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Frontiers in Immunology 2024To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC)...
OBJECTIVES
To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image.
METHODS
This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).
RESULTS
In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686.
CONCLUSION
The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.
Topics: Humans; Carcinoma, Non-Small-Cell Lung; Lung Neoplasms; Male; Female; Neoadjuvant Therapy; Middle Aged; Retrospective Studies; Aged; Tomography, X-Ray Computed; Treatment Outcome; Machine Learning; Immunotherapy; Adult; Pathologic Complete Response
PubMed: 38933281
DOI: 10.3389/fimmu.2024.1414954 -
Frontiers in Immunology 2024This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize...
BACKGROUND
This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response.
METHODS
A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness.
RESULTS
SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (0.0001) and the subsequent validation cohort (0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness.
CONCLUSIONS
The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.
Topics: Humans; Carcinoma, Hepatocellular; Liver Neoplasms; Immunotherapy; Prognosis; Biomarkers, Tumor; Male; Female; Transcriptome; Middle Aged; Gene Expression Regulation, Neoplastic; Gene Expression Profiling; Support Vector Machine; Treatment Outcome
PubMed: 38933262
DOI: 10.3389/fimmu.2024.1371829 -
Frontiers in Medicine 2024The objective of this research was to create a machine learning predictive model that could be easily interpreted in order to precisely determine the risk of premature...
OBJECTIVE
The objective of this research was to create a machine learning predictive model that could be easily interpreted in order to precisely determine the risk of premature death in patients receiving intensive care after pulmonary inflammation.
METHODS
In this study, information from the China intensive care units (ICU) Open Source database was used to examine data from 2790 patients who had infections between January 2019 and December 2020. A 7:3 ratio was used to randomly assign the whole patient population to training and validation groups. This study used six machine learning techniques: logistic regression, random forest, gradient boosting tree, extreme gradient boosting tree (XGBoost), multilayer perceptron, and K-nearest neighbor. A cross-validation grid search method was used to search the parameters in each model. Eight metrics were used to assess the models' performance: accuracy, precision, recall, F1 score, area under the curve (AUC) value, Brier score, Jordon's index, and calibration slope. The machine methods were ranked based on how well they performed in each of these metrics. The best-performing models were selected for interpretation using both the Shapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME) interpretable techniques.
RESULTS
A subset of the study cohort's patients (120/1668, or 7.19%) died in the hospital following screening for inclusion and exclusion criteria. Using a cross-validated grid search to evaluate the six machine learning techniques, XGBoost showed good discriminative ability, achieving an accuracy score of 0.889 (0.874-0.904), precision score of 0.871 (0.849-0.893), recall score of 0.913 (0.890-0.936), F1 score of 0.891 (0.876-0.906), and AUC of 0.956 (0.939-0.973). Additionally, XGBoost exhibited excellent performance with a Brier score of 0.050, Jordon index of 0.947, and calibration slope of 1.074. It was also possible to create an interactive internet page using the XGBoost model.
CONCLUSION
By identifying patients at higher risk of early mortality, machine learning-based mortality risk prediction models have the potential to significantly improve patient care by directing clinical decision making and enabling early detection of survival and mortality issues in patients with pulmonary inflammation disease.
PubMed: 38933112
DOI: 10.3389/fmed.2024.1399527 -
BMJ Neurology Open 2024Accurate outcome predictions for patients who had ischaemic stroke with successful reperfusion after endovascular thrombectomy (EVT) may improve patient treatment and...
BACKGROUND
Accurate outcome predictions for patients who had ischaemic stroke with successful reperfusion after endovascular thrombectomy (EVT) may improve patient treatment and care. Our study developed prediction models for key clinical outcomes in patients with successful reperfusion following EVT in an Australian population.
METHODS
The study included all patients who had ischaemic stroke with occlusion in the proximal anterior cerebral circulation and successful reperfusion post-EVT over a 7-year period. Multivariable logistic regression and Cox regression models, incorporating bootstrap and multiple imputation techniques, were used to identify predictors and develop models for key clinical outcomes: 3-month poor functional status; 30-day, 1-year and 3-year mortality; survival time.
RESULTS
A total of 978 patients were included in the analyses. Predictors associated with one or more poor outcomes include: older age (ORs for every 5-year increase: 1.22-1.40), higher premorbid functional modified Rankin Scale (ORs: 1.31-1.75), higher baseline National Institutes of Health Stroke Scale (ORs: 1.05-1.07) score, higher blood glucose (ORs: 1.08-1.19), larger core volume (ORs for every 10 mL increase: 1.10-1.22), pre-EVT thrombolytic therapy (ORs: 0.44-0.56), history of heart failure (outcome: 30-day mortality, OR=1.87), interhospital transfer (ORs: 1.42 to 1.53), non-rural/regional stroke onset (outcome: functional dependency, OR=0.64), longer onset-to-groin puncture time (outcome: 3-year mortality, OR=1.08) and atherosclerosis-caused stroke (outcome: functional dependency, OR=1.68). The models using these predictors demonstrated moderate predictive abilities (area under the receiver operating characteristic curve range: 0.752-0.796).
CONCLUSION
Our models using real-world predictors assessed at hospital admission showed satisfactory performance in predicting poor functional outcomes and short-term and long-term mortality for patients with successful reperfusion following EVT. These can be used to inform EVT treatment provision and consent.
PubMed: 38932996
DOI: 10.1136/bmjno-2024-000707 -
Ecology and Evolution Jun 2024For decades, researchers have employed sound to study the biology of wildlife, with the aim to better understand their ecology and behaviour. By utilizing on-animal...
For decades, researchers have employed sound to study the biology of wildlife, with the aim to better understand their ecology and behaviour. By utilizing on-animal recorders to capture audio from freely moving animals, scientists can decipher the vocalizations and glean insights into their behaviour and ecosystem dynamics through advanced signal processing. However, the laborious task of sorting through extensive audio recordings has been a major bottleneck. To expedite this process, researchers have turned to machine learning techniques, specifically neural networks, to streamline the analysis of data. Nevertheless, much of the existing research has focused predominantly on stationary recording devices, overlooking the potential benefits of employing on-animal recorders in conjunction with machine learning. To showcase the synergy of on-animal recorders and machine learning, we conducted a study at the Kutuharju research station in Kaamanen, Finland, where the vocalizations of rutting reindeer were recorded during their mating season. By attaching recorders to seven male reindeer during the rutting periods of 2019 and 2020, we trained convolutional neural networks to distinguish reindeer grunts with a 95% accuracy rate. This high level of accuracy allowed us to examine the reindeers' grunting behaviour, revealing patterns indicating that older, heavier males vocalized more compared to their younger, lighter counterparts. The success of this study underscores the potential of on-animal acoustic recorders coupled with machine learning techniques as powerful tools for wildlife research, hinting at their broader applications with further advancement and optimization.
PubMed: 38932958
DOI: 10.1002/ece3.11479 -
Ecology and Evolution Jun 2024Modeling ecological patterns and processes often involve large-scale and complex high-dimensional spatial data. Due to the nonlinearity and multicollinearity of...
Modeling ecological patterns and processes often involve large-scale and complex high-dimensional spatial data. Due to the nonlinearity and multicollinearity of ecological data, traditional geostatistical methods have faced great challenges in model accuracy. As machine learning has increased our ability to construct models on big data, the main focus of the study is to propose the use of statistical models that hybridize machine learning and spatial interpolation methods to cope with increasingly large-scale and complex ecological data. Here, two machine learning algorithms, boosted regression tree (BRT) and least absolute shrinkage and selection operator (LASSO), were combined with ordinary kriging (OK) to model plant invasions across the eastern United States. The accuracies of the hybrid models and conventional models were evaluated by 10-fold cross-validation. Based on an invasive plants dataset of 15 ecoregions across the eastern United States, the results showed that the hybrid algorithms were significantly better at predicting plant invasion when compared to commonly used algorithms in terms of RMSE and paired-samples -test (with the -value < .0001). Besides, the additional aspect of the combined algorithms is to have the ability to select influential variables associated with the establishment of invasive cover, which cannot be achieved by conventional geostatistics. Higher accuracy in the prediction of large-scale biological invasions improves our understanding of the ecological conditions that lead to the establishment and spread of plants into novel habitats across spatial scales. The results demonstrate the effectiveness and robustness of the hybrid BRTOK and LASOK that can be used to analyze large-scale and high-dimensional spatial datasets, and it has offered an optional source of models for spatial interpolation of ecology properties. It will also provide a better basis for management decisions in early-detection modeling of invasive species.
PubMed: 38932949
DOI: 10.1002/ece3.11605 -
Ecology and Evolution Jun 2024Wildlife observation is a popular activity, and sightings of rare or difficult-to-find animals are often highly desired. However, predicting the sighting probabilities...
Wildlife observation is a popular activity, and sightings of rare or difficult-to-find animals are often highly desired. However, predicting the sighting probabilities of these animals is a challenge for many observers, and it may only be possible by limited experts with intimate knowledge and skills. To tackle this difficulty, we developed user-friendly forecast systems of the daily observation probabilities of a rare Arctic seabird (Ross's Gull ) in a coastal area in northern Japan. Using a dataset gathered during 16 successive winters, we applied a machine learning technique of self-organizing maps and explored how days with gull sightings were related to the meteorological pressure patterns over the Sea of Okhotsk (Method A). We also built a regression model that explains the relationship between gull sightings and local-scale environmental factors (Method B). We then applied these methods with the operational global numerical weather prediction model (a computer simulation application about the fluid dynamics of Earth's atmosphere) to forecast the daily observation probabilities of our target. Method A demonstrated a strong dependence of gull sightings on the 16 representative weather patterns and forecasted stepwise observation probabilities ranging from 0% to 85.7%. Method B also showed that the strength of the northerly wind and the advancement of the season explained gull sightings and forecasted continuous observation probabilities ranging from 0% to 95.5%. Applying these two methods with the operational global numerical weather prediction model successfully forecasted the varied observation probabilities of Ross's Gull from 1 to 5 days ahead from November to February. A 2-year follow-up observation also validated both forecast systems to be effective for successful observation, especially when both systems forecasted higher observation probabilities. The developed forecast systems would therefore allow cost-effective animal observation and may facilitate a better experience for a variety of wildlife observers.
PubMed: 38932942
DOI: 10.1002/ece3.11388 -
Frontiers in Psychiatry 2024
PubMed: 38932940
DOI: 10.3389/fpsyt.2024.1435219 -
Frontiers in Public Health 2024In the contemporary context marked by globalization and the growing prominence of sustainable development, assessing urban tourism competitiveness has emerged as a...
In the contemporary context marked by globalization and the growing prominence of sustainable development, assessing urban tourism competitiveness has emerged as a crucial research domain. This paper aims to develop a comprehensive model for evaluating city tourism competitiveness, grounded in the principles of sustainable development. The model incorporates factors such as city tourism resources, environmental considerations, economic aspects, and societal factors. This holistic approach seeks to offer valuable insights for the city tourism industry. The study conducts a thorough analysis of current research both domestically and internationally, highlighting gaps and articulating the objectives and significance of the research. Employing a machine learning-based empowerment method, the paper determines the significance of evaluation indices and utilizes the Topsis method for assessing urban tourism competitiveness. Distinguishing itself from traditional evaluation methods, this model integrates the principles of sustainable development throughout the evaluation process, with environmental, social, and economic sustainability serving as pivotal evaluation indicators. Empirical analysis involves the evaluation of tourism competitiveness for select cities, facilitating inter-city comparisons. Results from empirical studies demonstrate the model's effectiveness in evaluating urban tourism competitiveness, providing targeted developmental recommendations for urban tourism.
Topics: Humans; Sustainable Development; Cities; Tourism; Models, Theoretical
PubMed: 38932779
DOI: 10.3389/fpubh.2024.1396134 -
Viruses Jun 2024Lipids, as a fundamental cell component, play an regulating role in controlling the different cellular biological processes involved in viral infections. A notable...
BACKGROUND
Lipids, as a fundamental cell component, play an regulating role in controlling the different cellular biological processes involved in viral infections. A notable feature of coronavirus disease 2019 (COVID-19) is impaired lipid metabolism. The function of lipophagy-related genes in COVID-19 is unknown. The present study aimed to investigate biomarkers and drug targets associated with lipophagy and lipophagy-based therapeutic agents for COVID-19 through bioinformatics analysis.
METHODS
Lipophagy-related biomarkers for COVID-19 were identified using machine learning algorithms such as random forest, Support Vector Machine-Recursive Feature Elimination, Generalized Linear Model, and Extreme Gradient Boosting in three COVID-19-associated GEO datasets: scRNA-seq (GSE145926) and bulk RNA-seq (GSE183533 and GSE190496). The cMAP database was searched for potential COVID-19 medications.
RESULTS
The lipophagy pathway was downregulated, and the lipid droplet formation pathway was upregulated, resulting in impaired lipid metabolism. Seven lipophagy-related genes, including , , , , , , and , were used as biomarkers and drug targets for COVID-19. Moreover, lipophagy may play a role in COVID-19 pathogenesis. As prospective drugs for treating COVID-19, seven potential downregulators (phenoxybenzamine, helveticoside, lanatoside C, geldanamycin, loperamide, pioglitazone, and trichostatin A) were discovered. These medication candidates showed remarkable binding energies against the seven biomarkers.
CONCLUSIONS
The lipophagy-related genes , , , , , , and can be used as biomarkers and drug targets for COVID-19. Seven potential downregulators of these seven biomarkers may have therapeutic effects for treating COVID-19.
Topics: Humans; SARS-CoV-2; COVID-19 Drug Treatment; Biomarkers; COVID-19; Lipid Metabolism; Antiviral Agents; Computational Biology; Machine Learning; Lactams, Macrocyclic; Hydroxamic Acids; Benzoquinones
PubMed: 38932215
DOI: 10.3390/v16060923