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An interpretable machine learning model combining MRI-DKI habitat radiomic features and clinical biomarkers for noninvasive prediction of lymphatic metastasis in rectal cancer: a prospective study

Crossref DOI link: https://doi.org/10.1186/s13244-026-02243-2

Published Online: 2026-03-25

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Peng, Leping

Li, Feixiang

Zhang, Fan

Ma, Fang

Zhang, Xiuling

Zhang, Xiaoyue

Chen, Dongdong

Huang, Gang

Wang, Lili https://orcid.org/0000-0003-4576-3776
Funding

Funding for this research was provided by:

Gansu Provincial Youth Fund Project (20JR5RA143)

Gansu Provincial Hospital Research Fund Project (23GSSYF-4)

Gansu Provincial Hospital Research Fund Project (21GSSYB-4)
License Information

Text and Data Mining valid from 2026-03-25

Version of Record valid from 2026-03-25
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Article History

Received: 23 September 2025

Accepted: 8 February 2026

First Online: 25 March 2026

Declarations

:

: This prospective study was approved by the Gansu Provincial Hospital Ethics Review Committee (approval number: 2022-370).

: The requirement for patient consent was waived by the IRB.

: One of the authors (Xiaoyue Zhang) is an employee of Philips Healthcare. The remaining authors declare that they have no competing interests.

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