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Related Concept Videos

Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Related Experiment Video

Updated: Jan 16, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Dual-Parallel Artificial Intelligence Framework for Breast Cancer Grading via High-Intensity Ultrasound and

Pritee Parwekar1, Krishna Kant Agrawal2, Jabir Ali3

  • 1GITAM School of Technology, GITAM Univeristy, Hyderabad, India.

Cancer Biotherapy & Radiopharmaceuticals
|September 30, 2025
PubMed
Summary

This study introduces a novel AI framework using ultrasound images and biomarkers for accurate, noninvasive breast cancer grading and therapy monitoring, achieving 97.8% accuracy and showing significant treatment response.

Keywords:
AI modelingand radiomicsbiomarker screeningbreast cancer analysisclinical validationdeep learningultrasound therapy

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate breast cancer grading and therapy monitoring are crucial but challenging.
  • Traditional methods like histopathology and imaging-only techniques have limitations in capturing tumor complexity.
  • Noninvasive approaches are needed for real-time monitoring and personalized treatment.

Purpose of the Study:

  • To develop a novel, noninvasive framework for breast cancer analysis and therapy monitoring.
  • To integrate high-intensity ultrasound imaging with patient-specific biomarkers for enhanced diagnostic depth.
  • To enable reliable cancer grading and quantitative assessment of treatment response.

Main Methods:

  • A dual-parallel artificial intelligence strategy combining two Convolutional Neural Network (CNN) streams.
  • Stream 1: Dual-stream CNN processing high-intensity ultrasound images for spatial and morphological features.
  • Stream 2: Biomarker-aware CNN utilizing breast cancer biomarkers (CA 15-3, CEA, HER2) for molecular indicators.

Main Results:

  • Achieved an overall breast cancer grading accuracy of 97.8% and an AUC of 0.981 for malignancy classification.
  • Demonstrated quantitative post-therapy analysis, showing an average tumor response improvement of 41.3%.
  • Validated effectiveness on pre- and post-chemotherapy patient cohorts.

Conclusions:

  • The dual-parallel AI strategy provides a promising noninvasive alternative to traditional methods.
  • This approach supports real-time cancer monitoring and personalized treatment evaluation.
  • Integration of imaging and biomolecular data enhances diagnostic depth for intelligent breast cancer management.