Evidence map›Paper›PMID 35909389›Full record

ReviewEuropean journal of radiology open2022

Importance of tumor subtypes in cancer imaging.

Ali Khader, Marta Braschi-Amirfarzan, Lacey J McIntosh, Babina Gosangi, Jeremy R Wortman, Christoph Wald, Richard Thomas

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in European journal of radiology open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 35% of its field
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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. What's new in cancer and molecular imaging.European journal of radiology open · 2022
    Article
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

7 authors at 4 institutions in 1 country.

Ali KhaderDepartment of Radiology, Lahey Hospital and Medical Center, Tufts University School of Medicine, 41 Mall Road, Burlington, MA 01805, the United States of America.
Marta Braschi-AmirfarzanDepartment of Radiology, Lahey Hospital and Medical Center, Tufts University School of Medicine, 41 Mall Road, Burlington, MA 01805, the United States of America.
Lacey J McIntoshUniversity of Massachusetts Chan Medical School/Memorial Health Care, Division of Oncologic and Molecular Imaging, 55 Lake Avenue North, Worcester, MA 01655, the United States of America.
Babina GosangiDepartment of Radiology, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, the United States of America.
Jeremy R WortmanDepartment of Radiology, Lahey Hospital and Medical Center, Tufts University School of Medicine, 41 Mall Road, Burlington, MA 01805, the United States of America.
Christoph WaldDepartment of Radiology, Lahey Hospital and Medical Center, Tufts University School of Medicine, 41 Mall Road, Burlington, MA 01805, the United States of America.
Richard ThomasDepartment of Radiology, Lahey Hospital and Medical Center, Tufts University School of Medicine, 41 Mall Road, Burlington, MA 01805, the United States of America.
Lahey Hospital and Medical Center · USTufts University · USUMass Memorial Health Care · USYale University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer therapy has evolved from being broadly directed towards tumor types, to highly specific treatment protocols that target individual molecular subtypes of tumors. With the ever-increasing data on imaging characteristics of tumor subtypes and advancements in imaging techniques, it is now often possible for radiologists to differentiate tumor subtypes on imaging. Armed with this knowledge, radiologists may be able to provide specific information that can obviate the need for invasive methods to identify tumor subtypes. Different tumor subtypes also differ in their patterns of metastatic spread. Awareness of these differences can direct radiologists to relevant anatomical sites to screen for early metastases that may otherwise be difficult to detect during cursory inspection. Likewise, this knowledge will help radiologists to interpret indeterminate findings in a more specific manner.

Indexed as

Artificial intelligenceCancerPrecision medicineRadiomicsTumor subtype

Identifiers

PMID35909389
PMCPMC9335388
OpenAlexW4288059654

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.