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<p>Breast cancer is a prevalent disease affecting women globally, and early detection and treatment can improve survival rates. Breast ultrasound is a common method for preoperative diagnosis, but its low resolution can lead to inaccurate judgments. Artificial intelligence (AI) can enhance diagnostic accuracy and aid in formulating effective treatment strategies. This research aimed to integrate medical ultrasound image analysis and AI to develop a multimodal nomogram for preoperative prediction of breast cancer. The study utilized a publicly available dataset that included B-mode ultrasound and color Doppler flow imaging (CDFI) scans from 611 patients, supplemented by relevant clinical variables. The study develops a multimodal deep learning radiomic nomogram model (DeepRadix) based on the ResNet50 backbone and channel attention mechanisms to classify breast malignancy. Radiomics features were extracted from lesion ROIs using PyRadiomics and subsequently reduced via minimum Redundancy–Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO)-based feature selection to identify the most informative radiomic predictors; in parallel, deep representations were learned using a ResNet50-based network and summarized as a deep learning score. The proposed multimodal model integrates predictive radiomic features, deep learning, and clinical information to overcome the limitations of existing methods and fully leverage their strengths. The model also introduced a nomogram-based visualization to enhance interpretability and improve clinical understanding of each patient’s characteristics. The research demonstrated strong discriminative ability in preoperative prediction of benign and malignant breast cancer, revealing potential associations among clinical features, ultrasound imaging features, and disease pathology. These findings could aid precision medicine and inform the design of diagnostic and therapeutic strategies for patients with breast cancer.</p>

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Multimodal Deep Learning Radiomics Nomogram for Preoperative Breast Cancer Prediction Using Ultrasound Imaging and Clinical Data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月19日

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  • Yucheng Guan,
  • Tariq Mahmood,
  • Yu Wang,
  • Muhammad I. Khan,
  • Muhammad Nazam

摘要

Breast cancer is a prevalent disease affecting women globally, and early detection and treatment can improve survival rates. Breast ultrasound is a common method for preoperative diagnosis, but its low resolution can lead to inaccurate judgments. Artificial intelligence (AI) can enhance diagnostic accuracy and aid in formulating effective treatment strategies. This research aimed to integrate medical ultrasound image analysis and AI to develop a multimodal nomogram for preoperative prediction of breast cancer. The study utilized a publicly available dataset that included B-mode ultrasound and color Doppler flow imaging (CDFI) scans from 611 patients, supplemented by relevant clinical variables. The study develops a multimodal deep learning radiomic nomogram model (DeepRadix) based on the ResNet50 backbone and channel attention mechanisms to classify breast malignancy. Radiomics features were extracted from lesion ROIs using PyRadiomics and subsequently reduced via minimum Redundancy–Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO)-based feature selection to identify the most informative radiomic predictors; in parallel, deep representations were learned using a ResNet50-based network and summarized as a deep learning score. The proposed multimodal model integrates predictive radiomic features, deep learning, and clinical information to overcome the limitations of existing methods and fully leverage their strengths. The model also introduced a nomogram-based visualization to enhance interpretability and improve clinical understanding of each patient’s characteristics. The research demonstrated strong discriminative ability in preoperative prediction of benign and malignant breast cancer, revealing potential associations among clinical features, ultrasound imaging features, and disease pathology. These findings could aid precision medicine and inform the design of diagnostic and therapeutic strategies for patients with breast cancer.