Evidence map›Paper›PMID 31273458›Full record

ArticleEuropean radiology2019

Temporal subtraction of computed tomography images improves detectability of bone metastases by radiology residents.

Koji Onoue, Mizuho Nishio, Masahiro Yakami, Ryo Sakamoto, Gakuto Aoyama, Keita Nakagomi, Yoshio Iizuka, Takeshi Kubo, Yutaka Emoto, Thai Akasaka and 4 more

Abstract readLetter
PubMed Publisher
In one paragraph

Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 45% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. CT temporal subtraction: techniques and clinical applications.Quantitative imaging in medicine and surgery · 2021
    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

14 authors at 5 institutions in 1 country.

Koji OnoueDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan. onoue.koji.88e@kyoto-u.jp.
Mizuho NishioDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan.
Masahiro YakamiDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan.
Ryo SakamotoDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan.
Gakuto AoyamaMedical Imaging System Development Center, R&D Headquarters, Canon Inc., 30-2, Shimomaruko 3-chome, Ohta-ku, Tokyo, 146-8501, Japan.
Keita NakagomiMedical Imaging System Development Center, R&D Headquarters, Canon Inc., 30-2, Shimomaruko 3-chome, Ohta-ku, Tokyo, 146-8501, Japan.
Yoshio IizukaMedical Imaging System Development Center, R&D Headquarters, Canon Inc., 30-2, Shimomaruko 3-chome, Ohta-ku, Tokyo, 146-8501, Japan.
Takeshi KuboDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan.
Yutaka EmotoKyoto College of Medical Science, 1-3 Imakita, Koyamahigashi-cho, Sonobe-cho, Nantan, Kyoto, 622-0041, Japan.
Thai AkasakaOsaka Red Cross Hospital, 5-30 Fudegasaki-cho, Tennoji-ku, Osaka, Osaka, 543-8555, Japan.
Kiyohide SatohMedical Imaging System Development Center, R&D Headquarters, Canon Inc., 30-2, Shimomaruko 3-chome, Ohta-ku, Tokyo, 146-8501, Japan.
Hiroyuki YamamotoMedical Imaging System Development Center, R&D Headquarters, Canon Inc., 30-2, Shimomaruko 3-chome, Ohta-ku, Tokyo, 146-8501, Japan.
Hiroyoshi IsodaDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan.
Kaori TogashiDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan.
Canon (Japan) · JPKyoto University · JPKyoto University Hospital · JPKyoto College of Medical Science · JPOsaka Red Cross Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTemporal subtraction of CT (TS) images improves detection of newly developed bone metastases (BM). We sought to determine whether TS improves detection of BM by radiology residents as well.

methodsWe performed an observer study using a previously reported dataset, consisting of 60 oncology patients, each with previous and current CT images. TS images were calculated using in-house software. Four residents independently interpreted twice the 60 sets of CT images, without and with TS. They identified BM by marking suspicious lesions likely to be BM. Lesion-based sensitivity and number of false positives per patient were calculated. Figure-of-merit (FOM) was calculated. Detectability of BM, with and without TS, was compared between radiology residents and board-certified radiologists, as published previously.

resultsFOM of residents significantly improved by implementing TS (p value < 0.0001). Lesion-based sensitivity, false positives per patients, and FOM were 40.8%, 0.121, and 0.657, respectively, without TS, and 58.1%, 0.0958, and 0.796, respectively, with TS. These findings were comparable with the previously published values for board-certified radiologists without TS (58.0%, 0.19, and 0.758, respectively).

conclusionThe detectability of BM by residents improved markedly by implementing TS and reached that of board-certified radiologists without TS. KEY POINTS: • Detectability of bone metastases on CT by residents improved significantly when using temporal subtraction of CT (TS). • Detections by residents with TS and board-certified radiologists without TS were comparable. • TS is useful for residents as it is for board-certified radiologists.

Indexed as

AlgorithmsBone NeoplasmsClinical CompetenceHumansImage Interpretation, Computer-AssistedInternship and ResidencyRadiologySensitivity and SpecificitySubtraction TechniqueTomography, X-Ray ComputedBone neoplasmsInternship and residencyTomographyX-Ray computed

Identifiers

PMID31273458
OpenAlexW2955424366

What OpenQuestion holds

Textmetadata
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.