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May CTC technologies promote better cancer management?

ABSTRACT: In the case of cancer, death is usually not due to the primary tumor itself but due to dissemination. Analysis of the circulating tumor cells (CTCs), i.e., cells responsible for a formation of metastases, should provide information useful for the management of cancer patients, fulfilling the objectives of predictive, preventive, and personalized medicine (PPPM). Despite promising results, the decisions on stage of disease and how to guide the adjuvant treatment still do not include results of CTC assessment. We want to describe two major reasons why the recent diagnostic value of CTC analysis is not sufficient for clinical use. The first reason arises from the biological nature of the tumor itself and the second reason is associated with an interdisciplinary status of CTC diagnostics in the sense that it is neither a theme purely for pathologists nor for haemato-oncologists nor clinical biochemists. We anticipate that there are at least three areas where CTCs can be useful for clinical practice. The first is monitoring of treatment efficacy of cancer patients. The second is a molecular characterization of captured CTCs for targeted treatment, and the third is a cultivation of captured CTCs for drug sensitivity testing. All of these approaches allow researchers recognize and respond to changes of phenotype of cancer cells during disease progression and introduce PPPM into clinical practice.

SUBMITTER: Pesta M

PROVIDER: S-EPMC4307224 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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May CTC technologies promote better cancer management?

Pesta Martin M Kulda Vlastimil V Narsanska Andrea A Fichtl Jakub J Topolcan Ondrej O

The EPMA journal 20150122 1

In the case of cancer, death is usually not due to the primary tumor itself but due to dissemination. Analysis of the circulating tumor cells (CTCs), i.e., cells responsible for a formation of metastases, should provide information useful for the management of cancer patients, fulfilling the objectives of predictive, preventive, and personalized medicine (PPPM). Despite promising results, the decisions on stage of disease and how to guide the adjuvant treatment still do not include results of CT ...[more]

PMID: 25628770

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Project description:ImportanceThe clinical decisions that arise from prostate magnetic resonance imaging (MRI) and genomic testing in patients with prostate cancer are not well understood.ObjectiveTo evaluate the association between regional uptake of prostate MRI and genomic testing and observation vs treatment for prostate cancer.Design, setting, and participantsThis retrospective cohort study of commercial insurance claims for prostate MRI and genomic testing included 65 530 patients 40 to 89 years of age newly diagnosed with prostate cancer from July 1, 2012, through June 30, 2019.ExposuresPatient- and regional-level use of prostate MRI and genomic testing.Main outcomes and measuresObservation vs definitive treatment for prostate cancer. Patient-level analyses examined the association between receipt of testing or residing in a hospital referral region (HRR) that adopted testing and observation. In regional-level analyses, the dependent variable was the change in the proportion of patients observed for prostate cancer at the HRR level between 2 periods: July 1, 2012, to June 30, 2014, and July 1, 2017, to June 20, 2019. The independent study variables included HRR-level changes in the proportion of men undergoing prostate MRI and genomic testing between these periods, and the models were adjusted for contextual factors associated with prostate cancer care and socioeconomic status.ResultsThis study identified 65 530 patients, including 27 679 in the early period (mean [SD] age, 58.0 [5.9] years) and 37 851 in the late period (mean [SD] age, 59.0 [5.7] years). Use of prostate MRI increased significantly from 7.2% (95% CI, 6.9%-7.5%) to 16.7% (95% CI, 16.3%-17.1%) from the early to late period. Use of genomic testing increased significantly from 1.3% (95% CI, 1.1%-1.4%) to 12.7% (95% CI, 12.3%-13.0%) from the early to late period. Compared with the lowest, the highest HRR quartiles of prostate MRI and genomic testing uptake were associated with an adjusted 4.1% (SE, 1.1%; P < .001) and 2.5% (SE, 1.1%; P = .03) absolute increase in the proportion of patients receiving observation, respectively.Conclusions and relevanceIn this cohort study, uptake of prostate MRI and genomic testing was associated with increased use of initial observation vs treatment for prostate cancer. Marked geographic variation supports the need for further patient-level research to optimize the dissemination and outcome of testing.

Project description:BackgroundPressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management.ObjectiveThe objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice.MethodsWe conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).ResultsA total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high.ConclusionsThere is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.

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May CTC technologies promote better cancer management?

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May CTC technologies promote better cancer management?

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