Fan Yang

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.

Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation.

PURPOSE: Camrelizumab is an antibody against programmed death protein 1. We assessed the activity and safety of camrelizumab plus apatinib, a tyrosine kinase inhibitor of vascular endothelial growth factor receptor-2, in patients with advanced cervical cancer. METHODS: This multicenter, open-label, single-arm, phase II study enrolled patients with advanced cervical cancer who progressed after at least one line of systemic therapy. Patients received camrelizumab 200 mg every 2 weeks and apatinib 250 mg once per day. The primary end point was objective response rate (ORR) assessed by investigators per RECIST version 1.1. Key secondary end points were progression-free survival (PFS), overall survival (OS), duration of response, and safety. RESULTS: Forty-five patients were enrolled and received treatment. Median age was 51.0 years (range, 33-67 years), and 57.8% of patients had previously received two or more lines of chemotherapy for recurrent or metastatic disease. Ten patients (22.2%) had received bevacizumab. Median follow-up was 11.3 months (range, 1.0-15.5 months). ORR was 55.6% (95% CI, 40.0% to 70.4%), with two complete and 23 partial responses. Median PFS was 8.8 months (95% CI, 5.6 months to not estimable). Median duration of response and median OS were not reached. Treatment-related grade 3 or 4 adverse events (AEs) occurred in 71.1% of patients, and the most common AEs were hypertension (24.4%), anemia (20.0%), and fatigue (15.6%). The most common potential immune-related AEs included grade 1-2 hypothyroidism (22.2%) and reactive cutaneous capillary endothelial proliferation (8.9%). CONCLUSION: Camrelizumab plus apatinib had promising antitumor activity and manageable toxicities in patients with advanced cervical cancer. Larger randomized controlled trials are warranted to validate our findings.

Multiple instance learning (MIL) is a typical weakly-supervised learning method where the label is associated with a bag of instances instead of a single instance. Despite extensive research over past years, effectively deploying MIL remains an open and challenging problem, especially when the commonly assumed standard multiple instance (SMI) assumption is not satisfied. In this paper, we propose a multiple instance learning method based on deep graph convolutional network and feature selection (FS-GCN-MIL) for histopathological image classification. The proposed method consists of three components, including instance-level feature extraction, instance-level feature selection, and bag-level classification. We develop a self-supervised learning mechanism to train the feature extractor based on a combination model of variational autoencoder and generative adversarial network (VAE-GAN). Additionally, we propose a novel instance-level feature selection method to select the discriminative instance features. Furthermore, we employ a graph convolutional network (GCN) for learning the bag-level representation and then performing the classification. We apply the proposed method in the prediction of lymph node metastasis using histopathological images of colorectal cancer. Experimental results demonstrate that the proposed method achieves superior performance compared to the state-of-the-art methods.