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The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized. While it is a multi-objective problem, current methods tend to treat feature selection as a single-objective optimization task. This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase (LMuMOGWO) for tackling feature selection problems. The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer (MOGWO): a Lévy flight and a mutation operator. The Lévy flight, a type of random walk with jump size determined by the Lévy distribution, enhances the global search capability of MOGWO, with the objective of maximizing classification accuracy while minimizing the number of selected features. The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy. As feature selection is a binary problem, the continuous search space is converted into a binary space using the sigmoid function. To evaluate the classification performance of the selected feature subset, the proposed approach employs a wrapper-based Artificial Neural Network (ANN). The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO, BMOGWO-S (based sigmoid), BMOGWO-V (based tanh) as well as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swarm Optimization (BMOPSO). The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem. Moreover, the proposed approach outperforms existing approaches in most cases in terms of classification error rate, feature reduction, and computational cost. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with cancer are often immuno-compromised, and at a high risk of experiencing various COVID-19-associated complications compared to the general population. Additionally, COVID-19 infection and lung toxicities due to cancer treatments can present with similar radiologic abnormalities, such as ground glass opacities or patchy consolidation, which poses further challenges for developing AI algorithms. To fill the gap, we carried out the first imaging AI study to investigate the performance of habitat imaging technique for COVID-19 severity prediction and detection specifically in the cancer patient population, and further tested its performance in the general population based on multicenter datasets. The proposed COVID-19 habitat imaging models trained separately on the cancer cohort outperformed those AI models (including deep learning) trained on the multicenter general population by a significant margin. This suggests that publicly available COVID-19 AI models developed for the general population will not be optimally applied to cancer. Objectives: Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. Methods: We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. Results: These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. Conclusions: Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population. [ABSTRACT FROM AUTHOR]

Backgrounds: Financial toxicity (FT) refers to the financial stress and detrimental impact on quality of life experienced by patients due to treatment costs. In patients with resected lung cancer (LC), we sought to identify those at risk of developing moderate or severe ("major") FT using machine learning (ML) techniques based on preoperative characteristics., Study Design: Patients who underwent LC resection at a single center between January 2016 and December 2021 were surveyed to ascertain demographic information, financial data, and presence of major FT. Clinicopathologic variables were extracted from a prospective database. Patients were randomly divided into training and test sets. First, we identified the most informative features. Then, 4 ML algorithms (decision tree, random forest [RF], gradient boosting, and extreme gradient boosting) were trained. We ensembled the 4 models' predictions to optimize the model., Results: There were 1477 patients identified, of whom 462 (31.3%) completed the survey. 46 (10.0%) patients experienced major FT. The variables most influential in our models included age, race/ethnicity, smoking status, household income, credit score, marital and employment status, size of residence, BMI, histology, extent of resection, and preoperative forced expiratory volume in 1 second. The ensemble model yielded an accuracy of 0.86, precision of 0.93, and sensitivity of 0.86, leading to an F1 score of 0.88, indicative of a reliable algorithm., Conclusion: ML algorithms can accurately identify patients at risk of experiencing major FT following LC surgery. Preoperatively identifying LC patients vulnerable to financial stress may allow an opportunity for intervention to address downstream cost considerations., (Copyright © 2025 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.)

Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA., Competing Interests: Competing interests The authors declare no competing interests. Additional Declarations: There is NO Competing Interest.