Knowledge graphs for covid-19: An exploratory review of the current landscape

Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research

Privacy-Preserving Predictive Models for Lung Cancer Survival Analysis

MAASTRO clinic, the Netherlands. Privacy-preserving data mining (PPDM) is a recent emergent research area that deals with the incorporation of privacy preserving concerns to data mining techniques. We consider a real clinical setting where the data is horizontally distributed among different institutions. Each one of the medical institutions involved in this work provides a database containing a subset of patients. There is recent work that shows the potential of the PPDM approach in medical applications. However, there is few work in developing/implementing PPDM for predictive personalized medicine. In this paper we use real data from several institutions across Europe to build models for survival prediction for non-small-cell lung cancer patients while addressing the potential privacy preserving issues that may arise when sharing data across institutions located in different countries. Our experiments in a real clinical setting show that the privacy preserving approach may result in improved models while avoiding the burdens of traditional data sharing (legal and/or anonymization expenses).

Individualized Positron Emission Tomography-Based Isotoxic Accelerated Radiation Therapy Is Cost-Effective Compared With Conventional Radiation Therapy: A Model-Based Evaluation

Purpose: To evaluate long-term health effects, costs, and cost-effectiveness of positron emission tomography (PET)-based isotoxic accelerated radiation therapy treatment (PET-ART) compared with conventional fixed-dose CT-based radiation therapy treatment (CRT) in non-small cell lung cancer (NSCLC). Methods and Materials: Our analysis uses a validated decision-model, based on data of 200 NSCLC patients with inoperable stage I-IIIB. Clinical outcomes, resource use, costs, and utilities were obtained from the Maastro Clinic and the literature. Primary model outcomes were the difference in life-years (LYs), quality-adjusted life-years (QALYs), costs, and the incremental cost-effectiveness and cost/utility ratio (ICER and ICUR) of PET-ART versus CRT. Model outcomes were obtained from averaging the predictions for 50,000 simulated patients. A probabilistic sensitivity analysis and scenario analyses were carried out. Results: The average incremental costs per patient of PET-ART were V569 (95% confidence interval [CI] (sic)-5327-(sic)6936) for 0.42 incremental LYs (95% CI 0.19-0.61) and 0.33 QALYs gained (95% CI 0.13-0.49). The base-case scenario resulted in an ICER of (sic)1360 per LY gained and an ICUR of (sic)1744 per QALY gained. The probabilistic analysis gave a 36% probability that PET-ART improves health outcomes at reduced costs and a 64% probability that PET-ART is more effective at slightly higher costs. Conclusion: On the basis of the available data, individualized PET-ART for NSCLC seems to be cost-effective compared with CRT

Analysis of tumor texture on a pre-treatment CT scan predicts treatment outcome in NSCLC patients

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Analysis of tumor texture on a pre-treatment CT scan predicts treatment outcome in NSCLC patients

Only abstrac

Combining Clinical, Pathological, and Demographic Factors Refines Prognosis of Lung Cancer: A Population-Based Study

In the treatment of lung cancer, an accurate estimation of patient clinical outcome is essential for choosing an appropriate course of therapy. It is important to develop a prognostic stratification model which combines clinical, pathological and demographic factors for individualized clinical decision making.A total of 234,412 patients diagnosed with adenocarcinomas or squamous cell carcinomas of the lung or bronchus between 1988 and 2006 were retrieved from the SEER database to construct a prognostic model. A model was developed by estimating a Cox proportional hazards model on 500 bootstrapped samples. Two models, one using stage alone and another comprehensive model using additional covariates, were constructed. The comprehensive model consistently outperformed the model using stage alone in prognostic stratification and on Harrell's C, Nagelkerke's R(2), and Brier Scores in the whole patient population as well as in specific treatment modalities. Specifically, the comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P<0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses. Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.These results demonstrate the feasibility of constructing a precise prognostic model combining multiple clinical, pathologic, and demographic factors. The comprehensive model significantly improves individualized prognosis upon AJCC tumor staging and is robust across a range of treatment modalities, the spectrum of patient risk, and in novel patient cohorts

Epigenetics in radiotherapy: Where are we heading?

Radiotherapy is an important component of anti-cancer treatment. However, not all cancer patients respond to radiotherapy, and with current knowledge clinicians are unable to predict which patients are at high risk of recurrence after radiotherapy. There is therefore an urgent need for biomarkers to guide clinical decision-making. Although the importance of epigenetic alterations is widely accepted, their application as biomarkers in radiotherapy has not been studied extensively. In addition, it has been suggested that radiotherapy itself introduces epigenetic alterations. As epigenetic alterations can potentially be reversed by drug treatment, they are interesting candidate targets for anticancer therapy or radiotherapy sensitizers. The application of demethylating drugs or histone deacetylase inhibitors to sensitize patients for radiotherapy has been studied in vitro, in vivo as well as in clinical trials with promising results. This review describes the current knowledge on epigenetics in radiotherapy

'Rapid Learning health care in oncology' – An approach towards decision support systems enabling customised radiotherapy' ☆ ☆☆

AbstractPurposeAn overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy.Material and resultsRapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes.ConclusionPersonalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making