Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a novel, clinically-oriented approach to uncertainty quantification (UQ) for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts

Updates on the pathologic diagnosis and classification of mesothelioma

Mesothelioma is a rare malignant tumor of the serosal membranes that can be challenging to diagnose, especially on small biopsy specimens. There are updated guidelines on the diagnosis and classification of mesothelioma, which incorporate advancements in understanding mesothelioma biology published in the literature over recent years. This review will discuss marked developments and/or improvements that have been made, including: (1) to the histologic classifications of mesothelioma; (2) the use of such classifications and nuclear grading in prognosis; (3) the indispensability of ancillary studies in the diagnosis of mesothelioma; (4) the application of these pleural based classifications and diagnostic schemes in peritoneal mesothelioma; and (5) the potential for diagnosis of mesothelioma in situ

Deep learning generates synthetic cancer histology for explainability and education

Abstract Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology

Author Correction:Pan-cancer image-based detection of clinically actionable genetic alterations (Nature Cancer, (2020), 1, 8, (789-799), 10.1038/s43018-020-0087-6)

In the version of this article initially published, the sample size (n = 794) was incorrect in Fig. 2f and Extended Data Fig. 4a,e; the correct sample size is ‘n = 397’. The sample size (n = 826) was also incorrect in Fig. 2h and Extended Data Fig. 4q,u; the correct sample size is ‘n = 413’. Also, the values in Supplementary Table 2, row ‘TCGA-HNSC’, column ‘Quality OK and tumor on slide’ (424, 424) were incorrect;the correct values are ‘457, 439’. The errors have been corrected in the HTML and PDF versions of the article