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<p>Accurate phenotyping of cells in the tumor microenvironment is essential for understanding cancer biology but typically requires precise cell segmentation, limiting scalability. Here, we introduce Contrastive Learning Enabled Accurate Registration of Immune and Tumor cells (CLEAR-IT), a self-supervised framework that learns cell-level features from multiplexed images using only cell locations. CLEAR-IT encoders achieve strong linear evaluation performance, improve substantially with hyperparameter optimization, and maintain high accuracy across imaging modalities and with up to 90% fewer labels. When substituted for handcrafted features in a state-of-the-art classifier, CLEAR-IT features yield higher performance, and their combination enables comparable accuracy with less than half of the labeled data otherwise required. The learned representations also support prognostic modeling: using annotations from a single patient, CLEAR-IT-based phenotyping identifies survival-associated tissue features that generalize across two cohorts and modalities. CLEAR-IT provides a segmentation-light, label-efficient approach for scalable cell phenotyping and enhances existing workflows in digital pathology and tumor microenvironment analysis.</p>

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CLEAR-IT, a framework for contrastive learning to capture the immune composition of tumor microenvironments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Daniel Spengler,
  • Serafim Korovin,
  • Kirti Prakash,
  • Peter Bankhead,
  • Reno Debets,
  • Hayri E. Balcioglu,
  • Carlas Smith

摘要

Accurate phenotyping of cells in the tumor microenvironment is essential for understanding cancer biology but typically requires precise cell segmentation, limiting scalability. Here, we introduce Contrastive Learning Enabled Accurate Registration of Immune and Tumor cells (CLEAR-IT), a self-supervised framework that learns cell-level features from multiplexed images using only cell locations. CLEAR-IT encoders achieve strong linear evaluation performance, improve substantially with hyperparameter optimization, and maintain high accuracy across imaging modalities and with up to 90% fewer labels. When substituted for handcrafted features in a state-of-the-art classifier, CLEAR-IT features yield higher performance, and their combination enables comparable accuracy with less than half of the labeled data otherwise required. The learned representations also support prognostic modeling: using annotations from a single patient, CLEAR-IT-based phenotyping identifies survival-associated tissue features that generalize across two cohorts and modalities. CLEAR-IT provides a segmentation-light, label-efficient approach for scalable cell phenotyping and enhances existing workflows in digital pathology and tumor microenvironment analysis.