Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data

Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma

BACKGROUND

To develop a multiclass radiomics model for differentiating chondroid bone tumors using preoperative MRI.

METHODS

This retrospective study included 120 patients (92 enchondromas, 16 low-grade chondrosarcomas, and 12 intermediate-to-high-grade chondrosarcomas) who underwent contrast-enhanced MRI between 2009 and 2019. Tumor segmentation was manually performed by a musculoskeletal radiologist and validated by a senior radiologist. We used least absolute shrinkage and selection operator (LASSO) and random forest (RF) for feature selection and classification, with and without synthetic minority oversampling technique (SMOTE). Model performance was evaluated using five-fold cross-validation with average precision, accuracy, area under the curve (AUC), and weighted kappa statistics.

RESULTS

The LASSO + RF model based on all sequences achieved the highest accuracy (0.826 ± 0.065) and AUC (0.967 ± 0.027). The highest mAP (0.750 ± 0.095) was observed in the SMOTE-enhanced T2WI-based model, highlighting the potential impact of class imbalance. Quadratic weighted kappa values ranged from 0.648 to 0.731 across models, indicating substantial agreement with pathological results.

CONCLUSIONS

Preoperative MRI-based radiomics provides a robust method for the classification of chondroid bone tumors, potentially enhancing clinical decision-making.

Longitudinal CT Radiomics to Predict Progression-free Survival in Patients with Locally Advanced Gastric Cancer After Neoadjuvant Chemotherapy

RATIONALE AND OBJECTIVES

To develop and validate a radiomics signature, utilizing baseline and restaging CT, for preoperatively predicting progression-free survival (PFS) after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC).

METHODS

A total of 316 patients with LAGC who received NAC followed by gastrectomy were retrospectively included in this single-center study; these patients were split into two cohorts, one for training (n = 243) and the other for validation (n = 73), based on the different districts of our hospital. A total of 1316 radiomics features were extracted from the volume of interest of the gastric-cancer lesion on venous phase CT images. Four radiomics signatures were built for predicting PFS based on baseline CT (Pre-Rad), restaging CT (Post-Rad), delta radiomics (Delta-Rad) and multi-time radiomics (PrePost-Rad), respectively. Then the PrePost-Rad was combined with clinical factors to establish a nomogram (Rad-clinical model). Kaplan-Meier survival curves with log-rank tests were used to assess the prognostic usefulness of the Rad-clinical model.

RESULTS

All baseline characteristics were not statistically different between the two cohorts. The PrePost-Rad achieved improved predictive value by a C-index of 0.724 (95% CI: 0.639-0.809) in the validation cohort [Pre-Rad: 0.715 (0.632-0.798); Post-Rad: 0.632 (0.538-0.725), Delta-Rad: 0.549 (0.447-0.651)]. In terms of clinical benefit, calibration capability, and prediction efficacy, the Rad-clinical model performed well for PFS prediction, with a C-index of 0.754 (95% CI: 0.707-0.800) and 0.719 (95% CI: 0.639-0.800) in the training and validation cohorts, respectively, superior to the clinical model (cN stage and CA199) but comparable to the PrePost-Rad. Moreover, the Rad-clinical model could accurately classify gastric-cancer patients after NAC into three PFS risk groups in both training and validation cohorts. The risk stratification also performed well in most subgroups (good responders, poor responders, ypTNM Ⅱ, and ypTNM Ⅲ/Ⅳ).

CONCLUSIONS

The Rad-clinical model integrating longitudinal radiomics score and clinical factors performed well in preoperatively predicting PFS of LAGC patients after NAC and surgery.

Transfer learning for accelerated failure time model with microarray data

BACKGROUND

In microarray prognostic studies, researchers aim to identify genes associated with disease progression. However, due to the rarity of certain diseases and the cost of sample collection, researchers often face the challenge of limited sample size, which may prevent accurate estimation and risk assessment. This challenge necessitates methods that can leverage information from external data (i.e., source cohorts) to improve gene selection and risk assessment based on the current sample (i.e., target cohort).

METHOD

We propose a transfer learning method for the accelerated failure time (AFT) model to enhance the fit on the target cohort by adaptively borrowing information from the source cohorts. We use a Leave-One-Out cross validation based procedure to evaluate the relative stability of selected genes and overall predictive power.

CONCLUSION

In simulation studies, the transfer learning method for the AFT model can correctly identify a small number of genes, its estimation error is smaller than the estimation error obtained without using the source cohorts. Furthermore, the proposed method demonstrates satisfactory accuracy and robustness in addressing heterogeneity across the cohorts compared to the method that directly combines the target and the source cohorts in the AFT model. We analyze the GSE88770 and GSE25055 data using the proposed method. The selected genes are relatively stable, and the proposed method can make an overall satisfactory risk prediction.

Computed tomography-based radiomics modeling to predict patient overall survival in cervical cancer with intensity-modulated radiotherapy combined with concurrent chemotherapy

ObjectiveThe objective of this study was to develop a predictive model combining radiomic characteristics and clinical features to forecast overall survival in cervical cancer patients treated with intensity-modulated radiotherapy and concurrent chemotherapy.MethodsIn this retrospective observational study, 159 patients were divided into a training group (n = 95) and a validation group (n = 64). Radiomic characteristics were extracted from contrast-enhanced computed tomography scans. The least absolute shrinkage and selection operator regression analysis was used to filter the extracted radiomic characteristics and reduce the dimensionality of the data. A radiomic score was calculated from the selected features, and multivariate Cox regression models were established to analyze overall survival. A nomogram combining radiomic score and clinical features was developed, and its reliability was assessed using the area under the receiver operating characteristic curve.ResultsFour radiomic characteristics and two clinical features were extracted for overall survival analysis. A nomogram combining these factors was developed and validated, showing good performance with a high C-index. Patients were categorized as low-risk or high-risk for overall survival based on a cut-off value.ConclusionsOur model combining computed tomography-extracted radiomic characteristics and clinical features shows good potential for evaluating overall survival in cervical cancer patients treated with intensity-modulated radiotherapy and concurrent chemotherapy.

Efficient Post-Shrinkage Estimation Strategies in High-Dimensional Cox's Proportional Hazards Models

Regularization methods such as LASSO, adaptive LASSO, Elastic-Net, and SCAD are widely employed for variable selection in statistical modeling. However, these methods primarily focus on variables with strong effects while often overlooking weaker signals, potentially leading to biased parameter estimates. To address this limitation, Gao, Ahmed, and Feng (2017) introduced a corrected shrinkage estimator that incorporates both weak and strong signals, though their results were confined to linear models. The applicability of such approaches to survival data remains unclear, despite the prevalence of survival regression involving both strong and weak effects in biomedical research. To bridge this gap, we propose a novel class of post-selection shrinkage estimators tailored to the Cox model framework. We establish the asymptotic properties of the proposed estimators and demonstrate their potential to enhance estimation and prediction accuracy through simulations that explicitly incorporate weak signals. Finally, we validate the practical utility of our approach by applying it to two real-world datasets, showcasing its advantages over existing methods.

Inferring Staphylococcus aureus host species and cross-species transmission from a genome-based model

BACKGROUND

Staphylococcus aureus is an important pathogen that can colonize humans and various animals. However, the host-associated determinants of S. aureus remain uncertain, which leads to difficulties in inferring its host species and cross-species transmission. We performed a 3-stage genome-wide association study (discovery, confirming, and validation) to compare genetic variation between pig and human S. aureus, aiming to elucidate the host-specific genetic elements (k-mers).

RESULTS

After 3-stage association analyses, we found a subset of 20 consensus-significant host-associated k-mers, which are significantly overrepresented in a specific host. Surprisingly for host prediction, both the final model with the top 5 k-mers and the simplest model with only the most important k-mer achieved a high classification accuracy of 98%, giving a simple target for predicting host species and cross-species transmission of S. aureus. The final classifier with the top 5 k-mers revealed that 97.5% of S. aureus isolates from livestock-exposed workers were predicted as pig origin, suggesting a high cross-species transmission risk. The time-based phylogeny inferred the cross-species transmission directions, indicating that ST9 can cross-species spread from animals to humans while ST59 can cross-species spread in the opposite direction.

CONCLUSION

Our findings provide novel insights into host-associated determinants and an accurate model for inferring S. aureus host species and cross-species transmission.

Computed tomography radiomics in predicting patient satisfaction after robotic-assisted total knee arthroplasty

PURPOSE

After robotic-assisted total knee arthroplasty (RA-TKA) surgery, some patients still experience joint discomfort. We aimed to establish an effective machine learning model that integrates radiomic features extracted from computed tomography (CT) scans and relevant clinical information to predict patient satisfaction three months postoperatively following RA-TKA.

MATERIALS AND METHODS

After careful selection, data from 142 patients were randomly divided into a training set (n = 99) and a test set (n = 43), approximately in a 7:3 ratio. A total of 1329 radiomic features were extracted from the regions of interest delineated in CT scans. The features were standardized using normalization algorithms, and the least absolute shrinkage and selection operator regression model was employed to select radiomic features with ICC > 0.75 and P < 0.05, generating the Rad-score as feature markers. Univariate and multivariate logistic regression was then used to screen clinical information (age, body mass index, operation time, gender, surgical side, comorbidities, preoperative KSS score, preoperative range of motion (ROM), preoperative and postoperative HKA angle, preoperative and postoperative VAS score) as potential predictive factors. The satisfaction scale ≥ 20 indicates patient satisfaction. Finally, three prediction models were established, focusing on radiomic features, clinical features, and their fusion. Model performance was evaluated using Receiver Operating Characteristic curves and decision curve analysis.

RESULTS

In the training set, the area under the curve (AUC) of the clinical model was 0.793 (95% CI 0.681-0.906), the radiomic model was 0.854 (95% CI 0.743-0.964), and the combined radiomic-clinical model was 0.899 (95% CI 0.804-0.995). In the test set, the AUC of the clinical model was 0.908 (95% CI 0.814-1.000), the radiomic model was 0.709 (95% CI 0.541-0.878), and the combined radiomic-clinical model was 0.928 (95% CI 0.842-1.000). The AUC of the radiomic-clinical model was significantly higher than the other two models. The decision curve analysis indicated its clinical application value.

CONCLUSION

We developed a radiomic-based nomogram model using CT imaging to predict the satisfaction of RA-TKA patients at 3 months postoperatively. This model integrated clinical and radiomic features and demonstrated good predictive performance and excellent clinical application potential.

Predictive potential of circular walking in prodromal Parkinson's disease

BackgroundDevelopment of objective, reliable and easy-to-use methods to detect the onset of motor changes in Parkinson's disease (PD) is required to identify the temporal window in which neuromodulatory therapies could be implemented. Turning impairments are present at early stages of PD. However, it is unclear, to date, whether circular walking is also altered in prodromal PD.ObjectiveExplore the predictive potential of circular walking in prodromal PD.MethodsWe included 102 subjects from a nine-year prospective cohort study (with 712 participants) in the current nested case-control analysis: 16 diagnosed with PD during follow-up (incident PD) and 96 healthy controls, matched in gender, age, and education with a 1:6 ratio. Forty-one gait features were extracted from baseline assessments with accelerometers under single and dual-tasking conditions. A Cox proportional hazards regression analysis was used to test the temporal association of non-correlated gait features to the probability of being diagnosed with PD.ResultsWe identified associations between time from baseline measurement to PD diagnosis for eleven gait features, mostly based on harmonic ratios, step and stride variability, and index of harmonicity, partially in combination with gait speed. Most significant associations indicated that low gait symmetry and low rhythmicity were associated with larger hazard of being diagnosed with PD. Area under the curve ranged 0.63-0.69.ConclusionsDespite low sensitivity and specificity, the findings potentially reflect prodromal motor impairments of PD manifested during circular walking, assessed quantitatively with a low-cost and wearable instrument. This will contribute to the characterization of pre-diagnostic PD motor symptoms.

Preoperative CT and Radiomics Nomograms for Distinguishing Bronchiolar Adenoma and Early-Stage Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

Evaluating the capability of CT nomograms and CT-based radiomics nomograms to differentiate between Bronchiolar Adenoma (BA) and Early-stage Lung Adenocarcinoma (LUAD).

MATERIALS AND METHODS

In this retrospective study; we analyzed data from 226 patients who were treated at our institution and pathologically confirmed to have either BA or Early-stage LUAD. Patients were randomly divided into a training cohort (n=158) and a testing cohort (n=68). All CT images were independently analyzed and measured by two radiologists using conventional computed tomography. Clinical predictive factors were identified using logistic regression. Multivariable logistic regression analysis was used to construct differential diagnostic models for BA and early-stage LUAD, including traditional CT and radiomics models. The performance of the models was determined based on the area under the receiver operating characteristic curve, discrimination ability, and decision curve analysis (DCA).

RESULTS

Lesion shape, tumor-lung interface, and pleural retraction signs were identified as independent clinical predictors. The areas under the curve for the CT nomogram, radiomic features, and radiomics nomogram were 0.854, 0.769, and 0.901, respectively. Both the CT nomogram and the radiomics nomogram demonstrated good generalizability in distinguishing between the two entities. DCA indicated that the nomograms achieved a higher net benefit compared to the use of radiomic features alone.

CONCLUSION

The two preoperative nomograms hold significant value in differentiating between patients with BA and those with Early-stage LUAD, and they contribute to informed clinical treatment decision-making.

Identification and Verification of Metabolism-related Immunotherapy Features and Prognosis in Lung Adenocarcinoma

BACKGROUND

Lung cancer is a frequent malignancy with a poor prognosis. Extensive metabolic alterations are involved in carcinogenesis and could, therefore, serve as a reliable prognostic phenotype.

AIMS

Our study aimed to develop a prognosis signature and explore the relationship between metabolic characteristic-related signature and immune infiltration in lung adenocarcinoma (LUAD).

OBJECTIVE

TCGA-LUAD and GSE31210 datasets were used as a training set and a validation set, respectively.

METHODS

A total of 513 LUAD samples collected from The Cancer Genome Atlas database (TCGA-LUAD) were used as a training dataset. Molecular subtypes were classified by consensus clustering, and prognostic genes related to metabolism were analyzed based on Differentially Expressed Genes (DEGs), Protein-Protein Interaction (PPI) network, the univariate/multivariate- and Lasso- Cox regression analysis.

RESULTS

Two molecular subtypes with significant survival differences were divided by the metabolism gene sets. The DEGs between the two subtypes were identified by integrated analysis and then used to develop an 8-gene signature (TTK, TOP2A, KIF15, DLGAP5, PLK1, PTTG1, ECT2, and ANLN) for predicting LUAD prognosis. Overexpression of the 8 genes was significantly correlated with worse prognostic outcomes. RiskScore was an independent factor that could divide LUAD patients into low- and high-risk groups. Specifically, high-risk patients had poorer prognoses and higher immune escape. The Receiver Operating Characteristic (ROC) curve showed strong performance of the RiskScore model in estimating 1-, 3- and 5-year survival in both training and validation sets. Finally, an optimized nomogram model was developed and contributed the most to the prognostic prediction in LUAD.

CONCLUSION

The current model could help effectively identify high-risk patients and suggest the most effective drug and treatment candidates for patients with LUAD.

Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicentre study

OBJECTIVES

Lymph node metastasis (LNM) is a pivotal determinant that influences the treatment strategies and prognosis for oropharyngeal squamous cell carcinoma (OPSCC) patients. This study aims to establish and verify a deep learning (DL) radiomics model for the prediction of LNM in OPSCCs using contrast-enhanced computed tomography (CECT).

METHODS

A retrospective analysis included 279 OPSCC patients from 3 institutions. CECT images were used for handcrafted (HCR) and DL feature extraction. Dimensionality reduction for HCR features used recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) algorithms, whereas DL feature dimensionality reduction used variance-threshold and RFE algorithms. Radiomics signatures were constructed using six machine learning classifiers. A combined model was then constructed using the screened DL, HCR, and clinical features. The area under the receiver operating characteristic curve (AUC) served to quantify the model's performance, and calibration curves were utilized to assess its calibration.

RESULTS

The combined model exhibited robust performance, achieving AUC values of 0.909 (95% CI, 0.861-0.957) in the training cohort, 0.884 (95% CI, 0.800-0.968) in the internal validation cohort, and 0.865 (95% CI, 0.791-0.939) in the external validation cohort. It outperformed both the clinical model and best-performing radiomics model. Moreover, calibration was deemed satisfactory.

CONCLUSIONS

The combined model based on CECT demonstrates the potential to predict LNM in OPSCCs preoperatively, offering a valuable tool for more precise and tailored treatment strategies.

ADVANCES IN KNOWLEDGE

This study presents a novel combined model integrating clinical factors with DL radiomics, significantly enhancing preoperative LNM prediction in OPSCC.

Development and validation of a radiomics-based nomogram for predicting pathological grade of upper urinary tract urothelial carcinoma

BACKGROUND

Upper urinary tract urothelial carcinoma (UTUC) is a rare and highly aggressive malignancy characterized by poor prognosis, making the accurate identification of high-grade (HG) UTUC essential for subsequent treatment strategies. This study aims to develop and validate a nomogram model using computed tomography urography (CTU) images to predict HG UTUC.

METHODS

A retrospective cohort study was conducted to include patients with UTUC who underwent radical nephroureterectomy and received a CTU examination prior to surgery. In the CTU images, tumor lesions located in the renal calyces, renal pelvis and ureter were segmented, and radiomics features from the unenhanced, medullary, and excretory phases were extracted. The maximum relevance minimum redundancy algorithm, least absolute shrinkage and selection operator, and various machine learning (ML) algorithms-including random forest, support vector machine, and eXtreme gradient boosting-were employed to select radiomics features and calculate radiomics scores. Logistic regression (LR) analysis was performed to identify the independent influencing factors of clinical baseline characteristics. Multiple datasets of radiomics features were constructed by integrating single-phase radiomics features with the most significant independent factor. Both LR and ML algorithms were utilized to develop predictive models. The area under the receiver operating characteristic curve (AUC values), accuracy, sensitivity, and specificity were assessed for model performance evaluation. Decision curve analysis was conducted to evaluate the clinical net benefits.

RESULTS

A total of 167 patients were enrolled in this study. Among them, 56 were diagnosed with low-grade UTUC (including papillary urothelial neoplasms with low malignant potential and low-grade urothelial carcinoma) as confirmed by postoperative pathological examination results, and 111 were of HG. These patients were randomly allocated to the training set and the validation set at a ratio of 7:3. The training set comprised 116 patients with a mean age of 63.5 ± 9.38 years and 38 males. The validation set comprised 51 patients with a mean age of 65.6 ± 11.1 years and 18 males. Hydronephrosis was identified as the most significant independent factor in the clinical baseline features. Models that include mixed-phase development achieve better performance compared to models that rely simply on single-phase development. The nomogram model had excellent predictive ability for HG UTUC, with AUC values of 0.844 and an accuracy of 0.793 in the validation sets. The nomogram model can enhance accuracy by 14.1% (79.3% vs. 65.2%) and sensitivity by 32.8% (93.2% vs. 60.4%) compared to urinary cytology.

CONCLUSIONS

This study developed a nomogram model, which significantly improved the diagnostic ability for HG UTUC compared to urinary cytology. Furthermore, the results of the decision curve analysis showed that the model had a net benefit and could provide a non-invasive and potentially diagnostic reference tool for HG UTUC.

A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer

OBJECTIVE

To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.

METHODS

A retrospective analysis was conducted involving 201 bladder cancer patients with definite pathological grading results after surgical excision at the organization between January 2019 and June 2023. The cohort was classified into a test set of 81 cases and a training set of 120 cases. Hand-crafted radiomics (HCR) and features derived from deep-learning (DL) were obtained from computed tomography (CT) images. The research builds a prediction model using 12 machine-learning classifiers, which integrate HCR, DL features, and clinical data. Model performance was estimated utilizing decision-curve analysis (DCA), the area under the curve (AUC), and calibration curves.

RESULTS

Among the classifiers tested, the logistic regression model that combined DL and HCR characteristics demonstrated the finest performance. The AUC values were 0.912 (training set) and 0.777 (test set). The AUC values of clinical model achieved 0.850 (training set) and 0.804 (test set). The AUC values of the combined model were 0.933 (training set) and 0.824 (test set), outperforming both the clinical and HCR-only models.

CONCLUSION

The CT-based combined model demonstrated considerable diagnostic capability in differentiating high-grade from low-grade bladder cancer, serving as a valuable noninvasive instrument for preoperative pathological evaluation.

Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study

Background

Accurate identification of pathologic grade before operation is helpful for guiding clinical treatment decisions and improving the prognosis for soft tissue sarcoma (STS).

Purpose

To construct and assess a magnetic resonance imaging (MRI)-based radiomics nomogram incorporating intratumoral habitats (subregions of clusters of voxels containing similar features) and peritumoral features for the preoperative prediction of the pathological grade of STS.

Methods

The MRI data of 145 patients with STS (74 low-grade and 71 high-grade) from 4 hospitals were retrospectively collected, including enhanced T1-weighted and fat-suppressed-T2-weighted sequences. The patients were divided into training cohort (n = 102) and validation cohort (n = 43). K-means clustering was used to divide intratumoral voxels into three habitats according to signal intensity. A number of radiomics features were extracted from tumor-related regions to construct radiomics prediction signatures for seven subgroups. Logistic regression analysis identified peritumoral edema as an independent risk factor. A nomogram was created by merging the best radiomics signature with the peritumoral edema. We evaluated the performance and clinical value of the model using area under the curve (AUC), calibration curves, and decision curve analysis.

Results

A multi-layer perceptron classifier model based on intratumoral habitats and peritumoral features combined gave the best radiomics signature, with an AUC of 0.856 for the validation cohort. The AUC of the nomogram in the validation cohort was 0.868, which was superior to the radiomics signature and the clinical model established by peritumoral edema. The calibration curves and decision curve analyses revealed good calibration and a high clinical application value for this nomogram.

Conclusion

The MRI-based nomogram is accurate and effective for predicting preoperative grading in patients with STS.

A Gluconeogenesis-Related Genes Model for Predicting Prognosis, Tumor Microenvironment Infiltration, and Drug Sensitivity in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a prevalent malignancy within the digestive system, known for its poor prognosis. Gluconeogenesis, a critical metabolic pathway, is responsible for the synthesis of glucose in the normal liver. This study aimed to examine the role of gluconeogenesis-related genes (GRGs) in HCC and evaluate their impact on the tumor microenvironment infiltration and drug sensitivity in HCC.

Methods

We retrieved gene expression and clinical pathological data of HCC from The Cancer Genome Atlas (TCGA) database. This dataset was utilized to develop a prognosis model. The data from The International Cancer Genome Consortium (ICGC) served as an independent validation cohort. A least absolute shrinkage and selection operator (LASSO) regression analysis was applied to a curated panel of GRGs to construct and validate the predictive model. Furthermore, unsupervised consensus clustering, based on the expression levels of GRGs, categorized HCC patients into distinct subgroups.

Results

A four-gene prognostic model, referred to as GRGs, has been successfully developed with high accuracy and stability for the prediction of HCC patient prognosis. This model enables the stratification of patients into high or low risk groups based on individual risk scores, revealing significant differences in immune infiltration patterns and anti-tumor drug responses. Unsupervised consensus clustering analysis delineated four distinct subgroups of patients, each characterized by a unique prognosis and tumor immune microenvironment (TIME).

Conclusion

This study is the first to develop a prognostic model incorporating 4-GRGs that effectively predicts the prognosis, tumor microenvironment infiltration, and drug sensitivity in HCC patients. The model based on 4 GRGs may contribute to predict the prognosis, immunotherapy and chemotherapy response of HCC patients.

Radiomics and Clinical Features for Distinguishing Kidney Stone-Associated Urinary Tract Infection: A Comprehensive Analysis of Machine Learning Classification

Background

This study investigated the abilities of radiomics and clinical feature models to distinguish kidney stone-associated urinary tract infections (KS-UTIs) using computed tomography.

Methods

A retrospective analysis was conducted on a single-center dataset comprising computed tomography (CT) scans and corresponding clinical information from 461 patients with kidney stones. Radiomics features were extracted from CT images and underwent dimensionality reduction and selection. Multiple machine learning (Three types of shallow learning and four types of deep learning) algorithms were employed to construct radiomics and clinical models in this study. Performance evaluation and optimal model selection were done using receiver operating characteristic (ROC) curve analysis and Delong test. Univariate and multivariate logistic regression analyzed clinical and radiomics features to identify significant variables and develop a clinical model. A combined model integrating radiomics and clinical features was established. Model performance was assessed by ROC curve analysis, clinical utility was evaluated through decision curve analysis, and the accuracy of the model was analyzed via calibration curve.

Results

Multilayer perceptron (MLP) showed higher classification accuracy than other classifiers (area under the curve (AUC) for radiomics model: train 0.96, test 0.94; AUC for clinical model: train 0.95, test 0.91. The combined radiomics-clinical model performed best (AUC for combined model: train 0.98, test 0.95). Decision curve and calibration curve analyses confirmed the model's clinical efficacy and calibration.

Conclusions

This study showed the effectiveness of combining radiomics and clinical features from CT scans to identify KS-UTIs. A combined model using MLP exhibited strong classification abilities.

A Flexible Adaptive Lasso Cox Frailty Model Based on the Full Likelihood

In this work, a method to regularize Cox frailty models is proposed that accommodates time-varying covariates and time-varying coefficients and is based on the full likelihood instead of the partial likelihood. A particular advantage of this framework is that the baseline hazard can be explicitly modeled in a smooth, semiparametric way, for example, via P-splines. Regularization for variable selection is performed via a lasso penalty and via group lasso for categorical variables while a second penalty regularizes wiggliness of smooth estimates of time-varying coefficients and the baseline hazard. Additionally, adaptive weights are included to stabilize the estimation. The method is implemented in the R function coxlasso, which is now integrated into the package PenCoxFrail, and will be compared to other packages for regularized Cox regression.

A comprehensive predictive model for postoperative joint function in robot-assisted total hip arthroplasty patients: combining radiomics and clinical indicators

Total hip arthroplasty (THA) effectively treats various end-stage hip conditions, offering pain relief and improved joint function. However, surgical outcomes are influenced by multifaceted factors. This research aims to create a predictive model, incorporating radiomic and clinical information, to forecast post-surgery joint function in robot-assisted THA (RA-THA) patients. The study set comprised 136 patients who underwent unilateral RA-THA, which were subsequently partitioned into a training set (n = 95) and a test set (n = 41) for analysis. Preoperative CT imaging was employed to derive 851 radiomic characteristics, selecting those with an intra-class correlation coefficient > 0.75 for analysis. Least absolute shrinkage and selection operator regression reduced redundancy to six significant radiomic features. Clinical data including preoperative Visual Analog Scale (VAS), Harris Hip Score (HHS), and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score were collected. Logistic regression identified significant predictors, and three models were developed. Receiver operating characteristic and decision curves evaluated the models. Preoperative VAS, HHS, WOMAC score, and radiomics feature scores were significant predictors. In the training set, the AUCs were 0.835 (clinical model), 0.757 (radiomic model), and 0.891 (combined model). In the test set, the AUCs were 0.777 (clinical model), 0.824 (radiomic model), and 0.881 (combined model). The constructed nomogram prediction model combines radiological features with relevant clinical data to accurately predict functional outcomes 3 years after RA-THA. This model has significant prediction accuracy and broad clinical application prospects and can provide a valuable reference for formulating personalized treatment plans and optimizing patient management strategies.

Preoperative MRI-based radiomic nomogram for distinguishing solitary fibrous tumor from angiomatous meningioma: a multicenter study

Purpose

This study evaluates the efficacy of radiomics-based machine learning methodologies in differentiating solitary fibrous tumor (SFT) from angiomatous meningioma (AM).

Materials and methods

A retrospective analysis was conducted on 171 pathologically confirmed cases (94 SFT and 77 AM) spanning from January 2009 to September 2020 across four institutions. The study comprised a training set (n=137) and a validation set (n=34). All patients underwent contrast-enhanced T1-weighted (CE-T1WI) and T2-weighted(T2WI) MRI scans, from which 1166 radiomics features were extracted. Subsequently, seventeen features were selected through minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was employed to assess the independence of these features as predictors. A clinical model, established via both univariate and multivariate logistic regression based on MRI morphological features, was integrated with the optimal radiomics model to formulate a radiomics nomogram. The performance of the models was assessed utilizing the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV).

Results

The radiomics nomogram demonstrated exceptional discriminative performance in the validation set, achieving an AUC of 0.989. This outperformance was evident when compared to both the radiomics algorithm (AUC= 0.968) and the clinical model (AUC = 0.911) in the same validation sets. Notably, the radiomics nomogram exhibited impressive values for ACC, SEN, and SPE at 97.1%, 93.3%, and 100%, respectively, in the validation set.

Conclusions

The machine learning-based radiomic nomogram proves to be highly effective in distinguishing between SFT and AM.

The digital revolution in pathology: Towards a smarter approach to research and treatment

Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.

Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1

INTRODUCTION

Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations.

METHODS

Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff et al. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort.

RESULTS

Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells.

DISCUSSION

Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.

Activated partial thromboplastin time predicts mortality in patients with severe fever with thrombocytopenia syndrome: A multicenter study in north China

Background

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high lethality. This study aimed to determine whether prolonged activated partial thromboplastin time (APTT) predicted SFTS mortality.

Methods

SFTS patients were enrolled from 6 hospitals in the north China. Subjects were divided into training cohort and 5 externally validation cohorts. The least absolute shrinkage and selection operator Cox regression model was performed to screen potential prognostic factors. Risk factors were analyzed using multivariable regression models. Prognostic models were established by Cox regression and random survival forest (RSF) methods, and evaluated regarding discrimination, validity and clinical benefit. Time-dependent receiver operating characteristic (ROC) curve was used to evaluate the predictive effectiveness of variables.

Results

1332 SFTS cases were included, in which 211 patients died. Six potential prognostic factors were screened, and pulse, breath, APTT and aspartic transaminase (AST) were independently associated with mortality in both training cohort (Yantai, N = 791) and external validation cohort (N = 541). APTT was steadily correlated with the fatality (HR: 1.039-1.144; all P < 0.01) in each five sub-validation cohorts (Dandong, Dalian, Tai'an, Qingdao and Beijing). RSF model with variables of APTT, AST, pulse and breath had considerable prognostic effectiveness, which APTT showed the highest prognostic ability with the area under the curve of 0.848 and 0.787 for 7-day and 14-day survival, respectively. Survival differences were found between high and low levels of APTT for mortality using 50s as the optimal cut-off.

Conclusions

SFTS patients have prolonged APTT, which is an independent risk factor for fatality. APTT≥50s was recommended as a biomarker to remind physicians to monitor and treat patients more aggressively to improve clinical prognosis.

Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation

Mortality risks of important diseases such as cancer can be estimated using gene profiles which are high-dimensional data obtained from gene expression sequences. However, it is impossible to analyze high-dimensional data with classical techniques due to multicollinearity, time-consuming processing load, and difficulty interpreting the results. For this purpose, extreme learning machine methods, which can solve regression and classification problems, have become one of the most preferred machine learning methods regarding fast data analysis and ease of application. The goal of this study is to compare estimation performance of risk score and short-term survival with survival extreme learning machine methods, L2-penalty Cox regression, and supervised principal components analysis in generated high-dimensional survival data. The survival models have been evaluated by Harrell’s concordance index, integrated Brier score, F1 score, kappa coefficient, the area under the curve, the area under precision-recall, accuracy, and Matthew’s correlation coefficient. Performances of risk score estimation and short-term survival prediction of the survival models for the censoring rates of 10%, 30%, 50% and 70% have been obtained in the range of 0.746-0.796, 0.739-0.798, 0.726-0.791, 0.708-0.784 for Harrell’s concordance index; 0.773-0.824, 0.772-0.824, 0.754-0.818, 0.739-0.808 for F1 score and 0.816-0.867, 0.808-0.865, 0.788-0.863, 0.776-0.851 for area under curve. All results showed that survival extreme learning machine methods that allow analyzing high-dimensional survival data without the necessity of dimension reduction perform very competitive with the other popular classical methods used in the study.

Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning

OBJECTIVES

To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs.

METHODS

Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan-Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model.

RESULTS

Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning-based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively.

CONCLUSIONS

A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs.

CLINICAL RELEVANCE STATEMENT

A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time.

KEY POINTS

• A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma. • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time. • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.

Identification and validation of a signature based on myofibroblastic cancer-associated fibroblast marker genes for predicting prognosis, immune infiltration, and therapeutic response in bladder cancer

PURPOSE

Myofibroblastic cancer-associated fibroblasts (myCAFs) are important components of the tumor microenvironment closely associated with tumor stromal remodeling and immunosuppression. This study aimed to explore myCAFs marker gene biomarkers for clinical diagnosis and therapy for patients with bladder cancer (BC).

MATERIALS AND METHODS

BC single-cell RNA sequencing (scRNA-seq) data were obtained from the National Center for Biotechnology Information Sequence Read Archive. Transcriptome and clinical data were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Subsequently, univariate Cox and LASSO (Least Absolute Shrinkage and Selection Operator regression) regression analyses were performed to construct a prognostic signature. Immune cell activity was estimated using single-sample gene set enrichment analysis whilst the TIDE (tumor immune dysfunction and exclusion) method was employed to assess patient response to immunotherapy. The chemotherapy response of patients with BC was evaluated using genomics of drug sensitivity in cancer. Furthermore, Immunohistochemistry was used to verify the correlation between MAP1B expression and immunotherapy efficacy. The scRNA-seq data were analyzed to identify myCAFs marker genes.

RESULTS

Combined with bulk RNA-sequencing data, we constructed a two-gene (COL6A1 and MAP1B) risk signature. In patients with BC, the signature demonstrated outstanding prognostic value, immune infiltration, and immunotherapy response. This signature served as a crucial guide for the selection of anti-tumor chemotherapy medications. Additionally, immunohistochemistry confirmed that MAP1B expression was significantly correlated with immunotherapy efficacy.

CONCLUSIONS

Our findings revealed a typical prognostic signature based on myCAF marker genes, which offers patients with BC a novel treatment target alongside theoretical justification.

A novel gene-based model for prognosis prediction of head and neck squamous cell carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is a significant global health challenge. The identification of reliable prognostic biomarkers and construction of an accurate prognostic model are crucial.

Methods

In this study, mRNA expression data and clinical data of HNSCC patients from The Cancer Genome Atlas were used. Overlapping candidate genes (OCGs) were identified by intersecting differentially expressed genes and prognosis-related genes. Best prognostic genes were selected using the least absolute shrinkage and selection operator Cox regression based on OCGs, and a risk score was developed using the Cox coefficient of each gene. The prognostic power of the risk score was assessed using Kaplan-Meier survival analysis and time-dependent receiver operating characteristic analysis. Univariate and multivariate Cox regression were performed to identify independent prognostic parameters, which were used to construct a nomogram. The predictive accuracy of the nomogram was evaluated using calibration plots. Functional enrichment analysis of risk score related genes was performed to explore the potential biological functions and pathways. External validation was conducted using data from the Gene Expression Omnibus and ArrayExpress databases.

Results

FADS3, TNFRSF12A, TJP3, and FUT6 were screened to be significantly related to prognosis in HNSCC patients. The risk score effectively stratified patients into high-risk group with poor overall survival (OS) and low-risk group with better OS. Risk score, age, clinical M stage and clinical N stage were regarded as independent prognostic parameters by Cox regression analysis and used to construct a nomogram. The nomogram performed well in 1-, 2-, 3-, 5- and 10-year survival predictions. Functional enrichment analysis suggested that tight junction was closely related to the cancer. In addition, the prognostic power of the risk score was validated by external datasets.

Conclusions

This study constructed a gene-based model integrating clinical prognostic parameters to accurately predict prognosis in HNSCC patients.

Predictors of chronic kidney disease survival in type 2 diabetes: a 12-year retrospective cohort study utilizing estimated glomerular filtration rate

Predicting the course of kidney disease in individuals with both type 1 and type 2 diabetes mellitus (DM) is a significant clinical and policy challenge. In several regions, DM is now the leading cause of end-stage renal disease. The aim of this study to identify both modifiable and non-modifiable risk factors, along with clinical markers and coexisting conditions, that increase the likelihood of stage 3-5 chronic kidney disease (CKD) development in individuals with type 2 DM in the United Arab Emirates (UAE). This was a single-center retrospective cohort study based on data derived from electronic medical records of UAE patients with DM who were registered at outpatient clinics at Tawam Hospital in Al Ain, UAE, between January 2011 and December 2021. Type 2 DM patients aged ≥ 18 years who had serum HbA1c levels ≥ 6.5% were included in the study. Patients with type 1 DM, who had undergone permanent renal replacement therapy, who had under 1 year of follow-up, or who had missing or incomplete data were excluded from the study. Factors associated with diabetic patients developing stage 3-5 CKD were identified through Cox regression analysis and a fine and gray competing risk model to account for competing events that could potentially hinder the development of CKD. A total of 1003 patients were recruited for the study. The mean age of the study cohort at baseline was 70.6 ± 28.2 years. Several factors were found to increase the risk of developing stage 3-5 CKD: advancing age (HR 1.005, 95% CI 1.002-1.009, p = 0.026), a history of hypertension (HR 1.69, 95% CI 1.032-2.8, p = 0.037), a history of heart disease (HR 1.49, 95% CI 1.16-1.92, p = 0.002), elevated levels of serum creatinine (HR 1.006, 95% CI 1.002-1.010, p = 0.003), decreased levels of estimated glomerular filtration rate (eGFR) (HR 0.943, 95% CI, 0.938-0.947; p < 0.001), and the use of beta-blockers (HR 139, 95% CI 112-173, p = 0.003). Implementing preventative measures, initiating early interventions, and developing personalized care plans tailored to address specific risk factors are imperative for reducing the impact of CKD. Additionally, the unforeseen findings related to eGFR highlight the ongoing need for research to deepen our understanding of the complexities of kidney disease.

Bayesian Inference for High Dimensional Cox Models with Gaussian and Diffused-Gamma Priors: A Case Study of Mortality in COVID-19 Patients Admitted to the ICU

Bayesian approaches have been utilized to address the challenge of variable selection and statistical inference in high-dimensional survival analysis. However, the discontinuity of the ℓ 0 -norm prior, including the useful spike-and-slab prior, may lead to computational and implementation challenges, potentially limiting the widespread use of Bayesian methods. The Gaussian and diffused-gamma (GD) prior has emerged as a promising alternative due to its continuous-and-differentiable ℓ 0 -norm approximation and computational efficiency in generalized linear models. In this paper, we extend the GD prior to semi-parametric Cox models by proposing a rank-based Bayesian inference procedure with the Cox partial likelihood. We develop a computationally efficient algorithm based on the iterative conditional mode (ICM) and Markov chain Monte Carlo methods for posterior inference. Our simulations demonstrate the effectiveness of the proposed method, and we apply it to an electronic health record dataset to identify risk factors associated with COVID-19 mortality in ICU patients at a regional medical center.

Characterization of immunogenic cell death regulators predicts survival and immunotherapy response in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is highly lethal tumor; understanding immune response is crucial for current effective treatment. Research investigated immunogenic cell death (ICD) impact on LUAD through 75 ICD-related genes which encompass cell damage, endoplasmic reticulum stress, microenvironment, and immunity. Transcriptome data and clinical info were analyzed, revealing two ICD-related clusters: B, an immune osmotic subgroup, had better prognosis, stronger immune signaling, and higher infiltration, while A represented an immune-deficient subgroup. Univariate Cox analysis identified six prognostic genes (AGER, CD69, CD83, CLEC9A, CTLA4, and NT5E), forming a validated risk score model. It was validated across datasets, showing predictive performance. High-risk group had unfavorable prognosis, lower immune infiltration, and higher chemotherapy sensitivity. Conversely, low-risk group had better prognosis, higher immune infiltration, and favorable immunotherapy response. The key gene NT5E was examined via immunohistochemistry, with higher expression linked to poorer prognosis. NT5E was predominantly expressed in B cells, fibroblasts, and endothelial cells, correlated with immune checkpoints. These outcomes suggest that NT5E can serve as a LUAD therapeutic target. The study highlights gene predictive value, offers an efficient tumor assessment tool, guides clinical treatment strategies, and identifies NT5E as therapeutic target for LUAD.

Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study

OBJECTIVE

To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients.

METHODS

We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model's performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan-Meier survival curve to analyze the prognosis of BCa patients.

RESULTS

The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659-1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival.

CONCLUSION

The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively.

CRITICAL RELEVANCE STATEMENT

Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients.

KEY POINTS

• Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival.

Construction of an ER stress-related prognostic signature for predicting prognosis and screening the effective anti-tumor drug in osteosarcoma

BACKGROUND

Osteosarcoma is the most common malignant primary bone tumor in infants and adolescents. The lack of understanding of the molecular mechanisms underlying osteosarcoma progression and metastasis has contributed to a plateau in the development of current therapies. Endoplasmic reticulum (ER) stress has emerged as a significant contributor to the malignant progression of tumors, but its potential regulatory mechanisms in osteosarcoma progression remain unknown.

METHODS

In this study, we collected RNA sequencing and clinical data of osteosarcoma from The TCGA, GSE21257, and GSE33382 cohorts. Differentially expressed analysis and the least absolute shrinkage and selection operator regression analysis were conducted to identify prognostic genes and construct an ER stress-related prognostic signature (ERSRPS). Survival analysis and time dependent ROC analysis were performed to evaluate the predictive performance of the constructed prognostic signature. The "ESTIMATE" package and ssGSEA algorithm were utilized to evaluate the differences in immune cells infiltration between the groups. Cell-based assays, including CCK-8, colony formation, and transwell assays and co-culture system were performed to assess the effects of the target gene and small molecular drug in osteosarcoma. Animal models were employed to assess the anti-osteosarcoma effects of small molecular drug.

RESULTS

Five genes (BLC2, MAGEA3, MAP3K5, STC2, TXNDC12) were identified to construct an ERSRPS. The ER stress-related gene Stanniocalcin 2 (STC2) was identified as a risk gene in this signature. Additionally, STC2 knockdown significantly inhibited osteosarcoma cell proliferation, migration, and invasion. Furthermore, the ER stress-related gene STC2 was found to downregulate the expression of MHC-I molecules in osteosarcoma cells, and mediate immune responses through influencing the infiltration and modulating the function of CD8+ T cells. Patients categorized by risk scores showed distinct immune status, and immunotherapy response. ISOX was subsequently identified and validated as an effective anti-osteosarcoma drug through a combination of CMap database screening and in vitro and in vivo experiments.

CONCLUSION

The ERSRPS may guide personalized treatment decisions for osteosarcoma, and ISOX holds promise for repurposing in osteosarcoma treatment.

Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review

PURPOSE OF REVIEW

The aim of the systematic review is to assess AI's capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice.

RECENT FINDINGS

In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy. The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.

Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma

BACKGROUND

Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC.

METHODS

Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio. Recurrence was defined as cytology/pathology-proven disease or morphological evidence of lesions on imaging examinations within 5 years after surgery. Radiomics features were extracted from manually segmented tumor on CT images and were then selected using four different feature selection methods sequentially. Multivariate logistic regression analysis was conducted to identify clinical features associated with recurrence. Radiomics, clinical, and combined models were constructed separately using logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and neural network (NN), respectively. Receiver operating characteristic analysis was performed to evaluate the model performance in predicting recurrence. A nomogram was established based on all relevant features, with its reliability and reproducibility verified using calibration curves and decision curve analysis (DCA).

RESULTS

Eighty-nine patients with PTC experienced recurrence. A total of 1218 radiomics features were extracted from each segmentation. Five radiomics and six clinical features were related to recurrence. Among the 4 radiomics models, the LR-based and SVM-based radiomics models outperformed the NN-based radiomics model (P = 0.032 and 0.026, respectively). Among the 4 clinical models, only the difference between the area under the curve (AUC) of the LR-based and NN-based clinical model was statistically significant (P = 0.035). The combined models had higher AUCs than the corresponding radiomics and clinical models based on the same classifier, although most differences were not statistically significant. In the validation cohort, the combined models based on the LR, SVM, KNN, and NN classifiers had AUCs of 0.746, 0.754, 0.669, and 0.711, respectively. However, the AUCs of these combined models had no significant differences (all P > 0.05). Calibration curves and DCA indicated that the nomogram have potential clinical utility.

CONCLUSIONS

The combined model may have potential for better prediction of PTC recurrence than radiomics and clinical models alone. Further testing with larger cohort may help reach statistical significance.

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

BACKGROUND

Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies.

PURPOSE

This study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM.

METHODS

We retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC).

RESULTS

The area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making.

CONCLUSION

The combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.

Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package

Analyzing different omics data types independently is often too restrictive to allow for detection of subtle, but consistent, variations that are coherently supported based upon different assays. Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package ( https://bioconductor.org/packages/iNETgrate/ ) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

Integrative analysis identified two subtypes and a taurine-related signature to predict the prognosis and efficacy of immunotherapy in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent subtypes of primary liver cancer, with high mortality and poor prognosis. Immunotherapy has revolutionized treatment strategies for many cancers. However, only a subset of patients with HCC achieve satisfactory benefits from immunotherapy. Therefore, a reliable biomarker that could predict the prognosis and immunotherapy response in patients with HCC is urgently needed. Taurine plays an important role in many physiological processes. However, its participation in the occurrence and progression of liver cancer and regulation of the composition and function of various components of the immune microenvironment remains elusive. In this study, we identified and validated two heterogeneous subtypes of HCC with different taurine metabolic profiles, presenting distinct genomic features, clinicopathological characteristics, and immune landscapes, using multiple bulk transcriptome datasets. Subsequently, we constructed a risk model based on genes related to taurine metabolism to assess the prognosis, immune cell infiltration, immunotherapy response, and drug sensitivity of patients with HCC. The risk model was validated using several independent external cohorts and showed a robust predictive performance. In addition, we evaluated the expression patterns of taurine metabolism-related genes in the tumor microenvironment and the heterogeneity of taurine metabolism among cancer cells using a single-cell transcriptome. In conclusion, our study provides insights into the important role played by taurine metabolism in tumor progression and immune regulation. Furthermore, the risk model can serve as a biomarker to assess patient prognosis and immunotherapy response, potentially helping clinicians make more precise and personalized clinical decisions.

Prediction of synchronous distant metastasis of primary pancreatic ductal adenocarcinoma using the radiomics features derived from 18F-FDG PET and MRI

AIM

To explore the potential of the joint radiomics analysis of positron-emission tomography (PET) and magnetic resonance imaging (MRI) of primary tumours for predicting the risk of synchronous distant metastasis (SDM) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

18F-FDG PET and MRI images of PDAC patients from January 2011 to December 2020 were collected retrospectively. Patients (n=66) who received 18F-FDG PET/CT and MRI were included in a development group. Patients (n=25) scanned with hybrid PET/MRI were incorporated in an external test group. A radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm to select PET-MRI radiomics features of primary PDAC tumours. A radiomics nomogram was developed by combining the radiomics signature and important clinical indicators using univariate and multivariate analysis to assess patients' metastasis risk. The nomogram was verified with the employment of an external test group.

RESULTS

Regarding the development cohort, the radiomics nomogram was found to be better for predicting the risk of distant metastasis (area under the curve [AUC]: 0.93, sensitivity: 87%, specificity: 85%) than the clinical model (AUC: 0.70, p<0.001; sensitivity:70%, specificity: 65%) and the radiomics signature (AUC: 0.89, p>0.05; sensitivity: 65%, specificity:100%). Concerning the external test cohort, the radiomics nomogram yielded an AUC of 0.85.

CONCLUSION

PET-MRI based radiomics analysis exhibited effective prediction of the risk of SDM for preoperative PDAC patients and may offer complementary information and provide hints for cancer staging.

Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors

OBJECTIVES

This study evaluated the ability of a preoperative contrast-enhanced CT (CECT)-based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT).

METHODS

Images and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis.

RESULTS

The radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model.

CONCLUSIONS

The preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning.

KEY POINTS

• A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis. • Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone. • For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.

Automated Prediction of Early Recurrence in Advanced Sinonasal Squamous Cell Carcinoma With Deep Learning and Multi-parametric MRI-based Radiomics Nomogram

RATIONALE AND OBJECTIVES

Preoperative prediction of the recurrence risk in patients with advanced sinonasal squamous cell carcinoma (SNSCC) is critical for individualized treatment. To evaluate the predictive ability of radiomics signature (RS) based on deep learning and multiparametric MRI for the risk of 2-year recurrence in advanced SNSCC.

MATERIALS AND METHODS

Preoperative MRI datasets were retrospectively collected from 265 SNSCC patients (145 recurrences) who underwent preoperative MRI, including T2-weighted (T2W), contrast-enhanced T1-weighted (T1c) sequences and diffusion-weighted (DW). All patients were divided into 165 training cohort and 70 test cohort. A deep learning segmentation model based on VB-Net was used to segment regions of interest (ROIs) for preoperative MRI and radiomics features were extracted from automatically segmented ROIs. Least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were applied for feature selection and radiomics score construction. Combined with meaningful clinicopathological predictors, a nomogram was developed and its performance was evaluated. In addition, X-title software was used to divide patients into high-risk or low-risk early relapse (ER) subgroups. Recurrence-free survival probability (RFS) was assessed for each subgroup.

RESULTS

The radiomics score, T stage, histological grade and Ki-67 predictors were independent predictors. The segmentation models of T2WI, T1c, and apparent diffusion coefficient (ADC) sequences achieved Dice coefficients of 0.720, 0.727, and 0.756, respectively, in the test cohort. RS-T2, RS-T1c and RS-ADC were derived from single-parameter MRI. RS-Combined (combined with T2WI, T1c, and ADC features) was derived from multiparametric MRI and reached area under curve (AUC) and accuracy of 0.854 (0.749-0.927) and 74.3% (0.624-0.840), respectively, in the test cohort. The calibration curve and decision curve analysis (DCA) illustrate its value in clinical practice. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (p < 0.001).

CONCLUSION

Automated nomograms based on multi-sequence MRI help to predict ER in SNSCC patients preoperatively.

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study

BACKGROUND

To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively.

METHODS

We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

RESULTS

The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature.

CONCLUSION

The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.

A novel necroptosis related gene signature and regulatory network for overall survival prediction in lung adenocarcinoma

We downloaded the mRNA expression profiles of patients with LUAD and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and used the Least Absolute Shrinkage and Selection Operator Cox regression model to construct a multigene signature in the TCGA cohort, which was validated with patient data from the GEO cohort. Results showed differences in the expression levels of 120 necroptosis-related genes between normal and tumor tissues. An eight-gene signature (CYLD, FADD, H2AX, RBCK1, PPIA, PPID, VDAC1, and VDAC2) was constructed through univariate Cox regression, and patients were divided into two risk groups. The overall survival of patients in the high-risk group was significantly lower than of the patients in the low-risk group in the TCGA and GEO cohorts, indicating that the signature has a good predictive effect. The time-ROC curves revealed that the signature had a reliable predictive role in both the TCGA and GEO cohorts. Enrichment analysis showed that differential genes in the risk subgroups were associated with tumor immunity and antitumor drug sensitivity. We then constructed an mRNA-miRNA-lncRNA regulatory network, which identified lncRNA AL590666. 2/let-7c-5p/PPIA as a regulatory axis for LUAD. Real-time quantitative PCR (RT-qPCR) was used to validate the expression of the 8-gene signature. In conclusion, necroptosis-related genes are important factors for predicting the prognosis of LUAD and potential therapeutic targets.

Construction of an Immunophenoscore-Related Signature for Evaluating Prognosis and Immunotherapy Sensitivity in Ovarian Cancer

Ovarian cancer (OC) is the deadliest gynecological malignancy in the world, and immunotherapy is emerging as a promising treatment. Immunophenoscore (IPS) is a robust biomarker distinguishing sensitive responders from immunotherapy. In this study, we aimed to construct a prognostic model for predicting overall survival (OS) and identifying patients who would benefit from immunotherapy. First, we combined The Cancer Genome Atlas (TCGA) and The Cancer Immune Atlas (TCIA) data sets and incorporated 229 OC samples into a training cohort. The validation cohort included 240 OC samples from the Gene Expression Omnibus (GEO) cohort. The training cohort was divided into high- and low-IPS subgroups to obtain differentially expressed genes (DEGs). DEGs with OS were identified by Univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct the prognostic model. Then, immune and mutation analyses were performed to explore the relationship between the model and the tumor microenvironment (TME) and tumor mutation burden (TMB). Eighty-three DEGs were obtained between the high-and low-IPS subgroups, where 17 DEGs were associated with OS. The five essential genes were selected to establish the prognostic model, which showed high accuracy for predicting OS and could be an independent survival indicator. OC samples that were divided by risk scores showed distinguished immune status, TME, TMB, immunotherapy response, and chemotherapy sensitivity. Similar results were validated in the GEO cohort. We developed an immunophenoscore-related signature associated with the TME to predict OS and response to immunotherapy in OC.

Healthcare center clustering for Cox's proportional hazards model by fusion penalty

There has been growing research interest in developing methodology to evaluate healthcare centers' performance with respect to patient outcomes. Conventional assessments can be conducted using fixed or random effects models, as seen in provider profiling. We propose a new method, using fusion penalty to cluster healthcare centers with respect to a survival outcome. Without any prior knowledge of the grouping information, the new method provides a desirable data-driven approach for automatically clustering healthcare centers into distinct groups based on their performance. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. The validity of our approach is demonstrated through simulation studies, and its practical application is illustrated by analyzing data from the national kidney transplant registry.

Combining single-cell and transcriptomic analysis revealed the immunomodulatory effect of GOT2 on a glutamine-dependent manner in cutaneous melanoma

Background: Reprogramming in glutamine metabolism is a hallmark of cancers, while its role in cutaneous melanoma has not been studied at great length. Methods: Here, we constructed a glutamine metabolism-related prognostic signature in cutaneous melanoma with a variety of bioinformatics methods according to the glutamine metabolism regulatory molecules. Moreover, experimental verification was carried out for the key gene. Results: We have identified two subgroups of cutaneous melanoma patients, each with different prognoses, immune characteristics, and genetic mutations. GOT2 was the most concerned key gene among the model genes. We verified its role in promoting tumor cell proliferation by CCK-8 and clone formation assays. Conclusion: Our study cast new light on the prognosis of cutaneous melanoma, and the internal mechanism regulating glutamine metabolism of GOT2 may provide a new avenue for treating the cutaneous melanoma disease precisely.

The solution surface of the Li-Stephens haplotype copying model

The Li-Stephens (LS) haplotype copying model forms the basis of a number of important statistical inference procedures in genetics. LS is a probabilistic generative model which supposes that a sampled chromosome is an imperfect mosaic of other chromosomes found in a population. In the frequentist setting which is the focus of this paper, the output of LS is a "copying path" through chromosome space. The behavior of LS depends crucially on two user-specified parameters, [Formula: see text] and [Formula: see text], which are respectively interpreted as the rates of mutation and recombination. However, because LS is not based on a realistic model of ancestry, the precise connection between these parameters and the biological phenomena they represent is unclear. Here, we offer an alternative perspective, which considers [Formula: see text] and [Formula: see text] as tuning parameters, and seeks to understand their impact on the LS output. We derive an algorithm which, for a given dataset, efficiently partitions the [Formula: see text] plane into regions where the output of the algorithm is constant, thereby enumerating all possible solutions to the LS model in one go. We extend this approach to the "diploid LS" model commonly used for phasing. We demonstrate the usefulness of our method by studying the effects of changing [Formula: see text] and [Formula: see text] when using LS for common bioinformatic tasks. Our findings indicate that using the conventional (i.e., population-scaled) values for [Formula: see text] and [Formula: see text] produces near optimal results for imputation, but may systematically inflate switch error in the case of phasing diploid genotypes.

A CT-based radiomics nomogram for distinguishing between malignant and benign Bosniak IIF masses: a two-centre study

AIM

To establish and assess a computed tomography (CT)-based radiomics nomogram for identifying malignant and benign Bosniak IIF masses.

MATERIALS AND METHODS

In total, 150 patients with Bosniak IIF masses were separated into a training set (n=106) and a test set (n=44) in a ratio of 7:3. A radiomics signature was calculated based on extracted features from the three phases of CT images. A clinical model was constructed based on clinical characteristics and CT features, and a nomogram incorporating the radiomics signature and independent clinical variables was established. The calibration ability, discrimination accuracy, and clinical value of the nomogram model were assessed.

RESULTS

Twelve features derived from CT images were applied to establish the radiomics signature. The performance levels of three machine-learning models were improved by adding the synthetic minority oversampling technique algorithm. The optimised machine learning model was a combination of the minimum redundancy maximum relevance-least absolute shrinkage and selection operator feature screening method + logistic regression classifier + synthetic minority oversampling technique algorithm, which demonstrated excellent identification ability on the test set (area under the curve [AUC], 0.970; 95% confidence interval [CI], 0.940-1.000). The nomogram model displayed outstanding discrimination ability on the test set (AUC, 0.972; 95% CI, 0.942-1.000).

CONCLUSIONS

The CT-based radiomics nomogram was useful for discriminating between malignant and benign Bosniak IIF masses, which improved the precision of preoperative diagnosis.

FGFR3 mutation characterization identifies prognostic and immune-related gene signatures in bladder cancer

BACKGROUND

Immunotherapy and FGFR3-targeted therapy play an important role in the management of locally advanced and metastatic bladder cancer (BLCA). Previous studies indicated that FGFR3 mutation (mFGFR3) may be involved in the alterations of immune infiltration, which may affect the priority or combination of these two treatment regimes. However, the specific impact of mFGFR3 on the immunity and how FGFR3 regulates the immune response in BLCA to affect prognosis remain unclear. In this study, we aimed to elucidate the immune landscape associated with mFGFR3 status in BLCA, screen immune-related gene signatures with prognostic value, and construct and validate a prognostic model.

METHODS

ESTIMATE and TIMER were used to assess the immune infiltration within tumors in the TCGA BLCA cohort based on transcriptome data. Further, the mFGFR3 status and mRNA expression profiles were analyzed to identify immune-related genes that were differentially expressed between patients with BLCA with wild-type FGFR3 or mFGFR3 in the TCGA training cohort. An FGFR3-related immune prognostic score (FIPS) model was established in the TCGA training cohort. Furthermore, we validated the prognostic value of FIPS with microarray data in the GEO database and tissue microarray from our center. Multiple fluorescence immunohistochemical analysis was performed to confirm the relationship between FIPS and immune infiltration.

RESULTS

mFGFR3 resulted in differential immunity in BLCA. In total, 359 immune-related biological processes were enriched in the wild-type FGFR3 group, whereas none were enriched in the mFGFR3 group. FIPS could effectively distinguish high-risk patients with poor prognosis from low-risk patients. The high-risk group was characterized by a higher abundance of neutrophils; macrophages; and follicular helper, CD4, and CD8 T-cells than the low-risk group. In addition, the high-risk group exhibited higher expression of PD-L1, PD-1, CTLA-4, LAG-3, and TIM-3 than the low-risk group, indicating an immune-infiltrated but functionally suppressed immune microenvironment. Furthermore, patients in the high-risk group exhibited a lower mutation rate of FGFR3 than those in the low-risk group.

CONCLUSIONS

FIPS effectively predicted survival in BLCA. Patients with different FIPS exhibited diverse immune infiltration and mFGFR3 status. FIPS might be a promising tool for selecting targeted therapy and immunotherapy for patients with BLCA.

Prediction of therapeutic response of unresectable hepatocellular carcinoma to hepatic arterial infusion chemotherapy based on pretherapeutic MRI radiomics and Albumin-Bilirubin score

PURPOSE

To construct and validate a combined nomogram model based on magnetic resonance imaging (MRI) radiomics and Albumin-Bilirubin (ALBI) score to predict therapeutic response in unresectable hepatocellular carcinoma (HCC) patients treated with hepatic arterial infusion chemotherapy (HAIC).

METHODS

The retrospective study was conducted on 112 unresectable HCC patients who underwent pretherapeutic MRI examinations. Patients were randomly divided into training (n = 79) and validation cohorts (n = 33). A total of 396 radiomics features were extracted from the volume of interest of the primary lesion by the Artificial Kit software. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify optimal radiomic features. After feature selection, three models, including the clinical, radiomics, and combined models, were developed to predict the non-response of unresectable HCC to HAIC treatment. The performance of these models was evaluated by the receiver operating characteristic curve. According to the most efficient model, a nomogram was established, and the performance of which was also assessed by calibration curve and decision curve analysis. Kaplan-Meier curve and log-rank test were performed to evaluate the Progression-free survival (PFS).

RESULTS

Using the LASSO regression, we ultimately selected three radiomics features from T2-weighted images to construct the radiomics score (Radscore). Only the ALBI score was an independent factor associated with non-response in the clinical model (P = 0.033). The combined model, which included the ALBI score and Radscore, achieved better performance in the prediction of non-response, with an AUC of 0.79 (95% CI 0.68-0.90) and 0.75 (95% CI 0.58-0.92) in the training and validation cohorts, respectively. The nomogram based on the combined model also had good discrimination and calibration (P = 0.519 for the training cohort and P = 0.389 for the validation cohort). The Kaplan-Meier analysis also demonstrate that the high-score patients had significantly shorter PFS than the low-score patients (P = 0.031) in the combined model, with median PFS 6.0 vs 9.0 months.

CONCLUSION

The nomogram based on the combined model consisting of MRI radiomics and ALBI score could be used as a biomarker to predict the therapeutic response of unresectable HCC after HAIC.

Microenvironment modulation by key regulators of RNA N6-methyladenosine modification in respiratory allergic diseases

BACKGROUND

RNA N6-methyladenosine (m6A) regulators are considered post-transcriptional regulators that affect several biological functions, and their role in immunity, in particular, is emerging. However, the role of m6A regulators in respiratory allergic diseases remains unclear. Therefore, we aimed to investigate the role of key m6A regulators in mediating respiratory allergic diseases and immune microenvironment infiltration characteristics.

METHODS

We downloaded gene expression profiles of respiratory allergies from the Gene Expression Omnibus (GEO) database and we performed hierarchical clustering, difference analysis, and construction of predictive models to identify hub m6A regulators that affect respiratory allergies. Next, we investigate the underlying biological mechanisms of key m6A regulators by performing PPI network analysis, functional enrichment analysis, and immune microenvironment infiltration analysis. In addition, we performed a drug sensitivity analysis on the key m6A regulator, hoping to be able to provide some implications for clinical medication.

RESULTS

In this study, we identified four hub m6A regulators that affect the respiratory allergy and investigated the underlying biological mechanisms. In addition, studies on the characteristics of immune microenvironment infiltration revealed that the expression of METTL14, METTL16, and RBM15B correlated with the infiltration of the mast and Th2 cells in respiratory allergy, and METTL16 expression was found to be significantly negatively correlated with macrophages for the first time (R = -0.53, P < 0.01). Finally, a key m6A regulator, METTL14, was screened by combining multiple algorithms. In addition, by performing a drug sensitivity analysis on METTL14, we hypothesized that it may play an important role in the improvement of allergic symptoms in the upper and lower airways with topical nasal glucocorticoids.

CONCLUSIONS

Our findings suggest that m6A regulators, particularly METTL14, play a crucial role in the development of respiratory allergic diseases and the infiltration of immune cells. These results may provide insight into the mechanism of action of methylprednisolone in treating respiratory allergic diseases.

ITGA3 acts as a purity-independent biomarker of both immunotherapy and chemotherapy resistance in pancreatic cancer: bioinformatics and experimental analysis

Contribution of integrin superfamily genes to treatment resistance remains uncertain. Genome patterns of thirty integrin superfamily genes were analyzed of using bulk and single-cell RNA sequencing, mutation, copy number, methylation, clinical information, immune cell infiltration, and drug sensitivity data. To select the integrins that are most strongly associated with treatment resistance in pancreatic cancer, a purity-independent RNA regulation network including integrins were constructed using machine learning. The integrin superfamily genes exhibit extensive dysregulated expression, genome alterations, epigenetic modifications, immune cell infiltration, and drug sensitivity, as evidenced by multi-omics data. However, their heterogeneity varies among different cancers. After constructing a three-gene (TMEM80, EIF4EBP1, and ITGA3) purity-independent Cox regression model using machine learning, ITGA3 was identified as a critical integrin subunit gene in pancreatic cancer. ITGA3 is involved in the molecular transformation from the classical to the basal subtype in pancreatic cancer. Elevated ITGA3 expression correlated with a malignant phenotype characterized by higher PD-L1 expression and reduced CD8+ T cell infiltration, resulting in unfavorable outcomes in patients receiving either chemotherapy or immunotherapy. Our findings suggest that ITGA3 is an important integrin in pancreatic cancer, contributing to chemotherapy resistance and immune checkpoint blockade therapy resistance.