Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient

Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem

Growth Factor Receptor Expression in Oropharyngeal Squamous Cell Cancer: Her1–4 and c-Met in Conjunction with the Clinical Features and Human Papillomavirus (p16) Status

Simple Summary Growth factor expression is a negative prognostic factor in head and neck squamous cell carcinoma (HNSCC). Targeted therapy has a limited effect on the treatment of advanced stages due to evolving resistance mechanisms. The aim of this study was to assess the distribution of growth factor receptors in oropharyngeal squamous cell cancer (OPSCC) and evaluate their role in the context of the human papillomavirus status, prognosis and possible relevance for targeted therapy. Tissue microarrays of 78 primary OPSCC, 35 related lymph node metastasis, 6 distant metastasis and 9 recurrent tumors were manufactured to evaluate the expression of human epidermal growth factor receptor (EGFR/erbB/Her)1-4 and c-Met by immunohistochemistry. EGFR and c-Met are relevant negative prognostic factors especially in noxae-induced OPSCC. Thus, dual targeting of EGFR and c-Met could be a promising prospective target in OPSCC treatment. Frequent coexpression of assessed receptors represents a possible intrinsic resistance mechanism in targeted therapy. This study aimed to assess the distribution of growth factor receptors in oropharyngeal squamous cell cancer (OPSCC) and evaluate their role in the context of human papillomavirus (HPV) status, prognosis and potential relevance for targeted therapy. The protein expression of human epidermal growth factor receptor (Her)1-4 and c-Met were retrospectively assessed using semiquantitative immunohistochemistry on tissue microarrays and analyzed for correlations as well as differences in the clinicopathological criteria. Her1-4 and c-met were overexpressed compared to normal mucosa in 46%, 4%, 17%, 27% and 23%, respectively. Interestingly, most receptors were coexpressed. Her1 and c-Met were inversely correlated with p16 (p = 0.04; p = 0.02). Her2 and c-Met were associated with high tobacco consumption (p = 0.016; p = 0.04). High EGFR, Her3, Her4 and c-Met expression were associated with worse overall and disease-free survival (p <= 0.05). Furthermore, EGFR and c-Met expression showed raised hazard ratios of 2.53 (p = 0.02; 95% CI 1.24-5.18) and 2.45 (p = 0.02; 95% CI 1.13-5.35), respectively. Her4 was expressed less in distant metastases than in corresponding primary tumors and was correlated to a higher T category. EGFR and c-Met are relevant negative prognostic factors in OPSCC, independent of known clinicopathological parameters. We suggest dual targeting of EGFR and c-Met as a promising strategy for OPSCC treatment