To visualize the performance of the various biomarkers in datasets including different number of patients, we have generated funnel plots depicting the hazard ratio (and confidence intervals) on the horizontal axis vs. the sample size on the vertical axis for each dataset. We also added an option to the online interface to simultaneously perform the analysis in each of the individual datasets. Finally, significance was set at p<0.01.
Tumor Markers
They can be detected in the blood, urine, or other bodily fluids and are used to help detect, diagnose, and manage certain types of cancers.
Tumor markers can provide valuable information about the presence, location, and stage of a cancer, as well as help monitor the effectiveness of treatment.
Common tumor markers include PSA for prostate cancer, CA-125 for ovarian cancer, and CEA for colorectal cancer.
While tumor markers are a useful tool, they must be interpreted carefuly in the context of other clinical findings, as their levels can be affected by non-cancerous conditions as well.
Researchers can use PubCompare.ai to optimise their tumor marker research protocols and easily locate the best protocols from literature, pre-prints, and patents using the powerful AI-driven platform, improving their research outcomes with data-driven insights.
Most cited protocols related to «Tumor Markers»
To visualize the performance of the various biomarkers in datasets including different number of patients, we have generated funnel plots depicting the hazard ratio (and confidence intervals) on the horizontal axis vs. the sample size on the vertical axis for each dataset. We also added an option to the online interface to simultaneously perform the analysis in each of the individual datasets. Finally, significance was set at p<0.01.
This study was conducted in accordance to REporting recommendations for tumour MARKer prognostic studies (REMARK)29 (link). The reporting standards of the current study fulfill these recommendations.
Following slide review, a new section was cut for H&E staining from a single representative tumor block in each case, and the new slides annotated for tissue microarray (TMA) construction. Three representative areas within the tumor center of each block were annotated for targeted coring (by an experienced biomedical scientist and confirmed by expert pathologists, MBL and JAJ). One millimeter diameter tissue cores were extracted from donor blocks and inserted into recipient blocks using a manual tissue arrayer (Estigen, Tartu, Estonia).
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Many clinical endpoints do not have standard definitions, although there have been some recent efforts to standardize definitions for some disease sites. The STandardized definitions for Efficacy End Points (STEEP) system [67] (link) proposed standardized endpoint definitions for adjuvant breast cancer trials to address inconsistencies such as the fact that new primary tumors, non-cancer death, and in situ cancers may or may not be included as events in DFS for breast cancer. Different names may be used interchangeably for one survival time outcome, for example, recurrence-free survival and DFS. Furthermore, there is not always agreement on which endpoint is the most relevant endpoint to consider in a particular disease setting. For example, reliable information about cause of death is sometimes not available, so considering death due to any cause is often preferred. In some situations, for example, in an older patient population with small risk of dying from the cancer, it can be argued that death due to cancer is more relevant because it is expected that many deaths will be unrelated to the cancer and including them in the endpoint could make the estimated prognostic effect of the marker difficult to interpret.
The endpoints to be examined should be decided on the basis of clinical relevance. The results for all endpoints that were examined should be reported regardless of the statistical significance of the findings (see Items 15 to 17 and
Most recents protocols related to «Tumor Markers»
Example 8
Serum samples from patients were tested with the FLNA IPMRM, as described above, using the anti-FLNA monoclonal antibodies of the invention. The results were combined with data on age, PSA, and Gleason score and subjected to regression modelling. As shown in
Samples of patient serum were also analyzed for the biomarkers FLNA, keratin 19 (KRT19) and age combined, versus PSA alone.
progression. Pelvic examination and plasma tumor markers were routinely
performed each time. Imaging tests including computed tomography (CT), positron
emission tomography-computed tomography (PET-CT), single-photon emission
computed tomography (SPECT), and magnetic resonance imaging (MRI) were used as
appropriate to determine the size of tumors. The Response Evaluation Criteria in
Solid Tumors (RECIST) version 1.1 was used to determine CR, PR, stable disease
(SD), or progressive disease (PD). During the nontreatment period, follow-up was
performed every 3 months until death or the patient was censored. Disease-free
survival (DFS) was defined as the period from the date of surgery to the
diagnosis of first recurrence/metastasis, and OS was defined as the period from
the beginning of treatment after first recurrence/metastasis to the death, with
patients alive at last follow-up censored on that date. PFS was defined as the
period from the treatment beginning after first recurrence/metastasis to the
second recurrence/metastasis or death, with patients censored if alive and with
no evidence of tumor recurrence/metastasis.
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More about "Tumor Markers"
These markers can be detected in bodily fluids like blood, urine, or other fluids, and are used to aid in the detection, diagnosis, and management of various types of cancers.
Common tumor markers include PSA (prostate-specific antigen) for prostate cancer, CA-125 for ovarian cancer, and CEA (carcinoembryonic antigen) for colorectal cancer.
These markers can provide valuable information about the presence, location, and stage of a cancer, as well as help monitor the effectiveness of treatment.
However, it's important to interpret tumor marker levels carefully, as they can be affected by non-cancerous conditions as well.
Researchers can use platforms like PubCompare.ai to optimize their tumor marker research protocols and easily locate the best protocols from literature, preprints, and patents using the powerful AI-driven platform.
This can help improve research outcomes with data-driven insights.
Analytical instruments like the Cobas e601, Prism 8, GraphPad Prism 7, Bio-Plex 200 system, GraphPad Prism 5, SPSS version 22.0, SPSS version 21, Cobas e602, Prism 6, and SPSS 24.0 can be used to analyze and interpret tumor marker data, providing valuable insights for clinicians and researchers.
By leveraging the power of these tools and the data-driven approach of PubCompare.ai, researchers can enhance their understanding of tumor markers and improve the overall quality and efficacy of their cancer research.