【Abstract】 Objective We aim to use machine learning methods to construct an online nomogram for predicting breast cancer based on quantitative ultrasound morphological features. Methods A total of 1046 patients with breast lesions who underwent ultrasound examination were retrospectively collected in the study from January 2019 and October 2020 were divided into 732 patients in the training set

and 314 patients in the validation set according to a 7:3 ratio of random numbers. The lesions of the training set were classified into benign and malignant groups according to pathology results

then the ultrasound morphological features were quantified using the ImageJ software. All features were evaluated for the association with the cancer risk using univariate and multivariate analysis by the machine learning methods

then a nomogram was established for the determined risk factors and was presented in the form of an online application. The diagnostic performance of the nomogram was evaluated by receiver operating characteristic (ROC) curve analysis and calibration plot. Results Pathological examination revealed a malignant lesion in 430 patients (58.7 %) for the training set and 199 patients (63.4 %) for the validation set. Univariate analysis showed that the morphological quantitative features AR

Circularity

MFA

and Solidity were statistically different between the benign and malignant groups (P<0.05). Multivariate analysis showed that AR

Circularity

and age were independent risk factors for malignancy (P<0.05). Based on the independent risk factors

a nomogram model for predicting breast cancer was constructed

and an online application was released. The area under the curve(AUC) of the nomogram was 0.931

0.901

the sensitivity was 88.1%

84.2%

and the specificity was 85.4%

85.8% in the internal and external validation

respectively. The calibration plot showed good agreement between predicted and actual risk. Conclusions The nomogram for predicting breast cancer based on quantitative ultrasound morphological features has good performance

and the presentation of the nomogram in the form of an internet online application makes it more practical

which may help tailor appropriate treatment decisions.