Evidence map›Paper›PMID 35084712›Full record

ArticleAnnals of nuclear medicine2022

Deep learning for image classification in dedicated breast positron emission tomography (dbPET).

Yoko Satoh, Tomoki Imokawa, Tomoyuki Fujioka, Mio Mori, Emi Yamaga, Kanae Takahashi, Keiko Takahashi, Takahiro Kawase, Kazunori Kubota, Ukihide Tateishi and 1 more

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Article in Annals of nuclear medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Review
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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.

Yoko SatohYamanashi PET Imaging Clinic, Chuo City, Yamanashi Prefecture, Japan.
Tomoki ImokawaDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
Tomoyuki FujiokaDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan. fjokmrad@tmd.ac.jp.ORCID http://orcid.org/0000-0002-7141-8901
Mio MoriDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
Emi YamagaDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
Kanae TakahashiDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
Keiko TakahashiDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
Takahiro KawaseDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
Kazunori KubotaDepartment of Radiology, Dokkyo Medical University Saitama Medical Center, Koshigaya City, Saitama Prefecture, Japan.
Ukihide TateishiDepartment of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo Ku, Tokyo, Japan.
Hiroshi OnishiDepartment of Radiology, University of Yamanashi, Chuo City, Yamanashi Prefecture, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to investigate and determine the best deep learning (DL) model to predict breast cancer (BC) with dedicated breast positron emission tomography (dbPET) images.

methodsOf the 1598 women who underwent dbPET examination between April 2015 and August 2020, a total of 618 breasts on 309 examinations for 284 women who were diagnosed with BC or non-BC were analyzed in this retrospective study. The Xception-based DL model was trained to predict BC or non-BC using dbPET images from 458 breasts of 109 BCs and 349 non-BCs, which consisted of mediallateral and craniocaudal maximum intensity projection images, respectively. It was tested using dbPET images from 160 breasts of 43 BC and 117 non-BC. Two expert radiologists and two radiology residents also interpreted them. Sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were calculated.

resultsOur DL model had a sensitivity and specificity of 93% and 93%, respectively, while radiologists had a sensitivity and specificity of 77-89% and 79-100%, respectively. Diagnostic performance of our model (AUC = 0.937) tended to be superior to that of residents (AUC = 0.876 and 0.868, p = 0.073 and 0.073), although not significantly different. Moreover, no significant differences were found between the model and experts (AUC = 0.983 and 0.941, p = 0.095 and 0.907).

conclusionsOur DL model could be applied to dbPET and achieve the same diagnostic ability as that of experts.

Indexed as

Breast NeoplasmsDeep LearningBreastFemaleFluorodeoxyglucose F18HumansPositron-Emission TomographyRetrospective StudiesFluorodeoxyglucose F18Breast cancerDedicated breast positron emission tomographyDeep learningImage classificationNeural network

Identifiers

PMID35084712

What OpenQuestion holds

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Registered trials

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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.