Evidence map›Paper›PMID 40647630›Full record

ArticleDiagnostics (Basel, Switzerland)2025

Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk.

Francesca Galati, Roberto Maroncelli, Chiara De Nardo, Lucia Testa, Gloria Barcaroli, Veronica Rizzo, Giuliana Moffa, Federica Pediconi

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. 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.

Francesca GalatiDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.ORCID 0000-0003-2103-2856
Roberto MaroncelliDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.ORCID 0009-0006-6626-2478
Chiara De NardoDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.
Lucia TestaDepartment of Informatic, Automatic and Gestional Engineering (DIAG), Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.
Gloria BarcaroliDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.
Veronica RizzoDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.ORCID 0000-0003-1781-3174
Giuliana MoffaDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.ORCID 0000-0002-6595-1611
Federica PediconiDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.ORCID 0000-0002-1432-2137

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

breast cancer risk stratificationdeep learningdigital breast tomosynthesisradiomicstransfer learning

Identifiers

PMID40647630
PMCPMC12249188

What OpenQuestion holds

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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.