NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer.

BACKGROUND: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists. RESULTS: This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes. CONCLUSIONS: This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.

Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings.

MOTIVATION: Nucleus detection, segmentation and classification are fundamental to high-resolution mapping of the tumor microenvironment using whole-slide histopathology images. The growing interest in leveraging the power of deep learning to achieve state-of-the-art performance often comes at the cost of explainability, yet there is general consensus that explainability is critical for trustworthiness and widespread clinical adoption. Unfortunately, current explainability paradigms that rely on pixel saliency heatmaps or superpixel importance scores are not well-suited for nucleus classification. Techniques like Grad-CAM or LIME provide explanations that are indirect, qualitative and/or nonintuitive to pathologists. RESULTS: In this article, we present techniques to enable scalable nuclear detection, segmentation and explainable classification. First, we show how modifications to the widely used Mask R-CNN architecture, including decoupling the detection and classification tasks, improves accuracy and enables learning from hybrid annotation datasets like NuCLS, which contain mixtures of bounding boxes and segmentation boundaries. Second, we introduce an explainability method called Decision Tree Approximation of Learned Embeddings (DTALE), which provides explanations for classification model behavior globally, as well as for individual nuclear predictions. DTALE explanations are simple, quantitative, and can flexibly use any measurable morphological features that make sense to practicing pathologists, without sacrificing model accuracy. Together, these techniques present a step toward realizing the promise of computational pathology in computer-aided diagnosis and discovery of morphologic biomarkers. AVAILABILITY AND IMPLEMENTATION: Relevant code can be found at github.com/CancerDataScience/NuCLS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Circulating soluble urokinase plasminogen activator receptor levels and peripheral arterial disease outcomes.

BACKGROUND AND AIMS: Circulating soluble urokinase plasminogen activator receptor (suPAR) is a marker of immune activation associated with atherosclerosis. Whether suPAR levels are associated with prevalent peripheral arterial disease (PAD) and its adverse outcomes remains unknown and is the aim of the study. METHODS: SuPAR levels were measured in 5810 patients (mean age 63 years, 63% male, 77% with obstructive coronary artery disease [CAD]) undergoing cardiac catheterization. The presence of PAD (n = 967, 17%) was classified as carotid (36%), lower/upper extremities (30%), aortic (15%) and multisite disease (19%). Multivariable logistic and Cox regression models were used to determine independent predictors of prevalent PAD and outcomes including all-cause death, cardiovascular death and PAD-related events after adjustment for age, gender, race, body mass index, smoking, diabetes, hypertension, hyperlipidemia, renal function, heart failure history, and obstructive CAD. RESULTS: Plasma suPAR levels were 22.5% (p < 0.001) higher in patients with PAD compared to those without PAD. Plasma suPAR was higher in patients with more extensive PAD (≥2 compared to single site) p < 0.001. After multivariable adjustment, suPAR was associated with prevalent PAD; odds ratio (OR) for highest compared to lowest tertile of 2.0, 95% CI (1.6-2.5) p < 0.001. In Cox survival analyses adjusted for clinical characteristics and medication regimen, suPAR (in the highest vs. lowest tertile) remained an independent predictor of all-cause death [HR 3.1, 95% CI (1.9-5.3)], cardiovascular death [HR 3.5, 95% CI (1.8-7.0)] and PAD-related events [HR = 1.8, 95% CI (1.3-2.6) p < 0.001 for all]. CONCLUSIONS: Plasma suPAR level is predictive of prevalent PAD and of incident cardiovascular and PAD-related events. Whether SuPAR measurement can help screen, risk stratify, or monitor therapeutic responses in PAD requires further investigation.

GATA3 immunohistochemical expression in invasive urothelial carcinoma.

INTRODUCTION: GATA binding protein 3 (GATA3) is a transcription factor, which belongs to a distinct family of tumor suppressor genes. It is involved in human cancer cell growth and differentiation, and plays an important role in cell proliferation and apoptosis. Although, its expression has been reported in various cancers, there are limited data in genitourinary malignancies. Recent studies found GATA3 to be a sensitive marker for urothelial carcinoma (UC) and associated with prognostic pathologic features. Its level of expression was found to be an independent factor predicting cancer recurrence. METHODS AND MATERIALS: In this article, immunohistochemical evaluation of GATA3 expression in genitourinary malignancies (invasive UC, renal cell carcinoma, and prostatic adenocarcinomas) was performed. RESULTS: GATA3 was positive in 56/79 (70.8%) of invasive UC, and was negative in all renal cell carcinoma and prostatic adenocarcinomas. The pattern of GATA3 staining, when positive, was intensely nuclear within the clusters of malignant cells. No cytoplasmic staining was noted. Negative controls were all negative. High GATA3 expression was associated with larger tumor size in invasive UC (3.19cm vs. 1.65cm, P = 0.01). GATA3 expression did not correlate with other clinicopathologic parameters in UC. CONCLUSIONS: This data suggest that GATA3 is a sensitive marker in confirming invasive UC, and may be helpful in differentiating it from metastatic tumors of renal and prostatic origin. Furthermore, strong GATA3 expression was noted to have an effect on tumor size in patients with UC.