Asset Details
Do you wish to reserve the book?
The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer
Do you wish to request the book?
The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer
2025
Overview
To investigate the diagnostic capability of multiple machine learning algorithms combined with intratumoral and peritumoral ultrasound radiomics models for non-massive breast cancer in dense breast backgrounds. Manual segmentation of ultrasound images was performed to define the intratumoral region of interest (ROI), and five peritumoral ROIs were generated by extending the contours by 1 to 5 mm. A total of 851 radiomics features were extracted from these regions and filtered using statistical methods. Thirteen machine learning algorithms were employed to create radiomics models for the intratumoral and peritumoral areas. The best model was combined with clinical ultrasound predictive factors to form a joint model, which was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).Based on this model, a nomogram was developed, demonstrating high predictive performance, with C-index values of 0.982 and 0.978.The model incorporating the intratumoral and peritumoral 2 mm regions outperformed other models, indicating its effectiveness in distinguishing between benign and malignant breast lesions. This study concludes that ultrasound imaging, particularly in the intratumoral and peritumoral 2 mm regions, has significant potential for diagnosing non-massive breast cancer, and the nomogram can assist clinical decision-making.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
