Evidence map›Paper›PMID 36612033›Full record

ArticleCancers2022

3T-MRI Artificial Intelligence in Patients with Invasive Breast Cancer to Predict Distant Metastasis Status: A Pilot Study.

Alessandro Calabrese, Domiziana Santucci, Michela Gravina, Eliodoro Faiella, Ermanno Cordelli, Paolo Soda, Giulio Iannello, Carlo Sansone, Bruno Beomonte Zobel, Carlo Catalano and 1 more

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

Article in Cancers, 2022. 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
0.6field-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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Applications of Artificial Intelligence (AI) in Breast Cancer Care Delivery and Education: A Scoping Review.International journal of environmental research and public health · 2026
    Article
  3. Review
  4. 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

11 authors at 4 institutions in 2 countries.

Alessandro CalabreseDepartment of Radiology, University of Rome "Sapienza", Viale del Policlinico 155, 00161 Roma, Italy.
Domiziana SantucciDepartment of Radiology, Sant'Anna Hospital, Via Ravona, 22042 San Fermo della Battaglia, Italy.
Michela GravinaDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80131 Naples, Italy.
Eliodoro FaiellaDepartment of Radiology, Sant'Anna Hospital, Via Ravona, 22042 San Fermo della Battaglia, Italy.
Ermanno CordelliUnit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome "Campus Bio-Medico", Via Alvaro del Portillo 21, 00128 Roma, Italy.
Paolo SodaUnit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome "Campus Bio-Medico", Via Alvaro del Portillo 21, 00128 Roma, Italy.ORCID 0000-0003-2621-072X
Giulio IannelloUnit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome "Campus Bio-Medico", Via Alvaro del Portillo 21, 00128 Roma, Italy.ORCID 0000-0003-3864-5800
Carlo SansoneDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80131 Naples, Italy.ORCID 0000-0002-8176-6950
Bruno Beomonte ZobelDepartment of Radiology, University of Rome "Campus Bio-medico", Via Alvaro del Portillo, 21, 00128 Rome, Italy.ORCID 0000-0001-9227-5535
Carlo CatalanoDepartment of Radiology, University of Rome "Sapienza", Viale del Policlinico 155, 00161 Roma, Italy.
Carlo de FeliceDepartment of Radiology, University of Rome "Sapienza", Viale del Policlinico 155, 00161 Roma, Italy.
Università Campus Bio-Medico · ITPoliclinico Umberto I · ITUniversity of Naples Federico II · ITOspedale Sant'Anna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe incidence of breast cancer metastasis has decreased over the years. However, 20-30% of patients with early breast cancer still die from metastases. The purpose of this study is to evaluate the performance of a Deep Learning Convolutional Neural Networks (CNN) model to predict the risk of distant metastasis using 3T-MRI DCE sequences (Dynamic Contrast-Enhanced).

methodsA total of 157 breast cancer patients who underwent staging 3T-MRI examinations from January 2011 to July 2022 were retrospectively examined. Patient data, tumor histological and MRI characteristics, and clinical and imaging follow-up examinations of up to 7 years were collected. Of the 157 MRI examinations, 39/157 patients (40 lesions) had distant metastases, while 118/157 patients (120 lesions) were negative for distant metastases (control group). We analyzed the role of the Deep Learning technique using a single variable size bounding box (SVB) option and employed a Voxel Based (VB) NET CNN model. The CNN performance was evaluated in terms of accuracy, sensitivity, specificity, and area under the ROC curve (AUC).

resultsThe VB-NET model obtained a sensitivity, specificity, accuracy, and AUC of 52.50%, 80.51%, 73.42%, and 68.56%, respectively. A significant correlation was found between the risk of distant metastasis and tumor size, and the expression of PgR and HER2.

conclusionsWe demonstrated a currently insufficient ability of the Deep Learning approach in predicting a distant metastasis status in patients with BC using CNNs.

Indexed as

3T-MRI Dynamic Contrast-Enhanced sequences (DCE)breast cancerconvolution neural network (CNN)Deep Learning (DL)metastasis

Identifiers

PMID36612033
PMCPMC9817717
OpenAlexW4313235938

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.