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.

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.

MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma.

OBJECTIVES: It is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous differentiation (UC w/SD) from pure UC in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. We developed a non-invasive automated machine learning (AutoML) model to preoperatively differentiate UC w/SD from pure UC in patients with MIBC. METHODS: A total of 119 MIBC patients who underwent baseline bladder MRI were enrolled in this study, including 38 patients with UC w/SD and 81 patients with pure UC. These patients were randomly assigned to a training set or a test set (3:1). An AutoML model was built from the training set, using 13 selected radiomic features from T2-weighted imaging, semantic features (ADC values), and clinical features (tumor length, tumor stage, lymph node metastasis status), and subsequent ten-fold cross-validation was performed. A test set was used to validate the proposed model. The AUC of the ROC curve was then calculated for the model. RESULTS: This AutoML model enabled robust differentiation of UC w/SD and pure UC in patients with MIBC in both training set (ten-fold cross-validation AUC = 0.955, 95% confidence interval [CI]: 0.944-0.965) and test set (AUC = 0.932, 95% CI: 0.812-1.000). CONCLUSION: The presented AutoML model, that incorporates the radiomic, semantic, and clinical features from baseline MRI, could be useful for preoperative differentiation of UC w/SD and pure UC. CLINICAL RELEVANCE STATEMENT: This MRI-based automated machine learning (AutoML) study provides a non-invasive and low-cost preoperative prediction tool to identify the muscle-invasive bladder cancer patients with variant histology, which may serve as a useful tool for clinical decision-making. KEY POINTS: • It is important to preoperatively diagnose variant histology from urothelial carcinoma in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. • An automated machine learning (AutoML) model based on baseline bladder MRI can identify the variant histology (squamous differentiation) from urothelial carcinoma preoperatively in patients with MIBC. • The developed AutoML model is a non-invasive and low-cost preoperative prediction tool, which may be useful for clinical decision-making.

Construction of 3D and 2D contrast-enhanced CT radiomics for prediction of CGB3 expression level and clinical prognosis in bladder cancer.

Objective: The purpose of this study was to construct a 3D and 2D contrast-enhanced computed tomography (CECT) radiomics model to predict CGB3 levels and assess its prognostic abilities in bladder cancer (Bca) patients. Methods: Transcriptome data and CECT images of Bca patients were downloaded from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database. Clinical data of 43 cases from TCGA and TCIA were used for radiomics model evaluation. The Volume of interest (VOI) (3D) and region of interest (ROI) (2D) radiomics features were extracted. For the construction of predicting radiomics models, least absolute shrinkage and selection operator regression were used, and the filtered radiomics features were fitted using the logistic regression algorithm (LR). The model's effectiveness was measured using 10-fold cross-validation and the area under the receiver operating characteristic curve (AUC of ROC). Result: CGB3 was a differential expressed prognosis-related gene and involved in the immune response process of plasma cells and T cell gamma delta. The high levels of CGB3 are a risk element for overall survival (OS). The AUCs of VOI and ROI radiomics models in the training set were 0.841 and 0.776, while in the validation set were 0.815 and 0.754, respectively. The Delong test revealed that the AUCs of the two models were not statistically different, and both models had good predictive performance. Conclusion: The CGB3 expression level is an important prognosis factor for Bca patients. Both 3D and 2D CECT radiomics are effective in predicting CGB3 expression levels.

Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism.

OBJECTIVES: We aimed to use machine learning (ML) algorithms to risk stratify the prognosis of critical pulmonary embolism (PE). MATERIAL AND METHODS: In total, 1229 patients were obtained from MIMIC-IV database. Main outcomes were set as all-cause mortality within 30 days. Logistic regression (LR) and simplified eXtreme gradient boosting (XGBoost) were applied for model constructions. We chose the final models based on their matching degree with data. To simplify the model and increase its usefulness, finally simplified models were built based on the most important 8 variables. Discrimination and calibration were exploited to evaluate the prediction ability. We stratified the risk groups based on risk estimate deciles. RESULTS: The simplified XGB model performed better in model discrimination, which AUC were 0.82 (95% CI: 0.78-0.87) in the validation cohort, compared with the AUC of simplified LR model (0.75 [95% CI: 0.69-0.80]). And XGB performed better than sPESI in the validation cohort. A new risk-classification based on XGB could accurately predict low-risk of mortality, and had high consistency with acknowledged risk scores. CONCLUSIONS: ML models can accurately predict the 30-day mortality of critical PE patients, which could further be used to reduce the burden of ICU stay, decrease the mortality and improve the quality of life for critical PE patients.

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.

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.

An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study.

BACKGROUND: Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. METHODS: In total, 69 204 images from 10 729 consecutive patients from 6 hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. RESULTS: The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974 to 0.979) in the internal validation set and 0.990 (95% CI = 0.979 to 0.996), 0.982 (95% CI = 0.974 to 0.988), 0.978 (95% CI = 0.959 to 0.989), and 0.991 (95% CI = 0.987 to 0.994) in different external validation sets. In the CAIDS vs urologists' comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902 to 0.964; sensitivity = 0.954, 95% CI = 0.902 to 0.983) with a short latency of 12 seconds, much more accurate and quicker than the expert urologists. CONCLUSIONS: The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.

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.

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.

Association of chromosome 7 aneuploidy measured by fluorescence in situ hybridization assay with muscular invasion in bladder cancer.

BACKGROUND: The preoperative prediction of muscular invasion status is important for adequately treating bladder cancer (BC) but nevertheless, there are some existing dilemmas in the current preoperative diagnostic accuracy of BC with muscular invasion. Here, we investigated the potential association between the fluorescence in situ hybridization (FISH) assay and muscular invasion among patients with BC. A cytogenetic-clinical nomogram for the individualized preoperative differentiation of muscle-invasive BC (MIBC) from non-muscle-invasive BC (NMIBC) is also proposed. METHODS: All eligible BC patients were preoperatively tested using a FISH assay, which included 4 sites (chromosome-specific centromeric probe [CSP] 3, 7, and 17, and gene locus-specific probe [GLP]-p16 locus). The correlation between the FISH assay and BC muscular invasion was evaluated using the Chi-square tests. In the training set, univariate and multivariate logistic regression analyses were used to develop a cytogenetic-clinical nomogram for preoperative muscular invasion prediction. Then, we assessed the performance of the nomogram in the training set with respect to its discriminatory accuracy and calibration for predicting muscular invasion, and clinical usefulness, which were then validated in the validation set. Moreover, model comparison was set to evaluate the discrimination and clinical usefulness between the nomogram and the individual variables incorporated in the nomogram. RESULTS: Muscular invasion was more prevalent in BC patients with positive CSP3, CSP7 and CSP17 status (OR [95% CI], 2.724 [1.555 to 4.774], P < 0.001; 3.406 [1.912 to 6.068], P < 0.001 and 2.483 [1.436 to 4.292], P = 0.001, respectively). Radiology-determined tumor size, radiology-determined clinical tumor stage and CSP7 status were identified as independent risk factors of BC muscular invasion by the multivariate regression analysis in the training set. Then, a cytogenetic-clinical nomogram incorporating these three independent risk factors was constructed and was observed to have satisfactory discrimination in the training (AUC 0.784; 95% CI: 0.715 to 0.853) and validation (AUC 0.743; 95% CI: 0.635 to 0.850) set. The decision curve analysis (DCA) indicated the clinical usefulness of our nomogram. In models comparison, using the receiver operator characteristic (ROC) analyses, the nomogram showed higher discriminatory accuracy than any variables incorporated in the nomogram alone and the DCAs also identified the nomogram as possessing the highest net benefits at wide range of threshold probabilities. CONCLUSION: CSP7 status was identified as an independent factor for predicting muscular invasion in BC patients and was successfully incorporated in a clinical nomogram combining the results of the FISH assay with clinical risk factors.

Long-Term Oncologic Outcomes After Laparoscopic and Robotic Tumor Enucleation for Renal Cell Carcinoma.

OBJECTIVES: Tumor enucleation (TE) optimizes parenchymal preservation with promising short-term oncologic outcomes compared with standard partial nephrectomy (SPN). However, researches/literatures about long-term oncologic outcomes for TE after minimally invasive surgery are scarce. We aim to analyze long-term oncologic outcomes after laparoscopic and robotic tumor enucleation for renal cell carcinoma (RCC). PATIENTS AND METHODS: We retrospectively analyzed 146 patients who underwent TE with either laparoscopic or robotic approach for localized RCC in our center. Local recurrence, cancer specific survival (CSS), recurrence free survival (RFS), and overall survival (OS) were the main outcomes. Survival curves were generated using a Kaplan-Meier method. Perioperative outcomes and pathological outcomes were also analyzed. RESULTS: Overall, 98 male and 48 female patients were eligible for the study. The median tumor size was 3.4 cm with a median R.E.N.A.L. score of seven. Warm ischemia was used in 143 patients with a median ischemia time of 20 min and three patients had zero ischemia. Five patients (3.4%) had major complications (> Clavien IIIa) and only two were related to urinary system. The median global glomerular filtration rate (GFR) preserved after surgery was 93%. Pseudocapsule invasion was reported in 50 tumors (34%) and positive surgical margins were found in 3/146 (2.1%) tumors. At a median follow-up of 66 months, local recurrence happened in two patients (1.4%), and systemic recurrence happened in six patients (4.2%). The 5-year CSS, RFS, OS were 95.7, 89.6, and 91.9%, and the 10-year CSS, RFS, OS were 93.8, 89.6, and 90.0%, respectively. CONCLUSION: This study indicates that tumor enucleation with laparoscopic or robotic approach in experienced hands for the treatment of RCC appears oncologically safe with a median follow-up of more than 5 years. Prospective studies with more patients and longer follow-up will be required to further evaluate oncologic safety after TE.

A nomogram for individualized estimation of survival among adult patients with adrenocortical carcinoma after surgery: a retrospective analysis and multicenter validation study.

BACKGROUND: Clinical outcome of adrenocortical carcinoma (ACC) varies because of its heterogeneous nature and reliable prognostic prediction model for adult ACC patients is limited. The objective of this study was to develop and externally validate a nomogram for overall survival (OS) prediction in adult patients with ACC after surgery. METHODS: Based on the data from the Surveillance Epidemiology, and End Results (SEER) database, adults patients diagnosed with ACC between January 1988 and December 2015 were identified and classified into a training set, comprised of 404 patients diagnosed between January 2007 and December 2015, and an internal validation set, comprised of 318 patients diagnosed between January 1988 and December 2006. The endpoint of this study was OS. The nomogram was developed using a multivariate Cox proportional hazards regression algorithm in the training set and its performance was evaluated in terms of its discriminative ability, calibration, and clinical usefulness. The nomogram was then validated using the internal SEER validation, also externally validated using the Cancer Genome Atlas set (TCGA, 82 patients diagnosed between 1998 and 2012) and a Chinese multicenter cohort dataset (82 patients diagnosed between December 2002 and May 2018), respectively. RESULTS: Age at diagnosis, T stage, N stage, and M stage were identified as independent predictors for OS. A nomogram incorporating these four predictors was constructed using the training set and demonstrated good calibration and discrimination (C-index 95% confidence interval [CI], 0.715 [0.679-0.751]), which was validated in the internal validation set (C-index [95% CI], 0.672 [0.637-0.707]), the TCGA set (C-index [95% CI], 0.810 [0.732-0.888]) and the Chinese multicenter set (C-index [95% CI], 0.726 [0.633-0.819]), respectively. Encouragingly, the nomogram was able to successfully distinguished patients with a high-risk of mortality in all enrolled patients and in the subgroup analyses. Decision curve analysis indicated that the nomogram was clinically useful and applicable. CONCLUSIONS: The study presents a nomogram that incorporates clinicopathological predictors, which can accurately predict the OS of adult ACC patients after surgery. This model and the corresponding risk classification system have the potential to guide therapy decisions after surgery.

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach.

BACKGROUND: Bladder cancer (BCa) can be divided into muscle-invasive BCa (MIBC) and non-muscle-invasive BCa (NMIBC). Whether the tumor infiltrates the detrusor muscle is a critical determinant of disease management in patients with BCa. However, the current preoperative diagnostic accuracy of muscular invasiveness is less than satisfactory. The authors report a radiomic-clinical nomogram for the individualized preoperative differentiation of MIBC from NMIBC. METHODS: In total, 2602 radiomics features were extracted from whole bladder tumors and the basal part of the lesions on T2-weighted magnetic resonance imaging. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set (n = 130). Furthermore, a radiomic-clinical nomogram was developed incorporating the radiomics signature and selected clinical predictors based on a multivariable logistic regression analysis. The performance of the nomogram (discrimination, calibration, and clinical usefulness) was assessed and validated in an independent validation set (n = 69). RESULTS: The radiomics signature, consisting of 23 selected features, showed good discrimination in the training and validation sets (area under the curve [AUC], 0.913 and 0.874, respectively). Incorporating the radiomics signature and magnetic resonance imaging-determined tumor size, the radiomic-clinical nomogram showed favorable calibration and discrimination in the training set with an AUC of 0.922, which was confirmed in the validation set (AUC, 0.876). Decision curve analysis and net reclassification improvement and integrated discrimination improvement indices (net reclassification improvement, 0.338, integrated discrimination improvement, 0.385) demonstrated the clinical usefulness of the nomogram. CONCLUSIONS: The proposed noninvasive radiomic-clinical nomogram can increase the accuracy of preoperatively discriminating MIBC from NMIBC, which may aid in clinical decision making and improve patient prognosis.

Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer.

BACKGROUND: Preoperative lymph node (LN) status is important for the treatment of bladder cancer (BCa). However, a proportion of patients are at high risk for inaccurate clinical nodal staging by current methods. Here, we report an accurate magnetic resonance imaging (MRI)-based radiomics signature for the individual preoperative prediction of LN metastasis in BCa. METHODS: In total, 103 eligible BCa patients were divided into a training set (n = 69) and a validation set (n = 34). And 718 radiomics features were extracted from the cancerous volumes of interest (VOIs) on T2-weighted MRI images. A radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm in the training set, whose performance was assessed and then validated in the validation set. Stratified analyses were also performed. Based on the multivariable logistic regression analysis, a radiomics nomogram was developed incorporating the radiomics signature and selected clinical predictors. Discrimination, calibration and clinical usefulness of the nomogram were assessed. FINDINGS: Consisting of 9 selected features, the radiomics signature showed a favorable discriminatory ability in the training set with an AUC of 0.9005, which was confirmed in the validation set with an AUC of 0.8447. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN negative (cN0) subgroup (AUC, 0.8406). The nomogram, consisting of the radiomics signature and the MRI-reported LN status, showed good calibration and discrimination in the training and validation sets (AUC, 0.9118 and 0.8902, respectively). The decision curve analysis indicated that the nomogram was clinically useful. INTERPRETATION: The MRI-based radiomics nomogram has the potential to be used as a non-invasive tool for individualized preoperative prediction of LN metastasis in BCa. External validation is further required prior to clinical implementation.

A Genomic-clinicopathologic Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer.

Preoperative lymph node (LN) status is important for the treatment of bladder cancer (BCa). Here, we report a genomic-clinicopathologic nomogram for preoperatively predicting LN metastasis in BCa. In the discovery stage, 325 BCa patients from TCGA were involved and LN-status-related mRNAs were selected. In the training stage, multivariate logistic regression analysis was used to developed a genomic-clinicopathologic nomogram for preoperative LN metastasis prediction in the training set (SYSMH set, n=178). In the validation stage, we validated the nomogram using two independent sample sets (SYSUCC set, n=142; RJH set, n=104) with respect to its discrimination, calibration and clinical usefulness. As results, we identified five LN-status-related mRNAs, including ADRA1D, COL10A1, DKK2, HIST2H3D and MMP11. Then, a genomic classifier was developed to classify patients into high- and low-risk groups in the training set. Furthermore, a nomogram incorporating the five-mRNA-based classifier, image-based LN status, transurethral resection (TUR) T stage, and TUR lymphovascular invasion (LVI) was constructed in the training set, which performed well in the training and validation sets. Decision curve analysis demonstrated the clinical value of our nomogram. Thus, our genomic-clinicopathologic nomogram shows favorable discriminatory ability and may aid in clinical decision-making, especially for cN-patients.

Computed tomography and magnetic resonance imaging evaluation of pelvic lymph node metastasis in bladder cancer.

BACKGROUND: Accurate evaluation of lymph node metastasis in bladder cancer (BCa) is important for disease staging, treatment selection, and prognosis prediction. In this study, we aimed to evaluate the diagnostic accuracy of computed tomography (CT) and magnetic resonance imaging (MRI) for metastatic lymph nodes in BCa and establish criteria of imaging diagnosis. METHODS: We retrospectively assessed the imaging characteristics of 191 BCa patients who underwent radical cystectomy. The data regarding size, shape, density, and diffusion of the lymph nodes on CT and/or MRI were obtained and analyzed using Kruskal-Wallis test and χ2 test. The optimal cutoff value for the size of metastatic node was determined using the receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 184 out of 3317 resected lymph nodes were diagnosed as metastatic lymph nodes. Among 82 imaging-detectable lymph nodes, 51 were confirmed to be positive for metastasis. The detection rate of metastatic nodes increased along with more advanced tumor stage (P < 0.001). Once the ratio of short- to long-axis diameter ≤ 0.4 or fatty hilum was observed in lymph nodes on imaging, it indicated non-metastases. Besides, lymph nodes with spiculate or obscure margin or necrosis indicated metastases. Furthermore, the short diameter of 6.8 mm was the optimal threshold to diagnose metastatic lymph node, with the area under ROC curve of 0.815. CONCLUSIONS: The probability of metastatic nodes significantly increased with more advanced T stages. Once lymph nodes are detected on imaging, the characteristic signs should be paid attention to. The short diameter > 6.8 mm may indicate metastatic lymph nodes in BCa.