Evidence map›Paper›PMID 39507406›Full record

ArticleJAMIA open2024

Targetable molecular algorithm and training platform development for the treatment of non-small cell lung cancer.

Krithika Bhuvaneshwar, Chul Kim, Kaushal Parikh, Joshua E Reuss, Camelia Bencheqroun, Anvitha G Agraharam, Ayesha Munir, Adil Alaoui, Yuriy Gusev, Irina G Veytsman

Abstract read
In one paragraph

Article in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Krithika BhuvaneshwarGeorgetown University Innovation Center for Biomedical Informatics (Georgetown-ICBI), Georgetown University Medical Center, Washington, DC 20007, United States.ORCID https://orcid.org/0000-0003-4015-7056
Chul KimOncology, Medstar Georgetown Cancer Center, Washington, DC 20007, United States.ORCID https://orcid.org/0000-0003-0191-8684
Kaushal ParikhOncology, Hackensack Meridian Health, Neptune City, NJ 07753, United States.
Joshua E ReussOncology, Medstar Georgetown Cancer Center, Washington, DC 20007, United States.
Camelia BencheqrounGeorgetown University Innovation Center for Biomedical Informatics (Georgetown-ICBI), Georgetown University Medical Center, Washington, DC 20007, United States.
Anvitha G AgraharamGeorgetown University Innovation Center for Biomedical Informatics (Georgetown-ICBI), Georgetown University Medical Center, Washington, DC 20007, United States.
Ayesha MunirOncology, Medstar Georgetown Cancer Center, Washington, DC 20007, United States.
Adil AlaouiGeorgetown University Innovation Center for Biomedical Informatics (Georgetown-ICBI), Georgetown University Medical Center, Washington, DC 20007, United States.
Yuriy GusevGeorgetown University Innovation Center for Biomedical Informatics (Georgetown-ICBI), Georgetown University Medical Center, Washington, DC 20007, United States.
Irina G VeytsmanOncology, Medstar Georgetown Cancer Center, Washington, DC 20007, United States.

Funding

Tissue Culture Shared ResourceP30CA051008 · NCI · GEORGETOWN UNIVERSITY · PI STEPHEN W BYERS · 1990 to 2026
$71.5M
NCI NIH HHS P30 CA051008
6 · The paper itself

Abstract

Background and Introduction: Over the last decade, treatment of patients with advanced non-small cell lung cancer (NSCLC) has become dependent on tissue and/or blood biomarkers to guide treatment decisions. Timely access to comprehensive biomarker and tumor signature information is crucial for diagnostic testing. With the rapid development and implementation of complex biomarker testing, comprehensive molecular profiling with in-depth analysis of DNA, RNA, and proteins can be easily performed. Initial data from the MedStar Health system showed considerable disparities in the use of next generation sequencing (NGS) between hospitals, and there is a clear need to improve education and understanding regarding which cases are appropriate for NGS, as well as the use of protocols to make those decisions in an expeditious manner. Materials and Methods: Clinical pathways are systems-based tools that aim to create greater transparency around care decision making, therapeutic selection, and care delivery. They enhance quality and efficiency by reducing non-value-added intra-provider variability in care. We aimed to create a comprehensive clinical pathway system, the Targetable Molecular Algorithm (TMA), to increase the understanding and use of NGS in NSCLC by physicians in training, oncology nurse navigators, nurse practitioners, and general oncologists. Discussion: We provide an overview of the implementation of the platform along with navigation guide. A realistic case study-a typical clinical workflow for a patient requiring NGS testing with an EGFR mutation-is also reviewed, demonstrating how the TMA platform can be applied. Additionally, we highlight the importance of the resource, and discuss its strengths, weaknesses, and potential future applications. Results and Conclusion: This new and innovative pathway system will make decision-making easier for clinicians trying to understand the appropriate tests and treatment algorithms for their patients. Our aim is to increase the appropriate and timely use of NGS among health-system providers with the hope that this system will empower physicians to provide better care by providing a quick, simple, user-friendly tool for comprehensive patient care.

Indexed as

biomarkersclinical pathwayselectronic learning systemnext generation sequencingNSCLCprecision oncologytargetable molecular algorithm

Identifiers

PMID39507406
PMCPMC11540162

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.