Lasso-Based Machine Learning Algorithm for Predicting Postoperative Lung Complications in Elderly: A Single-Center Retrospective Study from China

2.5D deep learning based on multi-parameter MRI to differentiate primary lung cancer pathological subtypes in patients with brain metastases

BACKGROUND

Brain metastases (BMs) represents a severe neurological complication stemming from cancers originating from various sources. It is a highly challenging clinical task to accurately distinguish the pathological subtypes of brain metastatic tumors from lung cancer (LC).The utility of 2.5-dimensional (2.5D) deep learning (DL) in distinguishing pathological subtypes of LC with BMs is yet to be determined.

METHODS

A total of 250 patients were included in this retrospective study, divided in a 7:3 ratio into training set (N=175) and testing set (N=75). We devised a method to assemble a series of two-dimensional (2D) images by extracting adjacent slices from a central slice in both superior-inferior and anterior-posterior directions to form a 2.5D dataset. Multi-Instance learning (MIL) is a weakly supervised learning method that organizes training instances into "bags" and provides labels for entire bags, with the purpose of learning a classifier based on the labeled positive and negative bags to predict the corresponding class for an unknown bag. Therefore, we employed MIL to construct a comprehensive 2.5D feature set. Then we used the single-slice as input for constructing the 2D model. DL features were extracted from these slices using the pre-trained ResNet101. All feature sets were inputted into the support vector machine (SVM) for evaluation. The diagnostic performance of the classification models were evaluated using five-fold cross-validation, with accuracy and area under the curve (AUC) metrics calculated for analysis.

RESULTS

The optimal performance was obtained using the 2.5D DL model, which achieved the micro-AUC of 0.868 (95% confidence interval [CI], 0.817-0.919) and accuracy of 0.836 in the test cohort. The 2D model achieved the micro-AUC of 0.836 (95 % CI, 0.778-0.894) and accuracy of 0.827 in the test cohort.

CONCLUSIONS

The proposed 2.5D DL model is feasible and effective in identifying pathological subtypes of BMs from lung cancer.

Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications

BACKGROUND

Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that use intraoperative respiratory features to predict PPCs.

METHODS

We analysed perioperative data from a cohort comprising patients aged 65 yr and older at an academic medical centre from 2019 to 2023. Two linear and four nonlinear learning models were developed and compared with the current gold-standard risk assessment tool ARISCAT (Assess Respiratory Risk in Surgical Patients in Catalonia Tool). The Shapley additive explanation of artificial intelligence was utilised to interpret feature importance and interactions.

RESULTS

Perioperative data were obtained from 10 284 patients who underwent 10 484 operations (mean age [range] 71 [65-98] yr; 42% female). An optimised XGBoost model that used preoperative variables and intraoperative respiratory variables had area under the receiver operating characteristic curves (AUROCs) of 0.878 (0.866-0.891) and 0.881 (0.879-0.883) in the validation and prospective cohorts, respectively. These models outperformed ARISCAT (AUROC: 0.496-0.533). The intraoperative dynamic features of respiratory dynamic system compliance, mechanical power, and driving pressure were identified as key modifiable contributors to PPCs. A simplified model based on XGBoost including 20 variables generated an AUROC of 0.864 (0.852-0.875) in an internal testing cohort. This has been developed into a web-based tool for further external validation (https://aorm.wchscu.cn/).

CONCLUSIONS

These findings suggest that real-time identification of surgical patients' risk of postoperative pulmonary complications could help personalise intraoperative ventilatory strategies and reduce postoperative pulmonary complications.

POLE -related gene signature predicts prognosis, immune feature, and drug therapy in human endometrioid carcinoma

The POLE subtype of Endometrial carcinoma (EC) is linked to a favourable prognosis in the molecular classification. We proposed to ascertain the potential connection between the POLE subtype and improved prognosis. In order to forecast the prognosis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis and weighted gene co-expression network analysis (WGCNA) were employed, and a POLE-related risk signature (PRS) model was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) with the "GSVA" package was employed to analyse immunity characteristics. Drug susceptibility studies were conducted to compare the half-maximal inhibitory concentration (IC50) of medicines between high- and low-risk groups. The PRS model was generated employing the LASSO Cox regression coefficients of the ELF1, MMADHC, andAL021707.6 genes. Our study demonstrated that the risk score was linked to tumour stage, grade, and survival. Furthermore, the low-risk group possessed elevated levels of gene expression connected with immunological checkpoints and HLA. Our outcomes emerged that the PRS model might have value in identifying patients with a good prognosis and in facilitating personalised treatment in the clinic.

A prediction model of elderly hip fracture mortality including preoperative red cell distribution width constructed based on the random survival forest (RSF) and Cox risk ratio regression

An independent correlation between pre-RDW and 1-year mortality after surgery in elderly hip fracture can be used to predict mortality in elderly hip fracture patients and has predictive significance in anemia patients. With further research, a treatment algorithm can be developed to potentially identify patients at high risk of preoperative mortality.

INTRODUCTION

Red blood cell distribution width (RDW) is an independent predictor of various disease states in elderly individuals, but its association with the prognosis of elderly hip fracture patients is controversial. This study aimed to evaluate the prognostic value of RDW in such patients, construct a prediction model containing RDW using random survival forest (RSF) and Cox regression analysis, and compare RDW in patients with and without anemia.

METHODS

We retrospectively analyzed the data of elderly patients who underwent hip fracture surgery, selected the best variables using RSF, stratified the independent variables by Cox regression analysis, constructed a 1-year mortality prediction model of elderly hip fracture with RDW, and conducted internal validation and external validation.

RESULTS

Two thousand one hundred six patients were included in this study. The RSF algorithm selects 12 important influencing factors, and Cox regression analysis showed that eight variables including preoperative RDW (pre-RDW) were independent risk factors for death within 1-year after hip fracture surgery in elderly patients. Stratified analysis showed that pre-RDW was still independently associated with 1-year mortality in the non-anemia group and not in the anemia group. The nomogram prediction model had high differentiation and fit, and the prediction model constructed by the total cohort of patients was also used for validation of patients in the anemia patients and obtained good clinical benefits.

CONCLUSION

An independent correlation between pre-RDW and 1-year mortality after surgery in elderly hip fracture can be used to predict mortality in elderly hip fracture patients and has predictive significance in anemia patients.

Ultrasound-based deep learning radiomics nomogram for risk stratification of testicular masses: a two-center study

OBJECTIVE

To develop an ultrasound-driven clinical deep learning radiomics (CDLR) model for stratifying the risk of testicular masses, aiming to guide individualized treatment and minimize unnecessary procedures.

METHODS

We retrospectively analyzed 275 patients with confirmed testicular lesions (January 2018 to April 2023) from two hospitals, split into training (158 cases), validation (68 cases), and external test cohorts (49 cases). Radiomics and deep learning (DL) features were extracted from preoperative ultrasound images. Following feature selection, we utilized logistic regression (LR) to establish a deep learning radiomics (DLR) model and subsequently derived its signature. Clinical data underwent univariate and multivariate LR analyses, forming the "clinic signature." By integrating the DLR and clinic signatures using multivariable LR, we formulated the CDLR nomogram for testicular mass risk stratification. The model's efficacy was gauged using the area under the receiver operating characteristic curve (AUC), while its clinical utility was appraised with decision curve analysis(DCA). Additionally, we compared these models with two radiologists' assessments (5-8 years of practice).

RESULTS

The CDLR nomogram showcased exceptional precision in distinguishing testicular tumors from non-tumorous lesions, registering AUCs of 0.909 (internal validation) and 0.835 (external validation). It also excelled in discerning malignant from benign testicular masses, posting AUCs of 0.851 (internal validation) and 0.834 (external validation). Notably, CDLR surpassed the clinical model, standalone DLR, and the evaluations of the two radiologists.

CONCLUSION

The CDLR nomogram offers a reliable tool for differentiating risks associated with testicular masses. It augments radiological diagnoses, facilitates personalized treatment approaches, and curtails unwarranted medical procedures.