Independent External Validation Research Articles

Identification of NINJ1 as a novel prognostic predictor for retroperitoneal liposarcoma

BackgroundRetroperitoneal liposarcoma (RPLS) is known for its propensity for local recurrence and short survival time. We aimed to identify a credible and specific prognostic biomarker for RPLS.MethodsCases from The Cancer Genome Atlas (TCGA) sarcoma dataset were included as the training group. Co-expression modules were constructed using weighted gene co-expression network analysis (WGCNA) to explore associations between modules and survival. Survival analysis of hub genes was performed using the Kaplan–Meier method. In addition, independent external validation was performed on a cohort of 135 Chinese RPLS patients from the REtroperitoneal SArcoma Registry (RESAR) study (NCT03838718).ResultsA total of 19 co-expression modules were constructed based on the expression levels of 26,497 RNAs in the TCGA cohort. Among these modules, the green module exhibited a positive correlation with overall survival (OS, p = 0.10) and disease-free survival (DFS, p = 0.06). Gene set enrichment analysis showed that the green module was associated with endocytosis and soft-tissue sarcomas. Survival analysis demonstrated that NINJ1, a hub gene within the green module, was positively associated with OS (p = 0.019) in the TCGA cohort. Moreover, in the validation cohort, patients with higher NINJ1 expression levels displayed a higher probability of survival for both OS (p = 0.023) and DFS (p = 0.012). Multivariable Cox analysis further confirmed the independent prognostic significance of NINJ1.ConclusionsWe here provide a foundation for the establishment of a consensus prognostic biomarker for RPLS, which should not only facilitate medical treatment but also guide the development of novel targeted drugs.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

The additive value of complementing diagnostic idiopathic intracranial hypertension criteria by MRI – an external validation study

BackgroundRecently, diagnostic criteria including a standardized MRI criterion were presented to identify patients suffering from idiopathic intracranial hypertension (IIH) proposing that IIH might be defined by two out of three objective findings (papilledema, ≥ 25 cm cerebrospinal fluid opening pressure (CSF-OP) and ≥ 3/4 neuroimaging signs).MethodsTo provide independent external validation, we retrospectively applied the proposed diagnostic criteria to our cohort of patients with clinical suspicion of IIH from the Vienna IIH database. Neuroimaging was reevaluated for IIH signs according to standardized definitions by a blinded expert neuroradiologist. We determined isolated diagnostic accuracy of the neuroimaging criterion (≥ 3/4 signs) as well as overall accuracy of the new proposed criteria.ResultsWe included patients with IIH (n = 102) and patients without IIH (no-IIH, n = 23). Baseline characteristics were balanced between IIH and no-IIH groups, but papilledema and CSF-OP were significantly higher in IIH. For the presence of ≥ 3/4 MRI signs, sensitivity was 39.2% and specificity was 91.3% with positive predictive value (PPV) of 95.2% and negative predictive value (NPV) 25.3%. Reclassifying our cohort according to the 2/3 IIH definition correctly identified 100% of patients without IIH, with definite IIH and suggested to have IIH without papilledema by Friedman criteria, respectively.ConclusionThe standardized neuroimaging criteria are easily applicable in clinical routine and provide moderate sensitivity and excellent specificity to identify patients with IIH. Defining IIH by 2/3 criteria significantly simplifies diagnosis without compromising accuracy.

Development and validation of a nomogram prediction model for ADHD in children based on individual, family, and social factors

ObjectivesA reliable, user-friendly, and multidimensional prediction tool can help to identify children at high risk for ADHD and facilitate early recognition and family management of ADHD. We aimed to develop and validate a risk nomogram for ADHD in children aged 3–17 years in the United States based on clinical manifestations and complex environments. MethodsA total of 141,356 cases were collected for the prediction model. Another 54,444 cases from a new data set were utilized for performing independent external validation. The LASSO regression was used to control possible variables. A final risk nomogram for ADHD was established based on logistic regression, and the discrimination and calibration of the established nomogram were evaluated by bootstrapping with 1000 resamples. ResultsA final risk nomogram for ADHD was established based on 13 independent predictors, including behavioral problems, learning disabilities, age, intellectual disabilities, anxiety symptoms, gender, premature birth, maternal age at childbirth, parent-child interaction patterns, etc. The C-index of this model was 0.887 in the training set, and 0.862 in the validation set. Internal and external validation proved that the model was reliable. ConclusionsA nomogram, a statistical prediction tool that assesses individualized ADHD risk for children is helpful for the early identification of children at high risk for ADHD and the construction of a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD.

External validation of the 2-year mortality prediction tool in hemodialysis patients developed using a Bayesian network

Abstract Background and hypothesis In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in haemodialysis patients, but only one, limited to patients aged over 70, has undergone sufficiently powerful external validation. Recently, using a national learning database and an innovative approach based on Bayesian networks and 14 carefully selected predictors, we have developed a clinical prediction tool to predict all-cause mortality at 2 years in all incident haemodialysis patients. In order to generalise the results of this tool and propose its use in routine clinical practice, we carried out an external validation using an independent external validation database. Methods A regional, multicenter, observational, retrospective cohort study was conducted to externally validate the tool for predicting 2-year all-cause mortality in incident and prevalent hemodialysis patients. This study recruited a total of 142 incident and 697 prevalent adult hemodialysis patients followed up in one of the 8 AURAL Alsace dialysis centers. Results In Incident patients, the 2-year all-cause mortality prediction tool had an area under the receiver curve of 0.73, an accuracy of 65%, a sensitivity of 71%, a specificity of 63%. In prevalent patients, the performance for the external validation were similar in terms of AUC-ROC, accuracy, and specificity but was lower in term of sensitivity. Conclusion The tool for predicting all-cause mortality at 2 years, developed using a Bayesian network and 14 routinely available explanatory variables, obtained satisfactory external validation in incident patients, but sensitivity was insufficient in prevalent patients.

Prognostic models and factors identifying end-of-life in non-cancer chronic diseases: a systematic review

BackgroundPrecise prognostic information, if available, is very helpful for guiding treatment decisions and resource allocation in patients with non-cancer non-communicable chronic diseases (NCDs). This study aimed to systematically review the...

Macrophage-coated tumor cluster aggravates hepatoma invasion and immunotherapy resistance via generating local immune deprivation.

Immune checkpoint inhibitors (ICIs) represent a promising treatment for hepatocellular carcinoma (HCC) due to their capacity for abundant lymphocyte infiltration. However, some patients with HCC respond poorly toICI therapy due to the presence of various immunosuppressive factors in the tumor microenvironment. Our research reveals that a macrophage-coated tumor cluster (MCTC) signifies a unique spatial structural organization in HCC correlating with diminished recurrence-free survival and overall survival in a total of 572 HCC cases from 3 internal cohorts and 2 independent external validation cohorts. Mechanistically, tumor-derived macrophage-associated lectin Mac-2 binding protein (M2BP) induces MCTC formation andtraps immunocompetent cells at the edge of MCTCs to induce intratumoral cytotoxic Tcell exclusion and local immune deprivation. Blocking M2BP with a Mac-2 antagonist might provide an effective approach to prevent MCTC formation, enhance Tcell infiltration, and thereby improve the efficacy of ICI therapy in HCC.

Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction.

An increasing amount of longitudinal health data is available on critically ill septic patients in the age of digital medicine, including daily sequential organ failure assessment (SOFA) score measurements. Thus, the assessment in sepsis focuses increasingly on the evaluation of the individual disease's trajectory. Machine learning (ML) algorithms may provide a promising approach here to improve the evaluation of daily SOFA score dynamics. We tested whether ML algorithms can outperform the conventional ΔSOFA score regarding the accuracy of 30-day mortality prediction. We used the multicentric SepsisDataNet.NRW study cohort that prospectively enrolled 252 sepsis patients between 03/2018 and 09/2019 for training ML algorithms, i.e. support vector machine (SVM) with polynomial kernel and artificial neural network (aNN). We used the Amsterdam UMC database covering 1,790 sepsis patients for external and independent validation. Both SVM (AUC 0.84; 95% CI: 0.71-0.96) and aNN (AUC 0.82; 95% CI: 0.69-0.95) assessing the SOFA scores of the first seven days led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score between day 1 and 7 (AUC 0.73; 95% CI: 0.65-0.80; p = 0.02 and p = 0.05, respectively). These differences were even more prominent the shorter the time interval considered. Using the SOFA scores of day 1 to 3 SVM (AUC 0.82; 95% CI: 0.68 0.95) and aNN (AUC 0.80; 95% CI: 0.660.93) led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score (AUC 0.66; 95% CI: 0.58-0.74; p < 0.01 and p < 0.01, respectively). Strikingly, all these findings could be confirmed in the independent external validation cohort. The ML-based algorithms using daily SOFA scores markedly improved the accuracy of mortality compared to the conventional ΔSOFA score. Therefore, this approach could provide a promising and automated approach to assess the individual disease trajectory in sepsis. These findings reflect the potential of incorporating ML algorithms as robust and generalizable support tools on intensive care units.

A machine learning-based diagnostic model for myocardial infarction patients: Analysis of neutrophil extracellular traps-related genes and eQTL Mendelian randomization.

To identify neutrophil extracellular trap (NET)-associated gene features in the blood of patients with myocardial infarction (MI) using bioinformatics and machine learning, with the aim of exploring potential diagnostic utility in atherosclerosis. The datasets GSE66360 and GSE48060 were downloaded from the Gene Expression Omnibus (GEO) public database. GSE66360 was used as the training set, and GSE48060 was used as an independent validation set. Differential genes related to NETs were screened using R software. Machine learning was performed based on the differential expression of NET-related genes across different samples. The advantages and disadvantages of 4 machine learning algorithms (Random Forest [RF], Extreme Gradient Boosting [XGBoost, XGB], Generalized Linear Models [GLM], and Support Vector Machine-Recursive Feature Elimination [SVM-RFE]) were compared, and the optimal method was used to screen feature genes and construct diagnostic models, which were then validated in the external validation dataset. Correlations between feature genes and immune cells were analyzed, and samples were reclustered based on the expression of feature genes. Differences in downstream molecular mechanisms and immune responses were explored for different clusters. Weighted Gene Co-expression Network Analysis was performed on different clusters, and disease-related NET genes were extracted, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Finally, Mendelian randomization was employed to further investigate the causal relationship between the expression of model genes and the occurrence of MI. Forty-seven NET-related differential genes were obtained, and after comparing the 4 machine learning methods, support vector machine was used to screen ATG7, MMP9, interleukin 6 (IL6), DNASE1, and PDE4B as key genes for the construction of diagnostic models. The diagnostic value of the model was validated in an independent external validation dataset. These five genes showed strong correlations with neutrophils. Different sample clusters also demonstrated differential enrichment in pathways such as nitrogen metabolism, complement and coagulation cascades, cytokine-cytokine receptor interaction, renin-angiotensin system, and steroid biosynthesis. The Mendelian randomization results demonstrate a causal relationship between the expression of ATG7 and the incidence of myocardial infarction. The feature genes ATG7, MMP9, IL6, DNASE1, and PDE4B, identified using bioinformatics, may serve as potential diagnostic biomarkers and therapeutic targets for Myocardial infarction. Specifically, the expression of ATG7 could potentially be a significant factor in the occurrence of MI.

Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images.

Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis. We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI. We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

BackgroundComputed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients.MethodsThis retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts.ResultsEighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869–0.906], 0.874 (95%CI 0.844–0.904) and 0.846 (95%CI 0.822–0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model.ConclusionsThe intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke.

The immune response is considered essential for pathology of ischemic stroke (IS), but it remains unclear which immune response-related proteins exhibit altered expression in IS patients. Here, we used Olink proteomics to examine the expression levels of 92 immune response-related proteins in the sera of IS patients (n = 88) and controls (n = 88), and we found that 59 of these proteins were differentially expressed. Feature variables were screened from the differentially expressed proteins by the least absolute shrinkage and selection operator (LASSO) and the random forest and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. Ultimately, we identified six potential protein biomarkers of IS, namely, MASP1, STC1, HCLS1, CLEC4D, PTH1R, and PIK3AP1, and established a logistic regression model that used these proteins to diagnose IS. The AUCs of the models in the internal validation and the test set were 0.962 (95% confidence interval (CI): 0.895-1.000) and 0.954 (95% CI: 0.884-1.000), respectively, and the same protein detection method was performed in an external independent validation set (AUC: 0.857 (95% CI: 0.801-0.913)). These proteins may play a role in immune regulation via the C-type lectin receptor signaling pathway, the PI3K-AKT signaling pathway, and the B-cell receptor signaling pathway.

Hydronephrosis severity as a predictor of postoperative renal function decline following laparoscopic radical nephroureterectomy.

This study aimed to investigate factors, including the degree of hydronephrosis, that may be associated with decreased renal function after radical nephroureterectomy (RNU). This study included 252 patients who underwent laparoscopic RNU with an estimated glomerular filtration rate (eGFR) ≥ 30ml/min/1.73 m2 in three institutions. We assessed the association between hydronephrosis grade and perioperative renal function and performed a stepwise multivariate linear regression analysis to identify factors associated with postoperative eGFR. Patients with preoperative eGFR ≥ 50ml/min/1.73 m2 were divided into a training set and an independent external validation set to develop a predictive model for postoperative renal function. The median preoperative and postoperative eGFR were 61.1 and 46.4ml/min/1.73 m2, respectively. The eGFR preservation rates were 66.9%, 66.6%, 88.1%, and 100.0% in groups without, with mild, moderate, and severe hydronephrosis, respectively, and this difference was statistically significant (p < 0.001). Multivariate analysis revealed that factors predictive of postoperative eGFR included sex, preoperative eGFR, clinical T stage (cT3-4), and the presence of moderate or severe hydronephrosis. Our predictive model, based on these factors, positively correlated with actual postoperative renal function, and the similarity in categories with or without renal function insufficiency between predicted and actual postoperative renal functions was 78% in both training and validation sets. Moderate or severe hydronephrosis is associated with a modest postoperative decline in renal function, while mild hydronephrosis is not. Our predictive model may be useful in predicting postoperative renal function insufficiency and guiding decision-making for perioperative medical treatment.

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review

BackgroundA comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation.MethodsPubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789).ResultsIn 20,887 screened references, 79 articles (82.5% in 2017–2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5–52,000, median 21) and large-span sample size (range 80–3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as “recommended”; however, 281 and 187 were “not recommended” and “warning,” respectively.ConclusionAI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.

Predictive value of C-reactive protein for postoperative liver-specific surgical site infections

BackgroundC-reactive protein is a useful biological tool to predict infectious complications, but its predictive value in detecting organ-specific surgical site infection after liver resection has never been studied. We aimed to evaluate the predictive value of c-reactive protein and determine the cut-off values to detect postoperative liver resection–surgical site infection. MethodsA multicentric analysis of consecutive patients with liver resection between 2018 and 2021 was performed. The predictive value of postoperative day 1, postoperative day 3, and postoperative day 5 C-reactive protein levels was evaluated using the area under the receiver operating characteristic curve. Cut-off values were determined using the Youden index in a 500-fold bootstrap resampling of 500 patients treated at 3 centers, who comprised the development cohort and were tested in an external independent validation cohort of 166 patients at a fourth center. ResultsAmong the 500 patients who underwent liver resection of the development cohort, liver resection–surgical site infection occurred in 66 patients (13.2%), and the median time to diagnosis was 6.0 days (interquartile range, 4.0–9.0) days. Median C-reactive protein levels were significantly higher on postoperative day 1, postoperative day 3, and postoperative day 5 in the liver resection–surgical site infection group compared with the non–surgical site infection group (50.5 vs 34.5 ng/mL, 148.0 vs 72.5 ng/mL, and 128.4 vs 35.2 ng/mL, respectively; P < .001). Postoperative day 3 and postoperative day 5 C-reactive protein–level area under the curve values were 0.76 (95% confidence interval, 0.64–0.88, P < .001) and 0.82 (95% confidence interval, 0.72–0.92, P < .001), respectively. Postoperative day 3 and postoperative day 5 optimal cut-off values of 100 mg/L and 87.0 mg/L could be used to rule out liver resection–surgical site infection, with a negative predictive value of 87.0% (interquartile range, 70.2–93.8) and 76.0% (interquartile range, 65.0–88.0), respectively, in the validation cohort. ConclusionPostoperative day 3 and postoperative day 5 C-reactive protein levels may be valuable predictive tools for liver resection–surgical site infection and aid in hospital discharge decision-making in the absence of other liver-related complications.

Identifying molecular subtypes and tumor microenvironment infiltration signatures in kidney renal clear cell carcinoma based on stemness-associated disulfidptosis genes by integrating machine learning, single-cell analyses and experimental validation

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignant tumor. Disulfidptosis is a new programmed cell death mechanism, which is characterized by the abnormal accumulation of intracellular disulfides that are highly toxic to cells. However, the contribution of disulfidptosis to ccRCC progression has not been fully clarified. In this study, two different molecular subtypes related to disulfidptosis were identified in ccRCC patients by the non-negative matrix factorization (NMF) algorithm. The cluster 1 was characterized by a worse prognosis and higher mRNAsi levels. Then, difference analysis and weighted gene co-expression network analysis (WGCNA) were conducted to search modular genes that are highly associated with tumor stemness and tumor microenvironment. Subsequently, a SADG signature containing nine genes was constructed stepwise by WGCNA and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The high-risk score group had a worse outcome, and immune regulation and metabolic signatures might be responsible for cancer progression in the high-risk group. After that, a predictive nomogram was constructed, and the predicting power of the risk model was verified using inter and three independent external validation datasets. Nine SADGs were shown to significantly correlate with immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) and immune checkpoint. In addition, based on the single-cell RNA sequencing dataset (GSE139555), the distribution and expression of nine hub genes in various types of immune cells were analyzed. Finally, the expression level of the nine genes was verified in clinical samples by qRT-PCR.

Development and validation of a 18F-FDG PET/CT radiomics nomogram for predicting progression free survival in locally advanced cervical cancer: a retrospective multicenter study

BackgroundThe existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) images to accurately predict the prognosis in patients.MethodsThe data from 190 consecutive patients with LACC who underwent pretreatment 18F-FDG PET-CT and CCRT at two cancer hospitals were retrospectively analyzed; 176 patients from the same hospital were randomly divided into training (n = 117) and internal validation (n = 50) cohorts. Clinical features were selected from the training cohort using univariate and multivariate Cox proportional hazards models; radiomic features were extracted from PET and CT images and filtered using least absolute shrinkage and selection operator and Cox proportional hazard regression. Three prediction models and a nomogram were then constructed using the previously selected clinical, CT and PET radiomics features. The external validation cohort that was used to validate the models included 23 patients with LACC from another cancer hospital. The predictive performance of the constructed models was evaluated using receiver operator characteristic curves, Kaplan Meier curves, and a nomogram.ResultsIn total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values.ConclusionsIn this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.

Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response

BackgroundsSince breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way. The interrelationship between the biomarker, TME phenotypes and recipients’ clinical response was also revealed.MethodsIn this retrospective multi-cohort investigation, five separate cohorts of breast cancer patients were recruited to measure breast cancer TME phenotypes via a radiomics signature, which was constructed and validated by integrating RNA-seq data with DCE-MRI images for predicting immunotherapy response. Initially, we constructed TME phenotypes using RNA-seq of 1089 breast cancer patients in the TCGA database. Then, parallel DCE-MRI images and RNA-seq of 94 breast cancer patients obtained from TCIA were applied to develop a radiomics-based TME phenotypes signature using random forest in machine learning. The repeatability of the radiomics signature was then validated in an internal validation set. Two additional independent external validation sets were analyzed to reassess this signature. The Immune phenotype cohort (n = 158) was divided based on CD8 cell infiltration into immune-inflamed and immune-desert phenotypes; these data were utilized to examine the relationship between the immune phenotypes and this signature. Finally, we utilized an Immunotherapy-treated cohort with 77 cases who received anti-PD-1/PD-L1 treatment to evaluate the predictive efficiency of this signature in terms of clinical outcomes.ResultsThe TME phenotypes of breast cancer were separated into two heterogeneous clusters: Cluster A, an "immune-inflamed" cluster, containing substantial innate and adaptive immune cell infiltration, and Cluster B, an "immune-desert" cluster, with modest TME cell infiltration. We constructed a radiomics signature for the TME phenotypes ([AUC] = 0.855; 95% CI 0.777–0.932; p < 0.05) and verified it in an internal validation set (0.844; 0.606–1; p < 0.05). In the known immune phenotypes cohort, the signature can identify either immune-inflamed or immune-desert tumor (0.814; 0.717–0.911; p < 0.05). In the Immunotherapy-treated cohort, patients with objective response had higher baseline radiomics scores than those with stable or progressing disease (p < 0.05); moreover, the radiomics signature achieved an AUC of 0.784 (0.643–0.926; p < 0.05) for predicting immunotherapy response.ConclusionsOur imaging biomarker, a practicable radiomics signature, is beneficial for predicting the TME phenotypes and clinical response in anti-PD-1/PD-L1-treated breast cancer patients. It is particularly effective in identifying the "immune-desert" phenotype and may aid in its transformation into an "immune-inflamed" phenotype.

P461 Deep learning model for endoscopic differential diagnosis between intestinal lymphoma and Crohn's disease: a multicenter study

Abstract Background Primary and secondary intestinal lymphoma (IL) are a relatively rare cancer, while easily misdiagnosed due to the unclear aetiology and unspecific clinical symptoms. Moreover, the endoscopic features of IL vary depending on pathological types, making it difficult to distinguish from Crohn's disease (CD). This study aims to develop deep learning models based on endoscopic images, facilitating the differential diagnosis of IL and CD. Methods We retrospectively recruited 271 patients with IL and 232 patients with CD from 6 tertiary centres in China. With out-of-focus or poor-bowel-prepared images excluded, we used 1739 IL and 1649 CD images for internal and external validation to build deep learning models. For internal validation, we used five-fold cross-validation with 1517 IL images and 1428 CD images from 5 centres randomly assigned without any patient overlap. 222 IL and 221 CD images from the 6th centre were used for independent external validation to measure the generalization ability of models. Further, we compared the performance of models with that of clinical physicians consisting of 2 junior physicians and 2 endoscopy specialists with more than ten years of clinical experience. We evaluated models using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) and used the kappa statistic to measure the inter-rater reliability between models and clinical physicians. Results We developed multiple convolutional neural network models, including ResNet18, ResNet34, ResNet50, VGG16, VGG19, DenseNet121, Inception v3, and Xception. The top 3 models in the internal validation were Inception v3, Xception, and DenseNet121. Inception v3 showed the best performance (accuracy: 83.1%; AUC: 0.910), while Xception performed better on average (average accuracy: 76.8%; average AUC: 0.834). In the external validation, the top 3 models were Inception v3, DenseNet121, and VGG19. Inception v3 achieved the highest accuracy of 81.9%, while DenseNet121 had the best AUC of 0.903. Compared with clinical physicians, Inception v3 performed better than junior physicians (average accuracy: 67.2%) and showed good agreement with the best endoscopy specialist (kappa: 0.604, p&amp;lt;0.05). Conclusion Convolutional neural network models using endoscopic images were able to differentiate PIL from CD, with better-diagnosing performance than junior physicians, good agreement with specialists, and good generation ability, which were expected to assist clinical physicians lacking experience to differentiate PIL and CD more precisely under endoscope.

Development and validation of an individualized and weighted Myeloma Prognostic Score System (MPSS) in patients with newly diagnosed multiple myeloma.

Current standard predictive models of disease risk do not adequately account for the heterogeneity of survival outcomes in patients with new-diagnosed multiple myeloma (NDMM). In this retrospective, multicohort study, we collected clinical and genetic data from 1792 NDMM patients and identified the prognostic impact of all features. Using the top-ranked predictive features, a weighted Myeloma Prognostic Score System (MPSS) risk model was formulated and validated to predict overall survival (OS). In the training cohort, elevated lactate dehydrogenase level (LDH), International Staging System (ISS) Stage III, thrombocytopenia, and cumulative high-risk cytogenetic aberration (HRA) numbers were found to have independent prognostic significance. Each risk factor was defined as its weighted value respectively according to their hazard ratio for OS (thrombocytopenia 2, elevated LDH 1, ISS III 2, one HRA 1, and ≥2 HRA 2, points). Patients were further stratified into four risk groups: MPSS I (22.5%, 0 points), II (17.6%, 1 points), III (38.6%, 2-3 points), and IV (21.3%, 4-7 points). MPSS risk stratification showed optimal discrimination, as well as calibration, of four risk groups with median OS of 91.0, 69.8, 45.0, and 28.0 months, for patients in MPSS I to IV groups (p < .001), respectively. Importantly, the MPSS model retained its prognostic value in the internal validation cohort and an independent external validation cohort, and exhibited significant risk distribution compared with conventional prognostic models (R-ISS, R2-ISS, and MASS). Utilization of the MPSS model in clinical practice could improve risk estimation in NDMM patients, thus prompting individualized treatment strategies.