The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.

An effective blood-based method for the diagnosis and prognosis of hepatocellular carcinoma (HCC) has not yet been developed. Circulating tumour DNA (ctDNA) carrying cancer-specific genetic and epigenetic aberrations may enable a noninvasive 'liquid biopsy' for diagnosis and monitoring of cancer. Here, we identified an HCC-specific methylation marker panel by comparing HCC tissue and normal blood leukocytes and showed that methylation profiles of HCC tumour DNA and matched plasma ctDNA are highly correlated. Using cfDNA samples from a large cohort of 1,098 HCC patients and 835 normal controls, we constructed a diagnostic prediction model that showed high diagnostic specificity and sensitivity (P < 0.001) and was highly correlated with tumour burden, treatment response, and stage. Additionally, we constructed a prognostic prediction model that effectively predicted prognosis and survival (P < 0.001). Together, these findings demonstrate in a large clinical cohort the utility of ctDNA methylation markers in the diagnosis, surveillance, and prognosis of HCC.

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The recent success of immunotherapies has highlighted the power of leveraging the immune system in the fight against cancer. In order for most immune‐based therapies to succeed, T cell subsets with the correct tumor‐targeting specificities must be mobilized. When such specificities are lacking, providing the immune system with tumor antigen material for processing and presentation is a common strategy for stimulating antigen‐specific T cell populations. While straightforward in principle, experience has shown that manipulation of the antigen presentation process can be incredibly complex, necessitating sophisticated strategies that are difficult to translate. Herein, the design of a biomimetic nanoparticle platform is reported that can be used to directly stimulate T cells without the need for professional antigen‐presenting cells. The nanoparticles are fabricated using a cell membrane coating derived from cancer cells engineered to express a co‐stimulatory marker. Combined with the peptide epitopes naturally presented on the membrane surface, the final formulation contains the necessary signals to promote tumor antigen‐specific immune responses, priming T cells that can be used to control tumor growth. The reported approach represents an emerging strategy that can be used to develop multiantigenic, personalized cancer immunotherapies.

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