Personalising lung cancer screening with machine learning

Personalised screening is based on a straightforward concept: repeated risk assessment linked to tailored management. However, delivering such programmes at scale is complex. In this work, I aimed to contribute to two areas: the simplification of risk assessment to facilitate the implementation of personalised screening for lung cancer; and, the use of synthetic data to support privacy-preserving analytics in the absence of access to patient records. I first present parsimonious machine learning models for lung cancer screening, demonstrating an approach that couples the performance of model-based risk prediction with the simplicity of risk-factor-based criteria. I trained models to predict the five-year risk of developing or dying from lung cancer using UK Biobank and US National Lung Screening Trial participants before external validation amongst temporally and geographically distinct ever-smokers in the US Prostate, Lung, Colorectal and Ovarian Screening trial. I found that three predictors – age, smoking duration, and pack-years – within an ensemble machine learning framework achieved or exceeded parity in discrimination, calibration, and net benefit with comparators. Furthermore, I show that these models are more sensitive than risk-factor-based criteria, such as those currently recommended by the US Preventive Services Taskforce. For the implementation of more personalised healthcare, researchers and developers require ready access to high-quality datasets. As such data are sensitive, their use is subject to tight control, whilst the majority of data present in electronic records are not available for research use. Synthetic data are algorithmically generated but can maintain the statistical relationships present within an original dataset. In this work, I used explicitly privacy-preserving generators to create synthetic versions of the UK Biobank before we performed exploratory data analysis and prognostic model development. Comparing results when using the synthetic against the real datasets, we show the potential for synthetic data in facilitating prognostic modelling

Evaluating patterns of national and international collaboration in Cuban science using bibliometric tools

Purpose -- The purpose of this paper is to explore the hypothesis that collaboration was a key characteristic of Cuban science to maintain their scientific capacity during a period of economic restrictions and an important feature of Cuban science policy and practice for the benefit of society. Design/methodology/approach -- Collaboration was studied through Cuban scientific publications listed in PubMed for the period 1990-2010. The search was carried out using the advanced search engine of PubMed indicating oCubaW in the affiliation field. To identify participating institutions a second search was performed to find the affiliations of all authors per article through the link to the electronic journal. A data set was created to identify institutional publication patterns for the surveyed period. Institutions were classified in three categories according to their scientific production as Central, Middle or Distal: the pattern of collaboration between these categories was analysed. Findings -- Results indicate that collaboration between scientifically advanced institutions (Central) and a wide range of national institutions is a consequence of the social character of science in Cuba in which cooperation prevails. Although this finding comes from a limited field of biomedical science it is likely to reflect Cuban science policy in general. Originality/value -- Using bibliometric tools the study suggests that Cuban science policy and practice ensure the application of science for social needs by harnessing human resources through national and international collaboration, building in this way stronger scientific capacity

Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study

BACKGROUND: Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. METHODS AND FINDINGS: For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables-age, smoking duration, and pack-years-achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts. CONCLUSIONS: We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings

Expression and Activity Patterns of Nitric Oxide Synthases and Antioxidant Enzymes Reveal a Substantial Heterogeneity Between Cardiac and Vascular Aging in the Rat

We investigated the effects of aging and ischemia-reperfusion (I/R) injury on the expression and activity of nitric oxide (•NO) synthases and superoxide dismutase (SOD) isoforms. To this end we perfused excised hearts from young (6months old) and old (31-34months old) rats according to the Langendorff technique. The isolated hearts were, after baseline perfusion for 30min, either subjected to 20min of global no-flow ischemia followed by 40min of reperfusion or were control-perfused (60min normoxic perfusion). Both MnSOD and Cu,ZnSOD expression remained unchanged with increasing age and remained unaltered by I/R. However, SOD activity decreased from 7.55 ± 0.1U/mg protein in young hearts to 5.94 ± 0.44 in old hearts (P<0.05). Furthermore, I/R led to a further decrease in enzyme activity (to 6.35 ± 0.41U/mg protein; P<0.05) in myocardium of young, but not in that of old animals. No changes in myocardial protein-bound 3-nitrotyrosine levels could be detected. Endothelial NOS (eNOS) expression and activity remained unchanged in aged left ventricles, irrespective of I/R injury. This was in steep contrast to peripheral (renal and femoral) arteries obtained from the same animals where a marked age-associated increase of eNOS protein expression could be demonstrated. Inducible NOS expression was undetectable either in the peripheral arteries or in the left ventricle, irrespective of age. In particular when associated with an acute pathology, which is furthermore limited to a certain time frame, changes in the aged myocardium with respect to enzymes crucially involved in maintaining the redox homeostasis, seem to be much less pronounced or even absent compared to the vascular aging process. This may point to heterogeneity in the molecular regulation of the cardiovascular aging proces

Expression and Activity Patterns of Nitric Oxide Synthases and Antioxidant Enzymes Reveal a Substantial Heterogeneity Between Cardiac and Vascular Aging in the Rat

We investigated the effects of aging and ischemia-reperfusion (I/R) injury on the expression and activity of nitric oxide (•NO) synthases and superoxide dismutase (SOD) isoforms. To this end we perfused excised hearts from young (6months old) and old (31-34months old) rats according to the Langendorff technique. The isolated hearts were, after baseline perfusion for 30min, either subjected to 20min of global no-flow ischemia followed by 40min of reperfusion or were control-perfused (60min normoxic perfusion). Both MnSOD and Cu,ZnSOD expression remained unchanged with increasing age and remained unaltered by I/R. However, SOD activity decreased from 7.55 ± 0.1U/mg protein in young hearts to 5.94 ± 0.44 in old hearts (P<0.05). Furthermore, I/R led to a further decrease in enzyme activity (to 6.35 ± 0.41U/mg protein; P<0.05) in myocardium of young, but not in that of old animals. No changes in myocardial protein-bound 3-nitrotyrosine levels could be detected. Endothelial NOS (eNOS) expression and activity remained unchanged in aged left ventricles, irrespective of I/R injury. This was in steep contrast to peripheral (renal and femoral) arteries obtained from the same animals where a marked age-associated increase of eNOS protein expression could be demonstrated. Inducible NOS expression was undetectable either in the peripheral arteries or in the left ventricle, irrespective of age. In particular when associated with an acute pathology, which is furthermore limited to a certain time frame, changes in the aged myocardium with respect to enzymes crucially involved in maintaining the redox homeostasis, seem to be much less pronounced or even absent compared to the vascular aging process. This may point to heterogeneity in the molecular regulation of the cardiovascular aging proces

Heart failure care in low-and middle-income countries: a systematic review and meta-analysis

In a systematic review and meta-analysis, Kazem Rahimi and colleagues examine the burden of heart failure in low- and middle-income countries. Please see later in the article for the Editors' Summar

HLA and cross-reactive antigen group matching for cadaver kidney allocation

Background. Allocation of cadaver kidneys by graded human leukocyte antigen (HLA) compatibility scoring arguably has had little effect on overall survival while prejudicing the transplant candidacy of African-American and other hard to match populations. Consequently, matching has been proposed of deduced amino acid residues of the individual HLA molecules shared by cross- reactive antigen groups (CREGs). We have examined the circumstances under which compatibility with either method impacted graft survival. Methods. Using Cox proportional hazards regression modeling, we studied the relationship between levels of conventional HLA mismatch and other donor and recipient factors on primary cadaver kidney survival between 1981 and 1995 at the University of Pittsburgh (n=1,780) and in the United Network for Organ Sharing (UNOS) Scientific Registry during 1991-1995 (n=31,291). The results were compared with those obtained by the matching of amino acid residues that identified CREG-compatible cases with as many as four (but not five and six) HLA mismatches. Results. With more than one HLA mismatch (>85% of patients in both series), most of the survival advantage of a zero mismatch was lost. None of the HLA loci were 'weak.' In the UNOS (but not Pittsburgh) category of one-HLA mismatch (n=1334), a subgroup of CREG-matched recipients (35.3%) had better graft survival than the remaining 64.7%, who were CREG-mismatched. There was no advantage of a CREG match in the two- to four-HLA incompatibility tiers. Better graft survival with tacrolimus was observed in both the Pittsburgh and UNOS series. Conclusions. Obligatory national sharing of cadaver kidneys is justifiable only for zero-HLA-mismatched kidneys. The potential value of CREG matching observed in the one-HLA-mismatched recipients of the UNOS (but not the Pittsburgh) experience deserves further study