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Detailed workflow. The search strategy is based on Column I (Medicinal Products with CDx) and Column II (Potential Biomarker for CDx). The information gathered in these two columns is used for a systematic keyword search in DMIDS (German Medical Devices Information and Database System [11]), integrating information on the detection method based on commercially available in vitro diagnostic medical devices. The resulting Summary of Column I and II is the foundation for an advanced keyword search in the PharmNet CT database, which was divided into four search categories by using different search operators.
Source publication
The European Union In Vitro Diagnostic Medical Devices Regulation (EU) 2017/746 (IVDR) introduces companion diagnostics (CDx) as a new legal term. CDx are applied in combination with a medicinal product to identify patient subgroups most likely to benefit from a treatment or who are at increased risk. This new regulation came into full effect on 26...
Citations
Oncogene amplification and tumour suppressor deletion can drive tumour initiation, progression and treatment resistance. Detection at diagnosis often signals poor prognosis, but it can also enable opportunities for treatment with highly effective targeted therapies. Predicting the likelihood that a patient will acquire these driver alterations in the future using a genomic test represents an opportunity to realise the benefits of interventions earlier, potentially with preventative intent. Here, we present a forecasting framework that takes as input a DNA copy number profile and predicts whether the tumour will acquire an oncogene amplification or tumour suppressor deletion in the future. This framework leverages mutation rate estimates from the input tumour, alongside gene-specific selection coefficients derived from a large cohort of 7,880 tumours. We demonstrate feasibility using 7,042 single-time-point samples and longitudinally collected tumour pairs from 44 prostate and 100 lung cancers, identifying tumours that went on to acquire amplifications at a later time point with an average AUC of 0.87. We show potential clinical utility by forecasting poor prognosis in low-grade gliomas via CDK4/PDGFRA amplification or CDKN2A deletion, and osimertinib resistance in lung cancers via MET amplification. This study serves as a proof-of-concept for a new class of biomarker, wherein selective pressures and mutation-generating processes can be harnessed to anticipate future genomic alterations.
High-throughput next-generation sequencing (NGS) has changed clinical oncology practice. Over the past two decades, this technology has evolved from the first oncogenomic projects and clinical trials to the introduction of comprehensive genomic profiling (CGP) into practical oncology guidelines. However, despite this fact NGS studies, with rare exceptions, are represented worldwide by commercial products that are not approved for clinical use. The legislative regulation of molecular genetic studies based on the NGS technology intended for clinical use is significantly inferior to the emergence rate of new technologies. For example, 11 years passed between the start of the first oncogenomic project and the approval of the world’s first targeted cancer NGS panel for comprehensive genomic profiling. This review analyzes the implementation of the NGS technology in oncology and the emergence of regulation of such molecular tests with the formation of recommendations for Russian regulatory practice.
A Tumor is a swelling in the body caused by cells that multiply abnormally.
Tumors or neoplasms consisting of benign tumors and malignant tumors.
Benign tumors can grow larger but do not spread to other body tissues.
Malignant tumors are cancers that attack the entire body and are
uncontrollable. Comparison between the cell nucleus with the cytoplasm of
malignant tumors, while benign tumors are the same as normal cells. Cancer
cells can develop rapidly. These cells attack and damage body tissues through
the bloodstream and lymph vessels so that they can grow in new places. One
way to detect tumor disease is by utilizing Artificial intelligence algorithms
for tumor Disease Detection. The purpose of this paper is for the development
of Artificial Intellegent algorithms for the detection of tumor Diseases and
optimization of Artificial Intellegent algorithms for the detection of tumor
Diseases. This research uses systematic literature review by using preferred
Reporting Items for Systematic Review (PRISMA). The results of screening
and selection of articles obtained 64 potential articles that have met the
inclusion criteria. The results showed that with earlier detection, a person can
check tumor disease earlier using the help of Artificial intelligence
algorithms. The results of research on the development of Artificial
intelligence algorithms for detection of tumor Diseases have found Artificial
intelligence algorithms that can be used to reduce the risk of tumor disease.
Optimization of Artificial Intelegency algorithms for tumor classification,
performing new data processing methods such as artificial intelligence can be
selected to provide the accuracy of classification and diagnosis, exploration
of detection limits is a very important aspect in tumor diagnosis based on
SERS, finding improved and suitable nanoparticle substrates so as to
significantly improve the original Raman signal.
A Tumor is a swelling in the body caused by cells that multiply abnormally.
Tumors or neoplasms consisting of benign tumors and malignant tumors.
Benign tumors can grow larger but do not spread to other body tissues.
Malignant tumors are cancers that attack the entire body and are
uncontrollable. Comparison between the cell nucleus with the cytoplasm of
malignant tumors, while benign tumors are the same as normal cells. Cancer
cells can develop rapidly. These cells attack and damage body tissues through
the bloodstream and lymph vessels so that they can grow in new places. One
way to detect tumor disease is by utilizing Artificial intelligence algorithms
for tumor Disease Detection. The purpose of this paper is for the development
of Artificial Intellegent algorithms for the detection of tumor Diseases and
optimization of Artificial Intellegent algorithms for the detection of tumor
Diseases. This research uses systematic literature review by using preferred
Reporting Items for Systematic Review (PRISMA). The results of screening
and selection of articles obtained 64 potential articles that have met the
inclusion criteria. The results showed that with earlier detection, a person can
check tumor disease earlier using the help of Artificial intelligence
algorithms. The results of research on the development of Artificial
intelligence algorithms for detection of tumor Diseases have found Artificial
intelligence algorithms that can be used to reduce the risk of tumor disease.
Optimization of Artificial Intelegency algorithms for tumor classification,
performing new data processing methods such as artificial intelligence can be
selected to provide the accuracy of classification and diagnosis, exploration
of detection limits is a very important aspect in tumor diagnosis based on
SERS, finding improved and suitable nanoparticle substrates so as to
significantly improve the original Raman signal.
