Data mining analyses for precision medicine in acromegaly: a proof of concept

Predicting which acromegaly patients could benefit from somatostatin receptor ligands (SRL) is a must for personalized medicine. Although many biomarkers linked to SRL response have been identified, there is no consensus criterion on how to assign this pharmacologic treatment according to biomarker levels. Our aim is to provide better predictive tools for an accurate acromegaly patient stratification regarding the ability to respond to SRL. We took advantage of a multicenter study of 71 acromegaly patients and we used advanced mathematical modelling to predict SRL response combining molecular and clinical information. Different models of patient stratification were obtained, with a much higher accuracy when the studied cohort is fragmented according to relevant clinical characteristics. Considering all the models, a patient stratification based on the extrasellar growth of the tumor, sex, age and the expression of E-cadherin, GHRL, IN1-GHRL, DRD2, SSTR5 and PEBP1 is proposed, with accuracies that stand between 71 to 95%. In conclusion, the use of data mining could be very useful for implementation of personalized medicine in acromegaly through an interdisciplinary work between computer science, mathematics, biology and medicine. This new methodology opens a door to more precise and personalized medicine for acromegaly patients

Musculo-Skeletal Stress Markers in Bioarchaeology: Indicators of Activity Levels or Human Variation? A re-analysis and Interpretation.

Musculoskeletal stress markers (MSM) have been widely used by bio-archaeologists as indicators of physical activity. These markers occur at the sites of attachment of soft tissue to bone. They are anomalies of bone formation or destruction at these sites and often called enthesopathies in clinical literature. The aims of this research were firstly to determine the aetiology of these features; in particular, whether they can be used as indicators of physical activity. Secondly, to create a new digital and quantifiable recording method, that is both cheap and simple to use. To achieve the first aim, several literature reviews were undertaken: of the bio-archaeological literature; of the anatomy of the attachment sites; of the relationship between trauma and enthesopathy formation; and of the relationship between enthesopathy formation and disease. Many diseases, for example DISH and ankylosing spondylitis, were found to be associated with enthesopathy formation. Findings of these reviews indicated current bio-archaeological recording methods and interpretive practises are at odds with clinical literature. The second aim had to take these factors into account. Pilot studies were undertaken to develop a new recording method. The final method used visual recording and measurement of enthese along with digitalisation of the surface in two-dimensions using a profile gauge. The digital curves were then quantified using roughness parameters commonly used in materials science. These described the surfaces and could also be used to determine whether this method was applicable to differentiate between normal entheses and those with enthesopathies. Discriminant function analysis demonstrated that this was possible. Stringent diagnostic criteria were also set in place to remove any individuals with possible disease-related enthesopathies. Using the same method, it was found that these could (in some circumstances) also be differentiated from the normal samples.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An intelligent decision support tool for early diagnosis of functional pituitary adenomas

In this work, a web based integrated Medical Decision Support System (MDSS) tool for mainly early diagnosis of functional pituitary adenomas (i.e., somatotrophinoma, corticotrophinoma and prolactinoma) is developed. In the MDSS tool, hormone diseases are described by means of well-classified set of attributes generated from the typical sign and symptoms of disorders.The proposed tool is based on a stationary linear stochastic system model which specifically predicts the selected hormone diseases employing certain system parameters. The MDSS tool is user friendly which includes questions and answers at the opening session of the self-test. Questions and answers session will be completed by “yes” or “no” type of simple-responses. Based on our clinical results, MDSS tool yields more than 99% correct decisions on the selected hormone diseases. It is expected that effective use of the proposed MDSS tool will save substantial amount of valuable time of an expert endocrinologists and minimizes the cost of diagnosis. Furthermore, it will provide the opportunity for early diagnosis for the patient and the expert medical doctor to take the necessary preventive measures.Publisher's Versio

Splicing Machinery is Dysregulated in Pituitary Neuroendocrine Tumors and is Associated with Aggressiveness Features

Pituitary neuroendocrine tumors (PitNETs) constitute approximately 15% of all brain tumors, and most have a sporadic origin. Recent studies suggest that altered alternative splicing and, consequently, appearance of aberrant splicing variants, is a common feature of most tumor pathologies. Moreover, spliceosome is considered an attractive therapeutic target in tumor pathologies, and the inhibition of SF3B1 (e.g., using pladienolide-B) has been shown to exert antitumor effects. Therefore, we aimed to analyze the expression levels of selected splicing-machinery components in 261 PitNETs (somatotropinomas/non-functioning PitNETS/corticotropinomas/prolactinomas) and evaluated the direct effects of pladienolide-B in cell proliferation/viability/hormone secretion in human PitNETs cell cultures and pituitary cell lines (AtT-20/GH3). Results revealed a severe dysregulation of splicing-machinery components in all the PitNET subtypes compared to normal pituitaries and a unique fingerprint of splicing-machinery components that accurately discriminate between normal and tumor tissue in each PitNET subtype. Moreover, expression of specific components was associated with key clinical parameters. Interestingly, certain components were commonly dysregulated throughout all PitNET subtypes. Finally, pladienolide-B reduced cell proliferation/viability/hormone secretion in PitNET cell cultures and cell lines. Altogether, our data demonstrate a drastic dysregulation of the splicing-machinery in PitNETs that might be associated to their tumorigenesis, paving the way to explore the use of specific splicing-machinery components as novel diagnostic/prognostic and therapeutic targets in PitNETs

Non-invasive health prediction from visually observable features [version 2; peer review: 1 approved, 1 approved with reservations]

Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches

Masculinization of postmenopausal female crania: fact or fiction?

The use of the Daubert Standard in court proceedings has highlighted the need to substantiate scientific findings or claims beyond simply accepting the word of a respected expert. The concept of postmenopausal masculinization of the skull in female crania falls into this category. Dr. Walker references this concept in several articles but there is no research to support this hypothesis. This project examines the theory of postmenopausal masculinization of female crania from several perspectives, using the visual sex estimation method set forth in Standards for Data Collection from Human Skeletal Remains edited by Jane E. Buikstra and Douglas H Ubelaker, photographic seriation of these sex estimation traits, and metric measurements in conjunction with Fordisc 3.1. A sample of 395 crania from the Hamann-Todd Collection at the Cleveland Museum of Natural History was analyzed using all three of these methods to determine if there was a pattern of masculinization in the postmenopausal female sample. The average age for the onset of menopause in the United States is 50, thus there should be an increase in "masculinization" observable through more rugged sex estimation traits, a higher number of females 50 or over being found below the midpoint in photographic seriations of sex estimation traits, and an increase in Fordisc 3.1 sex identification misclassifications in females in this age category. The results of the analyses revealed that there were statistically significant differences between ancestry groups, the sexes, and in some cases, age-groups. The results of this research indicate that though there are some differences between comparison groups, there does not appear to be a cohesive pattern of masculinization in female crania at or after the average age of onset of menopause. Human variation is endless, and even in areas of the skeleton for which it has been established that there is a significant degree of sexual dimorphism, there will be individuals who do not fit neatly into a binary conception of sexual divergence. Though these individuals may be misidentified as the opposite sex using one or all of the methods utilized in this project, this falls short of being classified as a part of the menopausal process in females

Automated Strategies in Multimodal and Multidimensional Ultrasound Image-based Diagnosis

Medical ultrasonography is an effective technique in traditional anatomical and functional diagnosis. However, it requires the visual examination by experienced clinicians, which is a laborious, time consuming and highly subjective procedure. Computer-aided diagnosis (CADx) have been extensively used in clinical practice to support the interpretation of images; nevertheless, current ultrasound CADx still entails a substantial user-dependency and are unable to extract image data for prediction modelling. The aim of this thesis is to propose a set of fully automated strategies to overcome the limitations of ultrasound CADx. These strategies are addressed to multiple modalities (B-Mode, Contrast-Enhanced Ultrasound-CEUS, Power Doppler-PDUS and Acoustic Angiography-AA) and dimensions (2-D and 3-D imaging). The enabling techniques presented in this work are designed, developed and quantitively validated to efficiently improve the overall patients’ diagnosis. This work is subdivided in 2 macro-sections: in the first part, two fully automated algorithms for the reliable quantification of 2-D B-Mode ultrasound skeletal muscle architecture and morphology are proposed. In the second part, two fully automated algorithms for the objective assessment and characterization of tumors’ vasculature in 3-D CEUS and PDUS thyroid tumors and preclinical AA cancer growth are presented. In the first part, the MUSA (Muscle UltraSound Analysis) algorithm is designed to measure the muscle thickness, the fascicles length and the pennation angle; the TRAMA (TRAnsversal Muscle Analysis) algorithm is proposed to extract and analyze the Visible Cross-Sectional Area (VCSA). MUSA and TRAMA algorithms have been validated on two datasets of 200 images; automatic measurements have been compared with expert operators’ manual measurements. A preliminary statistical analysis was performed to prove the ability of texture analysis on automatic VCSA in the distinction between healthy and pathological muscles. In the second part, quantitative assessment on tumor vasculature is proposed in two automated algorithms for the objective characterization of 3-D CEUS/Power Doppler thyroid nodules and the evolution study of fibrosarcoma invasion in preclinical 3-D AA imaging. Vasculature analysis relies on the quantification of architecture and vessels tortuosity. Vascular features obtained from CEUS and PDUS images of 20 thyroid nodules (10 benign, 10 malignant) have been used in a multivariate statistical analysis supported by histopathological results. Vasculature parametric maps of implanted fibrosarcoma are extracted from 8 rats investigated with 3-D AA along four time points (TPs), in control and tumors areas; results have been compared with manual previous findings in a longitudinal tumor growth study. Performance of MUSA and TRAMA algorithms results in 100% segmentation success rate. Absolute difference between manual and automatic measurements is below 2% for the muscle thickness and 4% for the VCSA (values between 5-10% are acceptable in clinical practice), suggesting that automatic and manual measurements can be used interchangeably. The texture features extraction on the automatic VCSAs reveals that texture descriptors can distinguish healthy from pathological muscles with a 100% success rate for all the four muscles. Vascular features extracted of 20 thyroid nodules in 3-D CEUS and PDUS volumes can be used to distinguish benign from malignant tumors with 100% success rate for both ultrasound techniques. Malignant tumors present higher values of architecture and tortuosity descriptors; 3-D CEUS and PDUS imaging present the same accuracy in the differentiation between benign and malignant nodules. Vascular parametric maps extracted from the 8 rats along the 4 TPs in 3-D AA imaging show that parameters extracted from the control area are statistically different compared to the ones within the tumor volume. Tumor angiogenetic vessels present a smaller diameter and higher tortuosity. Tumor evolution is characterized by the significant vascular trees growth and a constant value of vessel diameter along the four TPs, confirming the previous findings. In conclusion, the proposed automated strategies are highly performant in segmentation, features extraction, muscle disease detection and tumor vascular characterization. These techniques can be extended in the investigation of other organs, diseases and embedded in ultrasound CADx, providing a user-independent reliable diagnosis

Snoring and arousals in full-night polysomnographic studies from sleep apnea-hypopnea syndrome patients

SAHS (Sleep Apnea-Hypopnea Syndrome) is recognized to be a serious disorder with high prevalence in the population. The main clinical triad for SAHS is made up of 3 symptoms: apneas and hypopneas, chronic snoring and excessive daytime sleepiness (EDS). The gold standard for diagnosing SAHS is an overnight polysomnographic study performed at the hospital, a laborious, expensive and time-consuming procedure in which multiple biosignals are recorded. In this thesis we offer improvements to the current approaches to diagnosis and assessment of patients with SAHS. We demonstrate that snoring and arousals, while recognized key markers of SAHS, should be fully appreciated as essential tools for SAHS diagnosis. With respect to snoring analysis (applied to a 34 subjects¿ database with a total of 74439 snores), as an alternative to acoustic analysis, we have used less complex approaches mostly based on time domain parameters. We concluded that key information on SAHS severity can be extracted from the analysis of the time interval between successive snores. For that, we built a new methodology which consists on applying an adaptive threshold to the whole night sequence of time intervals between successive snores. This threshold enables to identify regular and non-regular snores. Finally, we were able to correlate the variability of time interval between successive snores in short 15 minute segments and throughout the whole night with the subject¿s SAHS severity. Severe SAHS subjects show a shorter time interval between regular snores (p=0.0036, AHI cp(cut-point): 30h-1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p=0.006, AHI cp: 30h-1) is seen for less severe SAHS subjects. Also, we have shown successful in classifying the subjects according to their SAHS severity using the features derived from the time interval between regular snores. Classification accuracy values of 88.2% (with 90% sensitivity, 75% specificity) and 94.1% (with 94.4% sensitivity, 93.8% specificity) for AHI cut-points of severity of 5 and 30h-1, respectively. In what concerns the arousal study, our work is focused on respiratory and spontaneous arousals (45 subjects with a total of 2018 respiratory and 2001 spontaneous arousals). Current beliefs suggest that the former are the main cause for sleep fragmentation. Accordingly, sleep clinicians assign an important role to respiratory arousals when providing a final diagnosis on SAHS. Provided that the two types of arousals are triggered by different mechanisms we hypothesized that there might exist differences between their EEG content. After characterizing our arousal database through spectral analysis, results showed that the content of respiratory arousals on a mild SAHS subject is similar to that of a severe one (p>>0.05). Similar results were obtained for spontaneous arousals. Our findings also revealed that no differences are observed between the features of these two kinds of arousals on a same subject (r=0.8, p<0.01 and concordance with Bland-Altman analysis). As a result, we verified that each subject has almost like a fingerprint or signature for his arousals¿ content and is similar for both types of arousals. In addition, this signature has no correlation with SAHS severity and this is confirmed for the three EEG tracings (C3A2, C4A1 and O1A2). Although the trigger mechanisms of the two arousals are known to be different, our results showed that the brain response is fairly the same for both of them. The impact that respiratory arousals have in the sleep of SAHS patients is unquestionable but our findings suggest that the impact of spontaneous arousals should not be underestimated