Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma

Abstract

ObjectiveThe aim of this study was to develop and validate an ultrasound-based radiomics nomogram model by integrating the clinical risk factors and radiomics score (Rad-Score) to predict the Ki-67 status in patients with breast carcinoma.MethodsUltrasound images of 284 patients (196 high Ki-67 expression and 88 low Ki-67 expression) were retrospectively analyzed, of which 198 patients belonged to the training set and 86 patients to the test set. The region of interest of tumor was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analysis by using the independent sample t test and least absolute shrinkage and selection operator (LASSO) algorithm. The support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction model based on the selected features. The classifier with the highest AUC value was selected to convert the output of the results into the Rad-Score and was regarded as Rad-Score model. In addition, the logistic regression method was used to integrate Rad-Score and clinical risk factors to generate the nomogram model. The leave group out cross-validation (LGOCV) method was performed 200 times to verify the reliability and stability of the nomogram model.ResultsSix classifier models were established based on the 15 non-zero coefficient features. Among them, the LR classifier achieved the best performance in the test set, with the area under the receiver operating characteristic curve (AUC) value of 0.786, and was obtained as the Rad-Score model, while the XGB performed the worst (AUC, 0.615). In multivariate analysis, independent risk factor for high Ki-67 status was age (odds ratio [OR] = 0.97, p = 0.04). The nomogram model based on the age and Rad-Score had a slightly higher AUC than that of Rad-Score model (AUC, 0.808 vs. 0.798) in the test set, but no statistical difference (p = 0.144, DeLong test). The LGOCV yielded a median AUC of 0.793 in the test set.ConclusionsThis study proposed a convenient, clinically useful ultrasound radiomics nomogram model that can be used for the preoperative individualized prediction of the Ki-67 status in patients with BC.

Development of an photoacoustic-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer

“…In this study, we employed a more versatile and cost-effective dual-modality photoacoustic imaging/ultrasound imaging tool to integrate features within the tumor extracted by radiomics and clinical risk factors, resulting in a nomogram model. Compared to the AUC of 0.808 reported in the training set by Liu et al [30], the AUC values for the training and validation sets in our study were 0.904 and 0.890, respectively, indicating superior diagnostic performance. Furthermore, we conducted feature extraction on photoacoustic images and ultrasound images acquired with a dual-modality photoacoustic/ultrasound imaging system to create a nomogram model.…”

Combining ultrasound radiomics, complete blood count, and serum biochemical biomarkers for diagnosing intestinal disorders in cats using machine learning

“…The use of DT features partially translated to an improved random forest model (RF only), as found in other studies 24,25 . The overall accuracies (standard deviation) of SVM, KNN, and RF models were 0.746 (0.158), 0.738 (0.167), and 0.737 (0.173), suggesting that for our data, these models performed consistently well and with smaller variations in outputs.…”

A Model Combining Conventional Ultrasound Characteristics, Strain Elastography and Clinicopathological Features to Predict Ki-67 Expression in Small Breast Cancer

“…In contrast with models 1 and 2, which yielded moderate diagnostic performance (AUC = 0.694 and 0.774, respectively), model 3, which further incorporated clinicopathological features, demonstrated a high predictive performance (AUC = 0.853). Other studies 38,39 have consistently found that this combination of US and clinicopathological features is better than either alone.…”