Highlights d AI system that can diagnose COVID-19 pneumonia using CT scans d Prediction of progression to critical illness d Potential to improve performance of junior radiologists to the senior level d Can assist evaluation of drug treatment effects with CT quantification

Tumor-associated macrophages (TAMs) are the most abundant inflammatory infiltrates in the tumor microenvironment and contribute to lymph node (LN) metastasis. However, the precise mechanisms of TAMs-induced LN metastasis remain largely unknown. Herein, we identify a long noncoding RNA, termed Lymph Node Metastasis Associated Transcript 1 (LNMAT1), which is upregulated in LN-positive bladder cancer and associated with LN metastasis and prognosis. Through gain and loss of function approaches, we find that LNMAT1 promotes bladder cancer-associated lymphangiogenesis and lymphatic metastasis. Mechanistically, LNMAT1 epigenetically activates CCL2 expression by recruiting hnRNPL to CCL2 promoter, which leads to increased H3K4 tri-methylation that ensures hnRNPL binding and enhances transcription. Furthermore, LNMAT1-induced upregulation of CCL2 recruits macrophages into the tumor, which promotes lymphatic metastasis via VEGF-C excretion. These findings provide a plausible mechanism for LNMAT1-modulated tumor microenvironment in lymphatic metastasis and suggest that LNMAT1 may represent a potential therapeutic target for clinical intervention in LN-metastatic bladder cancer.

To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in bladder cancer. A total of 118 eligible bladder cancer patients were divided into a training set ( = 80) and a validation set ( = 38). Radiomics features were extracted from arterial-phase CT images of each patient. A radiomics signature was then constructed with the least absolute shrinkage and selection operator algorithm in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. Nomogram performance was assessed in the training set and validated in the validation set. Finally, decision curve analysis was performed with the combined training and validation set to estimate the clinical usefulness of the nomogram. The radiomics signature, consisting of nine LN status-related features, achieved favorable prediction efficacy. The radiomics nomogram, which incorporated the radiomics signature and CT-reported LN status, also showed good calibration and discrimination in the training set [AUC, 0.9262; 95% confidence interval (CI), 0.8657-0.9868] and the validation set (AUC, 0.8986; 95% CI, 0.7613-0.9901). The decision curve indicated the clinical usefulness of our nomogram. Encouragingly, the nomogram also showed favorable discriminatory ability in the CT-reported LN-negative (cN0) subgroup (AUC, 0.8810; 95% CI, 0.8021-0.9598). The presented radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the radiomics signature and CT-reported LN status, shows favorable predictive accuracy for LN metastasis in patients with bladder cancer. Multicenter validation is needed to acquire high-level evidence for its clinical application. .

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It was recently brought to our attention that our paper was missing information regarding when the patient chest computed tomography (CT) scans were obtained and that there were some discrepancies in the clinical metadata, associated with the very large image dataset, that we made publicly available through the China National Center for Bioinformation (http://ncov-ai.big.ac.cn/ download?lang=en). All of the chest CT and clinical metadata used in our prognostic analysis were collected from patients at the time of hospital admission, and we have now added this statement to the STAR Methods section of our paper. We believe that the errors in the clinical metadata were introduced when the chest CT images, clinical metadata, and codes were transferred to the web server, and we have now corrected the errors manually. Although these corrections do not alter any of the conclusions made in the paper, we do apologize for these errors and any confusion that they may have caused.