CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice.

More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, serum protein biomarkers to help diagnose TB, which afflicts millions of people in high-burden countries. However, the protein biomarker pipeline is small. Here, we used the Diversity Outbred (DO) mouse population to address this gap, identifying five protein biomarker candidates. One protein biomarker, serum CXCL1, met the World Health Organization\u27s Targeted Product Profile for a triage test to diagnose active TB from latent M.tb infection (LTBI), non-TB lung disease, and normal sera in HIV-negative, adults from South Africa and Vietnam. To find the biomarker candidates, we quantified seven immune cytokines and four inflammatory proteins corresponding to highly expressed genes unique to progressor DO mice. Next, we applied statistical and machine learning methods to the data, i.e., 11 proteins in lungs from 453 infected and 29 non-infected mice. After searching all combinations of five algorithms and 239 protein subsets, validating, and testing the findings on independent data, two combinations accurately diagnosed progressor DO mice: Logistic Regression using MMP8; and Gradient Tree Boosting using a panel of 4: CXCL1, CXCL2, TNF, IL-10. Of those five protein biomarker candidates, two (MMP8 and CXCL1) were crucial for classifying DO mice; were above the limit of detection in most human serum samples; and had not been widely assessed for diagnostic performance in humans before. In patient sera, CXCL1 exceeded the triage diagnostic test criteria (\u3e90% sensitivity; \u3e70% specificity), while MMP8 did not. Using Area Under the Curve analyses, CXCL1 averaged 94.5% sensitivity and 88.8% specificity for active pulmonary TB (ATB) vs LTBI; 90.9% sensitivity and 71.4% specificity for ATB vs non-TB; and 100.0% sensitivity and 98.4% specificity for ATB vs normal sera. Our findings overall show that the DO mouse population can discover diagnostic-quality, serum protein biomarkers of human TB

Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels

Recent methods in computational pathology have trended towards semi- and weakly-supervised methods requiring only slide-level labels. Yet, even slide-level labels may be absent or irrelevant to the application of interest, such as in clinical trials. Hence, we present a fully unsupervised method to learn meaningful, compact representations of WSIs. Our method initially trains a tile-wise encoder using SimCLR, from which subsets of tile-wise embeddings are extracted and fused via an attention-based multiple-instance learning framework to yield slide-level representations. The resulting set of intra-slide-level and inter-slide-level embeddings are attracted and repelled via contrastive loss, respectively. This resulted in slide-level representations with self-supervision. We applied our method to two tasks— (1) non-small cell lung cancer subtyping (NSCLC) as a classification prototype and (2) breast cancer proliferation scoring (TUPAC16) as a regression prototype—and achieved an AUC of 0.8641 ± 0.0115 and correlation (R2) of 0.5740 ± 0.0970, respectively. Ablation experiments demonstrate that the resulting unsupervised slide-level feature space can be fine-tuned with small datasets for both tasks. Overall, our method approaches computational pathology in a novel manner, where meaningful features can be learned from whole-slide images without the need for annotations of slide-level labels. The proposed method stands to benefit computational pathology, as it theoretically enables researchers to benefit from completely unlabeled whole-slide images

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings

Bootstrapped comparison of proposed method with C1 readings.

<p>Bootstrapped comparison of proposed method with C1 readings.</p

Overview of model.

<p>64x64 tiles were extracted from annotated regions of whole-slide images. The tiles resulting from 32 of these slides comprised the training set, while tiles from 1 slide were withheld for testing. Additionally, multiple HPF regions were extracted from the test slide from areas without annotation. The inception model was trained on the training set and its performance evaluated on the tiles from the test set. Finally, the high power fields were segmented using the inception model and assessed by two separate pathologists to determine segmentation accuracy. Note that due to variability in the number of tiles each slide contributes, the size of these 33 training and testing sets varied slightly. On the training data set, the average validation accuracy was 86.7% (±0.82%).</p

ROC curves comparing inception and Alexnet results presented in Tables 2–9.

<p>Here, TP and FP stand for true positive and false positive, respectively. Top Left) ROC curve for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195621#pone.0195621.t002" target="_blank">Table 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195621#pone.0195621.t006" target="_blank">Table 6</a>. Top Right) ROC curve for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195621#pone.0195621.t003" target="_blank">Table 3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195621#pone.0195621.t007" target="_blank">Table 7</a>. Bottom Left) ROC curve for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195621#pone.0195621.t004" target="_blank">Table 4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195621#pone.0195621.t008" target="_blank">Table 8</a>. Bottom Right) ROC curve for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195621#pone.0195621.t005" target="_blank">Table 5</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195621#pone.0195621.t009" target="_blank">Table 9</a>.</p

Fine-tuned Alexnet compared to C2 readings.

<p>Fine-tuned Alexnet compared to C2 readings.</p

Example of a CNN.

<p><b>Each convolutional layer is typically followed by an activation and pooling layer.</b> The final pooling layer is followed by a series of fully-connected layers then a final classification layer.</p

Pancreas NET test image process.

<p>Top) Example of a cropped static image used during testing. Bottom) The proposed method identified tumor highlighted in light red while non-tumor was overlaid in light green. Distinct boundaries between tumor and non-tumor are delineated using red and green annotation lines, respectively.</p

Comparison of proposed method with C1 readings.

<p>Comparison of proposed method with C1 readings.</p