408 research outputs found

    HPV16 L1 and L2 DNA methylation predicts high-grade cervical intraepithelial neoplasia in women with mildly abnormal cervical cytology

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    Cancer Research UK, Queen Mary University of London. Grant Number: project grant C8162/A4609 and programme grants C8162/A10406, C569/A10404 and C236/A1179

    Analysis of contrast-enhanced medical images.

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    Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

    Classification of clinical outcomes using high-throughput and clinical informatics.

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    It is widely recognized that many cancer therapies are effective only for a subset of patients. However clinical studies are most often powered to detect an overall treatment effect. To address this issue, classification methods are increasingly being used to predict a subset of patients which respond differently to treatment. This study begins with a brief history of classification methods with an emphasis on applications involving melanoma. Nonparametric methods suitable for predicting subsets of patients responding differently to treatment are then reviewed. Each method has different ways of incorporating continuous, categorical, clinical and high-throughput covariates. For nonparametric and parametric methods, distance measures specific to the method are used to make classification decisions. Approaches are outlined which employ these distances to measure treatment interactions and predict patients more sensitive to treatment. Simulations are also carried out to examine empirical power of some of these classification methods in an adaptive signature design. Results were compared with logistic regression models. It was found that parametric and nonparametric methods performed reasonably well. Relative performance of the methods depends on the simulation scenario. Finally a method was developed to evaluate power and sample size needed for an adaptive signature design in order to predict the subset of patients sensitive to treatment. It is hoped that this study will stimulate more development of nonparametric and parametric methods to predict subsets of patients responding differently to treatment

    A Machine Learning Framework for Identifying Molecular Biomarkers from Transcriptomic Cancer Data

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    Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers. However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical. Traditional approaches for biomarker discovery calculate the fold change for each gene, comparing expression profiles between tumor and healthy samples, thus failing to capture the combined effect of the whole gene set. Also, these approaches do not always investigate cancer-type prediction capabilities using discovered biomarkers. In this work, we proposed a machine learning-based framework to address all of the above challenges in discovering lncRNA biomarkers. First, we developed a machine learning pipeline that takes lncRNA expression profiles of cancer samples as input and outputs a small set of key lncRNAs that can accurately predict multiple cancer types. A significant innovation of our work is its ability to identify biomarkers without using healthy samples. However, this initial framework cannot identify cancer-specific lncRNAs. Second, we extended our framework to identify cancer type and subtype-specific lncRNAs. Third, we proposed to use a state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. Thus, we proposed a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. Our deep learning-based pipeline significantly extended the previous state-of-the-art feature selection techniques. Finally, we showed that discovered biomarkers are biologically relevant using literature review and prognostically significant using survival analyses. The discovered novel biomarkers could be used as a screening tool for different cancer diagnoses and as therapeutic targets

    Machine learning-based analysis of [<sup>18</sup>F]DCFPyL PET radiomics for risk stratification in primary prostate cancer

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    PURPOSE: Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [18F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features. METHODS: In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [18F]DCFPyL PET-CT. Primary tumors were delineated using 50-70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC. RESULTS: The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance. CONCLUSION: Machine learning-based analysis of quantitative [18F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice

    Cervical and vaginal high-grade cancer precursors : age dependence of human papillomavirus genotypes and alternative management strategies

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    Nearly all humans acquire a human papillomavirus (HPV) infection during their lifetime. The vast majority of HPV infections regress spontaneously, even the precancerous lesions (intraepithelial neoplasias) of the female genital tract that HPV causes. Precancerous cervical lesions are treated with local excision, because the progressive or regressive nature of an individual lesion remains unknown. These procedures have a 90% initial cure rate but may predispose to preterm birth. Prophylactic HPV vaccines targeting the two most common HPV types in cervical cancer (HPV 16 and 18) have been available for a little over a decade. A near eradication of HPV infections and precancerous lesions has been demonstrated a decade after vaccination in adolescence; however, the full effect of mass vaccination, especially on cancer rates, will only be seen decades later. Characterising the prevaccination era HPV-type distribution can aid the assessment of the effect of vaccinations and refine screening strategies. Our study of 1279 women assessed for abnormal cytology found a distinct, age-related polarisation of HPV types. Histological high-grade cervical disease was diagnosed in 503 women, and two thirds of cases in young women were attributed to HPV16/18, whereas it was only found in one third of women ≥45. Other high-risk types and even HPV negativity were more common than HPV16/18 in the older women. We performed a meta-analysis on the outcomes of untreated cervical intraepithelial neoplasia grade 2 (CIN2). Summary estimates from 36 studies showed the overall regression rate at two years to be 50% and the progression rate 18%. The two-year regression rate was 60% and the progression rate was 11% in a subgroup analysis of women <30 years of age (approximately 1000 women). Overall progression to invasive cancer was rare (0.5%, n=15/3160). In addition, we assessed the performance of a DNA methylation panel (S5 classifier) in predicting progression of untreated histological CIN2 in a prospective cohort study of 149 women (18-30 years of age). S5 was independently able to predict progression even when adjusted for age, initial cytology, cigarette smoking, and HPV16/18 status. Vaginal intraepithelial neoplasia (VAIN) is more uncommon than CIN. Contemporary treatment is mostly laser vaporisation, but recurrence occurs in up to a third. HPV persistence is associated with recurrence. We recruited 30 women with high-grade VAIN into a randomised trial comparing the efficacy of self-administered vaginal immunomodulator imiquimod, laser vaporisation, and expectant management. No progressions were observed during the follow-up, and histological regression rates showed no significant differences between the study arms. HPV clearance, however, was significantly more common in the imiquimod arm (63%) than in the laser arm (11%).Lähes kaikki ihmiset saavat papilloomavirusinfektion (human papillomavirus, HPV). Valtaosa HPV-infektioista ja jopa sen aiheuttamista syövän esiastemuutoksista naisen synnytyselimissä paranee ilman hoitoa etenkin nuorilla. Kohdunkaulan esiastemuutokset hoidetaan paikallisella kirurgisella poistolla, koska yksittäisen muutoksen paranemista tai etenemistä ei voida ennustaa. Paikallisella poistolla on erinomainen onnistumisaste, mutta se voi altistaa ennenaikaiselle synnytykselle. Profylaktisia HPV-rokotteita kohdunkaulasyövän yleisimpiä HPV-tyyppejä (HPV16 ja 18) vastaan on ollut saatavilla reilun vuosikymmenen ajan. Lapsuudessa saatujen rokotusten jälkeen HPV-infektioiden ja esiastemuutosten on osoitettu lähes hävinneen. Väestötason rokottamisen vaikutusta etenkin syövän esiintymiseen joudutaan silti odottamaan vielä vuosikymmeniä. Totesimme 1279 kolposkopiaan solumuutoksen vuoksi lähetetyn naisen joukossa selvän ikään liittyvän jakauman HPV-tyypeissä. Histologinen vaikea-asteinen muutos todettiin 503 naisella, alle 30-vuotiailla naisilla kaksi kolmasosaa näistä liittyivät HPV16/18:aan kun taas vain kolmasosa yli 45-vuotialla. Vanhempien naisten vaikeissa esiastemuutoksissa muut korkean riskin virustyypit ja HPV-negatiivisuus olivat tavallisempia. Teimme meta-analyysin hoitamattomien keskivaikeiden kohdunkaulasyövän esiasteiden (cervical intraepithelial neoplasia grade 2, CIN2) luonnollisesta kulusta. 36 tutkimuksesta saatu arvio näytti CIN2-muutoksen paranevan 50 % tapauksista kahdessa vuodessa kun taas 18 % eteni. Vain alle 30-vuotiaita naisia sisältäneessä alaryhmäanalyysissä 60 % muutoksista parani kun vain 11 % eteni. Eteneminen syöväksi oli kaikkiaan harvinaista (0,5 %, n=15/3160). Lisäksi arvioimme DNA-metylaatioluokittelijaa (S5 classifier) CIN2-muutoksen etenemistä ennakoivana tekijänä 149 nuoren (18-30-vuotiaan) naisen etenevässä tutkimuksessa. S5 pystyi itsenäisesti ennustamaan muutoksen etenemistä iästä, lähtötilanteen solumuutoksen vaikeusasteesta, tupakoinnista ja HPV16/18-löydöksestä riippumatta. Emättimen esiastemuutokset (vaginal intraepithelial neoplasia, VAIN) ovat harvinaisempia kuin kohdunkaulan muutokset. Muutoksia hoidetaan usein laserilla, mutta tauti uusiutuu joka kolmannella. HPV:n säilyminen on uusiutumista ennakoiva tekijä. Rekrytoimme 30 naista, joilla oli todettu vaikea-asteinen VAIN-muutos, satunnaistettuun tutkimukseen, jossa verrattiin itseannostellun immunomodulaattori imikimodin, laserhoidon ja seurannan tehoa hoidossa. Yksikään muutoksista ei edennyt seurannassa ja paranemisaste oli ryhmissä yhtäläinen. HPV:n häviäminen oli kuitenkin tavallisempaa imikimodiryhmässä (63 %) kuin laserryhmässä (11 %)

    Supervised and Ensemble Classification of Multivariate Functional Data: Applications to Lupus Diagnosis

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    abstract: This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide predictors yet to be investigated for SLE identification. Functional nonparametric classifiers form a methodological basis, which is used herein to develop a) the family of ESFuNC segment-wise curve classification algorithms and b) per-pixel ensembles based on logistic regression and fused-LASSO. The proposed methods achieve test set accuracy rates as high as 94.3%, while returning information about regions of the temperature domain that are critical for population discrimination. The undertaken analyses suggest that derivate-based information contributes significantly in improved classification performance relative to recently published studies on SLE plasma thermograms.Dissertation/ThesisDoctoral Dissertation Applied Mathematics 201

    Deep Learning in Medical Image Analysis

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    The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

    Investigation of DNA methylation and microbial biomarkers to improve cervical cancer triage and early diagnosis

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    The shift towards primary HPV-based screening roused the search for a secondary triage test that provides sufficient sensitivity to detect high-grade cervical intraepithelial neoplasia (CIN) and cancer, but also brings high specificity to avoid unnecessary clinical work and colposcopy referrals. To date, molecular biomarkers like DNA methylation and microbial signatures were investigated for molecular triage purposes as they could offer an objective, cost-effective alternative to cytology. The S5 DNA-methylation classifier based on target CpG sites of the host gene EPB41L3, and viral gene regions of HPV16/18/31/33 demonstrated improved performance for detecting CIN2+ compared to either HPV16/18 genotyping, cytology or combination. We tested the performance of the S5 in detecting CIN3 and cancer from diverse geographic settings using the cut-off of 0.80 and the exploratory cut-off of 3.70 for use in low- and middle-income countries. Assays were performed using exfoliated cervical specimens and formalin-fixed biopsies from women with cytology negative results, CIN3 and cervical cancer diagnoses. We observed that S5 can accurately detect high-grade CIN and malignancy irrespective of geographic context and setting. We also show that adjustment of the S5 cut-off can be performed considering the relative importance given to sensitivity versus specificity, thus reflecting local triage modality needed. In parallel, we investigated biomarkers from the cervicovaginal microbiota and their association with CIN3 development and the S5 DNA methylation signatures. In a pilot longitudinal study, we identified S. amnii as a consistent microbial biomarker for CIN3 development. The increase in S. amnii abundance was directly proportional to the increase of S5 classifier scores and disease severity. S. amnii abundance might play a role in sustaining the epigenetic landscape of the cervicovaginal space. We also found that higher proportions of L.helveticus, L.suntoryeus and L.vaginalis might have a potential protective role against CIN3 development in women with persistent hrHPV infections
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