Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer

TL;DR

This study uses ultrasound radiomics and machine learning to predict the risk of breast cancer recurrence, offering a non-invasive tool for better diagnosis and treatment planning.

Contribution

The novel contribution is the development of a machine learning-based ultrasound radiomics model that effectively predicts breast cancer recurrence risk.

Findings

The Clin-US-Rad model achieved the highest AUC values (0.817 in test set and 0.851 in external validation set).

Rad-score is equally applicable across four breast cancer subtypes and correlates with recurrence risk (p < 0.05).

The model's calibration and decision curve analysis confirmed its strong clinical utility.

Abstract

To develop a radiomics model based on ultrasound images for predicting risk of recurrence in breast cancer patients.

In this retrospective study, 420 patients with pathologically confirmed breast cancer were included, randomly divided into training (70%) and test (30%) sets, with an independent external validation cohort of 90 patients. According to St. Gallen recurrence risk criteria, patients were categorized into two groups, low-medium-risk and high-risk. Radiomics features were extracted from a radiomics analysis set using Pyradiomics. The informative radiomics features were screened using the minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms. Subsequently, radiomics models were constructed with eight machine learning algorithms. Three distinct nomogram models were created using the features selected through multivariate logistic regression, including the Clinic-Ultrasound (Clin-US), Clinic-Radiomics (Clin-Rad), and Clinic-Ultrasound-Radiomics (Clin-US-Rad) models. The receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA) curves were used to evaluate the model’s clinical applicability and predictive performance.

A total of 12 ultrasound radiomics features were screened, of which wavelet.LHL first order Mean features weighed more and tended to have a high risk of recurrence. The higher the risk of recurrence, the higher the radiomics score (Rad-score) in all three sets (training, test, and external validation set, all p < 0.05). Rad-score is equally applicable in four different subtypes of breast cancer. In the test set and external validation set, the Clin-US-Rad model achieved the highest AUC values (AUC = 0.817 and 0.851, respectively). The calibration and DCA curves also demonstrated the good clinical utility of the combined model.

The machine learning-based ultrasound radiomics model were useful for predicting the risk of recurrence in breast cancer. The nomograms show promising potential in assessing the recurrence risk of breast cancer. This non-invasive approach offers crucial guidance for the diagnosis and treatment of the condition.

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