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| Asıl Yazarlar: | , , |
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| Materyal Türü: | Preprint |
| Baskı/Yayın Bilgisi: |
2025
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| Konular: | |
| Online Erişim: | https://arxiv.org/abs/2506.17536 |
| Etiketler: |
Etiketle
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| _version_ | 1866908415696044032 |
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| author | Peng, Hao Jiang, Steve Timmerman, Robert |
| author_facet | Peng, Hao Jiang, Steve Timmerman, Robert |
| contents | Personalized precision radiation therapy requires more than simple classification, it demands the identification of prognostic, spatially informative features and the ability to adapt treatment based on individual response. This study compares three approaches for predicting treatment response: standard radiomics, gradient based features, and convolutional neural networks enhanced with Class Activation Mapping. We analyzed 69 brain metastases from 39 patients treated with Gamma Knife radiosurgery. An integrated autoencoder classifier model was used to predict whether tumor volume would shrink by more than 20 percent at a three months follow up, framed as a binary classification task. The results highlight their strength in hierarchical feature extraction and the classifiers discriminative capacity. Among the models, pixel wise CAM provides the most detailed spatial insight, identifying lesion specific regions rather than relying on fixed patterns, demonstrating strong generalization. In non responding lesions, the activated regions may indicate areas of radio resistance. Pixel wise CAM outperformed both radiomics and gradient based methods in classification accuracy. Moreover, its fine grained spatial features allow for alignment with cellular level data, supporting biological validation and deeper understanding of heterogeneous treatment responses. Although further validation is necessary, these findings underscore the promise in guiding personalized and adaptive radiotherapy strategies for both photon and particle therapies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Strategies for Personalized Radiation Therapy Part I Unlocking Response-Related Tumor Subregions with Class Activation Mapping Peng, Hao Jiang, Steve Timmerman, Robert Medical Physics Artificial Intelligence Personalized precision radiation therapy requires more than simple classification, it demands the identification of prognostic, spatially informative features and the ability to adapt treatment based on individual response. This study compares three approaches for predicting treatment response: standard radiomics, gradient based features, and convolutional neural networks enhanced with Class Activation Mapping. We analyzed 69 brain metastases from 39 patients treated with Gamma Knife radiosurgery. An integrated autoencoder classifier model was used to predict whether tumor volume would shrink by more than 20 percent at a three months follow up, framed as a binary classification task. The results highlight their strength in hierarchical feature extraction and the classifiers discriminative capacity. Among the models, pixel wise CAM provides the most detailed spatial insight, identifying lesion specific regions rather than relying on fixed patterns, demonstrating strong generalization. In non responding lesions, the activated regions may indicate areas of radio resistance. Pixel wise CAM outperformed both radiomics and gradient based methods in classification accuracy. Moreover, its fine grained spatial features allow for alignment with cellular level data, supporting biological validation and deeper understanding of heterogeneous treatment responses. Although further validation is necessary, these findings underscore the promise in guiding personalized and adaptive radiotherapy strategies for both photon and particle therapies. |
| title | Exploring Strategies for Personalized Radiation Therapy Part I Unlocking Response-Related Tumor Subregions with Class Activation Mapping |
| topic | Medical Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2506.17536 |