Arterial phase CT radiomics for non-invasive prediction of Ki-67 proliferation index in pancreatic solid pseudopapillary neoplasms.
Background: This study aimed to preoperatively predict Ki-67 proliferation levels in patients with pancreatic solid pseudopapillary neoplasm (pSPN) using radiomics features extracted from arterial phase helical CT images. Methods: We retrospectively analyzed 92 patients (Ningbo Medical Center Lihuil...
| Publicado en: | Abdominal Radiology Vol. 50; no. 10; pp. 4635 - 4646 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188150398&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188150398 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Oct2025 vid: 50 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188150398 184217616 10.1007/s00261-025-04921-z 188150398 ppf: 4635 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Arterial phase CT radiomics for non-invasive prediction of Ki-67 proliferation index in pancreatic solid pseudopapillary neoplasms. aug: au: Liu, Jun Wu, Huanhua Ren, Dabin Huang, Hao Chen, Xinyue Liu, Liqiu Wang, Yongtao Wang, Guoyu affil: https://ror.org/040884w51 Taizhou Central Hospital, Taizhou, China sug: ab: Background: This study aimed to preoperatively predict Ki-67 proliferation levels in patients with pancreatic solid pseudopapillary neoplasm (pSPN) using radiomics features extracted from arterial phase helical CT images. Methods: We retrospectively analyzed 92 patients (Ningbo Medical Center Lihuili Hospital: n = 64, Taizhou Central Hospital: n = 28) with pathologically confirmed pSPN from June 2015 to June 2023. Ki-67 positivity > 3% was considered high. Radiomics features were extracted using PyRadiomics, with patients from training cohort (n = 64) and validation cohort (n = 28). A radiomics signature was constructed, and a CT radiomics score (CTscore) was calculated. Deep learning models were employed for prediction, with early stopping to prevent overfitting. Results: Seven key radiomics features were selected via LASSO regression with cross-validation. The deep learning model demonstrated improved accuracy with demographics and CTscore, with key features such as Morphology and CTscore contributing significantly to predictive accuracy. The best-performing models, including GBM and deep learning algorithms, achieved high predictive performance with an AUC of up to 0.946 in the training cohort. Conclusions: We developed a robust deep learning-based radiomics model using arterial phase CT images to predict Ki-67 levels in pSPN patients, identifying CTscore and Morphology as key predictors. This non-invasive approach has potential utility in guiding personalized preoperative treatment strategies. Clinical trial number: Not applicable. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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