Evidence map›Paper›PMID 32823322›Full record

ReviewYearbook of medical informatics2020

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care.

Ashley C Griffin, Umit Topaloglu, Sean Davis, Arlene E Chung

Abstract readReview
In one paragraph

Review in Yearbook of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Review
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

4 authors.

Ashley C GriffinUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Umit TopalogluWake Forest University School of Medicine, Winston-Salem, NC, USA.
Sean DavisNational Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Arlene E ChungUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Institutional Career Development CoreKL2TR001432 · NCATS · GEORGETOWN UNIVERSITY · PI UMANS, JASON G · 2015 to 2024
$7.5M
An Interdisciplinary Program for Advanced Training in Health Data AnalyticsT15LM012500 · NLM · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MOSTAFA, JAVED · 2017 to 2021
$2.9M
QuBBD: Statistical & Visualization Methods for PGHD to Enable Precision Medicine R01EB025024 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI CHUNG, ARLENE E. · 2017 to 2019
$918k
NCATS NIH HHS KL2 TR001432NCI NIH HHS L30 CA200405NCI NIH HHS P30 CA008748NIBIB NIH HHS R01 EB025024NIDDK NIH HHS L30 DK099910NLM NIH HHS T15 LM012500
6 · The paper itself

Abstract

objectivesConduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth: 1) digital health; 2) machine learning; and 3) precision oncology. We also highlight the ethical implications and future opportunities within each area.

methodsA search was conducted over a three-year period in two electronic databases (PubMed, Google Scholar) to identify peer-reviewed articles and conference proceedings. Search terms included variations of the following: neoplasms[MeSH], informatics[MeSH], cancer, oncology, clinical cancer informatics, medical cancer informatics. The search returned too many articles for practical review (23,994 from PubMed and 23,100 from Google Scholar). Thus, we conducted searches of key PubMed-indexed informatics journals and proceedings. We further limited our search to manuscripts that demonstrated a clear focus on clinical or translational cancer informatics. Manuscripts were then selected based on their methodological rigor, scientific impact, innovation, and contribution towards cancer informatics as a field or on their impact on cancer care and research.

resultsKey developments and opportunities in cancer informatics research in the areas of digital health, machine learning, and precision oncology were summarized.

conclusionWhile there are numerous innovations in the field of cancer informatics to advance prevention and clinical care, considerable challenges remain related to data sharing and privacy, digital accessibility, and algorithm biases and interpretation. The implementation and application of these findings in cancer care necessitates further consideration and research.

Indexed as

Machine LearningMedical InformaticsMedical OncologyPrecision MedicineHumansNeoplasmsPatient Participation

Identifiers

PMID32823322
PMCPMC7442514

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.