التفاصيل البيبلوغرافية
| العنوان: |
Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma. |
| المؤلفون: |
Zhang, Wanqiu, Patterson, Nathan Heath, Verbeeck, Nico, Moore, Jessica L., Ly, Alice, Caprioli, Richard M., De Moor, Bart, Norris, Jeremy L., Claesen, Marc |
| المصدر: |
PLoS ONE; 5/31/2024, Vol. 19 Issue 5, p1-16, 16p |
| مصطلحات موضوعية: |
DEEP learning, ARTIFICIAL neural networks, MELANOMA diagnosis, MASS spectrometry, HEMATOXYLIN & eosin staining, FEATURE extraction |
| مستخلص: |
Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments. [ABSTRACT FROM AUTHOR] |
|
Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder