Integrating Biomarkers and Targeted Therapy Into Colorectal Cancer Management

2019 ◽  
pp. 207-215 ◽  
Author(s):  
Christopher H. Lieu ◽  
Ryan B. Corcoran ◽  
Michael J. Overman
Keyword(s):  
Colorectal Cancer ◽  
Decision Making ◽  
Clinical Trials ◽  
Targeted Therapy ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Prognostic Indicators ◽  
Molecular Characteristics ◽  
Cancer Management ◽  
The Past

There have been substantial advances in the treatment of metastatic colorectal cancer (mCRC) over the past 15 years. Molecular characteristics of mCRC and identification of specific mutations can serve as predictive and prognostic indicators of disease and response to targeted therapies. When incorporated into clinical decision-making, these biomarkers can serve as critical tools in personalizing therapy to ensure the best outcomes. Additional improvements in the survival of patients with mCRC will be made possible with the identification of new predictive molecular biomarkers and their evaluation using rational and innovative clinical trials.

Highly specific detection of KRAS single nucleotide polymorphism by asymmetric PCR/SERS assay

The Analyst ◽  
2021 ◽  
Author(s):  
Nana Lyu ◽  
Vinoth Kumar Rajendran ◽  
Jun Li ◽  
Alexander Engel ◽  
Mark P. Molloy ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Nucleotide Polymorphism ◽  
Asymmetric Pcr ◽  
Specific Detection ◽  
Kras Mutations ◽  
Single Nucleotide ◽  
The Common

The molecular diagnosis of KRAS mutations has become crucial for clinical decision-making in colorectal cancer (CRC) treatments. Currently, the common methods for detecting mutations are based on quantitative PCR, DNA...

Beyond the Development of Health-Related Quality-of-Life (HRQOL) Measures: A Checklist for Evaluating HRQOL Outcomes in Cancer Clinical Trials—Does HRQOL Evaluation in Prostate Cancer Research Inform Clinical Decision Making?

2003 ◽  
Vol 21 (18) ◽  
pp. 3502-3511 ◽  
Author(s):  
Fabio Efficace ◽  
Andrew Bottomley ◽  
David Osoba ◽  
Carolyn Gotay ◽  
Henning Flechtner ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Clinical Trials ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Cancer Clinical Trials ◽  
Health Related Quality ◽  
Related Quality ◽  
Health Related

Purpose: The aim of this study was to evaluate whether the inclusion of health-related quality of life (HRQOL), as a part of the trial design in a randomized controlled trial (RCT) setting, has supported clinical decision making for the planning of future medical treatments in prostate cancer. Materials and Methods: A minimum standard checklist for evaluating HRQOL outcomes in cancer clinical trials was devised to assess the quality of the HRQOL reporting and to classify the studies on the grounds of their robustness. It comprises 11 key HRQOL issues grouped into four broader sections: conceptual, measurement, methodology, and interpretation. Relevant studies were identified in a number of databases, including MEDLINE and the Cochrane Controlled Trials Register. Both their HRQOL and traditional clinical reported outcomes were systematically analyzed to evaluate their consistency and their relevance for supporting clinical decision making. Results: Although 54% of the identified studies did not show any differences in traditional clinical end points between treatment arms and 17% showed a difference in overall survival, 74% of the studies showed some difference in terms of HRQOL outcomes. One third of the RCTs provided a comprehensive picture of the whole treatment including HRQOL outcomes to support their conclusions. Conclusion: A minimum set of criteria for assessing the reported outcomes in cancer clinical trials is necessary to make informed decisions in clinical practice. Using a checklist developed for this study, it was found that HRQOL is a valuable source of information in RCTs of treatment in metastatic prostate cancer.

scholarly journals Empowering Clinical Decision Making in Oligometastatic Colorectal Cancer: The Potential Role of Drug Screening of Patient-Derived Organoids

2021 ◽  
pp. 1192-1199
Author(s):  
Gianluca Mauri ◽  
Erika Durinikova ◽  
Alessio Amatu ◽  
Federica Tosi ◽  
Andrea Cassingena ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Decision Making ◽  
Drug Screening ◽  
Potential Role ◽  
Clinical Decision Making ◽  
Clinical Decision

When bright lines blur: Deconstructing distinctions between disorders of consciousness

2017 ◽  
Author(s):  
David B. Fischer ◽  
Robert D. Truog
Keyword(s):  
Decision Making ◽  
Diagnostic Criteria ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Disorders Of Consciousness ◽  
Ethical Management ◽  
The Past ◽  
Way Of Thinking

Disorders of consciousness are devastating to patients and present profound challenges to clinicians, scientists, philosophers, and ethicists alike. In the past, distinguishing between levels of these disorders has been vital to guiding important decisions. This chapter argues that these disorders are not sufficiently distinct, however, to dictate such decisions: diagnostic criteria are not discrete, nor do they reflect the conceptual definitions of these disorders. It argues that these non-distinct diagnostic boundaries reflect an inherent continuity between disorders of consciousness. In light of these points, a new way of thinking about disorders of consciousness is presented in the chapter to more effectively guide clinical decision-making. The chapter argues that these considerations bring clarity to disorders of consciousness and can improve the ethical management of patients suffering from these disorders.

Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making

2020 ◽  
pp. 084653712094143
Author(s):  
Jaryd R. Christie ◽  
Pencilla Lang ◽  
Lauren M. Zelko ◽  
David A. Palma ◽  
Mohamed Abdelrazek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Biomarker Discovery ◽  
Surgical Techniques ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Cancer Management

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)–based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

scholarly journals Predicting Clinical Outcomes Using Molecular Biomarkers

2016 ◽  
Vol 8 ◽  
pp. BIC.S33380 ◽  
Author(s):  
Harry B. Burke
Keyword(s):  
Decision Making ◽  
Clinical Outcomes ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Molecular Biomarkers ◽  
The Past ◽  
Diagnosis And Prognosis ◽  
Molecular Therapies ◽  
Physician Patient

Over the past 20 years, there has been an exponential increase in the number of biomarkers. At the last count, there were 768,259 papers indexed in PubMed.gov directly related to biomarkers. Although many of these papers claim to report clinically useful molecular biomarkers, embarrassingly few are currently in clinical use. It is suggested that a failure to properly understand, clinically assess, and utilize molecular biomarkers has prevented their widespread adoption in treatment, in comparative benefit analyses, and their integration into individualized patient outcome predictions for clinical decision-making and therapy. A straightforward, general approach to understanding how to predict clinical outcomes using risk, diagnostic, and prognostic molecular biomarkers is presented. In the future, molecular biomarkers will drive advances in risk, diagnosis, and prognosis, they will be the targets of powerful molecular therapies, and they will individualize and optimize therapy. Furthermore, clinical predictions based on molecular biomarkers will be displayed on the clinician's screen during the physician–patient interaction, they will be an integral part of physician–patient-shared decision-making, and they will improve clinical care and patient outcomes.

Sign in / Sign up

Export Citation Format

Share Document