Evidence map›Paper›PMID 41408239›Full record

Observational studyBMC pulmonary medicine2025

Screening for lung fibrosis using serum surfactant protein-D, KL-6, and a deep learning algorithm on chest radiographs: a prospective observational study.

Hirotaka Nishikiori, Naoya Yama, Kenichi Hirota, Yuki Mori, Ippei Neriai, Haruka Takenaka, Atsushi Saito, Mamoru Takahashi, Koji Kuronuma, Shinichiro Ueda and 2 more

Abstract readObservational Study
In one paragraph

Observational study in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Hirotaka NishikioriDepartment of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan. hnishiki@sapmed.ac.jp.
Naoya YamaDepartment of Diagnostic Radiology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.
Kenichi HirotaScholarly Communication Center Planning and Development Office, Sapporo Medical University, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.
Yuki MoriDepartment of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.
Ippei NeriaiDepartment of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.
Haruka TakenakaDepartment of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.
Atsushi SaitoDepartment of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.
Mamoru TakahashiDepartment of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.
Koji KuronumaDepartment of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.
Shinichiro UedaDepartment of Clinical Research Education and Management, University of Ryukyus Graduate School of Medicine, 207 Uebaru Nishihara Town, Okinawa, 9030215, Japan.
Masamitsu HatakenakaDepartment of Diagnostic Radiology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.
Hirofumi ChibaDepartment of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, South 1-West 16, Chuo-ku, Sapporo, Hokkaido, 0608543, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly identification of lung fibrosis remains difficult. In Japan, the serum biomarkers surfactant protein-D (SP-D) and KL-6 are commonly used to monitor interstitial lung diseases (ILD) in clinical practice, but their potential role in the early detection of lung fibrosis has not yet been fully clarified. Although chest radiography is also considered a possible tool for identifying subclinical pulmonary fibrosis, detecting early-stage disease remains challenging. A deep learning-based software, BMAX, was recently developed to identify fibrosing ILD on chest radiographs. Its capability to detect lung fibrosis in a health-checkup setting requires validation.

methodsStudy participants were randomly recruited from individuals undergoing routine health examinations. All participants underwent chest radiography and serum SP-D and KL-6 testing. Those with elevated biomarker levels (≥ 110 ng/mL for SP-D and ≥ 500 IU/mL for KL-6) or radiographic abnormalities were advised to undergo further evaluation with chest computed tomography (CT). Lung fibrosis on CT was assessed independently by one pulmonologist and one thoracic radiologist. BMAX assigned a confidence score for lung fibrosis (ranging from 0 to 1) on each radiograph. In participants who underwent CT, the sensitivity and specificity of BMAX (using a confidence score > 0.3 as the threshold), SP-D, and KL-6 for detecting lung fibrosis were evaluated.

resultsAmong the 2,751 individuals enrolled, 228 were recommended for CT, and 81 underwent the scan. Lung fibrosis was identified on chest CT in 8 of the 81 participants. The positivity rates for SP-D, KL-6, and BMAX (confidence score > 0.3) were 5.9%, 2.4%, and 5.9%, respectively. SP-D showed a sensitivity of 1.000 and a specificity of 0.315, while KL-6 showed a sensitivity of 0.750 and a specificity of 0.753. BMAX demonstrated a sensitivity of 1.000 and a specificity of 0.904.

conclusionsSP-D and KL-6 may be useful screening biomarkers for lung fibrosis in health checkup settings, offering high sensitivity and moderate positivity rates. BMAX also appears promising as a standalone screening tool for detecting lung fibrosis on chest radiographs.

Indexed as

Deep LearningMucin-1Pulmonary FibrosisPulmonary Surfactant-Associated Protein DAdultAgedAlgorithmsBiomarkersFemaleHumansJapanMaleMiddle AgedProspective StudiesRadiography, ThoracicSensitivity and SpecificityBiomarkersMUC1 protein, humanMucin-1Pulmonary Surfactant-Associated Protein DArtificial intelligenceDeep learningHealth checkupInterstitial lung abnormalityInterstitial lung diseaseLung fibrosisPulmonary fibrosisSerum biomarker

Identifiers

PMID41408239
PMCPMC12821957

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.