AI-driven gamma-ray precision: Effect of convolutional neural networks on chemotherapy side effects.

e15000 Background: Chemotherapy remains a primary cancer treatment but causes significant systemic toxicity that impacts patient quality of life. Precision therapies enabled by artificial intelligence (AI), particularly convolutional neural networks (CNNs), offer promising solutions by enhancing tumor targeting and reducing collateral damage. This study integrates a ResNet-50-based CNN with a robotic gamma-ray delivery system to achieve targeted radiation therapy. Methods: Data Acquisition: A dataset of 12,000 annotated medical imaging samples (MRI, CT, PET scans) was curated. Images underwent preprocessing (normalization, segmentation, augmentation) to ensure robustness. Model Development: A ResNet-50 CNN was trained (80/20 split) using stochastic gradient descent (learning rate = 0.001). Metrics assessed included sensitivity, specificity, and Dice similarity coefficient (DSC). Gamma-Ray Delivery: The CNN guided a robotic gamma-ray delivery system in real time, reducing healthy tissue exposure. Validation: Tissue phantoms simulated clinical scenarios. Statistical significance was assessed using paired t -tests. Results: Model Performance: Sensitivity: 97.2% Specificity: 94.8% DSC: 89% Radiation Outcomes: Healthy tissue damage reduced by 92%. Radiation dosage optimized (35% reduction). Patient Simulations: Side effects reduced by 78%. Quality-of-life scores improved by 39 points (100-point scale). Conclusions: This AI-driven framework significantly enhances cancer treatment precision, reducing systemic toxicity and improving patient outcomes. Integration of CNNs with robotic systems demonstrates high efficacy, offering a scalable solution for personalized oncology. Future work includes clinical trials and exploring additional radiation modalities. Summary of treatment outcomes demonstrating improved precision and reduced adverse effects. Measure Improvement (%) Healthy Tissue Damage Reduction 92 Radiation Dosage Reduction 35 Side Effect Reduction 78 Quality-of-Life Score Improvement 39

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Publication Details

Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.e15000
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

AI-driven gamma-ray precision: Effect of convolutional neural networks on chemotherapy side effects.

Salma Siddiqui
Journal of Clinical Oncology
Radiomics and Machine Learning in Medical Imaging
article

AI-driven gamma-ray precision: Effect of convolutional neural networks on chemotherapy side effects.

Salma Siddiqui
article en

Abstract

e15000 Background: Chemotherapy remains a primary cancer treatment but causes significant systemic toxicity that impacts patient quality of life. Precision therapies enabled by artificial intelligence (AI), particularly convolutional neural networks (CNNs), offer promising solutions by enhancing tumor targeting and reducing collateral damage. This study integrates a ResNet-50-based CNN with a robotic gamma-ray delivery system to achieve targeted radiation therapy. Methods: Data Acquisition: A dataset of 12,000 annotated medical imaging samples (MRI, CT, PET scans) was curated. Images underwent preprocessing (normalization, segmentation, augmentation) to ensure robustness. Model Development: A ResNet-50 CNN was trained (80/20 split) using stochastic gradient descent (learning rate = 0.001). Metrics assessed included sensitivity, specificity, and Dice similarity coefficient (DSC). Gamma-Ray Delivery: The CNN guided a robotic gamma-ray delivery system in real time, reducing healthy tissue exposure. Validation: Tissue phantoms simulated clinical scenarios. Statistical significance was assessed using paired t -tests. Results: Model Performance: Sensitivity: 97.2% Specificity: 94.8% DSC: 89% Radiation Outcomes: Healthy tissue damage reduced by 92%. Radiation dosage optimized (35% reduction). Patient Simulations: Side effects reduced by 78%. Quality-of-life scores improved by 39 points (100-point scale). Conclusions: This AI-driven framework significantly enhances cancer treatment precision, reducing systemic toxicity and improving patient outcomes. Integration of CNNs with robotic systems demonstrates high efficacy, offering a scalable solution for personalized oncology. Future work includes clinical trials and exploring additional radiation modalities. Summary of treatment outcomes demonstrating improved precision and reduced adverse effects. Measure Improvement (%) Healthy Tissue Damage Reduction 92 Radiation Dosage Reduction 35 Side Effect Reduction 78 Quality-of-Life Score Improvement 39

Journal of Clinical OncologyVol. 43(16_suppl)
Openalex Percentile: Top 82%
Radiomics and Machine Learning in Medical Imaging
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AI-driven gamma-ray precision: Effect of convolutional neural networks on chemotherapy side effects. — Salma Siddiqui · Journal of Clinical Oncology (2025) | TGRS Research Map | TGRS