Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer

Wang, Hui; Ding, Jun; Deng, Shuitang; Mao, Guoqun; Tian, Shuyuan; Ao, Weiqun

doi:10.1007/s00261-022-03759-z

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…Then 18 features significantly correlated with Ki-67 expression in rectal cancer were selected to construct models, among which wavelet features account for the majority (14/18, 77.8%), followed by original_shape features (3/18, 16.6%) and original_firstorder features (1/18, 5.6%). Other studies [ 30 , 31 ] also showed that “wavelet” features had powerful prognostic abilities and were major components in building radiomic model or signature, which is consistent with our study. “Wavelet” features [ 30 , 32 ], which are derived from the wavelet transform algorithm, can describe the texture information of images at different scales and provide valuable feature information for discrimination and classification of lesions that cannot be identified by the naked eye.…”

Section: Discussionsupporting

confidence: 92%

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study

Yao,

Ao,

Zhu

et al. 2023

BMC Med Imaging

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Background To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. Methods Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms. Results Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians. Conclusion The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

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