The Emergence of the Potential Therapeutic Targets: Ultrasound-Based Radiomics in the Prediction of Human Epidermal Growth Factor Receptor 2-Low Breast Cancer

Personalized prediction of breast cancer candidates for Anti-HER2 therapy using 18F-FDG PET/CT parameters and machine learning: a dual-center study

Background

Accurately evaluating human epidermal growth factor receptor (HER2) expression status in breast cancer enables clinicians to develop individualized treatment plans and improve patient prognosis. The purpose of this study was to assess the performance of a machine learning (ML) model that was developed using 18F-FDG PET/CT parameters and clinicopathological features in distinguishing different levels of HER2 expression in breast cancer.

Methods

This retrospective study enrolled breast cancer patients who underwent 18F-FDG PET/CT scans prior to treatment at Lianyungang First People's Hospital (centre 1, n=157) and the Third Affiliated Hospital of Soochow University (centre 2, n=84). Two classification tasks were analysed: distinguishing HER2-zero expression from HER2-low/positive expression (Task 1) and distinguishing HER2-low expression from HER2-positive expression (Task 2). For each task, patients from Centre 1 were randomly divided into training and internal test sets at a 7:3 ratio, whereas patients from Centre 2 served as an external test set. The prediction models included logistic regression (LR), support vector machine (SVM), extreme gradient boosting (XGBoost) and multilayer perceptron (MLP), and SHAP analysis provided model interpretability. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results

XGBoost models exhibited the best predictive performance in both tasks. For Task 1, recursive feature elimination (RFE) was used to select 8 features, excluding pathological features, and the XGBoost model achieved AUCs of 0.888, 0.844 and 0.759 for the training, internal and external testing sets, respectively. The top three features according to the SHAP values were the tumour minimum diameter, mean standardized uptake value (SUVmean) and CTmean. For Task 2, 9 features were selected, including progesterone receptor (PR) status as a pathological feature. The XGBoost model achieved AUCs of 0.920, 0.814 and 0.693 for the training, internal and external testing sets, respectively. The top three features according to the SHAP values were the PR status, maximum tumour diameter and metabolic tumour volume (MTV).

Conclusions

ML models that incorporate 18F-FDG PET/CT parameters and clinicopathological features can aid in the prediction of different HER2 expression statuses in breast cancer.

Improving Prediction Accuracy of Residual Axillary Lymph Node Metastases in Node-Positive Triple-Negative Breast Cancer: A Radiomics Analysis of Ultrasound-Guided Clip Locations Using the SHAP Method

RATIONALE AND OBJECTIVES

To construct a radiomics nomogram derived from multiparametric ultrasound (US) imaging using the SHapley Additive exPlanations (SHAP) method for the accurate identification of residual axillary lymph node metastases post-neoadjuvant chemotherapy (NAC) among patients with triple-negative breast cancer (TNBC).

METHODS

A total of 405 consecutive patients with pathologically confirmed TNBC between 2016 and 2023 were recruited in the study and were divided into training (n = 284) and validation cohorts (n = 121). Radiomics features capturing detailed tumor characteristics were extracted from pre-NAC gray-scale US images at the locations of US-guided clip placement. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy algorithm were employed to identify key features and formulate the radiomics signature (RS). A nomogram based on US radiomics was then constructed using multivariable logistic regression analysis. The predictive efficacy of this model was evaluated through receiver operating characteristic curve analysis, calibration assessment, and decision curve analysis. SHAP summary plots were used to visualize the distribution of SHAP values across all features.

RESULTS

The nomogram integrates clinical and US characteristics with RS, yielded optimal AUC of 0.922 (95% CI, 0.890-0.954) in the training cohort, 0.904 (95% CI, 0.853-0.955) in the validation cohort. The calibration and decision curves confirmed favorable calibration and clinical value of the nomogram. SHAP provided further insight into the contributions of each feature to the model's outcomes.

CONCLUSION

The combined multiparametric US based radiomics nomogram plays a potential role in predicting residual axillary lymph node metastases after NAC in TNBCs.

Intratumoral and peritumoral ultrasound-based radiomics for preoperative prediction of HER2-low breast cancer: a multicenter retrospective study

OBJECTIVES

Recent advances in human epidermal growth factor receptor 2 (HER2)-targeted therapies have opened up new therapeutic options for HER2-low cancers. This study aimed to establish an ultrasound-based radiomics model to identify three different HER2 states noninvasively.

METHODS

Between May 2018 and December 2023, a total of 1257 invasive breast cancer patients were enrolled from three hospitals. The HER2 status was divided into three classes: positive, low, and zero. Four peritumoral regions of interest (ROI) were auto-generated by dilating the manually segmented intratumoral ROI to thicknesses of 5 mm, 10 mm, 15 mm, and 20 mm. After image preprocessing, 4720 radiomics features were extracted from each image of every patient. The least absolute shrinkage and selection operator and LightBoost algorithm were utilized to construct single- and multi-region radiomics signatures (RS). A clinical-radiomics combined model was developed by integrating discriminative clinical-sonographic factors with the optimal RS. A data stitching strategy was used to build patient-level models. The Shapley additive explanations (SHAP) approach was used to explain the contribution of internal prediction.

RESULTS

The optimal RS was constructed by integrating 12 tumor features and 9 peritumoral-15mm features. Age, tumor size, and seven qualitative ultrasound features were retained to construct the clinical-radiomics combined model with the optimal RS. In the training, validation, and test cohorts, the patient-level combined model showed the best discrimination ability with the macro-AUCs of 0.988 (95% CI: 0.983-0.992), 0.915 (95% CI: 0.851-0.965), and 0.862 (95% CI: 0.820-0.899), respectively.

CONCLUSION

This study built a robust and interpretable clinical-radiomics model to evaluate three classes of HER2 status based on ultrasound images.

CRITICAL RELEVANCE STATEMENT

Ultrasound-based radiomics method can noninvasively identify three different states of HER2, which may guide treatment decisions and the implementation of personalized HER2-targeted treatment for invasive breast cancer patients.

KEY POINTS

Determination of HER2 status can affect treatment options for breast cancer. The ultrasound-based clinical-radiomics model can discriminate the three different HER2 statuses. Our developed model can assist in providing personalized recommendations for novel HER2-targeted therapies.

Multiparameter and Ultrasound Radiomics Nomogram to Predict the Aggressiveness of Papillary Thyroid Carcinomas: A Multicenter, Retrospective Study

RATIONALE AND OBJECTIVES

To construct a multiparameter radiomics nomogram based on ultrasound (US) to predict the aggressiveness of thyroid papillary carcinoma (PTC).

MATERIALS AND METHODS

In total, 471 consecutive patients from three institutions were included in this study. Among them, patients from institution 1 were used for training (n = 294) and internal validation (n = 92), while 85 patients from institution 2 and institution 3 were used for external validation. Radiomics features were extracted from the conventional US. The least absolute shrinkage was employed to select the most relevant features for the aggressiveness of PTC, along with the maximum relevance minimum redundancy algorithm and selection operator. These features were then used to construct the radiomics signature (RS). Subsequently, relevant multiparameter ultrasound (MPUS) features from shear-wave elastic (SWE) and strain elastography (SE) will be extracted using multivariable logistic regression. The final radionics nomogram was conducted using the RS, clinical information, and conventional US and MPUS features. The receiver operating characteristic (ROC), calibration, and decision curves were used to evaluate the performance of the nomogram.

RESULTS

Multivariable logistic regression analysis indicated that age, nodule size, capsule abutment, SWV tumor, and RS were independent predictors of the aggressiveness of PTC. The radiomics nomogram, utilizing these characteristics, displayed impressive performance with an AUC of 0.920 [95% CI, 0.889-0.950], 0.901 [95% CI, 0.839-0.963], and 0.896 [95% CI, 0.823-0.969] in the training, internal, and external validation cohort. It outperformed the clinical US, MPUS, and RS models (p < 0.05). The decision curve analysis indicated that the nomogram offered valuable clinical utility.

CONCLUSION

The nomogram incorporated MPUS and radiomics have good diagnostic performance in predicting the aggressiveness of PTC which may help in the selection of the surgical modality.