Evidence map›Paper›PMID 29380154›Full record

ArticleJournal of digital imaging2018

Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT.

Young Jae Kim, Seung Hyun Lee, Kun Young Lim, Kwang Gi Kim

Open access · bronzeAbstract readComparative Study
In one paragraph

Article in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 32% 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, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  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

4 authors at 3 institutions in 1 country.

Young Jae KimDepartment of Biomedical Engineering, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea.
Seung Hyun LeeDepartment of Plazma Bio Display, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul, 01897, Republic of Korea.
Kun Young LimDepartment of Diagnostic Radiology, Center for Lung Cancer, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang, 10408, Gyeonggi-do, Republic of Korea.
Kwang Gi KimDepartment of Biomedical Engineering, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea. kimkg@gachon.ac.kr.
Kwangwoon University · KRGachon University · KRNational Cancer Center · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The set of criteria called Response Evaluation Criteria In Solid Tumors (RECIST) is used to evaluate the remedial effects of lung cancer, whereby the size of a lesion can be measured in one dimension (diameter). Volumetric evaluation is desirable for estimating the size of a lesion accurately, but there are several constraints and limitations to calculating the volume in clinical trials. In this study, we developed a method to detect lesions automatically, with minimal intervention by the user, and calculate their volume. Our proposed method, called a spherical region-growing method (SPRG), uses segmentation that starts from a seed point set by the user. SPRG is a modification of an existing region-growing method that is based on a sphere instead of pixels. The SPRG method detects lesions while preventing leakage to neighboring tissues, because the sphere is grown, i.e., neighboring voxels are added, only when all the voxels meet the required conditions. In this study, two radiologists segmented lung tumors using a manual method and the proposed method, and the results of both methods were compared. The proposed method showed a high sensitivity of 81.68-84.81% and a high dice similarity coefficient (DSC) of 0.86-0.88 compared with the manual method. In addition, the SPRG intraclass correlation coefficient (ICC) was 0.998 (CI 0.997-0.999, p < 0.01), showing that the SPRG method is highly reliable. If our proposed method is used for segmentation and volumetric measurement of lesions, then objective and accurate results and shorter data analysis time are possible.

Indexed as

AdultAlgorithmsCohort StudiesDatabases, FactualDiagnosis, Computer-AssistedFemaleHumansImage Processing, Computer-AssistedLung NeoplasmsMaleMiddle AgedRadiography, ThoracicRepublic of KoreaSensitivity and SpecificityTomography, X-Ray ComputedTumor BurdenComputed tomographyComputer-assisted diagnosisLung cancerResponse evaluation criteria in solid tumorsTumor volume

Identifiers

PMID29380154
PMCPMC6113144
OpenAlexW2792165479

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.