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Narrative review of developing new biomarkers for decision making in advanced testis cancer.

ABSTRACT: Management of testicular germ cell tumor (GCT) patients is based on clinical determinants, mainly CT scan and serum tumor markers (alpha-fetoprotein, beta subunit of HCG and LDH). Treatment decisions are usually straightforward for patients with clear evidence of metastatic disease, confirmed either by imaging tests or by unequivocal elevated tumor markers. However, there are several clinical scenarios where the assessment of metastatic disease is complicated by the limited specificity of the current imaging tests and serum tumor markers. These include patients with clinical stage IIA GCT with negative tumor markers and patients with post-chemotherapy residual disease where, in absence of clear indicators of GCT, decision making and patient treatment allocation become challenging. Therefore, more accurate biomarkers are critical to reduce the risk of under-or over-treatment and to always deliver the most optimal therapy. The objectives of this narrative review are to review the available publications about micro-RNAs in GCT s and their potential clinical applications. Two clusters of micro-RNAs, miR-371a-3p and miR-302/367, specifically expressed by both seminoma and non-seminoma GCT and easily detectable in the peripheral blood, have demonstrated to be promising in this endeavor. Large prospective trials are ongoing to define the operating characteristics of these biomarkers and their clinical utility to improve GCT patient management and reduce the error rate deriving from clinical uncertainty, therefore reducing the risk of sub-optimal treatments.

SUBMITTER: Nappi L

PROVIDER: S-EPMC8575592 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Narrative review of developing new biomarkers for decision making in advanced testis cancer.

Nappi Lucia L Nichols Craig C Kollmannsberger Christian C

Translational andrology and urology 20211001 10

Management of testicular germ cell tumor (GCT) patients is based on clinical determinants, mainly CT scan and serum tumor markers (alpha-fetoprotein, beta subunit of HCG and LDH). Treatment decisions are usually straightforward for patients with clear evidence of metastatic disease, confirmed either by imaging tests or by unequivocal elevated tumor markers. However, there are several clinical scenarios where the assessment of metastatic disease is complicated by the limited specificity of the cu ...[more]

PMID: 34804849

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Project description:ObjectivesTo provide a comprehensive overview of interventions that support shared decision-making (SDM) for treatment modality decisions in advanced kidney disease (AKD). To provide summarised information on their content, use and reported results. To provide an overview of interventions currently under development or investigation.DesignThe JBI methodology for scoping reviews was followed. This review conforms to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist.Data sourcesMEDLINE, Embase, Web of Science, Cochrane Library, Emcare, PsycINFO, PROSPERO and Academic Search Premier for peer-reviewed literature. Other online databases (eg, clinicaltrials.gov, OpenGrey) for grey literature.Eligibility for inclusionRecords in English with a study population of patients >18 years of age with an estimated glomerular filtration rate <30 mL/min/1.73 m2. Records had to be on the subject of SDM, or explicitly mention that the intervention reported on could be used to support SDM for treatment modality decisions in AKD.Data extraction and synthesisTwo reviewers independently screened and selected records for data extraction. Interventions were categorised as prognostic tools (PTs), educational programmes (EPs), patient decision aids (PtDAs) or multicomponent initiatives (MIs). Interventions were subsequently categorised based on the decisions they were developed to support.ResultsOne hundred forty-five interventions were identified in a total of 158 included records: 52 PTs, 51 EPs, 29 PtDAs and 13 MIs. Sixteen (n=16, 11%) were novel interventions currently under investigation. Forty-six (n=46, 35.7%) were reported to have been implemented in clinical practice. Sixty-seven (n=67, 51.9%) were evaluated for their effects on outcomes in the intended users.ConclusionThere is no conclusive evidence on which intervention is the most efficacious in supporting SDM for treatment modality decisions in AKD. There is a lot of variation in selected outcomes, and the body of evidence is largely based on observational research. In addition, the effects of these interventions on SDM are under-reported.

Project description:ObjectiveIn this review, we aim to present frontier studies in patients with lung cancer as it related to artificial intelligence (AI)-assisted decision-making and summarize the latest advances, challenges and future trend in this field.BackgroundDespite increasing survival rate in cancer patients over the last decades, lung cancer remains one of the leading causes of death worldwide. The early diagnosis, accurate evaluation and individualized treatment are vital approaches to improve the survival rate of patients with lung cancer. Thus, decision making based on these approaches requires accuracy and efficiency beyond manpower. Recent advances in AI and precision medicine have provided a fertile environment for the development of AI-based models. These models have the potential to assist radiologists and oncologists in detecting lung cancer, predicting prognosis and developing personalized treatment plans for better outcomes of the patients.MethodsWe searched literature from 2000 through July 31th, 2021 in Medline/PubMed, the Web of Science, the Cochrane Library, ACM Digital Library, INSPEC and EMBASE. Key words such as "artificial intelligence", "AI", "deep learning", "lung cancer", "NSCLC", "SCLC" were combined to identify related literatures. These literatures were then selected by two independent authors. Articles chosen by only one author will be examined by another author to determine whether this article was relative and valuable. The selected literatures were read by all authors and discussed to draw reliable conclusions.ConclusionsAI, especially for those based on deep learning and radiomics, is capable of assisting clinical decision making from many aspects, for its quantitatively interpretation of patients' information and its potential to deal with the dynamics, individual differences and heterogeneity of lung cancer. Hopefully, remaining problems such as insufficient data and poor interpretability may be solved to put AI-based models into clinical practice.

Project description:IntroductionPatients with advanced kidney disease (AKD) have to make difficult treatment modality decisions as their disease progresses towards end-stage kidney disease. International guidelines in nephrology suggest shared decision-making (SDM) to help patients make timely treatment modality decisions that align with their values and preferences. However, systematic reviews or scoping reviews on these SDM interventions and on their reported use or outcomes are lacking. This limits the adoption of SDM in clinical practice and hampers further research and development on the subject. Our aim is to provide a comprehensive and up-to-date overview of these SDM interventions by means of a scoping review of the literature. Scoping reviews can provide a broad overview of a topic, identify gaps in the research knowledge base and report on the types of evidence that address and inform practices. This paper presents our study protocol.Methods and analysisThe proposed scoping review will be performed in accordance with the Joanna Briggs Institute's (JBI) methodology for scoping reviews. It will cover both qualitative and quantitative scientific literature, as well as the grey literature on SDM interventions for treatment modality decisions in AKD. Only literature written in English will be considered for inclusion. Two independent reviewers will participate in an iterative process of screening the literature, paper selection and data extraction. Disagreements between the reviewers will be resolved by discussion until consensus is reached or after consultation with the research team when needed. Results will be reported with descriptive statistics and diagrammatic or tabular displayed information, accompanied by narrative summaries as explained in the JBI guidelines.Ethics and disseminationEthical approval for the conduct of this study is not required. We will analyse previously collected data for the proposed scoping review. Our results will be published in a peer-reviewed journal and disseminated through conferences and/or seminars.

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Narrative review of developing new biomarkers for decision making in advanced testis cancer.

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Narrative review of developing new biomarkers for decision making in advanced testis cancer.

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