Evidence map›Paper›PMID 39594731›Full record

ArticleCancers2024

A Machine Learning Model Based on Thyroid US Radiomics to Discriminate Between Benign and Malignant Nodules.

Antonino Guerrisi, Elena Seri, Vincenzo Dolcetti, Ludovica Miseo, Fulvia Elia, Gianmarco Lo Conte, Giovanni Del Gaudio, Patrizia Pacini, Angelo Barbato, Emanuele David and 1 more

Abstract read
In one paragraph

Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Prediction of neoadjuvant chemotherapy efficacy in patients with HER2-low breast cancer based on ultrasound radiomics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
    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

11 authors.

Antonino GuerrisiRadiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Via Elio Chianesi 53, 00144 Rome, Italy.
Elena SeriDepartment of Radiological, Anatomo-Pathological Sciences, "Sapienza" University of Rome, Viale Regina Elena 324, 00161 Rome, Italy.
Vincenzo DolcettiDepartment of Radiological, Anatomo-Pathological Sciences, "Sapienza" University of Rome, Viale Regina Elena 324, 00161 Rome, Italy.ORCID 0000-0001-8397-8343
Ludovica MiseoRadiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Via Elio Chianesi 53, 00144 Rome, Italy.
Fulvia EliaRadiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Via Elio Chianesi 53, 00144 Rome, Italy.
Gianmarco Lo ConteDepartment of Radiological, Anatomo-Pathological Sciences, "Sapienza" University of Rome, Viale Regina Elena 324, 00161 Rome, Italy.ORCID 0009-0007-0151-3317
Giovanni Del GaudioDepartment of Radiological, Anatomo-Pathological Sciences, "Sapienza" University of Rome, Viale Regina Elena 324, 00161 Rome, Italy.
Patrizia PaciniDepartment of Radiological, Anatomo-Pathological Sciences, "Sapienza" University of Rome, Viale Regina Elena 324, 00161 Rome, Italy.
Angelo BarbatoLocal Health Authority of Rieti, Via del Terminillo 42, 02100 Rieti, Italy.
Emanuele DavidDepartment of Radiological, Anatomo-Pathological Sciences, "Sapienza" University of Rome, Viale Regina Elena 324, 00161 Rome, Italy.
Vito CantisaniDepartment of Radiological, Anatomo-Pathological Sciences, "Sapienza" University of Rome, Viale Regina Elena 324, 00161 Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

machine learningnodulesradiomicsultrasound

Identifiers

PMID39594731
PMCPMC11592088

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.