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Improving Augmented Human Intelligence to Distinguish Burkitt Lymphoma From Diffuse Large B-Cell Lymphoma Cases
Jeffrey S Mohlman1,2, Samuel D Leventhal3, Taft Hansen1,2
1Department of Pathology, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City.
Insights
A deep convolutional neural network (CNN) shows promise in assisting hematopathologists to differentiate Burkitt lymphoma (BL) from diffuse large B-cell lymphoma (DLBCL) using histologic images. The best performing CNN achieved 94% accuracy, highlighting its potential as an AI tool.
Area of Science:
- Computational pathology
- Artificial intelligence in medicine
- Hematopathology
Background:
- Distinguishing Burkitt lymphoma (BL) from diffuse large B-cell lymphoma (DLBCL) is critical for appropriate treatment.
- Hematopathologists face challenges in accurately classifying these similar-appearing B-cell lymphomas based on histologic images.
Purpose of the Study:
- To evaluate the effectiveness of a deep convolutional neural network (CNN) in assisting hematopathologists.
- To improve the diagnostic accuracy of differentiating BL from DLBCL using machine learning on histologic images.
Main Methods:
- A deep, densely connected CNN was trained and applied to a dataset of 10,818 histologic images from BL and DLBCL cases.
- Various network parameters, including image augmentation and network depth, were optimized to achieve the best performance.
Main Results:
- The best performing CNN achieved 94% accuracy in correctly classifying BL and DLBCL cases (17 out of 18).
- The optimal network utilized all training images, specific image augmentation techniques, and a depth of 22 layers.
- Receiver operating characteristic curve analysis showed an area under the curve of 0.92 for both lymphoma types.
Conclusions:
- Deep convolutional neural networks show significant potential as augmented intelligence tools for pathologists.
- CNNs can effectively assist in differentiating challenging cases of Burkitt lymphoma and diffuse large B-cell lymphoma.
Objectives:
To assess and improve the assistive role of a deep, densely connected convolutional neural network (CNN) to hematopathologists in differentiating histologic images of Burkitt lymphoma (BL) from diffuse large B-cell lymphoma (DLBCL).
Methods:
A total of 10,818 images from BL (n = 34) and DLBCL (n = 36) cases were used to either train or apply different CNNs. Networks differed by number of training images and pixels of images, absence of color, pixel and staining augmentation, and depth of the network, among other parameters.
Results:
Cases classified correctly were 17 of 18 (94%), nine with 100% of images correct by the best performing network showing a receiver operating characteristic curve analysis area under the curve 0.92 for both DLBCL and BL. The best performing CNN used all available training images, two random subcrops per image of 448 × 448 pixels, random H&E staining image augmentation, random horizontal flipping of images, random alteration of contrast, reduction on validation error plateau of 15 epochs, block size of six, batch size of 32, and depth of 22. Other networks and decreasing training images had poorer performance.
Conclusions:
CNNs are promising augmented human intelligence tools for differentiating a subset of BL and DLBCL cases.
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