Development and internal validation of a high-resolution computed tomography radiomics and three-dimensional deep learning diagnostic prediction model for preoperative differentiation of minimally invasive and invasive adenocarcinoma in subsolid nodules.
- DOI
- 10.21037/jtd-2026-0924
- Published
- 2026-07-28
- Container
- J Thorac Dis
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.21037/jtd-2026-0924,
title = {Development and internal validation of a high-resolution computed tomography radiomics and three-dimensional deep learning diagnostic prediction model for preoperative differentiation of minimally invasive and invasive adenocarcinoma in subsolid nodules.},
author = {Li P and Wang H and Zhang Q and Li H.},
year = {2026},
journal = {J Thorac Dis},
doi = {10.21037/jtd-2026-0924},
url = {https://doi.org/10.21037/jtd-2026-0924}
}RIS
TY - JOUR TI - Development and internal validation of a high-resolution computed tomography radiomics and three-dimensional deep learning diagnostic prediction model for preoperative differentiation of minimally invasive and invasive adenocarcinoma in subsolid nodules. AU - Li P AU - Wang H AU - Zhang Q AU - Li H. PY - 2026 JO - J Thorac Dis DO - 10.21037/jtd-2026-0924 UR - https://doi.org/10.21037/jtd-2026-0924 ER -
APA
P, L., H, W., Q, Z., & H., L. (2026). Development and internal validation of a high-resolution computed tomography radiomics and three-dimensional deep learning diagnostic prediction model for preoperative differentiation of minimally invasive and invasive adenocarcinoma in subsolid nodules.. J Thorac Dis. https://doi.org/10.21037/jtd-2026-0924
Source records
- europe-pmc · retrieved 2026-09-25T23:51:37.700Z