Motivation While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. Results We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. Availability and Implementation Dataset is freely available at: https://goo.gl/cNM4EL. Supplementary information Supplementary data are available at Bioinformatics online.

We investigated direct oral anticoagulant (DOAC) use in venous thromboembolism and thrombophilia. A comprehensive search identified 10 studies, 8 of which were included in a meta‐analysis. DOACs were overall safe and effective in patients with venous thromboembolism and thrombophilia. Efficacy/safety of DOACs was maintained in low‐risk antiphospholipid syndrome patient subgroup. Summary BackgroundDirect oral anticoagulants (DOACs) are increasingly used in acute and long‐term treatment of venous thromboembolism (VTE). However, their role in management of thrombophilia‐associated VTE is controversial. MethodsThrough a comprehensive search on MEDLINE, Cochrane Library, and Clinicaltrials.gov, we identified 10 eligible studies, 8 of which reporting data on 1994 thrombophilia patients were included in a random‐effects meta‐analysis. Eligible studies were phase 2 to 3 randomized controlled trials comparing DOACs to vitamin K antagonists (VKAs) in patients with VTE, including those with thrombophilia. ResultsOf eight studies included in meta‐analysis, four evaluated rivaroxaban, three dabigatran, and one edoxaban. No results could be obtained on apixaban use. The rates of VTE recurrence (RR, 0.70; 95% CI, 0.34–1.44; I2 = 0%) and major/clinically relevant non‐major bleeding events (RR, 0.92; 95% CI, 0.62–1.36; I2 = 23%) were similar between thrombophilia patients treated with DOACs compared to VKAs. Results were comparable to findings in patients without known thrombophilia: RR, 1.02; 95% CI, 0.80–1.30; I2 = 46% for VTE recurrence and RR, 0.72; 95% CI, 0.57–0.90; I2 = 84% for major/clinically relevant non‐major bleeding events. ConclusionsRates of VTE recurrence and bleeding events were both low and comparable in patients with various thrombophilias receiving either treatment, suggesting that DOACs are an appropriate treatment option in this population. Due to limited data, it is unclear whether these findings apply to specific subgroups such as high‐risk antiphospholipid syndrome, uncommon thrombophilias, or the use of apixaban.

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.

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This is the second-largest retrospective analysis addressing the controversy of whether adult rhabdomyosarcoma (RMS) should be treated with chemotherapy regimens adopted from pediatric RMS protocols or adult soft-tissue sarcoma protocols. A comprehensive database search identified 553 adults with primary non-metastatic RMS. Increasing age, intermediate-risk disease, no chemotherapy use, anthacycline-based and poor chemotherapy response were significant predictors of poor overall and progression-free survival. In contrast, combined cyclophosphamide-based, cyclophosphamide + anthracycline-based, or cyclophosphamide + ifosfamide + anthracycline-based regimens significantly improved outcomes. Intermediate-risk disease was a significant predictor of poor chemotherapy response. Overall survival of clinical group-III patients was significantly improved if they underwent delayed complete resection. Non-parameningeal clinical group-I patients had the best local control, which was not affected by additional adjuvant radiotherapy. This study highlights the superiority of chemotherapy regimens –adapted from pediatric protocols- compared to anthracycline-based regimens. There is lack of data to support the routine use of adjuvant radiotherapy for non-parameningeal group-I patients. Nonetheless, intensive local therapy should be always considered for those at high risk for local recurrence, including intermediate-risk disease, advanced IRS stage, large tumors or narrow surgical margins. Although practically difficult (due to tumor’s rarity), there is a pressing need for high quality randomized controlled trials to provide further guidance.