Dataset Information

Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

ABSTRACT: The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ?4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n?=?311) or no cancer (n?=?195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n?=?433; test set: n?=?73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve?=?0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity?=?84.8% (73.9/76.1%, respectively), specificity?=?55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making.

SUBMITTER: Levitsky A

PROVIDER: S-EPMC6848139 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

Levitsky Adrian A Pernemalm Maria M Bernhardson Britt-Marie BM Forshed Jenny J Kölbeck Karl K Olin Maria M Henriksson Roger R Lehtiö Janne J Tishelman Carol C Eriksson Lars E LE

Scientific reports 20191111 1

The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/67 ...[more]

PMID: 31712735

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Project description:Lung cancer (LC) generally lacks unique core symptoms or signs. However, there are a multitude of bodily sensations that are often non-specific, not easily understood, and many times initially not recognized as indicative of LC by the affected person, which often leads to late diagnosis. In this international qualitative study, we inductively analyzed retrospective accounts of 61 people diagnosed with LC in Denmark, England and Sweden. Using the bodily sensations they most commonly spoke about (tiredness, breathlessness, pain, and cough), we constructed four sensation-based cases to understand the pre-diagnostic processes of reasoning and practice triggered by these key indicators of LC. We thereafter critically applied Hay's model of sensations to symptoms transformation, examining its central concepts of duration, disability and vulnerability, to support understanding of these processes. We found that while duration and disability are clearly relevant, vulnerability is more implicitly expressed in relation to perceived threat. Tiredness, even when of long duration and causing disability, was often related to normal aging, rather than a health threat. Regardless of duration, breathlessness was disturbing and threatening enough to lead to care-seeking. Pain varied by location, duration and degree of disability, and thus also varied in degree of threat perceived. Preconceived, but unmet expectations of what LC-related cough and pain would entail could cause delays by misleading participants; if cough lasted long enough, it could trigger health care contact. Duration, disability, and sense of threat, rather than vulnerability, were found to be relevant concepts for understanding the trajectory to diagnosis for LC among these participants. The process by which an individual, their family and health care providers legitimize sensations, allowing them to be seen as potential symptoms of disease, is also an essential, but varying part of the diagnostic processes described here.

Project description:AimsProspective studies on the mental health of university students highlighted a major concern. Specifically, young adults in academia are affected by markedly worse mental health status than their peers or adults in other vocations. This situation predisposes to exacerbated disability-adjusted life-years.MethodsWe enroled 1,388 students at the baseline, 557 of whom completed follow-up after 6 months, incorporating their demographic information and self-report questionnaires on depressive, anxiety and obsessive-compulsive symptoms. We applied multiple regression modelling to determine associations - at baseline - between demographic factors and self-reported mental health measures and supervised machine learning algorithms to predict the risk of poorer mental health at follow-up, by leveraging the demographic and clinical information collected at baseline.ResultsApproximately one out of five students reported severe depressive symptoms and/or suicidal ideation. An association of economic worry with depression was evidenced both at baseline (when high-frequency worry odds ratio = 3.11 [1.88-5.15]) and during follow-up. The random forest algorithm exhibited high accuracy in predicting the students who maintained well-being (balanced accuracy = 0.85) or absence of suicidal ideation but low accuracy for those whose symptoms worsened (balanced accuracy = 0.49). The most important features used for prediction were the cognitive and somatic symptoms of depression. However, while the negative predictive value of worsened symptoms after 6 months of enrolment was 0.89, the positive predictive value is basically null.ConclusionsStudents' severe mental health problems reached worrying levels, and demographic factors were poor predictors of mental health outcomes. Further research including people with lived experience will be crucial to better assess students' mental health needs and improve the predictive outcome for those most at risk of worsening symptoms.

Project description:Purpose Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features. Methods One hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters—four clinical factors, four laboratory indicators, and 91 [18F]F-FDG PET/CT radiomic features—were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance. Results Boruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model. Conclusions The proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails.

Dataset Information

Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

Publications

Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

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