Evaluation of the radiomics model.

<p>(A) ROC curves of the radiomics model for the training and testing sets. (B) Calibration curves of the radiomics model for the training and testing sets. (C) DCA curve of the radiomics model for the training set. (D) DCA curve of the radiomics model for the testing set.</p>

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Bibliographic Details
Main Author:	Pan Tang (4411270) (author)
Other Authors:	Qi Zhang (28502) (author), Ling-cui Meng (22386132) (author), Miao Chen (213356) (author), Sai-Feng He (22386135) (author), Jian-Xing Zhang (22386138) (author)
Published:	2025
Subjects:	Medicine Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified probability threshold range personalized treatment planning offering valuable guidance least absolute shrinkage invasive predictive tool independent risk factors guiding treatment strategies 3 positive alns 755 – 0 678 – 0 05 – 0 integrating clinical data reported aln status nomogram model vs derived radiomics features based radiomics nomogram radiomics nomogram model ultrasound imaging features based nomogram nomogram model radiomics features clinical model radiomics model ultrasound imaging best features clinical pathology radiomics score combined model xlink "> ultrasound images tumor burden training set testing set statistically significant serologic markers selection operator selected using roc curve results showed calibration curves 4161 ).
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Evaluation of the radiomics model.

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