1,365 research outputs found
Biological function and clinical implication of coagulation proteins during malignant transformation of pancreatic cells
The premalignant pancreatic cellular genotype can remain stable for years before rapid malignant transformation, often associated with inflammation. Tissue factor (TF) is an inflammatory modulator regulated by factor VIIa (fVIIa) for its levels and activity. The presence of TF in PDAC and its role in cell proliferation, angiogenesis, and metastasis suggests that TF may be a marker of the inflammatory microenvironment driving precursor lesions of pancreatic cancer. This study examined the in vitro influence of TF on pancreatic epithelial cells and its clinical value in detecting malignant transformation within pancreatic cyst fluid (PCyF). PCyF from 27 patients with pancreatic cystic lesions was analysed in a blinded fashion. TF and fVIIa levels were measured (ELISA), and the fVIIa:TF ratios were calculated. A cut-off value for TF concentration was determined and compared to the conventional assessment parameters (radiological features, CEA and amylase). Patients were categorised into four groups based on cytopathology and two groups based on indication for resection (‘resective’). Significant histological stage-dependent increases in TF levels were observed. Mean TF concentration was significantly higher (p=0.006) in the resective (high-grade dysplasia & malignant; 1.17 ng/ml, 95% CI 0.68, 1.67) vs non-resective group (benign & low-grade dysplasia; 0.27 ng/ml, 95% CI 0.1, 0.44), with a strong positive correlation (r= 0.746, p <0.001, TF cut-off 0.75 ng/ml, AUC 0.877, p=0.002). The fVIIa:TF ratio did not add further value. Incubation of pancreatic cells with recombinant TF resulted in increased expression of a marker of epithelial to mesenchymal transition (Vimentin). This influence was moderated by supplementation with fVIIa in benign (hTERT-HPNE) but not overtly malignant pancreatic cells (AsPC-1). Cyst-associated TF levels appear to correlate with cytological progression to the malignant phenotype and may allow better discrimination (specificity 94%) of the ‘resective’ lesion, reduce healthcare costs and offer a more nuanced tool for monitoring indeterminate cystic lesions
A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool
Background: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. Objective: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP 3 ) framework benefiting the pharmaceutical paradigm. Method: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdge TM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. Conclusions: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdge TM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm
Artificial Intelligence and Chest Computational Tomography to predict prognosis in Pulmonary Hypertension and lung disease.
Pulmonary hypertension (PH) is an incurable severe condition with poor survival and multiple clinically distinct sub-groups and phenotypes. Accurate diagnosis and identification of the underlying phenotype is an integral step in patient management as it informs treatment choice. Outcomes vary significantly between phenotypes. Patients presenting with signs of both PH and lung disease pose a clinical dilemma between two phenotypes - idiopathic pulmonary arterial hypertension (IPAH) and pulmonary hypertension secondary to lung disease (PH-CLD) as they can present with overlapping features. The impact of lung disease on outcomes is not well understood and this is a challenging area in the literature with limited progress. All patients suspected with PH undergo routine chest Computed Tomography Pulmonary Angiography (CTPA) imaging. Despite this, the prognostic significance of commonly visualised lung parenchymal patterns is currently unknown. Current radiological assessment is also limited by its visual and subjective nature. Recent breakthroughs in deep-learning Artificial Intelligence (AI) approaches have enabled automated quantitative analysis of medical imaging features.
This thesis demonstrates the prognostic impact of common lung parenchymal patterns on CT in IPAH and PH-CLD. It describes how this data could aid in phenotyping, and in identification of new sub-groups of patients with distinct clinical characteristics, imaging features and prognostic profiles. It further develops and clinically evaluates an automated CT AI model which quantifies the percentage of lung involvement of prognostic lung parenchymal patterns. Combining this AI model with radiological assessment improves the prognostic predictive strength of lung disease severity in these patients
Evidence-based oncology: the use of methodologically complex systematic reviews to inform cancer research and clinical practice
Background: Systematic reviews are produced to inform health research and clinical practice, e.g., by identifying research gaps and by formulating recommendations in clinical practice guidelines. Standardised methodology exists for the conduct of systematic reviews of interventions. To answer clinically diverse research questions, new methods are constantly being developed for the systematic synthesis of results from different types of studies. Moreover, constant monitoring of newly available evidence, particularly in
clinical areas that are rapidly evolving, is important to ensure the currency of systematic reviews.
Objective: The primary objective of this cumulative dissertation was to conduct systematic reviews using new and complex systematic review methods, and to contribute to the
further development and refinement of these methods. Secondary objective was to conduct clinically relevant systematic reviews to provide meaningful evidence that can inform clinical practice and health care in oncology.
Methods: Two clinically relevant systematic reviews using novel and complex methodological approaches were conducted:
Systematic review I: A systematic review with network meta analysis and an adapted living approach to evaluate and compare the benefits and harms of first-line therapies for adults with advanced renal cell carcinoma.
Systematic review II: A systematic review with meta-analysis of prognostic factor studies to explore the interim positron emission tomography (PET) scan result as a prognostic factor in adults with newly diagnosed Hodgkin lymphoma.
Results: Methodological results Systematic review I: The evidence for the currently recommended treatments and important comparisons in this review stem from direct evidence from one trial per comparison only. This is due to the great lack of head-to-head comparisons of the many treatment options available. Statistical validation of the homogeneity and consistency assumptions was not possible for every network meta-analysis, so the validity of estimates is largely based on the transitivity assumption. When a strong
evidence base is missing, the results of a network meta-analysis, including the ranking of treatments, should be interpreted with caution. The adapted living approach, where monthly update searches were conducted during the conduct of the review, was an appropriate method to maintain the currency of the evidence in such a rapidly evolving treatment landscape.
Systematic review II: The greatest methodological challenges identified in synthesising evidence from prognostic factor studies were that, firstly, searching for prognosis studies is challenging due to insufficient indexing and missing search filters that are specific and sensitive enough to identify prognostic factor studies. Secondly, extracting and analysing outcome results was particularly difficult due to
incomplete reporting of important data in the, usually retrospective, studies. Thirdly, available methods for the quality assessments had to be adapted to fit to the review question. Lastly, methods for the certainty assessment of the evidence from prognosis studies had to be developed during the conduct of the review as there was no official guidance at that time. The challenges encountered during the conduct of both reviews were discussed and resolved through the involvement of methodological and clinical experts as coauthors.
Clinical results: Systematic Review I: Combinations of novel therapies (e.g., a checkpoint inhibitor with a tyrosine kinase inhibitor) appear to be superior to monotherapy with sunitinib (a tyrosine kinase inhibitor) as first-line therapy in terms of survival for adults with advanced renal cell carcinoma. However, these novel treatments may cause more (serious) side effects. Moreover, the question on the potential impact of these novel treatments on the quality of life of affected individuals remains unanswered.
Systematic Review II: Evidence was found on the prognostic ability of the interim PET-scan result to predict survival in adults with Hodgkin lymphoma. It successfully distinguishes between PET-negative people, who have a better outcome prognosis, and PET-positive people, who have a worse outcome prognosis.
Conclusion:Future methodological research needs to further address these different challenges, for example the challenges one encounters when trying to search for and identify prognostic factor studies, or the limitations one encounters when underlying assumptions of a network meta analysis cannot be verified. When evidence from such methodologically complex systematic reviews shall be used to inform clinical practice guidelines and, thereby, health care decision making, all involved stakeholders need to be aware of the methodological complexity and limitations behind the evidence produced
A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool
Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD.This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm.The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers.Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm
Patient Risk-Minimizing Tube Current Modulation in X-Ray Computed Tomography
This dissertation proposes a patient-specific tube current modulation for computed tomography (CT) that minimizes the individual patient risk (riskTCM). Modern CT scanners use automatic exposure control (AEC) techniques including tube current modulation (TCM) to reduce the radiation dose delivered to the patient while maintaining image quality. Today's TCM implementations aim at minimizing the tube current-time (mAs) product as a surrogate for patient dose, which is why they are referred to as mAsTCM hereafter. However, the actual patient risk, e.g., in the form of risk measures such as the effective dose Deff representing the sensitivity of individual organs with respect to ionizing radiation, is not taken into account.
In order to be able to optimize the effective dose Deff or another biologically meaningful measure, organ doses must be estimated before the actual CT scan in order to compute an optimized riskTCM curve. This can be achieved using a machine learning approach and based on these information, the new patient risk-minimizing TCM curve can be obtained. The proposed riskTCM algorithm was evaluated in a simulation study for circular scans and compared against the current gold standard method mAsTCM and to a constant tube current as well as an organ-specific tube current modulation technique.
The results illustrate that all anatomical regions can benefit from riskTCM and a reduction of effective dose of up to 30% can be expected compared to mAsTCM. Furthermore, riskTCM was extended to a spiral trajectory that is commonly used in clinical routine and initial measurements with phantoms have been performed. The introduction of riskTCM into clinical practice would only require a software update since almost all CT systems are already capable of modulating the tube current
A Deep Learning Approach to Evaluating Disease Risk in Coronary Bifurcations
Cardiovascular disease represents a large burden on modern healthcare systems, requiring significant resources for patient monitoring and clinical interventions. It has been shown that the blood flow through coronary arteries, shaped by the artery geometry unique to each patient, plays a critical role in the development and progression of heart disease. However, the popular and well tested risk models such as Framingham and QRISK3 current cardiovascular disease risk models are not able to take these differences when predicting disease risk.
Over the last decade, medical imaging and image processing have advanced to the point that non-invasive high-resolution 3D imaging is routinely performed for any patient suspected of coronary artery disease. This allows for the construction of virtual 3D models of the coronary anatomy, and in-silico analysis of blood flow within the coronaries. However, several challenges still exist which preclude large scale patient-specific simulations, necessary for incorporating haemodynamic risk metrics as part of disease risk prediction. In particular, despite a large amount of available coronary medical imaging, extraction of the structures of interest from medical images remains a manual and laborious task. There is significant variation in how geometric features of the coronary arteries are measured, which makes comparisons between different studies difficult. Modelling blood flow conditions in the coronary arteries likewise requires manual preparation of the simulations and significant computational cost.
This thesis aims to solve these challenges. The "Automated Segmentation of Coronary Arteries (ASOCA)" establishes a benchmark dataset of coronary arteries and their associated 3D reconstructions, which is currently the largest openly available dataset of coronary artery models and offers a wide range of applications such as computational modelling, 3D printed for experiments, developing, and testing medical devices such as stents, and Virtual Reality applications for education and training. An automated computational modelling workflow is developed to set up, run and postprocess simulations on the Left Main Bifurcation and calculate relevant shape metrics. A convolutional neural network model is developed to replace the computational fluid dynamics process, which can predict haemodynamic metrics such as wall shear stress in minutes, compared to several hours using traditional computational modelling reducing the computation and labour cost involved in performing such simulations
Biological function and clinical implication of coagulation proteins during malignant transformation of pancreatic cells
The premalignant pancreatic cellular genotype can remain stable for years before rapid
malignant transformation, often associated with inflammation. Tissue factor (TF) is an
inflammatory modulator regulated by factor VIIa (fVIIa) for its levels and activity. The
presence of TF in PDAC and its role in cell proliferation, angiogenesis, and metastasis suggests
that TF may be a marker of the inflammatory microenvironment driving precursor lesions of
pancreatic cancer. This study examined the in vitro influence of TF on pancreatic epithelial
cells and its clinical value in detecting malignant transformation within pancreatic cyst fluid
(PCyF). PCyF from 27 patients with pancreatic cystic lesions was analysed in a blinded fashion.
TF and fVIIa levels were measured (ELISA), and the fVIIa:TF ratios were calculated. A cut-off
value for TF concentration was determined and compared to the conventional assessment
parameters (radiological features, CEA and amylase). Patients were categorised into four
groups based on cytopathology and two groups based on indication for resection (‘resective’).
Significant histological stage-dependent increases in TF levels were observed. Mean TF
concentration was significantly higher (p=0.006) in the resective (high-grade dysplasia &
malignant; 1.17 ng/ml, 95% CI 0.68, 1.67) vs non-resective group (benign & low-grade
dysplasia; 0.27 ng/ml, 95% CI 0.1, 0.44), with a strong positive correlation (r= 0.746, p <0.001,
TF cut-off 0.75 ng/ml, AUC 0.877, p=0.002). The fVIIa:TF ratio did not add further value.
Incubation of pancreatic cells with recombinant TF resulted in increased expression of a
marker of epithelial to mesenchymal transition (Vimentin). This influence was moderated by
supplementation with fVIIa in benign (hTERT-HPNE) but not overtly malignant pancreatic cells
(AsPC-1). Cyst-associated TF levels appear to correlate with cytological progression to the
malignant phenotype and may allow better discrimination (specificity 94%) of the ‘resective’
lesion, reduce healthcare costs and offer a more nuanced tool for monitoring indeterminate
cystic lesions
Radiomics in the evaluation of ovarian masses — a systematic review
Objectives The study aim was to conduct a systematic review of the literature reporting the application of radiomics
to imaging techniques in patients with ovarian lesions.
Methods MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant
articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently
by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS)
was utilised to assess radiomic methodology.
Results After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications,
10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome
prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound
and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were
used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared
radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included
external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias.
The highest RQS achieved was 61.1%, and the lowest was − 16.7%.
Conclusion Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow
better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature
extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical
translation.
Clinical relevance statement Radiomics shows promising results in improving lesion stratification, treatment
selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical
translation.
Key points
• Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses.
• Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction.
• Modelling with larger cohorts and real-world evaluation is required before clinical translation
Morphometric analysis of airways in pre-COPD and mild COPD lungs using continuous surface representations of the bronchial lumen
Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory disease that presents a high rate of underdiagnosis during onset and early stages. Studies have shown that in mild COPD patients, remodeling of the small airways occurs concurrently with morphological changes in the proximal airways. Despite this evidence, the geometrical study of the airway tree from computed tomography (CT) lung images remains underexplored due to poor representations and limited tools to characterize the airway structure.Methods: We perform a comprehensive morphometric study of the proximal airways based on geometrical measures associated with the different airway generations. To this end, we leverage the geometric flexibility of the Snakes IsoGeometric Analysis method to accurately represent and characterize the airway luminal surface and volume informed by CT images of the respiratory tree. Based on this framework, we study the airway geometry of smoking pre-COPD and mild COPD individuals.Results: Our results show a significant difference between groups in airway volume, length, luminal eccentricity, minimum radius, and surface-area-to-volume ratio in the most distal airways.Discussion: Our findings suggest a higher degree of airway narrowing and collapse in COPD patients when compared to pre-COPD patients. We envision that our work has the potential to deliver a comprehensive tool for assessing morphological changes in airway geometry that take place in the early stages of COPD
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