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Related Concept Videos

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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Related Experiment Video

Updated: Jul 17, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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A multi-model machine learning framework for breast cancer risk stratification using clinical and imaging data.

Lu Miao1, Zidong Li2, Jinnan Gao1

  • 1Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.

Journal of X-Ray Science and Technology
|February 20, 2025
PubMed
Summary

This study developed a machine learning framework integrating clinical and deep learning imaging features for breast cancer malignancy assessment. The stacking-based ensemble model achieved a peak AUC of 0.94, showing high accuracy in risk stratification.

Keywords:
breast cancerclinical datadeep learningimaging datamachine learningstacking ensemble

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Accurate breast cancer malignancy assessment is crucial for effective patient management.
  • Integrating diverse data sources can improve diagnostic performance.
  • Deep learning offers advanced methods for extracting imaging features.

Purpose of the Study:

  • To develop and evaluate a machine learning framework for breast cancer malignancy assessment.
  • To integrate clinical features with deep learning-derived imaging features.
  • To compare the performance of imaging-only, hybrid, and ensemble models.

Main Methods:

  • Utilized a dataset of 1668 patients with mammographic images and clinical data.
  • Employed four Convolutional Neural Network (CNN) architectures (EfficientNet, ResNet, DenseNet, InceptionNet) for feature extraction.
  • Developed imaging-only, hybrid (imaging + clinical), and stacking-based ensemble models.
  • Applied twelve feature selection techniques and evaluated performance using accuracy and AUC with 5-fold cross-validation.

Main Results:

  • Imaging-only models showed strong performance (EfficientNet AUC=0.76).
  • The hybrid model achieved 83% accuracy and 0.87 AUC, demonstrating the value of data integration.
  • The stacking-based ensemble model reached a peak AUC of 0.94, outperforming other configurations.

Conclusions:

  • Integrating clinical and deep imaging features significantly enhances breast cancer risk stratification.
  • The developed stacking-based ensemble model shows high potential as a reliable tool for malignancy risk assessment.
  • This framework offers a promising approach for improving diagnostic accuracy in breast cancer.