Evidence map›Paper›PMID 31820265›Full record

ArticleJapanese journal of radiology2020

Diagnostic performance and inter-operator variability of apparent diffusion coefficient analysis for differentiating pleomorphic adenoma and carcinoma ex pleomorphic adenoma: comparing one-point measurement and whole-tumor measurement including radiomics approach.

Takeshi Wada, Hajime Yokota, Takuro Horikoshi, Jay Starkey, Shinya Hattori, Jun Hashiba, Takashi Uno

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Japanese journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

7 authors.

Takeshi WadaDepartment of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan.
Hajime YokotaDiagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana Chuo-ku, Chiba, 260-8670, Japan. hjmykt@chiba-u.jp.ORCID http://orcid.org/0000-0003-2389-0299
Takuro HorikoshiDepartment of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan.
Jay StarkeyDepartment of Radiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR, 97239, USA.
Shinya HattoriDepartment of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan.
Jun HashibaDepartment of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan.
Takashi UnoDiagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana Chuo-ku, Chiba, 260-8670, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeThe purpose of this study was to compare the diagnostic performance between apparent diffusion coefficient (ADC) analysis of one-point measurement and whole-tumor measurement, including radiomics for differentiating pleomorphic adenoma (PA) from carcinoma ex pleomorphic adenoma (CXPA), and to evaluate the impact of inter-operator segmentation variability. MATERIALS AND

methodsOne hundred and fifteen patients with PA and 22 with CXPA were included. Four radiologists with different experience independently placed one-point and whole-tumor ROIs and a radiomics-predictive model was constructed from the extracted imaging features. We calculated the area under the receiver-operator characteristic curve (AUC) for the diagnostic performance of imaging features and the radiomics-predictive model.

resultsAUCs of the imaging features from whole-tumor varied between readers (0.50-0.89). The most experienced radiologist (Reader 1) produced significantly high AUCs than less experienced radiologists (Reader 3 and 4; P = 0.01 and 0.009). AUCs were higher for the radiomics-predictive model (0.82-0.87) than for one-point (0.66-0.79) in all readers.

conclusionSome imaging features of whole-tumor and radiomics-predictive model had higher diagnostic performance than one-point. The diagnostic performance of imaging features from whole-tumor alone varied depending on operator experience. Operator experience appears less likely to affect diagnostic performance in the radiomics-predictive model.

Indexed as

Adenoma, PleomorphicAlgorithmsArea Under CurveClinical CompetenceDiagnosis, DifferentialDiffusion Magnetic Resonance ImagingFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedObserver VariationReproducibility of ResultsROC CurveSalivary Gland NeoplasmsSalivary GlandsCarcinoma ex pleomorphic adenomaDiagnostic performanceMachine learningPleomorphic adenomaRadiomics

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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.