Bibliometric insights into drug resistance in bladder cancer: Two decades of progress (1999-2022).

Aims: To provide a comprehensive bibliometric overview of drug resistance in bladder cancer (BC) from 1999 to 2022, aiming to illuminate its historical progression and guide future investigative avenues. Methods: Literature on BC drug resistance between 1999 and 2022 was sourced from the Web of Science. Visual analyses were executed using Vosviewer and Citespace software, focusing on contributions by countries, institutions, journals, authors, references, and keywords. Results: From 2727 publications, a marked growth in BC drug resistance studies was discerned over the two decades. Prominent among all institutions is the University of Texas System. The majority of top-ranked journals were American. In authorship significance, McConkey DJ led in publications, while Bellmunt J dominated in citations. Research topics predominantly spanned cancer demographics, drug efficacy evaluations, molecular features, oncology subtypes, and individualized treatment strategies, with a notable contemporary emphasis on molecular mechanisms behind drug resistance and nuances of ICIs. Conclusions: Our bibliometric analysis charts the landscape of BC drug resistance research from 1999 to 2022. While the study of resistance mechanisms has been robust, there's an evident need for deeper exploration into the molecular intricacies and the potential of ICIs and targeted therapeutic strategies.

Development and validation of an artificial intelligence-based model for detecting urothelial carcinoma using urine cytology images: a multicentre, diagnostic study with prospective validation.

Background: Urine cytology is an important non-invasive examination for urothelial carcinoma (UC) diagnosis and follow-up. We aimed to explore whether artificial intelligence (AI) can enhance the sensitivity of urine cytology and help avoid unnecessary endoscopy. Methods: In this multicentre diagnostic study, consecutive patients who underwent liquid-based urine cytology examinations at four hospitals in China were included for model development and validation. Patients who declined surgery and lacked associated histopathology results, those diagnosed with rare subtype tumours of the urinary tract, or had low-quality images were excluded from the study. All liquid-based cytology slides were scanned into whole-slide images (WSIs) at 40 × magnification and the WSI-labels were derived from the corresponding histopathology results. The Precision Urine Cytology AI Solution (PUCAS) was composed of three distinct stages (patch extraction, features extraction, and classification diagnosis) and was trained to identify important WSI features associated with UC diagnosis. The diagnostic sensitivity was mainly used to validate the performance of PUCAS in retrospective and prospective validation cohorts. This study is registered with the ChiCTR, ChiCTR2300073192. Findings: Between January 1, 2018 and October 31, 2022, 2641 patients were retrospectively recruited in the training cohort, and 2335 in retrospective validation cohorts; 400 eligible patients were enrolled in the prospective validation cohort between July 7, 2023 and September 15, 2023. The sensitivity of PUCAS ranged from 0.922 (95% CI: 0.811-0.978) to 1.000 (0.782-1.000) in retrospective validation cohorts, and was 0.896 (0.837-0.939) in prospective validation cohort. The PUCAS model also exhibited a good performance in detecting malignancy within atypical urothelial cells cases, with a sensitivity of over 0.84. In the recurrence detection scenario, PUCAS could reduce 57.5% of endoscopy use with a negative predictive value of 96.4%. Interpretation: PUCAS may help to improve the sensitivity of urine cytology, reduce misdiagnoses of UC, avoid unnecessary endoscopy, and reduce the clinical burden in resource-limited areas. The further validation in other countries is needed. Funding: National Natural Science Foundation of China; Key Program of the National Natural Science Foundation of China; the National Science Foundation for Distinguished Young Scholars; the Science and Technology Planning Project of Guangdong Province; the National Key Research and Development Programme of China; Guangdong Provincial Clinical Research Centre for Urological Diseases.

Estimation of the tumor size at cure threshold among adult patients with adrenocortical carcinoma: A populational-based study.

Background: The prognostic significance of tumor size with adrenocortical carcinoma (ACC) patients has not yet been thoroughly evaluated. Our objective was to investigate the influence of tumor size on prognostic value in adult ACC patients. Methods: The Surveillance, Epidemiology and End Results Program (SEER) was employed to identify adult ACC patients who had been diagnosed from 2004 to 2015. The "X-Tile" program determined the optimal cutoff value of tumor size. Cancer-specific survival (CSS) and overall survive (OS) were estimated. The survival outcomes and risk factors were analyzed by the Kaplan-Meier methods and the multivariable cox regression respectively. Results: A total 426 adult ACC patients were included. Univariable and multivariable cox analysis revealed age, larger tumor size and metastasis as consistent predictors of lower CSS and OS. The optimal cutoff value of tumor size was identified as 8.5 cm using X-tile software, and Kaplan-Meier method showed dramatic prognostic difference between patients with larger tumors (＞8.5 cm) and smaller tumors (≤8.5 cm) (log-rank test, P < 0.001). Subgroup analyses revealed no statistical significance and a consistent proportionate effect of tumor size on CSS and OS across all eight pre-specified subgroups. Interestingly, an additional subgroup analysis showed that ACC patients could not benefit from chemotherapy in terms of CSS and OS. Conclusion: The study suggests that tumor size is a crucial prognostic factor in ACC patients and a cutoff value 8.5 cm might indicate a poor outcome. Given the limitations of the available data, it is challenging to conclusively determine the benefit of chemotherapy in adult ACC patients across different tumor size ranges.

An artificial intelligence model for detecting pathological lymph node metastasis in prostate cancer using whole slide images: a retrospective, multicentre, diagnostic study.

Background: The pathological examination of lymph node metastasis (LNM) is crucial for treating prostate cancer (PCa). However, the limitations with naked-eye detection and pathologist workload contribute to a high missed-diagnosis rate for nodal micrometastasis. We aimed to develop an artificial intelligence (AI)-based, time-efficient, and high-precision PCa LNM detector (ProCaLNMD) and evaluate its clinical application value. Methods: In this multicentre, retrospective, diagnostic study, consecutive patients with PCa who underwent radical prostatectomy and pelvic lymph node dissection at five centres between Sep 2, 2013 and Apr 28, 2023 were included, and histopathological slides of resected lymph nodes were collected and digitised as whole-slide images for model development and validation. ProCaLNMD was trained at a dataset from a single centre (the Sun Yat-sen Memorial Hospital of Sun Yat-sen University [SYSMH]), and externally validated in the other four centres. A bladder cancer dataset from SYSMH was used to further validate ProCaLNMD, and an additional validation (human-AI comparison and collaboration study) containing consecutive patients with PCa from SYSMH was implemented to evaluate the application value of integrating ProCaLNMD into the clinical workflow. The primary endpoint was the area under the receiver operating characteristic curve (AUROC) of ProCaLNMD. In addition, the performance measures for pathologists with ProCaLNMD assistance was also assessed. Findings: In total, 8225 slides from 1297 patients with PCa were collected and digitised. Overall, 8158 slides (18,761 lymph nodes) from 1297 patients with PCa (median age 68 years [interquartile range 64-73]; 331 [26%] with LNM) were used to train and validate ProCaLNMD. The AUROC of ProCaLNMD ranged from 0.975 (95% confidence interval 0.953-0.998) to 0.992 (0.982-1.000) in the training and validation datasets, with sensitivities > 0.955 and specificities > 0.921. ProCaLNMD also demonstrated an AUROC of 0.979 in the cross-cancer dataset. ProCaLNMD use triggered true reclassification in 43 (4.3%) slides in which micrometastatic tumour regions were initially missed by pathologists, thereby correcting 28 (8.5%) missed-diagnosed cases of previous routine pathological reports. In the human-AI comparison and collaboration study, the sensitivity of ProCaLNMD (0.983 [0.908-1.000]) surpassed that of two junior pathologists (0.862 [0.746-0.939], P = 0.023; 0.879 [0.767-0.950], P = 0.041) by 10-12% and showed no difference to that of two senior pathologists (both 0.983 [0.908-1.000], both P > 0.99). Furthermore, ProCaLNMD significantly boosted the diagnostic sensitivity of two junior pathologists (both P = 0.041) to the level of senior pathologists (both P > 0.99), and substantially reduced the four pathologists' slide reviewing time (-31%, P < 0.0001; -34%, P < 0.0001; -29%, P < 0.0001; and -27%, P = 0.00031). Interpretation: ProCaLNMD demonstrated high diagnostic capabilities for identifying LNM in prostate cancer, reducing the likelihood of missed diagnoses by pathologists and decreasing the slide reviewing time, highlighting its potential for clinical application. 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, the Guangdong Provincial Clinical Research Centre for Urological Diseases, and the Science and Technology Projects in Guangzhou.

A bibliometric insight into neoadjuvant chemotherapy in bladder cancer: trends, collaborations, and future avenues.

Background: Neoadjuvant chemotherapy (NAC) followed by radical cystectomy (RC) remains the cornerstone of treatment for muscle-invasive bladder cancer (MIBC). While platinum-based regimens have demonstrated benefits in tumor downstaging and improved long-term survival for selected patients, they may pose risks for those who are ineligible or unresponsive to chemotherapy. Objective: We undertook a bibliometric analysis to elucidate the breadth of literature on NAC in bladder cancer, discern research trajectories, and underscore emerging avenues of investigation. Methods: A systematic search of the Web of Science Core Collection (WoSCC) was conducted to identify articles pertaining to NAC in bladder cancer from 1999 to 2022. Advanced bibliometric tools, such as VOSviewer, CiteSpace, and SCImago Graphica, facilitated the examination and depicted the publication trends, geographic contributions, institutional affiliations, journal prominence, author collaborations, and salient keywords, emphasizing the top 25 citation bursts. Results: Our analysis included 1836 publications spanning 1999 to 2022, indicating a growing trend in both annual publications and citations related to NAC in bladder cancer. The United States emerged as the predominant contributor in terms of publications, citations, and international collaborations. The University of Texas was the leading institution in publication output. "Urologic Oncology Seminars and Original Investigations" was the primary publishing journal, while "European Urology" boasted the highest impact factor. Shariat, Shahrokh F., and Grossman, H.B., were identified as the most prolific and co-cited authors, respectively. Keyword analysis revealed both frequency of occurrence and citation bursts, highlighting areas of concentrated study. Notably, the integration of immunochemotherapy is projected to experience substantial growth in forthcoming research. Conclusions: Our bibliometric assessment provides a panoramic view of the research milieu surrounding neoadjuvant chemotherapy for bladder cancer, encapsulating the present state, evolving trends, and potential future directions, with a particular emphasis on the promise of immunochemotherapy.

Immune regulation and the tumor microenvironment in anti-PD-1/PDL-1 and anti-CTLA-4 therapies for cancer immune evasion: A bibliometric analysis.

This study aims to conduct a bibliometric analysis, employing visualization tools to examine literature pertaining to tumor immune evasion related to anti-CTLA-4 and anti-PD-1/PD-L1 therapy from 1999 to 2022. A special emphasis is placed on the interplay between tumor microenvironment, signaling pathways, immune cells and immune evasion, with data sourced from the Web of Science core collection (WoSCC). Advanced tools, including VOSviewer, Citespace, and Scimago Graphica, were utilized to analyze various parameters, such as co-authorship/co-citation patterns, regional contributions, journal preferences, keyword co-occurrences, and significant citation bursts. Out of 4778 publications reviewed, there was a marked increase in research focusing on immune evasion, with bladder cancer being notably prominent. Geographically, China, the USA, and Japan were the leading contributors. Prestigious institutions like MD Anderson Cancer Center, Harvard Medical School, Fudan University, and Sun Yat Sen University emerged as major players. Renowned journals in this domain included Frontiers in Immunology, Cancers, and Frontiers in Oncology. Ehen LP and Wang W were identified as prolific authors on this topic, while Topalian SL stood out as one of the most cited. Research current situation is notably pivoting toward challenges like immunotherapy resistance and the intricate signaling pathways driving drug resistance. This bibliometric study seeks to provide a comprehensive overview of past and current research trends, emphasizing the potential role of tumor microenvironment, signaling pathways and immune cells in the context of immune checkpoint inhibitors (ICIs) and tumor immune evasion.

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.

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.