Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images
Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images
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多模态化学和全玻片成像数据的深度学习用于直接从组织图像预测前列腺癌
DOI：
10.1101/2022.05.11.491570
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
O. Ovchinnikova
中科院分区：
文献类型：
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作者：
Md Inzamam Ul Haque;Debangshu Mukherjee;S. Stopka;N. Agar;Jacob Hinkle;O. Ovchinnikova
前列腺癌是全球最常见的癌症之一，也是美国男性人群中第二常见的癌症。在这里，我们开发了一项研究，该研究基于将H& E染色的活检数据与相应组织的MALDI质谱成像相关联，以确定癌性区域及其独特的化学特征，以及预测区域与原始病理注释的变化。我们将通过深度学习从整个载玻片H&E染色数据的高分辨率光学显微照片中获得的特征与MSI数据进行空间配准，以将化学签名与数据的组织解剖结构相关联，然后使用学习的相关性从观察到的H&E图像中预测前列腺癌，使用训练的共同配准MSI数据。我们发现，该系统比从单一成像模式预测更鲁棒，并且可以预测癌症区域，准确率为80%。还发现两种化学生物标志物可以预测真实的癌症区域。这将通过直接从H& E染色的活检图像更准确地预测前列腺癌来改进患者治疗轨迹的生成。
Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the H&E-stained biopsy data with MALDI mass-spectrometric imaging of the corresponding tissue to determine the cancerous regions and their unique chemical signatures, and variation of the predicted regions with original pathological annotations. We spatially register features obtained through deep learning from high-resolution optical micrographs of whole slide H&E stained data with MSI data to correlate the chemical signature with the tissue anatomy of the data, and then use the learned correlation to predict prostate cancer from observed H&E images using trained co-registered MSI data. We found that this system is more robust than predicting from a single imaging modality and can predict cancerous regions with ∼80% accuracy. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This will improve on generating patient treatment trajectories by more accurately predicting prostate cancer directly from H&E-stained biopsy images.