Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics

A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology

INTRODUCTION

Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer.

METHODS

This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models.

RESULTS

Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001).

CONCLUSIONS

In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.

Advances in cell-based delivery of oncolytic viruses as therapy for lung cancer

Lung cancer's intractability is enhanced by its frequent resistance to (chemo)therapy and often high relapse rates that make it the leading cause of cancer death worldwide. Improvement of therapy efficacy is a crucial issue that might lead to a significant advance in the treatment of lung cancer. Oncolytic viruses are desirable combination partners in the developing field of cancer immunotherapy due to their direct cytotoxic effects and ability to elicit an immune response. Systemic oncolytic virus administration through intravenous injection should ideally lead to the highest efficacy in oncolytic activity. However, this is often hampered by the prevalence of host-specific, anti-viral immune responses. One way to achieve more efficient systemic oncolytic virus delivery is through better protection against neutralization by several components of the host immune system. Carrier cells, which can even have innate tumor tropism, have shown their appropriateness as effective vehicles for systemic oncolytic virus infection through circumventing restrictive features of the immune system and can warrant oncolytic virus delivery to tumors. In this overview, we summarize promising results from studies in which carrier cells have shown their usefulness for improved systemic oncolytic virus delivery and better oncolytic virus therapy against lung cancer.

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

Quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning

The determination of a treatment plan for a target patient with tumor is a difficult problem due to the existence of heterogeneity in patients' responses, incomplete information about tumor states, and asymmetric knowledge between doctors and patients, and so on. In this paper, a method for quantitative risk analysis of treatment plans for patients with tumor is proposed. To reduce the impacts of the heterogeneity in patients' responses on analysis results, the method conducts risk analysis by mining historical similar patients from Electronic Health Records (EHRs) in multiple hospitals using federated learning (FL). For this, the Recursive Feature Elimination based on the Support Vector Machine (SVM) and Deep Learning Important FeaTures (DeepLIFT) are extended into the FL framework to select key features and determine key feature weights for identifying historical similar patients. Then, in the database of each collaborative hospital, the similarities between the target patient and all historical patients are calculated, and the historical similar patients are determined. According to the statistics of tumor states and treatment outcomes of historical similar patients in all collaborative hospitals, the related data (including the probabilities of different tumor states and possible outcomes of different treatment plans) for risk analysis of the alternative treatment plans can be obtained, which can eliminate the asymmetric knowledge between doctors and patients. The related data are valuable for the doctor and patient to make their decisions. Experimental studies have been conducted to verify the feasibility and effectiveness of the proposed method.

Effect of pre-analytical variables on Raman and FTIR spectral content of lymphocytes

The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study, we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n = 3). Pre-analytical variables investigated were associated with (i) sample preparation, (blood collection systems, anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-operator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze-thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed. Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by a lack of standardisation and harmonisation of protocols related to the preparation, storage, and processing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further development of protocols is required to successfully integrate these techniques into current clinical workflows.

Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review

Knowledge discovery in databases (KDD) is crucial in analyzing data to extract valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in diseases with high incidence, mortality, and costs, like cancer. ML techniques can develop more accurate predictive models for cancer patients' clinical outcomes, aiding informed healthcare decision-making. However, cancer prediction modeling faces challenges because of the unbalanced nature of the datasets, where there is a small minority category of patients with a cancer diagnosis compared to a majority category of cancer-free patients. Imbalanced datasets pose statistical hurdles like bias and overfitting when developing accurate prediction models. This systematic review focuses on breast cancer prediction articles published from 2008 to 2023. The objective is to examine ML methods used in three critical steps of KDD: preprocessing, data mining, and interpretation which address the imbalanced data problem in breast cancer prediction. This work synthesizes prior research in ML methods for breast cancer prediction. The findings help identify effective preprocessing strategies, including balancing and feature selection methods, robust predictive models, and evaluation metrics of those models. The study aims to inform healthcare providers and researchers about effective techniques for accurate breast cancer prediction.

Exploratory study on noninvasive biomarker of silicosis in exhaled breath by solid-phase microextraction-gas chromatography-mass spectrometry analysis

BACKGROUND

As a chronic occupational disease, silicosis could cause irreversible and incurable impair to the lung. The current diagnosis of silicosis relies on imaging of X-ray or CT, but these methods cannot detect lung lesions in the early stage of silicosis.

OBJECTIVE

To establish a regular screening and early diagnosis methods for silicosis, which could be helpful for the prevention and treatment of silicosis.

METHODS

A total of 161 subjects were enrolled in the study, including 69 patients with silicosis (SILs) and 92 healthy controls. The exhaled breath samples of the subjects were collected with breath sampler and Tedlar bag. The analysis of volatile organic compounds (VOCs) in exhaled breath was performed by solid-phase microextraction (SPME) combined with gas chromatography mass spectrometry (GC-MS).

RESULTS

After excluding the pollutants from sampling bags and instruments, 86 VOCs have been identified in the exhaled breath. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed for the screening of potential biomarkers of silicosis. Those components that related to smoking were also excluded from the biomarkers. Finally, nine possible biomarkers for silicosis were screened out, including 2,3-butanedione, ethyl acetate, chlorobenzene, o-cymene, 4-ethylhex-2-ynal, 3,5-dimethyl-3-heptanol, hydroquinone, phthalic anhydride and 5-(2-methylpropyl)nonane. Based on these biomarkers screened, a predicted model for silicosis was generated with the accuracy of 89.61%.

CONCLUSION

The nine biomarkers in exhaled breath were preliminarily screened out for the early diagnosis of silicosis, which can be helpful to the establishment of a noninvasive screening method for silicosis. Follow-up studies should be conducted to further verify these markers.

Identification through machine learning of potential immune- related gene biomarkers associated with immune cell infiltration in myocardial infarction

BACKGROUND

To investigate the potential role of immune-related genes (IRGs) and immune cells in myocardial infarction (MI) and establish a nomogram model for diagnosing myocardial infarction.

METHODS

Raw and processed gene expression profiling datasets were archived from the Gene Expression Omnibus (GEO) database. Differentially expressed immune-related genes (DIRGs), which were screened out by four machine learning algorithms-partial least squares (PLS), random forest model (RF), k-nearest neighbor (KNN), and support vector machine model (SVM) were used in the diagnosis of MI.

RESULTS

The six key DIRGs (PTGER2, LGR6, IL17B, IL13RA1, CCL4, and ADM) were identified by the intersection of the minimal root mean square error (RMSE) of four machine learning algorithms, which were screened out to establish the nomogram model to predict the incidence of MI by using the rms package. The nomogram model exhibited the highest predictive accuracy and better potential clinical utility. The relative distribution of 22 types of immune cells was evaluated using cell type identification, which was done by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. The distribution of four types of immune cells, such as plasma cells, T cells follicular helper, Mast cells resting, and neutrophils, was significantly upregulated in MI, while five types of immune cell dispersion, T cells CD4 naive, macrophages M1, macrophages M2, dendritic cells resting, and mast cells activated in MI patients, were significantly downregulated in MI.

CONCLUSION

This study demonstrated that IRGs were correlated with MI, suggesting that immune cells may be potential therapeutic targets of immunotherapy in MI.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

GraphChrom: A Novel Graph-Based Framework for Cancer Classification Using Chromosomal Rearrangement Endpoints

Chromosomal rearrangements are generally a consequence of improperly repaired double-strand breaks in DNA. These genomic aberrations can be a driver of cancers. Here, we investigated the use of chromosomal rearrangements for classification of cancer tumors and the effect of inter- and intrachromosomal rearrangements in cancer classification. We used data from the Catalogue of Somatic Mutations in Cancer (COSMIC) for breast, pancreatic, and prostate cancers, for which the COSMIC dataset reports the highest number of chromosomal aberrations. We developed a framework known as GraphChrom for cancer classification. GraphChrom was developed using a graph neural network which models the complex structure of chromosomal aberrations (CA) and provides local connectivity between the aberrations. The proposed framework illustrates three important contributions to the field of cancers. Firstly, it successfully classifies cancer types and subtypes. Secondly, it evolved into a novel data extraction technique which can be used to extract more informative graphs (informative aberrations associated with a sample); and thirdly, it predicts that interCAs (rearrangements between two or more chromosomes) are more effective in cancer prediction than intraCAs (rearrangements within the same chromosome), although intraCAs are three times more likely to occur than intraCAs.

Oncolytic Vaccinia Virus in Lung Cancer Vaccines

Therapeutic cancer vaccines represent a promising therapeutic modality via the induction of long-term immune response and reduction in adverse effects by specifically targeting tumor-associated antigens. Oncolytic virus, especially vaccinia virus (VV) is a promising cancer treatment option for effective cancer immunotherapy and thus can also be utilized in cancer vaccines. Non-small cell lung cancer (NSCLC) is likely to respond to immunotherapy, such as immune checkpoint inhibitors or cancer vaccines, since it has a high tumor mutational burden. In this review, we will summarize recent applications of VV in lung cancer treatment and discuss the potential and direction of VV-based therapeutic vaccines.

Physical Activity and Its Association with Traditional Outcome Measures in Pulmonary Arterial Hypertension

Rationale Limitation of physical activity is a common presenting complaint for patients with pulmonary arterial hypertension (PAH). Physical activity is thought to be determined by cardiopulmonary function, yet there are limited data that investigate this relationship. Objective We aimed to study the relationship between right ventricular function and daily activity and its impact on health-related quality of life (HRQoL) in PAH. Methods Baseline data for 55 patients enrolled in PHANTOM, an ongoing multicenter randomized controlled trial of anastrozole in PAH were used. Post-menopausal women and men were eligible and underwent six-minute walk testing, echocardiography and completed HRQoL questionnaires. Each patient wore an accelerometer for 7-days. Multivariable linear regression models were used to study the association between tricuspid annular plane systolic excursion (TAPSE) and vector magnitude counts, and between daily activity and HRQoL. Principal component analysis and K-means clustering were used to identify activity-based phenotypes. K-nearest neighbors' classification was applied to an independent cross-sectional cohort from the University of Pennsylvania. Results The mean age of patients in PHANTOM was 61 years. 67% were women with idiopathic PAH as the most common etiology. A 0.4 cm increase in TAPSE was associated with an increase in daily vector magnitude counts (ß:34000, 95%CI:900-67000, p=0.004) after adjustment for age, sex, body mass index, etiology of PAH and wear time. A 1-standard deviation increase in vector magnitude counts was associated with higher six-minute walk distance (ß: 56.1 meters, 95%CI:28.6-83.7, p<0.001) and lower emPHasis-10 scores (ß:-3.3, 95%CI:0.3-6.4, p=0.03). Three activity phenotypes, low, medium, and high, were identified. The most active phenotype had greater six-minute walk distances (p=0.001) and lower emPHasis-10 scores (p=0.009) after adjustment for age, sex, body mass index, WHO functional class and parenteral prostacyclin use. Phenotypes of physical activity were reproduced in the second cohort and were independently associated with six-minute walk distance. Conclusion Better right ventricular systolic function was associated with increased levels of activity in PAH. Increased daily activity was associated with greater six-minute walk distance and better HRQoL. Distinct activity-based phenotypes may be helpful in risk stratification of PAH patients or provide novel endpoints for clinical trials.

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

Pulmonary hypertension (PH) is a common disease that affects the normal functioning of the human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an ideal source for a minimally invasive disease diagnosis. This study hypothesized that the transcriptional fluctuations in the PMBCs exposed to the PH arteries may stably reflect the disease. However, the dimension of a human transcriptome is much higher than the number of samples in all the existing datasets. So, an ensemble feature selection algorithm, EnRank, was proposed to integrate the ranking information of four popular feature selection algorithms, i.e., T-test (Ttest), Chi-squared test (Chi2), ridge regression (Ridge), and Least Absolute Shrinkage and Selection Operator (Lasso). Our results suggested that the EnRank-detected biomarkers provided useful information from these four feature selection algorithms and achieved very good prediction accuracy in predicting the PH patients. Many of the EnRank-detected biomarkers were also supported by the literature.

Exploring Volatile Organic Compounds in Breath for High-Accuracy Prediction of Lung Cancer

(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants' exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.