Evidence map›Paper›PMID 37373993›Full record

ArticleJournal of personalized medicine2023

Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction.

Marianna Inglese, Matteo Ferrante, Tommaso Boccato, Allegra Conti, Chiara A Pistolese, Oreste C Buonomo, Rolando M D'Angelillo, Nicola Toschi

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

Article in Journal of personalized medicine, 2023. 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
0.5field-weighted citation impact, top 33% 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, 3 citations in OpenAlex.

  1. 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

8 authors at 2 institutions in 3 countries.

Marianna IngleseDepartment of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy.
Matteo FerranteDepartment of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy.
Tommaso BoccatoDepartment of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy.
Allegra ContiDepartment of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy.ORCID 0000-0003-4210-5065
Chiara A PistoleseDepartment of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy.
Oreste C BuonomoU.O.S.D. Breast Unit, Department of Surgical Science, Policlinico Tor Vergata, 00133 Rome, Italy.
Rolando M D'AngelilloDepartment of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy.
Nicola ToschiDepartment of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy.ORCID 0000-0003-1929-5833
University of Rome Tor Vergata · ITPoliclinico Tor Vergata · IT

Funding

MUR-PNRR M4C2I1.3 PE6 PE00000019National Recovery and Resilience Plan DN. 1553 11.10.2022National Recovery and Resilience Plan PE0000006The European Innovation Council 101070908The European Innovation Council 101099355
6 · The paper itself

Abstract

Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic

Indexed as

breast cancerclassificationmachine learningprognosisradiomics

Identifiers

PMID37373993
PMCPMC10303631
OpenAlexW4380990570

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.