Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

Ribocentre-switch: a database of riboswitches.

Riboswitches are regulatory elements found in the untranslated regions (UTRs) of certain mRNA molecules. They typically comprise two distinct domains: an aptamer domain that can bind to specific small molecules, and an expression platform that controls gene expression. Riboswitches work by undergoing a conformational change upon binding to their specific ligand, thus activating or repressing the genes downstream. This mechanism allows gene expression regulation in response to metabolites or small molecules. To systematically summarise riboswitch structures and their related ligand binding functions, we present Ribocentre-switch, a comprehensive database of riboswitches, including the information as follows: sequences, structures, functions, ligand binding pockets and biological applications. It encompasses 56 riboswitches and 26 orphan riboswitches from over 430 references, with a total of 89 591 sequences. It serves as a good resource for comparing different riboswitches and facilitating the identification of potential riboswitch candidates. Therefore, it may facilitate the understanding of RNA structural conformational changes in response to ligand signaling. The database is publicly available at https://riboswitch.ribocentre.org.

Tumor-associated neutrophils suppress CD8+ T cell immunity in urothelial bladder carcinoma through the COX-2/PGE2/IDO1 Axis.

BACKGROUND: Many urothelial bladder carcinoma (UBC) patients don't respond to immune checkpoint blockade (ICB) therapy, possibly due to tumor-associated neutrophils (TANs) suppressing lymphocyte immune response. METHODS: We conducted a meta-analysis on the predictive value of neutrophil-lymphocyte ratio (NLR) in ICB response and investigated TANs' role in UBC. We used RNA-sequencing, HALO spatial analysis, single-cell RNA-sequencing, and flow cytometry to study the impacts of TANs and prostaglandin E2 (PGE2) on IDO1 expression. Animal experiments evaluated celecoxib's efficacy in targeting PGE2 synthesis. RESULTS: Our analysis showed that higher TAN infiltration predicted worse outcomes in UBC patients receiving ICB therapy. Our research revealed that TANs promote IDO1 expression in cancer cells, resulting in immunosuppression. We also found that PGE2 synthesized by COX-2 in neutrophils played a key role in upregulating IDO1 in cancer cells. Animal experiments showed that targeting PGE2 synthesis in neutrophils with celecoxib enhanced the efficacy of ICB treatment. CONCLUSIONS: TAN-secreted PGE2 upregulates IDO1, dampening T cell function in UBC. Celecoxib targeting of PGE2 synthesis represents a promising approach to enhance ICB efficacy in UBC.

Long-Term Outcomes of Orthotopic Neobladder Versus Ileal Conduit Urinary Diversion in Robot-Assisted Radical Cystectomy (RARC): Multicenter Results from the Asian RARC Consortium.

PURPOSE: Robot-assisted radical cystectomy (RARC) has gained traction in the management of muscle invasive bladder cancer. Urinary diversion for RARC was achieved with orthotopic neobladder and ileal conduit. Evidence on the optimal method of urinary diversion was limited. Long-term outcomes were not reported before. This study was designed to compare the perioperative and oncological outcomes of ileal conduit versus orthotopic neobladder cases of nonmetastatic bladder cancer treated with RARC. PATIENTS AND METHODS: The Asian RARC consortium was a multicenter registry involving nine Asian centers. Consecutive patients receiving RARC were included. Cases were divided into the ileal conduit and neobladder groups. Background characteristics, operative details, perioperative outcomes, recurrence information, and survival outcomes were reviewed and compared. Primary outcomes include disease-free and overall survival. Secondary outcomes were perioperative results. Multivariate regression analyses were performed. RESULTS: From 2007 to 2020, 521 patients who underwent radical cystectomy were analyzed. Overall, 314 (60.3%) had ileal conduit and 207 (39.7%) had neobladder. The use of neobladder was found to be protective in terms of disease-free survival [Hazard ratio (HR) = 0.870, p = 0.037] and overall survival (HR = 0.670, p = 0.044) compared with ileal conduit. The difference became statistically nonsignificant after being adjusted in multivariate cox-regression analysis. Moreover, neobladder reconstruction was not associated with increased blood loss, nor additional risk of major complications. CONCLUSIONS: Orthotopic neobladder urinary diversion is not inferior to ileal conduit in terms of perioperative safety profile and long-term oncological outcomes. Further prospective studies are warranted for further investigation.

Trends and socioeconomic inequality of the burden of congenital abnormalities of the kidney and urinary tract among children and adolescents.

BACKGROUND: Although congenital abnormalities of the kidney and urinary tract (CAKUT) is the leading cause of childhood onset chronic kidney disease (CKD) and kidney failure, comprehensive information on the disease burden among children and adolescents globally is lacking. We aim to report the trends and socioeconomic inequality of CAKUT burden for people aged 0-24 years from 1990 to 2019·. METHODS: We reported the prevalence, mortality and disability-adjusted life-years (DALYs) for CAKUT based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, quantified the association of disease burden and socio-demographic index (SDI), calculated the slope index of inequality (SII) the relative index of inequality (RII) and concentration index. RESULTS: In 2019, the global prevalence, mortality, and DALYs of CAKUT among individuals aged 0-24 years were 167.11 (95%Confident Interval 166.97, 167.25), 0.30 (0.29, 0.30), and 32.22 (32.16, 32.29) per 100 000 population. The greatest prevalence, mortality and DALYs were recorded in the 0-4 year age group. The greatest mortality and DALYs were recorded in low SDI countries and territories. During 1990 to 2019, the prevalence, mortality and DALYs decreased globally, while in low and low-middle countries and territories the reduction was much less slower. India, Nigeria and Pakistan had the highest DALYs. Saudi Arabia and China exhibited a markedly decrease of CAKUT burden. Globally for every 0.1 increase in SDI, there was a 20.53% reduction in mortality, a 16.31% decrease in DALYs, but a 0.38% rise in prevalence. CONCLUSIONS: Inequality for disease burden of varying SDI was increasing globally. Thus, specific preventive and health service measures are needed to reduce the global burden from CAKUT.

Establishment of a prognostic model to predict chemotherapy response and identification of RAC3 as a chemotherapeutic target in bladder cancer.

Cisplatin-based chemotherapy is considered the primary treatment option for patients with advanced bladder cancer (BCa). However, the objective response rate to chemotherapy is often unsatisfactory, leading to a poor 5-year survival rate. Furthermore, current strategies for evaluating chemotherapy response and prognosis are limited and inefficient. In this study, we aimed to address these challenges by establishing a chemotherapy response type gene (CRTG) signature consisting of 9 genes and verified the prognostic value of this signature using TCGA and GEO BCa cohorts. The risk scores based on the CRTG signature were found to be associated with advanced clinicopathological status and demonstrated favorable predictive power for chemotherapy response in the TCGA cohort. Meanwhile, tumors with high risk scores exhibited a tendency toward a "cold tumor" phenotype. These tumors showed a low abundance of T cells, CD8+ T cells and cytotoxic lymphocytes, along with a high abundance of cancer-associated fibroblasts. Moreover, they displayed higher mRNA levels of these immune checkpoints: CD200, CD276, CD44, NRP1, PDCD1LG2 (PD-L2), and TNFSF9. Furthermore, we developed a nomogram that integrated the CRTG signature with clinicopathologic risk factors. This nomogram proved to be a more effective tool for predicting the prognosis of BCa patients. Additionally, we identified Rac family small GTPase 3 (RAC3) as a biomarker in our model. RAC3 was found to be overexpressed in chemoresistant BCa tissues and enhance the chemotherapeutic resistance of BCa cells in vitro and in vivo by regulating the PAK1-ERK1/2 pathway. In conclusion, our study presents a novel CRTG model for predicting chemotherapy response and prognosis in BCa. We also highlight the potential of combining chemotherapy with immunotherapy as a promising strategy for chemoresistant BCa and that RAC3 might be a latent target for therapeutic intervention.

Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study.

BACKGROUND: Accurate lymph node staging is important for the diagnosis and treatment of patients with bladder cancer. We aimed to develop a lymph node metastases diagnostic model (LNMDM) on whole slide images and to assess the clinical effect of an artificial intelligence-assisted (AI) workflow. METHODS: In this retrospective, multicentre, diagnostic study in China, we included consecutive patients with bladder cancer who had radical cystectomy and pelvic lymph node dissection, and from whom whole slide images of lymph node sections were available, for model development. We excluded patients with non-bladder cancer and concurrent surgery, or low-quality images. Patients from two hospitals (Sun Yat-sen Memorial Hospital of Sun Yat-sen University and Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China) were assigned before a cutoff date to a training set and after the date to internal validation sets for each hospital. Patients from three other hospitals (the Third Affiliated Hospital of Sun Yat-sen University, Nanfang Hospital of Southern Medical University, and the Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong, China) were included as external validation sets. A validation subset of challenging cases from the five validation sets was used to compare performance between the LNMDM and pathologists, and two other datasets (breast cancer from the CAMELYON16 dataset and prostate cancer from the Sun Yat-sen Memorial Hospital of Sun Yat-sen University) were collected for a multi-cancer test. The primary endpoint was diagnostic sensitivity in the four prespecified groups (ie, the five validation sets, a single-lymph-node test set, the multi-cancer test set, and the subset for a performance comparison between the LNMDM and pathologists). FINDINGS: Between Jan 1, 2013 and Dec 31, 2021, 1012 patients with bladder cancer had radical cystectomy and pelvic lymph node dissection and were included (8177 images and 20 954 lymph nodes). We excluded 14 patients (165 images) with concurrent non-bladder cancer and also excluded 21 low-quality images. We included 998 patients and 7991 images (881 [88%] men; 117 [12%] women; median age 64 years [IQR 56-72]; ethnicity data not available; 268 [27%] with lymph node metastases) to develop the LNMDM. The area under the curve (AUC) for accurate diagnosis of the LNMDM ranged from 0·978 (95% CI 0·960-0·996) to 0·998 (0·996-1·000) in the five validation sets. Performance comparisons between the LNMDM and pathologists showed that the diagnostic sensitivity of the model (0·983 [95% CI 0·941-0·998]) substantially exceeded that of both junior pathologists (0·906 [0·871-0·934]) and senior pathologists (0·947 [0·919-0·968]), and that AI assistance improved sensitivity for both junior (from 0·906 without AI to 0·953 with AI) and senior (from 0·947 to 0·986) pathologists. In the multi-cancer test, the LNMDM maintained an AUC of 0·943 (95% CI 0·918-0·969) in breast cancer images and 0·922 (0·884-0·960) in prostate cancer images. In 13 patients, the LNMDM detected tumour micrometastases that had been missed by pathologists who had previously classified these patients' results as negative. Receiver operating characteristic curves showed that the LNMDM would enable pathologists to exclude 80-92% of negative slides while maintaining 100% sensitivity in clinical application. INTERPRETATION: We developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work. FUNDING: National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, and the Guangdong Provincial Clinical Research Centre for Urological Diseases.

A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study.

BACKGROUND: The aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients. METHODS: Four independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC). RESULTS: A total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI. DISCUSSION: The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy.

The tumour-associated stroma correlates with poor clinical outcomes and immunoevasive contexture in patients with upper tract urothelial carcinoma: results from a multicenter real-world study (TSU-01 Study).

BACKGROUND: In this real-world study, we aimed to elucidate the predictive value of tumour-associated stroma for clinical prognostic and therapeutic response in upper tract urothelial carcinoma (UTUC) by reviewing the clinicopathologic characteristics of 1015 UTUC patients through a nationwide multicenter analysis. METHODS: The tumour-stroma ratio (TSR) was assessed based on tissue sections stained for hematoxylin and eosin (H&E), and patients were further stratified into stroma-high (>50% stroma) and stroma-low group (≤50% stroma). Kaplan-Meier curve and Cox regression hazard analysis were conducted to assess the survival outcomes of UTUC patients. Bioinformatics analysis and immunostaining analysis were applied to portray the tumour microenvironment (TME). RESULTS: Stroma-high UTUC was significantly associated with poorer survival outcomes and inferior chemotherapeutic responsiveness. Our established nomogram achieved a high prognostic accuracy in predicting overall survival and cancer-specific survival in both of the discovery cohort (area under the curve [AUC] 0.663 and 0.712) and the validation cohort (AUC 0.741 and 0.747). Moreover, stroma-high UTUC was correlated with immunoevasive TME accompanied by increased cancer-associated fibroblasts, tumour-associated macrophages and, conspicuously a cluster of highly exhausted CD8+ T cells. CONCLUSION: Our results showed stroma-high UTUC was associated with an inferior prognosis and an immunoevasive TME with exhausted CD8+ T cells in UTUC patients. Our TSR-based nomogram could be used to refine prognosis and inform treatment decisions of patients with UTUC.

An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer.

BACKGROUND: Accurate pathological diagnosis of invasion depth and histologic grade is key for clinical management in patients with bladder cancer (BCa), but it is labour-intensive, experience-dependent and subject to interobserver variability. Here, we aimed to develop a pathological artificial intelligence diagnostic model (PAIDM) for BCa diagnosis. METHODS: A total of 854 whole slide images (WSIs) from 692 patients were included and divided into training and validation sets. The PAIDM was developed using the training set based on the deep learning algorithm ScanNet, and the performance was verified at the patch level in validation set 1 and at the WSI level in validation set 2. An independent validation cohort (validation set 3) was employed to compare the PAIDM and pathologists. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUCs of the PAIDM were 0.878 (95% CI 0.875-0.881) at the patch level in validation set 1 and 0.870 (95% CI 0.805-0.923) at the WSI level in validation set 2. In comparing the PAIDM and pathologists, the PAIDM achieved an AUC of 0.847 (95% CI 0.779-0.905), which was non-inferior to the average diagnostic level of pathologists. There was high consistency between the model-predicted and manually annotated areas, improving the PAIDM's interpretability. CONCLUSIONS: We reported an artificial intelligence-based diagnostic model for BCa that performed well in identifying invasion depth and histologic grade. Importantly, the PAIDM performed admirably in patch-level recognition, with a promising application for transurethral resection specimens.

Preoperative fluorescence in situ hybridization analysis as a predictor of tumor recurrence in patients with non-muscle invasive bladder cancer: a bi-institutional study.

BACKGROUND: Non-muscle invasive bladder cancer (NMIBC) is known for its elevated recurrence rate, necessitating an enhancement in the current risk stratification for recurrence. The urine-based fluorescence in situ hybridization (FISH) assay has emerged as a noninvasive auxiliary tool for detecting bladder cancer. The aim of this study was to explore the potential relationship between the preoperative FISH assay and recurrence, and to develop a FISH-clinical nomogram for predicting the recurrence-free survival (RFS) in NMIBC patients. METHODS: In total, 332 eligible patients were enrolled from two hospitals. The SYSMH cohort was randomly assigned to the training set (n = 168) and the validation set I (n = 72) at a ratio of 7:3, while the SYSUTH cohort was allocated to the validation set II (n = 92). The correlation between the preoperative FISH assay and recurrence was determined through the Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used for model construction. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness. RESULTS: We uncovered that chromosome 7 aneuploidy, p16 locus loss, number of the positive FISH sites, and the FISH test result were significantly associated with tumor recurrence. Then, a FISH-clinical nomogram incorporating the FISH test result, T stage, associated CIS, tumor grade, and tumor status was developed. It showed favorable calibration and discrimination with a C-index of 0.683 (95%CI, 0.611-0.756) in the training set, which was confirmed in the validation set I and validation set II with C-indexes of 0.665 (95%CI, 0.565-0.765) and 0.778 (95%CI, 0.665-0.891), respectively. Decision curve analysis revealed the clinical usefulness of the nomogram. Moreover, our proposed nomogram significantly outperformed the guideline-recommended EORTC and CUETO scoring models. CONCLUSION: Our study confirmed the prognostic value of the preoperative FISH assay and proposed a FISH-clinical nomogram to predict RFS in NMIBC patients. Our nomogram can serve as a more precise tool for recurrence risk stratification, which may optimize disease management in bladder cancer and improve patient prognosis.

Comparison of minimally invasive versus open pelvic organ-preserving radical cystectomy in female patients with bladder cancer: a multicenter propensity score matching analysis.

PURPOSE: To compare the perioperative and oncologic outcomes between minimally invasive pelvic organ-preserving radical cystectomy (MIPOPRC) and open pelvic organ-preserving radical cystectomy (open POPRC) among female patients with bladder cancer (BCa). METHODS: We identified female patients who underwent POPRC for BCa at three centers between January 2006 and April 2018. Female patients who underwent open POPRC were matched with those who underwent MIPOPRC using 1:1 propensity score (PS) matching. The patient demographics and perioperative and oncologic outcomes were evaluated for the comparison between MIPOPRC and open POPRC. RESULTS: Among the 158 patients enrolled, 83 patients underwent MIPOPRC, and 75 underwent open POPRC. A total of 60 MIPOPRC and 60 open POPRC patients were matched successfully. The cancer-specific survival (CSS) and recurrence-free survival (RFS) did not differ significantly in the propensity score-weighted cohort (p = 0.297 and p = 0.600, respectively). Subgroup analysis by age and pathologic stage in the matched cohort revealed that CSS and RFS were with no differences among all subgroups. Moreover, multivariable Cox regression analyses showed that the surgical approach (MIPOPRC vs open POPRC) was not a predictor of CSS (p = 0.250). CONCLUSION: MIPOPRC was non-inferior to open POPRC in terms of oncologic outcomes among female patients. MIPOPRC could be technically feasible in selected female patients with BCa.

A Novel Lymphangiogenesis-Related Gene Signature can Predict Prognosis and Immunosuppressive Microenvironment in Patients with Clear Cell Renal Cell Carcinoma.

Background: Lymphangiogenesis represents a key event in the progression and metastasis of patients with clear cell renal cell carcinoma (ccRCC). Nevertheless, the prognostic value of lymphangiogenesis-related genes (LRGs) in ccRCC patients remains unknown. Method: Differential analyses were performed to identify differentially expressed LRGs between normal and tumor tissues. A univariate Cox analysis was performed to identify differently expressed LRGs associated with overall survival (OS). LASSO and multivariate Cox analyses were performed to construct and optimize the LRG signature. To further explore the molecular characterization of the LRG signature, a functional enrichment analysis, immune signature, somatic mutations, and drug sensitivity were assessed. Immunohistochemistry (IHC) and immunofluorescence staining were performed to validate the relationship between lymphangiogenesis and immunity using our ccRCC samples. Results: Four candidate genes (IL4, CSF2, PROX1, and TEK) were eventually available to construct the LRG signature in the training set. Patients in the high-risk group had a shorter survival than those in the low-risk group. The LRG signature was an independent prognostic factor of OS. These results were confirmed in the validation group. The LRG signature was correlated with immunosuppressive cell infiltration, T cell exhaustion markers, somatic mutations, and drug sensitivity. The IHC and immunofluorescence staining results confirmed the correlation between lymphangiogenesis and CD163+ macrophages, exhausted CD8+PD-1+, and CD8+ LAG3+ T cells. Conclusion: A novel prognostic signature based on LRGs could provide insight into the prognostic evaluation and treatment of ccRCC patients.

Identification of an IDO1-based immune classifier for survival prediction of upper tract urothelial carcinoma.

The limited response rate of immunotherapy in upper tract urothelial carcinoma (UTUC) might be attributed to additional immunosuppressive mechanisms in vivo. As a promising immune checkpoint target, the expression and prognostic role of indoleamine 2,3-dioxygenase 1 (IDO1) in UTUC remains unknown. In this study, the expression and prognostic value of IDO1 was analyzed in 251 patients from 3 independent cohorts. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to construct an IDO1-based immune classifier and external validation was performed to further validate the classifier. RNA sequencing and immunofluorescence were used to explore the immune contexture of different risk groups stratified by classifier. We found that high IDO1 expression on tumor cells (TC) indicated a poorer overall survival and disease-free survival in all cohorts. Patients with high expression of IDO1 TC possessed increased infiltration of CD4+ , CD8+ and Foxp3+ T cells. An immune classifier based on intratumoral CD8+ lymphocytes, IDO1 TC, and stromal PD-L1 expression status was developed, with its area under the curves (AUCs) values for overall survival at 5 y being 0.79 (95% confidence interval [CI] 0.65-0.93) in the discovery cohort, 0.75 (95% CI 0.58-0.92) and 0.78 (95% CI 0.65-0.92) in the internal and external validation cohorts, respectively. The high-risk group stratified by the immune classifier was associated with immunosuppressive contexture, accompanied by enhanced CD8+ T cells exhaustion patterns. Our IDO1-based immune classifier can provide a superior accuracy for survival prediction and lead to individual stratification of UTUC immune subtypes.

Intravesical Pseudomonas aeruginosa mannose-sensitive Hemagglutinin vaccine triggers a tumor-preventing immune environment in an orthotopic mouse bladder cancer model.

Bacillus Calmette-Guerin (BCG) immunotherapy can prevent recurrence and progression in selected patients with non-muscle-invasive bladder cancer (NMIBC); however, significant adverse events and treatment failure suggest the need for alternative agents. A commercial anti-infection vaccine comprises a genetically engineered heat-killed Pseudomonas aeruginosa (PA) expressing many mannose-sensitive hemagglutination (MSHA) fimbriae, termed PA-MSHA, which could be a candidate for bladder cancer intravesical therapy. In an immunocompetent orthotopic MB49 bladder cancer model, we characterized the antitumor effects and mechanisms of PA-MSHA compared with those of BCG. Three weekly intravesical PA-MSHA or BCG treatments reduced tumor involvement; however, only PA-MSHA prolonged survival against MB49 implantation significantly. In non-tumor-bearing mice after treatment, flow-cytometry analysis showed PA-MSHA and BCG induced an increased CD4/CD8 ratio, the levels of effector memory T cell phenotypes (CD44, CXCR-3, and IFN-γ), and the proportion of CD11b+Ly6G-Ly6C-IA/IE+ mature macrophages, but a decrease in the proportion of CD11b+Ly6G-Ly6C+IA/IE- monocytic myeloid-derived suppressor cells (Mo-MDSCs) and the expression of suppressive molecules on immune cells (PD-L1, PD-1, TIM-3, and LAG-3). Notably, PA-MSHA, but not BCG, significantly reduced PD-1 and TIM-3 expression on CD4+ T cells, which might account for the better effects of PA-MSHA than BCG. However, in tumor-bearing mice after treatment, the increased proportion of Mo-MDSCs and high expression of PD-L1 might be involved in treatment failure. Thus, modulating the balance among adaptive and innate immune responses was identified as a key process underlying PA-MSHA-mediated treatment efficacy. The results demonstrated mechanisms underlying intravesical PA-MSHA therapy, pointing at its potential as an alternative effective treatment for NMIBC.

Global research trends and foci of artificial intelligence-based tumor pathology: a scientometric study.

BACKGROUND: With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve the challenges faced by traditional pathology. This technology has attracted the attention of scholars in many fields and a large amount of articles have been published. This study mainly summarizes the knowledge structure of AI-based tumor pathology through bibliometric analysis, and discusses the potential research trends and foci. METHODS: Publications related to AI-based tumor pathology from 1999 to 2021 were selected from Web of Science Core Collection. VOSviewer and Citespace were mainly used to perform and visualize co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references and keywords in this field. RESULTS: A total of 2753 papers were included. The papers on AI-based tumor pathology research had been continuously increased since 1999. The United States made the largest contribution in this field, in terms of publications (1138, 41.34%), H-index (85) and total citations (35,539 times). We identified the most productive institution and author were Harvard Medical School and Madabhushi Anant, while Jemal Ahmedin was the most co-cited author. Scientific Reports was the most prominent journal and after analysis, Lecture Notes in Computer Science was the journal with highest total link strength. According to the result of references and keywords analysis, "breast cancer histopathology" "convolutional neural network" and "histopathological image" were identified as the major future research foci. CONCLUSIONS: AI-based tumor pathology is in the stage of vigorous development and has a bright prospect. International transboundary cooperation among countries and institutions should be strengthened in the future. It is foreseeable that more research foci will be lied in the interpretability of deep learning-based model and the development of multi-modal fusion model.

Development of a radiomics model to diagnose pheochromocytoma preoperatively: a multicenter study with prospective validation.

BACKGROUND: Preoperative diagnosis of pheochromocytoma (PHEO) accurately impacts preoperative preparation and surgical outcome in PHEO patients. Highly reliable model to diagnose PHEO is lacking. We aimed to develop a magnetic resonance imaging (MRI)-based radiomic-clinical model to distinguish PHEO from adrenal lesions. METHODS: In total, 305 patients with 309 adrenal lesions were included and divided into different sets. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and radiomics signature building. In addition, a nomogram incorporating the obtained radiomics signature and selected clinical predictors was developed by using multivariable logistic regression analysis. The performance of the radiomic-clinical model was assessed with respect to its discrimination, calibration, and clinical usefulness. RESULTS: Seven radiomics features were selected among the 1301 features obtained as they could differentiate PHEOs from other adrenal lesions in the training (area under the curve [AUC], 0.887), internal validation (AUC, 0.880), and external validation cohorts (AUC, 0.807). Predictors contained in the individualized prediction nomogram included the radiomics signature and symptom number (symptoms include headache, palpitation, and diaphoresis). The training set yielded an AUC of 0.893 for the nomogram, which was confirmed in the internal and external validation sets with AUCs of 0.906 and 0.844, respectively. Decision curve analyses indicated the nomogram was clinically useful. In addition, 25 patients with 25 lesions were recruited for prospective validation, which yielded an AUC of 0.917 for the nomogram. CONCLUSION: We propose a radiomic-based nomogram incorporating clinically useful signatures as an easy-to-use, predictive and individualized tool for PHEO diagnosis.

NONO Inhibits Lymphatic Metastasis of Bladder Cancer via Alternative Splicing of SETMAR.

Bladder cancer patients with lymph node (LN) metastasis have an extremely poor prognosis and no effective treatment. The alternative splicing of precursor (pre-)mRNA participates in the progression of various tumors. However, the precise mechanisms of splicing factors and cancer-related variants in LN metastasis of bladder cancer remain largely unknown. The present study identified a splicing factor, non-POU domain-containing octamer-binding protein (NONO), that was significantly downregulated in bladder cancer tissues and correlated with LN metastasis status, tumor stage, and prognosis. Functionally, NONO markedly inhibited bladder cancer cell migration and invasion in vitro and LN metastasis in vivo. Mechanistically, NONO regulated the exon skipping of SETMAR by binding to its motif, mainly through the RRM2 domain. NONO directly interacted with splicing factor proline/glutamine rich (SFPQ) to regulate the splicing of SETMAR, and it induced metastasis suppression of bladder cancer cells. SETMAR-L overexpression significantly reversed the metastasis of NONO-knockdown bladder cancer cells, both in vitro and in vivo. The further analysis revealed that NONO-mediated SETMAR-L can induce H3K27me3 at the promotor of metastatic oncogenes and inhibit their transcription, ultimately resulting in metastasis suppression. Therefore, the present findings uncover the molecular mechanism of lymphatic metastasis in bladder cancer, which may provide novel clinical markers and therapeutic strategies for LN-metastatic bladder cancer.

A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning.

Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Therefore, we aimed to develop a radiomic model for preoperatively identifying infection stones with multicenter validation. In total, 1198 eligible patients with urolithiasis from three centers were divided into a training set, an internal validation set, and two external validation sets. Stone composition was determined by Fourier transform infrared spectroscopy. A total of 1316 radiomic features were extracted from the pre-treatment Computer Tomography images of each patient. Using the least absolute shrinkage and selection operator algorithm, we identified a radiomic signature that achieved favorable discrimination in the training set, which was confirmed in the validation sets. Moreover, we then developed a radiomic model incorporating the radiomic signature, urease-producing bacteria in urine, and urine pH based on multivariate logistic regression analysis. The nomogram showed favorable calibration and discrimination in the training and three validation sets (area under the curve [95% confidence interval], 0.898 [0.840-0.956], 0.832 [0.742-0.923], 0.825 [0.783-0.866], and 0.812 [0.710-0.914], respectively). Decision curve analysis demonstrated the clinical utility of the radiomic model. Thus, our proposed radiomic model can serve as a non-invasive tool to identify urinary infection stones in vivo, which may optimize disease management in urolithiasis and improve patient prognosis.