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<p>Although significant advances have been made in the early detection of many cancers, challenges remain in the early diagnosis of rare cancers, including Wilms tumor, Clear Cell Sarcoma of the Kidney, Neuroblastoma, Osteosarcoma, and Acute Myeloid Leukemia, perhaps due to their relative obscurity and scarce data compared to common cancers. Application of artificial intelligence or deep learning has shown promising results in disease diagnosis including in their ability to diagnose cancers and detect their tissue of origin. However, their ability to detect rare cancers is yet to be comprehensively assessed. This motivated us to develop, RareNet, which leverages transfer learning of an established deep learning model, namely, CancerNet, to classify rare cancers. The transfer learning framework of RareNet utilized DNA methylation data of various biopsied rare cancers to learn epigenetic signatures of rare cancers. RareNet achieved an overall accuracy (F1 score) of ~ 96%, outperforming other machine learning models including Random Forest, K Nearest Neighbors, Decision Tree Classifier, and Support Vector Classifier.</p>

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RareNet: a deep learning model for rare cancer diagnosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

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  • Danyang Shao,
  • Sohan Addagudi,
  • Joseph Cowles,
  • Arnav Jain,
  • Lauren D’Souza,
  • Steven Gore,
  • Rajeev K. Azad

摘要

Although significant advances have been made in the early detection of many cancers, challenges remain in the early diagnosis of rare cancers, including Wilms tumor, Clear Cell Sarcoma of the Kidney, Neuroblastoma, Osteosarcoma, and Acute Myeloid Leukemia, perhaps due to their relative obscurity and scarce data compared to common cancers. Application of artificial intelligence or deep learning has shown promising results in disease diagnosis including in their ability to diagnose cancers and detect their tissue of origin. However, their ability to detect rare cancers is yet to be comprehensively assessed. This motivated us to develop, RareNet, which leverages transfer learning of an established deep learning model, namely, CancerNet, to classify rare cancers. The transfer learning framework of RareNet utilized DNA methylation data of various biopsied rare cancers to learn epigenetic signatures of rare cancers. RareNet achieved an overall accuracy (F1 score) of ~ 96%, outperforming other machine learning models including Random Forest, K Nearest Neighbors, Decision Tree Classifier, and Support Vector Classifier.