Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy

Abstract

Objective: To construct a combined radiomics model based on pre-treatment ultrasound for predicting of advanced breast cancers sensitive to neoadjuvant chemotherapy (NAC). Methods: A total of 288 eligible breast cancer patients who underwent NAC before surgery were enrolled in the retrospective study cohort. Radiomics features reflecting the phenotype of the pre-NAC tumors were extracted. With features selected using the least absolute shrinkage and selection operator (LASSO) regression, radiomics signature (Rad-score) was established based on the pre-NAC ultrasound. Then, radiomics nomogram of ultrasound (RU) was established on the basis of the best radiomic signature incorporating independent clinical features. The performance of RU was evaluated in terms of calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Results: Nine features were selected to construct the radiomics signature in the training cohort. Combined with independent clinical characteristics, the performance of RU for identifying Grade 4–5 patients was significantly superior than the clinical model and Rad-score alone (p < 0.05, as per the Delong test), which achieved an AUC of 0.863 (95% CI, 0.814–0.963) in the training group and 0.854 (95% CI, 0.776–0.931) in the validation group. DCA showed that this model satisfactory clinical utility, suggesting its robustness as a response predictor. Conclusion: This study demonstrated that RU has a potential role in predicting drug-sensitive breast cancers. Advances in knowledge: Aiming at early detection of Grade 4–5 breast cancer patients, the radiomics nomogram based on ultrasound has been approved as a promising indicator with high clinical utility. It is the first application of ultrasound-based radiomics nomogram to distinguish drug-sensitive breast cancers.

Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

“…Radiomics can extract information that doctors cannot identify from images of primary breast cancer. It can then associate that information with lymph node status, molecular subtypes of breast cancer, treatment response, tumor recurrence, survival time, and other patient characteristics, thus providing evidence‐based support for making decisions in the clinic [ 10 , 11 , 12 , 13 , 14 , 15 ]. The workflow of ultrasound radiomics generally includes image acquisition, image segmentation, model construction and validation, and database establishment [ 16 ].…”

Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors

“…Radiomics can extract a large number of high-dimensional data from traditional medical images, and has been applied to predict therapeutic response. However, most of previous radiomics studies based on MRI [5,6], or CT [7], or PET/CT images [8], only few studies utilized US radiomics for prediction [9]. Moreover, prior researchers mainly focused on parameters within intratumoral structure, while the considerations about peritumoral region, which has been described as a "reactive zone" surrounding the tumor were limited.…”

“…Compared to previous radiomics studies, most of which used the least absolute shrinkage and selection operator (LASSO) regression to establish predictive nomogram [5][6][7][8][9], machine learning (ML) algorithms such as linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), logistic regression (LR) and adaptive boosting (Adaboost) can handle abundant quantitative radiomics features powerfully and effectively [12]. Nevertheless, the application of ML-based PURS and IURS to predict NAC effect has not been explored.…”