostic and therapeutic methods is complemented in the text by numerous treatment algorithms, giving the reader the knowledge and tools needed for effective clinical practice.
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Ferrell, Betty Rolling, and Judith A. Paice, eds. Oxford Textbook of Palliative Nursing. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190862374.001.0001.

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The Oxford Textbook of Palliative Nursing is a comprehensive textbook on the art and science of palliative care nursing. Including new chapters on advance care planning, organ donation, self-care, global palliative care, and the ethos of palliative nursing, each chapter is rich with tables and figures, case examples for improved learning, and a strong evidence-based practice to support the highest quality of care. The book offers a valuable and practical resource for students and clinicians across all settings of care. Developed with the intention of emphasizing the need to extend palliative care beyond the specialty to be integrated in all settings and by all clinicians caring for the seriously ill, this new edition will continue to serve as the cornerstone of palliative care education. The content is relevant for specialty hospice agencies and palliative care programs, as well as generalist knowledge for schools of nursing, oncology, critical care, and pediatric.
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Book chapters on the topic "Oncology Knowledge Base"

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Williams, Paul J., James A. Uchizono, and Ene I. Ette. "Pharmacometric Knowledge-Based Oncology Drug Development." In Handbook of Anticancer Pharmacokinetics and Pharmacodynamics, 149–67. Totowa, NJ: Humana Press, 2004. http://dx.doi.org/10.1007/978-1-59259-734-5_11.

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El Naqa, Issam. "Knowledge-Based Treatment Planning." In Machine Learning in Radiation Oncology, 193–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18305-3_10.

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Zhang, Jiahan, Yaorong Ge, and Q. Jackie Wu. "Knowledge-Based Treatment Planning." In Machine and Deep Learning in Oncology, Medical Physics and Radiology, 307–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83047-2_13.

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Kolari, Pentti, Jussi Yliaho, Kari Näriäinen, Simo Hyödynmaa, Antti Ojala, Jukka Rantanen, and Niilo Saranummi. "CARTES — A Prototype Decision Support System in Oncology." In Knowledge Based Systems in Medicine: Methods, Applications and Evaluation, 148–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-08131-0_12.

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Elsayed, Zeinab, Mohamed Reda Kelany, and Ahmed Magdy Rabea. "Establishing a Continuing Educational Program Based on the ESMO/ASCO Recommendations for a Global Curriculum in Egypt and Other Educational Initiatives." In Improving Oncology Worldwide, 19–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96053-7_3.

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AbstractThis chapter contains descriptions of three different postgraduate educational endeavors in Egypt that are addressing the need for continuing medical education in oncology. Two initiatives are addressing the medical oncology training in general, whereas one focuses on a rare subset of tumors (sarcomas) that requires a well-coordinated approach to the best patient care possible. The authors of this chapter have successfully studied in the Advanced Oncology study program of Ulm University and strive to transfer the knowledge and the know-how to their workplaces. Thereby, they are facing similar and also different challenges that may be present in other low- and middle-income countries (LMICs).
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Mardani, Morteza, Yong Yang, Yinyi Ye, Stephen Boyd, and Lei Xing. "From model-driven to knowledge- and data-based treatment planning." In Big Data in Radiation Oncology, 97–109. Boca Raton : Taylor & Francis, 2018. | Series: Imaging in medical diagnosis and therapy ; 30: CRC Press, 2019. http://dx.doi.org/10.1201/9781315207582-7.

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Knaup, Petra, Timm Wiedemann, Andreas Bachert, Ursula Creutzig, Reinhold Haux, Michael Schäfer, and Freimut Schilling. "Experiences of Using a Computer-Aided Therapy Planning for Pediatric Oncology in Clinical Routine." In XPS-99: Knowledge-Based Systems. Survey and Future Directions, 210–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/10703016_17.

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Fdez-Olivares, Juan, Juan A. Cózar, and Luis Castillo. "OncoTheraper: Clinical Decision Support for Oncology Therapy Planning Based on Temporal Hierarchical Tasks Networks." In Knowledge Management for Health Care Procedures, 25–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03262-2_3.

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Cuccurullo, Corrado, Luca D’Aniello, and Maria Spano. "Thematic atlas of Italian oncological research: the analysis of public IRCCS." In Proceedings e report, 109–14. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.22.

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This paper has been developed in the frame of the research project “V:ALERE 2019” focused on Italian public-owned Academic Medical Centers. The main aim of the project is to provide evidence, advice, and remarks to help the agents of the public health system to address the many challenges that they face. In recent years, there is an increasing recognition of the potential value of research evidence as one of the many factors considered by policymakers and practitioners. Even more, in the case of medical science, the analysis of research and its impact is indispensable, in light of its implications for public health. The starting point for mapping a research area is to review the related scientific literature because by synthesizing past research findings, it is possible to effectively use the existing knowledge base and advance lines of future researches. In this sense, bibliometrics becomes useful, by providing a structured analysis to a large body of information, to infer trends over time, themes researched, and to show the “big picture” of extant research. In particular, in this work, we focus our attention on the scientific production of the last 20 years of the Scientific Institutes for Research, Hospitalization, and Healthcare (IRCCS “Istituto di Ricovero e Cura a Carattere Scientifico”) specialized in the oncology research. IRCCS are biomedical institutions of relevant national interest that drive clinical assistance in strong relation to research activities. They are committed to being a benchmark for the whole public health system for both the quality of patient care and the innovation skills in the field of the organization. All the analyses were carried out by using the Bibliometrix, an open-source tool for quantitative research in scientometrics and bibliometrics that includes all the main bibliometric methods of analysis.
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Guo, Yanbo, Chen Wang, Raymond Moore, Hongfang Liu, and Feichen Shen. "POKR: Building a Computable Heterogeneous Knowledge Resource for Precision Oncology." In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220071.

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Precision oncology is expected to improve selection of targeted therapies, tailored to individual patients and ultimately improve cancer patients’ outcomes. Several cancer genetics knowledge databases have been successfully developed for such purposes, including CIViC and OncoKB, with active community-based curations and scoring of genetic-treatment evidences. Although many studies were conducted based on each knowledge base respectively, the integrative analysis across both knowledge bases remains largely unexplored. Thus, there exists an urgent need for a heterogeneous precision oncology knowledge resource with computational power to support drug repurposing discovery in a timely manner, especially for life-threatening cancer. In this pilot study, we built a heterogeneous precision oncology knowledge resource (POKR) by integrating CIViC and OncoKB, in order to incorporate unique information contained in each knowledge base and make associations amongst biomedical entities (e.g., gene, drug, disease) computable and measurable via training POKR graph embeddings. All the relevant codes, database dump files, and pre-trained POKR embeddings can be accessed through the following URL: https://github.com/shenfc/POKR.
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Conference papers on the topic "Oncology Knowledge Base"

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Documet, Jorge R., Brent Liu, Anh Le, and Maria Law. "A DICOM-RT radiation oncology ePR with decision support utilizing a quantified knowledge base from historical data." In Medical Imaging, edited by Katherine P. Andriole and Khan M. Siddiqui. SPIE, 2008. http://dx.doi.org/10.1117/12.773087.

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Schwertner, Marco Antonio, Sandro Jose Rigo, Denis Andrei Araujo, Alan Barcelos Silva, and Bjoern Eskofier. "Fostering Natural Language Question Answering Over Knowledge Bases in Oncology EHR." In 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2019. http://dx.doi.org/10.1109/cbms.2019.00102.

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3

Fahrudin, Tresna Maulana, Iwan Syarif, and Ali Ridho Barakbah. "The determinant factor of breast cancer on medical oncology using feature selection based clustering." In 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC). IEEE, 2016. http://dx.doi.org/10.1109/kcic.2016.7883652.

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4

Ceccarelli, Michele, Antonio Donatiello, and Dante Vitale. "KON^3: A Clinical Decision Support System, in Oncology Environment, Based on Knowledge Management." In 2008 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2008. http://dx.doi.org/10.1109/ictai.2008.46.

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