Independent External Validation Cohort Research Articles

Development and validation of a nomogram for predicting sepsis-induced coagulopathy in septic patients: mixed cohort study.

Sepsis-induced coagulopathy (SIC) is a common cause of poor prognosis in critically ill patients in the intensive care unit (ICU). This study aimed to develop a predictive nomogram incorporating clinical markers and scoring systems to individually predict the probability of SIC in septic patients. Patients consecutively recruited in the stage between January 2022 and April 2023 constituted the development cohort for retrospective analysis to internally test the nomogram, and patients in the stage between May 2023 to November 2023 constituted the validation cohort for prospective analysis to external validate the nomogram. The nomogram was validated in an independent external validation cohort, involving discrimination and calibration. A decision curve analysis was also performed to evaluate the net benefit of the insertion decision with this nomogram. A total of 548 and 245 patients were included in the development and validation cohort, respectively. Predictors contained in the prediction nomogram included shock, platelets and INR. Patients with shock (OR, 4.499; 95% CI, 2.730-7.414; P < 0.001) , higher INR (OR, 349.384; 95% CI, 62.337-1958.221; P < 0.001) and lower platelet (OR, 0.985; 95% CI, 0.982-0.988; P < 0.001) had higher probabilities of SIC. The development model showed good discrimination, with an AUROC of 0.879（95%CI, 0.850-0.908）and good calibration. Application of the nomogram in the validation cohort also gave good discrimination with an AUROC of 0.872（95%CI,0.826-0.917）and good calibration. By incorporating shock, platelets and INR in the model, this useful nomogram could be accessibly utilized to predict SIC occurrence in septic patients.

A risk score for predicting in-stent restenosis in patients with premature acute myocardial infarction undergoing percutaneous coronary intervention with drug-eluting stent

BackgroundThis study aimed at developing and validating a risk score to predict in-stent restenosis (ISR) in patients with premature acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI) with drug-eluting stent (DES). MethodsThis was a two-center retrospective study. A total of 2185 patients firstly diagnosed with premature AMI (age ≥18 years and <55 years in men, <65 years in women) from Xinjiang cohort were retrospectively analyzed. After filtering by exclusion criteria, patients were randomly divided into training cohort (n = 434) and internal validation cohort (n = 186) at a 7:3 ratio. Several candidate variables associated with ISR in the training cohort were assessed by the least absolute shrinkage and selection operator and logistic regression analysis. The ISR risk nomogram score based on the superior predictors was finally developed, and then validated in the internal validation cohort and in an independent Chengdu external validation cohort (n = 192). The higher total nomogram score, the greater the ISR risk. ResultsThe eight variables in the final risk nomogram score, cardiovascular-kidney-metabolic (CKM) score included age, diabetes mellitus (DM), body mass index (BMI), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDLC), estimated glomerular filtration rate (eGFR), stent in left anterior descending coronary artery, minimum stent diameter <3 mm. The areas under the curve (AUC) and C-statistics [training cohort: 0.834 (95%CI: 0.787 to 0.882); internal validation cohort: 0.852 (95%CI: 0.784 to 0.921); Chengdu external validation cohort: 0.787 (95%CI: 0.692 to 0.882), respectively)] demonstrated the good discrimination of the CKM score. The Hosmer-Lemeshow test (χ2 = 7.86, P = 0.448; χ2 = 5.17, P = 0.740; χ2 = 6.35, P = 0.608, respectively) and the calibration curve confirmed the good calibration of the CKM score. Decision curve analysis (DCA) testified the clinical net benefit of the CKM score in the training and validation cohort. ConclusionThis study provided a well-developed and validated risk nomogram score, the CKM score to predict ISR in patients with premature AMI undergoing PCI with DES. Given that these variables are readily available and practical, the CKM score should be widely adopted for individualized assessment and management of premature AMI.

Machine Learning–Based Models for Advanced Fibrosis and Cirrhosis Diagnosis in Chronic Hepatitis B Patients With Hepatic Steatosis

Background and AimsThe global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants non-invasive, precise tools for assessing fibrosis progression. This study leveraged machine learning (ML) to develop diagnostic models for advanced fibrosis and cirrhosis in this patient population. MethodsTreatment-naive CHB patients with concurrent HS who underwent liver biopsy in ten medical centers were enrolled as a training cohort and an independent external validation cohort (NCT05766449). Six ML models were implemented to predict advanced fibrosis and cirrhosis. The final models, derived from Shapley Additive exPlanations, were compared to Fibrosis-4 Index (FIB-4), Nonalcoholic fatty liver disease Fibrosis Score (NFS), and Aspartate transaminase to platelet ratio index (APRI) using the area under receiver operating characteristic curve (AUROC), and decision curve analysis (DCA). ResultsOf 1,198 eligible patients, the random forest (RF) model achieved AUROCs of 0.778 [95% confidence interval (CI) 0.749-0.807] for diagnosing advanced fibrosis (RF-AF model) and 0.777 (95%CI 0.748-0.806) for diagnosing cirrhosis (RF-C model) in the training cohort, and maintained high AUROCs in the validation cohort. In the training cohort, the RF-AF model obtained an AUROC of 0.825 (95% CI 0.787-0.862) in patients with HBV DNA ≥105 IU/ml, and RF-C model had an AUROC of 0.828 (95% CI 0.774-0.883) in female patients. The two models outperformed FIB-4, NFS, and APRI in the training cohort, and also performed well in the validation cohort. ConclusionThe RF models provide reliable, non-invasive tools for identifying advanced fibrosis and cirrhosis in CHB patients with concurrent HS, offering a significant advancement in the co-management of the two diseases.

Prognosis recovery score of apical surgery Guided Bone Regeneration using cone beam computed tomography and digital bioinformatics

ObjectiveGuided Bone Regeneration (GBR) is a dental surgical procedure that uses barrier membranes to prevent soft tissue invasion and conduct new bone growth. This study aimed to define a Prognosis Recovery score (PR score) to objectively classify post-surgery responders from non-responder patients who underwent GBR using Cone Beam Computed Tomography (CBCT). MethodsThis prospective-observational-longitudinal-cohort study recruited 250 individuals who were assigned to: Conventional-Apical-Surgery (CAS, n = 39), Apical-Surgery using human fascia lata Membrane placement (ASM, n = 42), and Apical-Surgery using human fascia lata Membrane placement and lyophilized allograft Bone powder (ASMB, n = 39); and Apical-Surgery using collagen membrane Porcine origin and Bovine Bone-matrix (ASPBB, n = 130), an independent external validation cohort. Surgery was performed, and evolution was monitored by CBCTs at 0, 6-, 12-, 18-, and 24 months post-surgery. ResultsNormalized lesion volumes were calculated, and non-linear time evolution morphology curves were characterized. The three-time evolution bone growth patterns were: a linear tendency (PR0), “S'' shaped log-logistic (PR1), and “C" cellular growth (PR2). The treatment success rates were PR2-46 %, PR2-88 %, and PR2-95 %/PR1-5% for CAS, ASM, and ASMB groups. The xenograft ASPBB counterpart achieved PR2-92 % and PR1-8%. The score PR had a sensitivity, specificity, and accuracy of 100 %. ConclusionsPatients' treatment success can be quantitatively, objectively, and precisely predicted with the Prognosis Recovery score (using only two CBCTs), according to their biological response to allograft or xenograft materials (time-evolution bone growth curves), reducing cost and radiation exposure. Clinical significanceThrough digital imaging and bioinformatic analysis of bone regeneration observed in CBCTs, we defined a Prognosis Recovery (PR) score using only two CBCT volume assessments (0 and 6 months). The PR score allowed us to define three time-evolution curves depending on the biomaterials used and to classify patients in a quantitative, objective, and accurate way.

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.

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.

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 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.

Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer

ObjectiveThis study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. MethodsIn this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. ResultsThe nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813–0.953) in internal validation cohort and 0.862 (95 % CI: 0.756–0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. ConclusionsA CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process.

Leveraging a gene signature associated with disulfidptosis identified by machine learning to forecast clinical outcomes, immunological heterogeneities, and potential therapeutic targets within lower-grade glioma.

Disulfidptosis, a newly defined type of programmed cell death, has emerged as a significant regulatory process in the development and advancement of malignant tumors, such as lower-grade glioma (LGG). Nevertheless, the precise biological mechanisms behind disulfidptosis in LGG are yet to be revealed, considering the limited research conducted in this field. We obtained LGG data from the TCGA and CGGA databases and performed comprehensive weighted co-expression network analysis, single-sample gene set enrichment analysis, and transcriptome differential expression analyses. We discovered nine genes associated with disulfidptosis by employing machine learning methods like Cox regression, LASSO regression, and SVM-RFE. These were later used to build a predictive model for patients with LGG. To confirm the expression level, functional role, and impact on disulfidptosis of ABI3, the pivotal gene of the model, validation experiments were carried out in vitro. The developed prognostic model successfully categorized LGG patients into two distinct risk groups: high and low. There was a noticeable difference in the time the groups survived, which was statistically significant. The model's predictive accuracy was substantiated through two independent external validation cohorts. Additional evaluations of the immune microenvironment and the potential for immunotherapy indicated that this risk classification could function as a practical roadmap for LGG treatment using immune-based therapies. Cellular experiments demonstrated that suppressing the crucial ABI3 gene in the predictive model significantly reduced the migratory and invasive abilities of both SHG44 and U251 cell lines while also triggering cytoskeletal retraction and increased cell pseudopodia. The research suggests that the prognostic pattern relying on genes linked to disulfidptosis can provide valuable insights into the clinical outcomes, tumor characteristics, and immune alterations in patients with LGG. This could pave the way for early interventions and suggests that ABI3 might be a potential therapeutic target for disulfidptosis.

Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study.

Radiomics models could help assess the benign and malignant invasiveness and prognosis of pulmonary nodules. However, the lack of interpretability limits application of these models. We thus aimed to construct and validate an interpretable and generalized computed tomography (CT) radiomics model to evaluate the pathological invasiveness in patients with a solitary pulmonary nodule in order to improve the management of these patients. We retrospectively enrolled 248 patients with CT-diagnosed solitary pulmonary nodules. Radiomic features were extracted from nodular region and perinodular regions of 3 and 5 mm. After coarse-to-fine feature selection, the radiomics score (radscore) was calculated using the least absolute shrinkage and selection operator logistic method. Univariate and multivariate logistic regression analyses were performed to determine the invasiveness-related clinicoradiological factors. The clinical-radiomics model was then constructed using the logistic and extreme gradient boosting (XGBoost) algorithms. The Shapley additive explanations (SHAP) method was then used to explain the contributions of the features. After removing batch effects with the ComBat algorithm, we assessed the generalization of the explainable clinical-radiomics model in two independent external validation cohorts (n=147 and n=149). The clinical-radiomic XGBoost model integrating the radscore, CT value, nodule length, and crescent sign demonstrated better predictive performance than did the clinical-radiomics logistic model in assessing pulmonary nodule invasiveness, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.889 [95% confidence interval (CI), 0.848-0.927] in the training cohort. The SHAP algorithm illustrates the contribution of each feature in the final model. The specific model decision process was visualized using a tree-based decision heatmap. Satisfactory generalization performance was shown with AUCs of 0.889 (95% CI, 0.823-0.942) and 0.915 (95% CI, 0.851-0.963) in the two external validation cohorts. An interpretable and generalized clinical-radiomics model for predicting pulmonary nodule invasibility was constructed to help clinicians determine the invasiveness of pulmonary nodules and devise assessment strategies in an easily understandable manner.

Development and Validation of an Individualized and Weighted Prognostic Model in Patients with Newly Diagnosed Multiple Myeloma

Introduction Multiple myeloma (MM) is a malignancy of terminally differentiated antibody-secreting plasma cells in the bone marrow with highly variable survival outcomes. Precise risk stratification not only plays a vital role in predicting patient prognoses, but critically can be used to develop risk-adapted treatment regimens. However, current risk stratification models do not adequately account for the heterogeneity of patients with new-diagnosed multiple myeloma (NDMM). Methods In this retrospective, multicohort study, we collected clinical data from 1792 NDMM patients and formulated a weighted Myeloma Prognostic Score System (MPSS) risk model to predict overall survival (OS). Construction of the MPSS model was carried out based on the National Longitudinal Cohort of Hematological Diseases in China (NCT04645199) cohort, external validation cohort was created using TT2, TT3a, and TT3b cohorts from a series of randomized prospective studies from the University of Arkansas for Medical Sciences (UAMS). Results 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 high-risk cytogenetic aberration [HRA] 1, and ≥2 HRA 2 points). In the training cohort, patients were furtherly 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&amp;lt; 0.001), 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). Conclusions In conclusion, we formulated and validated the MPSS risk model to predict the prognosis of patients with MM using readily available standard clinical and genetic test data. The established MPSS profile shows a better performance in risk discrimination than the current R-ISS, R2-ISS, and MASS. A score-based risk stratification is derived, and identifies patients with high and ultra-high risk of death after diagnosis and may therefore aid the development of more personalized treatment strategies, especially for patients for whom current therapies are likely to fail.

Pretreatment MRI–Based Radiomics for Prediction of Rectal Cancer Outcome: A Discovery and Validation Study

Accurate prediction of local recurrence or distant metastasis is critical for developing individualized therapies for locally advanced rectal cancer (LARC) patients after standard therapy. This study aims to develop and validate a multiparameter MRI-based radiomics signature (RS) for prognostic prediction in LARC patients receiving neoadjuvant chemoradiotherapy (nCRT) and total mesorectal excision (TME) and to explore the ability of RS for personalized survival risk stratification. In this multi-center study, 454 patients who received nCRT and TME and completed 3 years of follow-up participated. RS was constructed for prognostic prediction based on features extracted from pretreatment multiparameter MRI in a training cohort (TC; n=298), which was tested in an internal validation cohort (IVC; n=75) and further validated in an independent external validation cohort (EVC; n=81). Furthermore, the ability of RS for personalized survival risk stratification was explored using the Kaplan-Meier survival curves. The RS model showed satisfactory accuracy for prognostic prediction with AUCs of 0.83, 0.81 and 0.82 in the TC, IVC and EVC, respectively. In addition, RS helped to refine risk stratification for LARC patients on the basis of significantly different 3-year disease-free survival rates, independent of their pathological stage, pre-surgery CEA, and even treatment modality. The proposed RS can be used not only to predict local recurrence or distant metastasis but also to serve as an effective postoperative survival risk stratification tool for clinicians to facilitate decision-making for LARC patients receiving standard treatment.

Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation

ObjectivesIndependent internal and external validation of three previously published CT-based radiomics models to predict local tumor progression (LTP) after thermal ablation of colorectal liver metastases (CRLM).Materials and methodsPatients with CRLM treated with thermal ablation were collected from two institutions to collect a new independent internal and external validation cohort. Ablation zones (AZ) were delineated on portal venous phase CT 2–8 weeks post-ablation. Radiomics features were extracted from the AZ and a 10 mm peri-ablational rim (PAR) of liver parenchyma around the AZ. Three previously published prediction models (clinical, radiomics, combined) were tested without retraining. LTP was defined as new tumor foci appearing next to the AZ up to 24 months post-ablation.ResultsThe internal cohort included 39 patients with 68 CRLM and the external cohort 52 patients with 78 CRLM. 34/146 CRLM developed LTP after a median follow-up of 24 months (range 5–139). The median time to LTP was 8 months (range 2–22). The combined clinical-radiomics model yielded a c-statistic of 0.47 (95%CI 0.30–0.64) in the internal cohort and 0.50 (95%CI 0.38–0.62) in the external cohort, compared to 0.78 (95%CI 0.65–0.87) in the previously published original cohort. The radiomics model yielded c-statistics of 0.46 (95%CI 0.29–0.63) and 0.39 (95%CI 0.28–0.52), and the clinical model 0.51 (95%CI 0.34–0.68) and 0.51 (95%CI 0.39–0.63) in the internal and external cohort, respectively.ConclusionThe previously published results for prediction of LTP after thermal ablation of CRLM using clinical and radiomics models were not reproducible in independent internal and external validation.Clinical relevance statementLocal tumour progression after thermal ablation of CRLM cannot yet be predicted with the use of CT radiomics of the ablation zone and peri-ablational rim. These results underline the importance of validation of radiomics results to test for reproducibility in independent cohorts.Key Points• Previous research suggests CT radiomics models have the potential to predict local tumour progression after thermal ablation in colorectal liver metastases, but independent validation is lacking.• In internal and external validation, the previously published models were not able to predict local tumour progression after ablation.• Radiomics prediction models should be investigated in independent validation cohorts to check for reproducibility.Graphical

Novel nomogram for predicting survival in advanced non-small cell lung cancer receiving anti-PD-1 plus chemotherapy with or without antiangiogenic therapy.

This study aimed to develop and validate a novel nomogram to predict survival in advanced non-small cell lung cancer (NSCLC) receiving programmed cell death 1 (PD-1) inhibitor plus chemotherapy with or without antiangiogenic therapy. A total of 271 patients with advanced NSCLC who received anti-PD-1 plus chemotherapy with or without antiangiogenic therapy were enrolled in our center and randomized into the training cohort (n = 133) and the internal validation cohort (n = 138). Forty-five patients from another center were included as an independent external validation cohort. The nomogram was created based on the multivariate Cox regression analysis to predict overall survival (OS) and progression-free survival (PFS). The performance of the nomogram was assessed using the concordance index (C-index), the time-dependent area under the receiver operating (ROC) curves (AUCs), calibration curves, and decision curve analysis (DCA). Four factors significantly associated with OS were utilized to create a nomogram to predict OS: Eastern Cooperative Oncology Group performance status (ECOG PS), programmed cell death-ligand 1 (PD-L1) expression, chemotherapy cycle, and pretreatment lactate dehydrogenase-albumin ratio (LAR). Six variables significantly associated with PFS were incorporated into the development of a nomogram for predicting PFS: ECOG PS, histology, PD-L1 expression, chemotherapy cycle, pretreatment platelet to lymphocyte (PLR), and pretreatment LAR. The C-indexes of the nomogram for predicting OS and PFS were 0.750 and 0.747, respectively. The AUCs for predicting the 6-month, 12-month, and 18-month OS and PFS were 0.847, 0.791, and 0.776 and 0.810, 0.787, and 0.861, respectively. The calibration curves demonstrated a good agreement between predictions and actual observations. The DCA curves indicated that the nomograms had good net benefits. Furthermore, the nomogram model was well-validated in the internal and external cohorts. The novel nomogram for predicting the prognosis of advanced NSCLC receiving anti-PD-1 plus chemotherapy with or without antiangiogenic therapy may help guide clinical treatment decisions.

Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists

ObjectivesTo develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesions. Materials and methodsThis retrospective two-center study included 1146 adrenal lesions from 1059 patients who underwent multiphase CT scanning between January 2008 and March 2021. The study encompassed 564 surgically confirmed adenomas, along with 135 benign lesions and 447 metastases confirmed by observation. DL models based on multiphase CT images were developed, validated and tested. The diagnostic performance of the classification models, imaging phases and radiologists with or without DL were compared using accuracy (ACC) and receiver operating characteristic (ROC) curves. Integrated discrimination improvement (IDI) analysis and the DeLong test were used to compare the area under the curve (AUC) among models. Decision curve analysis (DCA) was used to assess the clinical usefulness of the predictive models. ResultsThe DL signature based on LASSO (DLSL) had a higher AUC than that of the other classification models (IDI > 0, P < 0.05). Furthermore, the precontrast phase (PCP)-based DLSL performed best in the independent external validation (AUC = 0.881, ACC = 82.9 %) and clinical test cohorts (AUC = 0.790, ACC = 70.4 %), outperforming DLSL based on the other single-phase or three-phase images (IDI > 0, P < 0.05). DCA demonstrated that PCP-based DLSL provided a higher net benefit (0.01–0.95). The diagnostic performance led to statistically significant improvements when radiologists incorporated the DL model, with the AUC improving by 0.056–0.159 and the ACC improving by 0.069–0.178 (P < 0.05). ConclusionThe DL model based on PCP CT images performed acceptably in differentiating adrenal metastases from benign lesions, and it may assist radiologists in accurate tumor staging for patients with a history of extra-adrenal malignancy.

Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults

Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur. Do ML alerts, telemedicine system (TS)-generated alerts, or biomedical monitors (BMs) have superior performance for predicting episodes of intubation or administration of vasopressors? An ML algorithm was trained to predict intubation and vasopressor initiation events among critically ill adults. Its performance was compared with BM alarms and TS alerts. ML notifications were substantially more accurate and precise, with 50-fold lower alarm burden than TS alerts for predicting vasopressor initiation and intubation events. ML notifications of internal validation cohorts demonstrated similar performance for independent academic medical center external validation and COVID-19 cohorts. Characteristics were also measured for a control group of recent patients that validated event detection methods and compared TS alert and BM alarm performance. The TS test characteristics were substantially better, with 10-fold less alarm burden than BM alarms. The accuracy of ML alerts (0.87-0.94) was in the range of other clinically actionable tests; the accuracy of TS (0.28-0.53) and BM (0.019-0.028) alerts were not. Overall test performance (F scores) for ML notifications were more than fivefold higher than for TS alerts, which were higher than those of BM alarms. ML-derived notifications for clinically actioned hemodynamic instability and respiratory failure events represent an advance because the magnitude of the differences of accuracy, precision, misclassification rate, and pre-event lead time is large enough to allow more proactive care and has markedly lower frequency and interruption of bedside physician work flows.

Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study

This study aims to evaluate the feasibility and effectiveness of deep transfer learning (DTL) and clinical-radiomics in differentiating thymoma from thymic cysts. Clinical and imaging data of 196 patients pathologically diagnosed with thymoma and thymic cysts were retrospectively collected from center 1. (training cohort: n=137; internal validation cohort: n=59). An independent external validation cohort comprised 68 thymoma and thymic cyst patients from center 2. Region of interest (ROI) delineation was performed on contrast-enhanced chest computed tomography (CT) images, and eight DTLmodelsincluding Densenet 169, Mobilenet V2, Resnet 101, Resnet 18, Resnet 34, Resnet 50, Vgg 13, Vgg 16 were constructed. Radiomics features were extracted from the ROI on the CT images of thymoma and thymic cyst patients, and feature selection was performed using intra-observer correlation coefficient (ICC), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) algorithm. Univariate analysis and multivariable logistic regression (LR) were used to select clinical-radiological features. Six machine learning classifiers, including LR, support vector machine (SVM), k-nearest neighbors (KNN), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP), were used to construct Radiomics and Clinico-radiologic models. The selected features from the Radiomics and Clinico-radiologic models were fused to build a Combined model. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical utility of the models, respectively. The Delong test was used to compare the AUC between different models. K-means clustering was used to subdivide the lesions of thymomas or thymic cysts into subregions, and traditional radiomics methods were used to extract features and compare the ability of Radiomics and DTL models to reflect intratumoral heterogeneity using correlation analysis. The Densenet 169 based on DTL performed the best, with AUC of 0.933 (95% CI: 0.875-0.991) in the internal validation cohort and 0.962 (95% CI: 0.923-1.000) in the external validation cohort. The AdaBoost classifier achieved AUC of 0.965 (95% CI: 0.923-1.000) and 0.959 (95% CI: 0.919-1.000) in the internal and external validation cohorts, respectively, for the Radiomics model. The LightGBM classifier achieved AUC of 0.805 (95% CI: 0.690-0.920) and 0.839 (95% CI: 0.736-0.943) in the Clinico-radiologic model. The AUC of the Combined model in the internal and external validation cohorts was0.933 (95% CI: 0.866-1.000) and 0.945 (95% CI: 0.897-0.994), respectively. The results of the Delong test showed that the Radiomics model, DTL model, and Combined model outperformed the Clinico-radiologic model in both internal and external validation cohorts (p-values were 0.002, 0.004, and 0.033 in the internal validation cohort, while in the external validation cohort, the p-values were 0.014, 0.006, and 0.015, respectively). But there was no statistical difference in performance among the three models (all p-values <0.05). Correlation analysis showed that radiomics performed better than DTL in quantifying intratumoral heterogeneity differences between thymoma and thymic cysts. The developed DTL model and the Combined model based on radiomics and clinical-radiologic features achieved excellent diagnostic performance in differentiating thymic cysts from thymoma. They can serve as potential tools to assist clinical decision-making, particularly when endoscopic biopsy carries a high risk.

The Characteristics of Transcription Factors Regulating T Cell Exhaustion Were Analyzed to Predict the Prognosis and Therapeutic Effect in Patients with HCC.

Hepatocellular carcinoma (HCC) ranks as the third leading cause of cancer-related deaths, posing a significant threat to people in diverse regions. T-cell exhaustion (Tex) can hinder the efficacy of immunotherapy in patients with HCC, and the transcription factors that regulate Tex in HCC have not yet been fully elucidated. We used the single sample gene set enrichment analysis (ssGSEA) method to define the transcription factor pathway that regulates Tex and employed LASSO regression analysis to establish Tex related genes (TEXRS). To predict differences in immunotherapy efficacy between the two groups, we used the immunophenotype score and submap algorithm. RT-qPCR was used to detect the expression levels of the model genes in 21 pairs of HCC tissues. Finally, we assessed the cell communication strength and identified ligand receptors using the "CellChat" R package. Nine Tex transcription factors were identified as regulators of the HCC immune microenvironment, with Tex scores affecting patient survival. Patients with a high Tex Risk Score (TEXRS) had significantly worse overall survival compared to patients with low TEXRS. After adjusting for confounding factors, TEXRS remained an independent prognostic factor. Importantly, TEXRS performed well in multiple independent external validation cohorts. Various algorithms have shown that patients in the low-TEXRS group might benefit more from immunotherapy. Finally, RT-qPCR analysis of 21 HCC samples showed that C7, CD5L, and SDS were significantly downregulated in HCC tissues, consistent with the bioinformatics analysis results. TEXRS proved to be a valuable predictor of immunotherapy and transcatheter arterial chemoembolization efficacy in patients with HCC. This holds promise for enhancing the prognosis and treatment outcomes of patients with HCC.