The detection of prostate cancer within H&E-stained tissue slides helps identify the presence, location, and extent of the disease. Machine learning approaches have been developed to accomplish this task for biopsies and radical prostatectomies. Deep learning approaches, primarily using convolutional neural networks (CNN), have demonstrated substantial ability to identify cancer in each of these sample types. Some models trained on biopsies have even been approved for clinical application. Biopsies are small tissue samples acquired through a syringe to establish cancer diagnosis while radical prostatectomies are large planar sections of prostate tissue acquired post-surgery. This leads to differing morphology and heterogeneity between sample types, making it unclear whether algorithms trained on biopsies are robust enough to perform well on radical prostatectomies and vice versa. Our goal was to investigate whether morphological differences between sample types affected the performance of radical prostatectomy trained CNN models when applied to biopsies and vice versa. Radical prostatectomies (N=100) and biopsies (N=50) were acquired from The University of Pennsylvania to train (80%) and validate (20%) a CNN using the Densenet architecture for biopsies (MB), radical prostatectomies (MR), and a combined dataset (MB+R). Model performance was compared using sensitivity, specificity, and F1 score. To isolate the effect of morphological differences on performance, we acquired all data from the same institution, ensured no batch effects were present via UMAP, applied data augmentation during training, and applied stain normalization. Additionally, the radical prostatecomy training set was optimized for transfer to biopsies by reducing heterogeneity (MR*). This reduction was based on distance from the centroid of a texture feature distribution. Models performed well when applied to their own sample type (F1 > 0.88) but performed poorly when applied across sample types (F1 < 0.65). Reducing heterogeneity to optimize the radical prostatectomy training set resulted in MR* (F1=0.72) noticeably outperforming MR (F1=0.53) on biopsies. This indicates that differences in morphology and heterogeneity drive performance differences between cancer detection models trained on different sample types. Our results suggest that these morphological differences can be overcome with sample type specific models or training set optimization methods.
Detecting the background is often one of the first tasks when analyzing whole slide images. However, these methods greatly vary and are generally not reported in sufficient detail for subsequent studies to be able to reproduce. Therefore, we sought to determine the effect of varying background detection methods on downstream performance metrics. Specifically, we applied attention-based multiple instance learning, CLAM, and Attention2majority to classify whole slide images from Camelyon16 using four different background detection methods. Our results show that performance metrics (i.e., accuracy and area under the curve - AUC) differ when different background detection methods are utilized. Furthermore, all classification methods perform worse when using hand-drawn tissue annotations for background detection. Finally, we show that depending on the background detection method, the best performing method changes. We conclude that background detection methods must be fully reported so that results can be accurately reproduced and techniques can be fairly compared.
Accurate quantification of renal fibrosis has profound importance in the assessment of chronic kidney disease (CKD). Visual analysis of a biopsy stained with trichrome under the microscope by a pathologist is the gold standard for evaluation of fibrosis. Trichrome helps to highlight collagen and ultimately interstitial fibrosis. However, trichrome stains are not always reproducible, can underestimate collagen content and are not sensitive to subtle fibrotic patterns. Using the Dual-mode emission and transmission (DUET) microscopy approach, it is possible to capture both brightfield and fluorescence images from the same area of a tissue stained with hematoxylin and eosin (H&E) enabling reproducible extraction of collagen with high sensitivity and specificity. Manual extraction of spectrally overlapping collagen signals from tubular epithelial cells and red blood cells is still an intensive task. We employed a UNet++ architecture for pixel-level segmentation and quantification of collagen using 760 whole slide image (WSI) patches from six cases of varying stages of fibrosis. Our trained model (Deep-DUET) used the supervised extracted collagen mask as ground truth and was able to predict the extent of collagen signal with a MSE of 0.05 in a holdout testing set while achieving an average AUC of 0.94 for predicting regions of collagen deposits. Expanding this work to the level of the WSI can greatly improve the ability of pathologists and machine learning (ML) tools to quantify the extent of renal fibrosis reproducibly and reliably.
Reference histomorphometric data of healthy human kidneys are lacking due to laborious quantitation requirements. We leveraged deep learning to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine in a multinational set of reference kidney tissue sections. A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in digitized images of 79 periodic acid-Schiff (PAS)-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were measured from the segmented classes. Regression analysis was used to determine the relationship of histomorphometric parameters with age, sex, and serum creatinine. The model achieved high segmentation performance for all test compartments. We found that the size and density of nephrons, arteries/arterioles, and the baseline level of interstitium vary significantly among healthy humans, with potentially large differences between subjects from different geographic locations. Nephron size in any region of the kidney was significantly dependent on patient creatinine. Slight differences in renal vasculature and interstitium were observed between sexes. Finally, glomerulosclerosis percentage increased and cortical density of arteries/arterioles decreased as a function of age. We show that precise measurements of kidney histomorphometric parameters can be automated. Even in reference kidney tissue sections with minimal pathologic changes, several histomorphometric parameters demonstrated significant correlation to patient demographics and serum creatinine. These robust tools support the feasibility of deep learning to increase efficiency and rigor in histomorphometric analysis and pave the way for future large-scale studies.
