(a) Contribution of radiomic features to model performance; (b) Binomial deviance calculation for radiomic features; (c) Contribution of clinical features to model performance; (d) Changes in coefficients during LASSO regularization.

<p>(a) Contribution of radiomic features to model performance; (b) Binomial deviance calculation for radiomic features; (c) Contribution of clinical features to model performance; (d) Changes in coefficients during LASSO regularization.</p>

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Bibliographic Details
Main Author:	Ningxin Chen (12965441) (author)
Other Authors:	Boyang Zang (21464459) (author), Yangjia Chen (17177836) (author)
Published:	2025
Subjects:	Medicine Cell Biology Biotechnology Cancer Virology Space Science Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified linear discriminant analysis g ., age colorectal liver metastasis mlp ), k predict early recurrence high &# 8211 associated clinical data random forest algorithm low &# 8211 et ), adaboost 993 radiomic features et model showed best performance using 197 crlm patients random forest radiomic features clinical features surgical recurrence high risk combining radiomic combines clinical gb ), year post xlink "> typically correlates strong demand run entropy retrospective single predicting one order statistics order characteristics nearest neighbors models improved model achieved interpretable approaches including shape identifying patients gradient boosting extra trees dicom images developing robust decomposed image decision trees crlm ). center design center cohorts carcinoembryonic antigen bilobar disease based metrics band .)
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(a) Contribution of radiomic features to model performance; (b) Binomial deviance calculation for radiomic features; (c) Contribution of clinical features to model performance; (d) Changes in coefficients during LASSO regularization.

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