Deep Learning Can Accurately Predict the Prognosis of Gynecologic Smooth Muscle Tumors of Uncertain Malignant Potential: A Multicenter Pilot Study.

Costa J, Le VL, De Leo A, Ravaioli C, Velasco V, Davidson B, Skeie-Jensen T, Devouassoux-Shisheboran M, Trecourt A, Bartosch C, Rios E, Genestie C, Pautier P, Lebreton C, Guyon F, Babin G, Coindre JM, Le Loarer F, Saut O, Croce S

Open source

DOI: 10.1016/j.labinv.2025.104211
Published: 2025 Oct
Container: Laboratory investigation; a journal of technical methods and pathology
Publisher: Not recorded
Open access: unknown

Credibility signals

limited evidence Score 43/100 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. Source: Crossref; license: CC0 metadata
cautionDOI resolves: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.1016/j.labinv.2025.104211,
  title = {Deep Learning Can Accurately Predict the Prognosis of Gynecologic Smooth Muscle Tumors of Uncertain Malignant Potential: A Multicenter Pilot Study.},
  author = {Costa J and Le VL and De Leo A and Ravaioli C and Velasco V and Davidson B and Skeie-Jensen T and Devouassoux-Shisheboran M and Trecourt A and Bartosch C and Rios E and Genestie C and Pautier P and Lebreton C and Guyon F and Babin G and Coindre JM and Le Loarer F and Saut O and Croce S},
  year = {2025},
  journal = {Laboratory investigation; a journal of technical methods and pathology},
  doi = {10.1016/j.labinv.2025.104211},
  url = {https://doi.org/10.1016/j.labinv.2025.104211}
}

RIS

TY  - JOUR
TI  - Deep Learning Can Accurately Predict the Prognosis of Gynecologic Smooth Muscle Tumors of Uncertain Malignant Potential: A Multicenter Pilot Study.
AU  - Costa J
AU  - Le VL
AU  - De Leo A
AU  - Ravaioli C
AU  - Velasco V
AU  - Davidson B
AU  - Skeie-Jensen T
AU  - Devouassoux-Shisheboran M
AU  - Trecourt A
AU  - Bartosch C
AU  - Rios E
AU  - Genestie C
AU  - Pautier P
AU  - Lebreton C
AU  - Guyon F
AU  - Babin G
AU  - Coindre JM
AU  - Le Loarer F
AU  - Saut O
AU  - Croce S
PY  - 2025
JO  - Laboratory investigation; a journal of technical methods and pathology
DO  - 10.1016/j.labinv.2025.104211
UR  - https://doi.org/10.1016/j.labinv.2025.104211
ER  -

APA

J, C., VL, L., A, D. L., C, R., V, V., B, D., T, S., M, D., A, T., C, B., E, R., C, G., P, P., C, L., F, G., G, B., JM, C., F, L. L., O, S., & S, C. (2025). Deep Learning Can Accurately Predict the Prognosis of Gynecologic Smooth Muscle Tumors of Uncertain Malignant Potential: A Multicenter Pilot Study.. Laboratory investigation; a journal of technical methods and pathology. https://doi.org/10.1016/j.labinv.2025.104211

Source records

pubmed · retrieved 2026-09-25T19:41:20.193Z