First-order statistical speckle models improve robustness and reproducibility of contrast-enhanced ultrasound perfusion estimates

Contrast-enhanced ultrasound (CEUS) permits the quantification and monitoring of adaptive tumor responses in the face of anti-angiogenic treatment, with the goal of informing targeted therapy. However, conventional CEUS image analysis relies on mean signal intensity as an estimate of tracer concentration in indicator-dilution modeling. This discounts additional information that may be available from the first-order speckle statistics in a CEUS image. Heterogeneous vascular networks, typical of tumor-induced angiogenesis, lead to heterogeneous contrast enhancement of the imaged tumor cross-section. To address this, a linear (B-mode) processing approach was developed to quantify the change in the first-order speckle statistics of B-mode cine loops due to the incursion of microbubbles. The technique, named the EDoF (effective degrees of freedom) method, was developed on tumor bearing mice (MDA-MB-231LN mammary fat pad inoculation) and evaluated using nonlinear (two-pulse amplitude modulated) contrast microbubble-specific images. To improve the potential clinical applicability of the technique, a second-generation compound probability density function for the statistics of two-pulse amplitude modulated contrast-enhanced ultrasound images was developed. The compound technique was tested in an antiangiogenic drug trial (bevacizumab) on tumor bearing mice (MDA-MB-231LN), and evaluated with gold-standard histology and contrast-enhanced X-ray computed tomography. The compound statistical model could more accurately discriminate anti-VEGF treated tumors from untreated tumors than conventional CEUS image. The technique was then applied to a rapid patient-derived xenograft (PDX) model of renal cell carcinoma (RCC) in the chorioallantoic membrane (CAM) of chicken embryos. The ultimate goal of the PDX model is to screen RCC patients for de novo sunitinib resistance. The analysis of the first-order speckle statistics of contrast-enhanced ultrasound cine loops provides more robust and reproducible estimates of tumor blood perfusion than conventional image analysis. Theoretically this form of analysis could quantify perfusion heterogeneity and provide estimates of vascular fractal dimension, but further work is required to determine what physiological features influence these measures. Treatment sensitivity matrices, which combine vascular measures from CEUS and power Doppler, may be suitable for screening of de novo sunitinib resistance in patients diagnosed with renal cell carcinoma. Further studies are required to assess whether this protocol can be predictive of patient outcome

Applications of Raman spectroscopy to urology

Raman spectroscopy is an optical technique that can interrogate biological tissues. In doing so it gives us an understanding of the changes in the molecular structure that are associated with disease development. The Kerr gating technique uses a picosecond pulsed laser and fast temporal gating of inelastically (Raman) scattered light. The tissue samples used were taken following fully informed consent and ethics approval. Bladder samples were obtained by taking a biopsy during a TURBT or TURP, prostate samples were taken during TURP and the liver and kidney (pigs) were bought at a supermarket. The bladder and prostate samples were snap frozen in liquid nitrogen and stored in an -80°C freezer until required for experimentation. The liver and kidney tissue were used fresh. The constituent samples were bought from Sigma – Aldrich. Multivariate and least squares analysis were used to ascertain the biochemical basis of the differing pathologies within the bladder and the prostate gland, as well as to test diagnostic algorithms produced by a colleague in our group. Depth profiling through the bladder and prostate gland was shown to be feasible by utilizing the Kerr gating technique as was the suppression of fluorescence from dark tissue (liver and kidney). We have shown for the first time, that we can utilise Raman spectroscopy to determine the biochemical basis of pathologies of the bladder and the prostate gland. With the help of the Kerr gating technique we also obtained spectra from different depths through them. We also suppressed fluorescence and resonantly enhanced Raman spectra from dark tissue. These have major implications in terms of understanding pathogenesis and disease progression and also the potential to accurately assess depth of tumour invasion

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Diagnóstico no invasivo de patologías humanas combinando análisis de aliento y modelización con redes neuronales

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Químicas, leída el 09-09-2016It is currently known that there is a direct relation between the moment a disease is detected or diagnosed and the consequences it will have on the patient, as an early detection is generally linked to a more favorable outcome. This concept is the basis of the present research, due to the fact that its main goal is the development of mathematical tools based on computational artificial intelligence to safely and non-invasively attain the detection of multiple diseases. To reach these devices, this research has focused on the breath analysis of patients with diverse diseases, using several analytical methodologies to extract the information contained in these samples, and multiple feature selection algorithms and neural networks for data analysis. In the past, it has been shown that there is a correlation between the molecular composition of breath and the clinical status of a human being, proving the existence of volatile biomarkers that can aid in disease detection depending on their presence or amount. During this research, two main types of analytical approaches have been employed to study the gaseous samples, and these were cross-reactive sensor arrays (based on organically functionalized silicon nanowire field-effect transistors (SiNW FETs) or gold nanoparticles (GNPs)) and proton transfer reaction-mass spectrometry (PTR-MS). The cross-reactive sensors analyze the bulk of the breath samples, offering global, fingerprint-like information, whereas PTR-MS quantifies the volatile molecules present in the samples. All of the analytical equipment employed leads to the generation of large amounts of data per sample, forcing the need of a meticulous mathematical analysis to adequately interpret the results. In this work, two fundamental types of mathematical tools were utilized. In first place, a set of five filter-based feature selection algorithms (χ2 (chi2) score, Fisher’s discriminant ratio, Kruskal-Wallis test, Relief-F algorithm, and information gain test) were employed to reduce the amount of independent in the large databases to the ones which contain the greatest discriminative power for a further modeling task. On the other hand, and in relation to mathematical modeling, artificial neural networks (ANNs), algorithms that are categorized as computational artificial intelligence, have been employed. These non-linear tools have been used to locate the relations between the independent variables of a system and the dependent ones to fulfill estimations or classifications. The type of ANN that has been used in this thesis coincides with the one that is more commonly employed in research, which is the supervised multilayer perceptron (MLP), due to its proven ability to create reliable models for many different applications...Actualmente es sabido que existe una relación directa entre el momento en el cual se detecta o diagnostica una enfermedad y las consecuencias que tendrá sobre el paciente, ya que una detección temprana va generalmente ligada a un desarrollo más favorable. Este concepto es el cimiento de la presente investigación, cuyo objetivo fundamental es el desarrollo de herramientas basadas en inteligencia artificial computacional que consigan, mediante medios seguros y no invasivos, la detección de diversas enfermedades. Para alcanzar dichos sistemas, los estudios han sido enfocados en el análisis de muestras de aliento de pacientes de diversas enfermedades, empleando varias técnicas para extraer información, y diversos algoritmos de selección de variables y redes neuronales para el procesamiento matemático. En el pasado, se ha comprobado que hay una correlación entre la composición molecular del aliento y el estado clínico de una persona, evidenciando la existencia de biomarcadores volátiles que pueden ayudar a detectar enfermedades, ya sea por su presencia o por su cantidad. Durante el transcurso de esta investigación, se han empleado esencialmente dos tipos de técnicas analíticas para estudiar las muestras gaseosas, y estas son conjuntos de sensores de reactividad cruzada (basados en transistores de efecto de campo con nanocables de silicio (SiNW FETs) o en nanopartículas de oro (GNPs), ambos funcionalizados con cadenas orgánicas) y equipos de reacción de transferencia de protones con espectrometría de masas (PTR-MS). Los sensores de reactividad cruzada analizan el aliento en su conjunto, extrayéndose información de la muestra global, mientras que usando PTR-MS, se cuantifican las moléculas volátiles presentes en las muestras analizadas. Todas las técnicas empleadas desembocan en la generación de grandes cantidades de datos por muestra, por lo que un análisis matemático exhaustivo es necesario para poder sacar el máximo rendimiento de los estudios. En este trabajo, se emplearon principalmente dos tipos de herramientas matemáticas. Las primeras son un grupo de cinco algoritmos de selección de variables, concretamente, filtros de variables (cálculos basados en estadística de χ2 (chi2), ratio discriminante de Fisher, análisis de Kruskal-Wallis, algoritmo relief-F y test de ganancia de información), que se han empleado en las bases de datos con grandes cantidades de variables independientes para localizar aquellas con mayor importancia o poder discriminativo para una tarea de modelización matemática posterior. Por otro lado, en cuando a dicha modelización, se ha empleado un tipo de algoritmo que se cataloga dentro del área de la inteligencia artificial computacional: las redes neuronales artificiales (ANNs). Estas herramientas matemáticas de naturaleza no lineal se han utilizado para localizar las relaciones existentes entre las variables independientes de un sistema y las variables dependientes o parámetros a estimar o clasificar. Se ha empleado el tipo de ANN supervisada más extensamente usado en investigación, que son los perceptrones multicapa (MLPs), debido a su habilidad contrastada para originar modelos fiables para numerosas aplicaciones...Fac. de Ciencias QuímicasTRUEunpu

Challenges in cancer research and multifaceted approaches for cancer biomarker quest

AbstractRecent advances in cancer biology have subsequently led to the development of new molecularly targeted anti-cancer agents that can effectively hit cancer-related proteins and pathways. Despite better insight into genomic aberrations and diversity of cancer phenotypes, it is apparent that proteomics too deserves attention in cancer research. Currently, a wide range of proteomic technologies are being used in quest for new cancer biomarkers with effective use. These, together with newer technologies such as multiplex assays could significantly contribute to the discovery and development of selective and specific cancer biomarkers with diagnostic or prognostic values for monitoring the disease state. This review attempts to illustrate recent advances in the field of cancer biomarkers and multifaceted approaches undertaken in combating cancer

Role of machine learning in early diagnosis of kidney diseases.

Machine learning (ML) and deep learning (DL) approaches have been used as indispensable tools in modern artificial intelligence-based computer-aided diagnostic (AIbased CAD) systems that can provide non-invasive, early, and accurate diagnosis of a given medical condition. These AI-based CAD systems have proven themselves to be reproducible and have the generalization ability to diagnose new unseen cases with several diseases and medical conditions in different organs (e.g., kidneys, prostate, brain, liver, lung, breast, and bladder). In this dissertation, we will focus on the role of such AI-based CAD systems in early diagnosis of two kidney diseases, namely: acute rejection (AR) post kidney transplantation and renal cancer (RC). A new renal computer-assisted diagnostic (Renal-CAD) system was developed to precisely diagnose AR post kidney transplantation at an early stage. The developed Renal-CAD system perform the following main steps: (1) auto-segmentation of the renal allograft from surrounding tissues from diffusion weighted magnetic resonance imaging (DW-MRI) and blood oxygen level-dependent MRI (BOLD-MRI), (2) extraction of image markers, namely: voxel-wise apparent diffusion coefficients (ADCs) are calculated from DW-MRI scans at 11 different low and high b-values and then represented as cumulative distribution functions (CDFs) and extraction of the transverse relaxation rate (R2*) values from the segmented kidneys using BOLD-MRI scans at different echotimes, (3) integration of multimodal image markers with the associated clinical biomarkers, serum creatinine (SCr) and creatinine clearance (CrCl), and (4) diagnosing renal allograft status as nonrejection (NR) or AR by utilizing these integrated biomarkers and the developed deep learning classification model built on stacked auto-encoders (SAEs). Using a leaveone- subject-out cross-validation approach along with SAEs on a total of 30 patients with transplanted kidney (AR = 10 and NR = 20), the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified 10-fold cross-validation approach, the Renal-CAD system demonstrated its reproduciblity and robustness with a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. In addition, a new renal cancer CAD (RC-CAD) system for precise diagnosis of RC at an early stage was developed, which incorporates the following main steps: (1) estimating the morphological features by applying a new parametric spherical harmonic technique, (2) extracting appearance-based features, namely: first order textural features are calculated and second order textural features are extracted after constructing the graylevel co-occurrence matrix (GLCM), (3) estimating the functional features by constructing wash-in/wash-out slopes to quantify the enhancement variations across different contrast enhanced computed tomography (CE-CT) phases, (4) integrating all the aforementioned features and modeling a two-stage multilayer perceptron artificial neural network (MLPANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype. On a total of 140 RC patients (malignant = 70 patients (ccRCC = 40 and nccRCC = 30) and benign angiomyolipoma tumors = 70), the developed RC-CAD system was validated using a leave-one-subject-out cross-validation approach. The developed RC-CAD system achieved a sensitivity of 95.3% ± 2.0%, a specificity of 99.9% ± 0.4%, and Dice similarity coefficient of 0.98 ± 0.01 in differentiating malignant from benign renal tumors, as well as an overall accuracy of 89.6% ± 5.0% in the sub-typing of RCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The results obtained using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, and relational functional gradient boosting) as well as other different approaches from the literature. In summary, machine and deep learning approaches have shown potential abilities to be utilized to build AI-based CAD systems. This is evidenced by the promising diagnostic performance obtained by both Renal-CAD and RC-CAD systems. For the Renal- CAD, the integration of functional markers extracted from multimodal MRIs with clinical biomarkers using SAEs classification model, potentially improved the final diagnostic results evidenced by high accuracy, sensitivity, and specificity. The developed Renal-CAD demonstrated high feasibility and efficacy for early, accurate, and non-invasive identification of AR. For the RC-CAD, integrating morphological, textural, and functional features extracted from CE-CT images using a MLP-ANN classification model eventually enhanced the final results in terms of accuracy, sensitivity, and specificity, making the proposed RC-CAD a reliable noninvasive diagnostic tool for RC. The early and accurate diagnosis of AR or RC will help physicians to provide early intervention with the appropriate treatment plan to prolong the life span of the diseased kidney, increase the survival chance of the patient, and thus improve the healthcare outcome in the U.S. and worldwide

Tissue Identification by Differential Mobility Spectrometry

Perimämme muuttuu jatkuvasti luonnollisten mutaatioiden sekä ulkoisten tekijöiden vaikutuksesta. Muutosten kumuloituessa elämämme aikana hallitsemattomasti jakaantuvien ja ympäröiviin kudoksiin sekä lymfaattiseen järjestelmään ja verenkiertoon tunkeutuvien solujen syntymisen todennäköisyys kasvaa. Tämän tyyppistä pahanlaatuista solukasvua kutsutaan syöväksi. Syöpä vaikuttaa joko suoraan tai epäsuorasti suurimpaan osaan ihmisistä yhtenä yleisimmistä kuolinsyistä. Syöpä on monimuotoinen tauti, joka voi syntyä käytännössä mihin kehon osaan tahansa. Riippuen syövän kohdekudoksesta ja kasvun aggressiivisuudesta mahdolliset hoitomuodot, selviytymisennusteet ja kuolleisuus vaihtelevat huomattavasti. Yleisesti syövän rooli kuolinsyynä kuitenkin korostuu jatkuvasti, ja huomattavasta rahallisesta panostuksesta ja vuosikymmenten tutkimustyöstä huolimatta uusille ja paremmille hoito- ja diagnosointimenetelmille on jatkuva tarve. Kiinteiden syöpien leikkaushoito on yksi erityisalue, joka hyötyisi uusista, hoitoa tehostavista innovaatioista. Syövän leikkaushoidossa on yleisesti tavoitteena poistaa kasvain elimistöstä täydellisesti ja täten saavuttaa negatiivinen tervekudosmarginaali. Huomattavassa osassa syöpäleikkauksia poisto on kuitenkin epätäydellinen. Tällöin potilaaseen jääneet syöpäsolut vaativat jatkohoitotoimenpiteitä, joihin yleensä sisältyy myös syövän uusintaleikkaus. Uusintaleikkauksen tarve on erittäin vahingollista potilaan yleiselle hyvinvoinnille ja tuo mukanaan huomattavia lisäterveydenhuoltokustannuksia. Jos vältettävissä olevien uusintaleikkausten määrä voitaisiin puolittaa nykyisestä, säästöjä mitattaisiin jo miljardeissa. Selkeästä säästöpotentiaalista huolimatta syöpien turhat uusintaleikkaukset ovat edelleen ratkaisematon ongelma johtuen etenkin leikkauksenaikaisista haasteista erottaa hyvänlaatuinen kudos pahanlaatuisesta. Solujen rakenteen ja toiminnan määräävä molekulaarinen sisältö eroaa riippuen solujen syntykudoksesta, ja samankaltaisia eroja havaitaan myös pahanlaatuisten ja hyvänlaatuisten solujen välillä. Biomolekyylejä, jotka mahdollistavat kudostyyppien erojen havaitsemisen, kutsutaan biomarkkereiksi tai bioilmaisimiksi, ja tutkimuksissa onkin löydetty satoja proteiineja, rasva-aineita ja aineenvaihduntatuotteita, joiden pitoisuus solussa vaihtelee hyvänlaatuisen ja pahanlaatuisen kudoksen välillä. Tiettyjen biomarkkereiden pitoisuuksien vaihteluvälit hyvänlaatuisissa ja pahanlaatuisissa kudoksissa ovat kuitenkin erittäin suuria, ja molekyylitason erot kudosten välillä aiheuttavat harvoin selkeää makroskooppisesti näkyvää muutosta. Siksi syöpäkudoksen ja tervekudoksen välisen rajan silmämääräinen arvioiminen on erittäin haastavaa. Silti lähes kaikki syöpäkirurgit käyttävät ainoastaan visuaalista arviointia ja tunnustelua operaatiotilanteessa. Lisää haasteellisuutta syövän kokonaispoistoon tuovat myös nykysuositukset, joiden mukaan syövän ympäriltä poistetun tervekudoksen määrä pyritään minimoimaan. Tämä tavoite ja rajan subjektiivinen arviointi johtavat suureen hajontaan eri maiden ja sairaaloiden positiivisten marginaalien määrissä sekä yleisesti korkeaan uusintaleikkausten määrään. Positiivisista tervekudosmarginaalilöydöksistä johtuvia uusintaleikkauksia on pyritty vähentämään tutkimalla ja ottamalla käyttöön useita erilaisia leikkauksenaikaista kudostunnistusta auttavia menetelmiä, mutta niiden kliininen käyttö on ollut rajallista johtuen kunkin menetelmän rajoitteista ja haitoista. Tässä väitöskirjassa esitellään kudostunnistusjärjestelmä, jota voidaan mahdollisesti tulevaisuudessa hyödyntää leikkauksenaikaisessa tervekudosmarginaalin arvioinnissa. Järjestelmän kehitystä ja soveltuvuutta kudostunnistukseen tarkastellaan viiden osatyön kautta. Järjestelmä pohjautuu sähkökirurgiassa tuotetun kudossavun mittaamiseen liikkuvuuserospektrometrialla (differential mobility spectrometry, DMS). DMS on normaali-ilmanpaineessa toimiva mittausteknologia, joka tuottaa informaatiota kaasumaisen näytteen molekulaarisesta rakenteesta erottamalla ionisoidut molekyylit toisistaan voimakkaassa, epäsymmetrisesti muuttuvassa sähkökentässä. DMS vertautuu massaspektrometriaan (MS) mutta on analyyttiseltä suorituskyvyltään sitä heikompi. DMS-teknologian etuna on kuitenkin sen yksinkertaisuus, pienempi koko sekä pienemmät kustannukset MS-teknologiaan verrattuna. DMS-teknologiaa on aiemmin käytetty itsenäisenä mittausmenetelmänä erilaisissa kaasumittaussovelluksissa sekä biolääketieteellisessä käytössä muun muassa hengitysilman mittaamiseen. Nämä sovellukset ovat kuitenkin aina sallineet kontrolloidun ympäristön ja suhteellisen pitkän mittauksen keston. Siksi reaaliaikainen DMS-pohjainen sovellus vaatii ympärilleen lisälaitteistoa ja järjestelmän parametrien optimointia. Lisäksi DMS-data ei suoraan tuota määrällistä tietoa näytteessä olevista biomolekyyleista vaan luo pikemminkin kokonaiskuvan näytteen sisältämien aineiden seoksesta. Spektrin tulkinta ja kudostyypin määritys ei siis ole suoraviivaista, ja yhdestä näytteestä saatavan suuren datamäärän vuoksi analysointi soveltuu parhaiten koneoppimismenetelmille. Järjestelmän poikkitieteellinen näkökulma sekä kokonaisuuden toiminnan ja suorituskyvyn tutkiminen kudostunnistuksessa ovat tämän väitöskirjan pääsisältö. Väitöskirjan kolmessa ensimmäisessä osatyössä tavoitteena oli tutkia menetelmän soveltuvuutta kudostunnistukseen eläinkudosnäytteillä sekä ihmisen rintasyöpänäytteillä. Tulokset laboratorio-olosuhteissa hallitulla näytteentuotolla olivat lupaavia, ja diagnostinen suorituskyky osoitti teknologian potentiaalin kudostunnistuksessa. Neljännessä osatyössä laitteistoa muokattiin mahdollistamaan reaaliaikaiset mittaukset sekä luokittelutuloksen esitys välittömästi mittauksen jälkeen. Tulokset osoittivat, että järjestelmä soveltuu reaaliaikaiseen kudostunnistukseen vähintään eläinnäytteillä laboratorio-olosuhteissa. Viidennessä osatyössä järjestelmää käytettiin rintasyöpäleikkauksissa. Diagnostisen suorituskyvyn osalta tulokset eivät olleet vertailukelpoisia laboratoriotutkimuksiin, mutta tutkimus osoitti, että järjestelmän integroiminen osaksi syöpäkirurgiaa onnistuu käyttäjiä häiritsemättä ja että se pystyy tuottamaan informaatiota leikatusta kudoksesta operaation aikana. Kokonaisuudessaan väitöskirjatutkimuksen tulokset osoittavat DMS-pohjaisen kudostunnistusjärjestelmän potentiaalin ja soveltuvuuden reaaliaikaiseen käyttöön riittävällä diagnostisella suorituskyvyllä. Tulevaisuudessa tässä työssä esitetty järjestelmä voi jatkokehityksen jälkeen toimia syöpäkirurgin apuna tervekudosmarginaalin tunnistuksessa ja auttaa suojelemaan syöpäpotilaiden hyvinvointia vähentämällä tarpeettomia syövän uusintaleikkauksia.The human genome is constantly changing due to natural mutations and environmental exposure. As these changes accumulate over our lifetime, it increases the likelihood of the creation of cells that proliferate uncontrollably and ultimately invade surrounding tissue and the blood circulation or the lymphatic system. This type of malignant neoplasm, more commonly known as cancer, is a disease that either directly or indirectly affects the majority of the population as one of the leading causes of death. Cancer is a versatile disease that can affect practically any part of the body. Depending on the tissue of origin and the aggressiveness of the malignancy, the treatment options, prognosis and mortality rates can vary significantly. In general, the role of cancer as a cause of death is constantly increasing, and despite significant global financial investments and decades of research, new and better methods of treatment and diagnosis are in continuous demand. One particular area that requires more attention and innovation is the surgical treatment of solid cancers. The general aim of surgical treatment is to remove all malignant cells from the patient’s body – that is to say, to achieve a negative surgical margin. The resected tumour has a negative margin, when the outermost surface area has no cancerous cells. However, in a considerable number of surgeries, the removal is incomplete. The resulting residual cancer almost always triggers additional treatment steps, which often involve a reoperation. The need for a reoperation is a major detriment for the well-being of the patient, and the added healthcare costs are substantial. If the number of avoidable reoperations could be halved from their current level, the saving potential in annual global healthcare costs would already be measured in billions of dollars. The reason why the problem of reoperations persists despite the notable financial incentives lies in the difficulty of discriminating malignant tissue from benign, especially during a surgical procedure. The molecular contents that define the structure and function of a cell are different depending on the organ of origin, and similar differences are also present between malignant and benign cells. The biomolecules that enable the identification of the types of tissues are called biomarkers, and the research on this area has revealed hundreds of proteins, fatty acids and metabolic products that exhibit differences in quantities based on tissue malignancy. However, the variation of specific marker molecules is often high, and the molecular differences rarely translate into clear macroscopic differences. This means that visual assessment of the margin between benign and cancerous tissue is extremely challenging. Still, almost all surgeons rely only on visual assessment and palpation in cancer surgeries. The challenge of complete excision is further accentuated by the current resection guidelines that instruct surgeons to preserve as much non-cancerous tissue as possible. This aim and its subjective execution lead not only to high variation in positive margin rates between institutions and regions, but also to a high number of required reoperations in general. To reduce the reoperations caused by positive surgical margins, several technologies have been studied and introduced to aid in intraoperative tissue identification, but the clinical adoption has been limited due to various impeding factors involved in their use. In this thesis, a concept that could potentially be used in the assessment of the intraoperative surgical margin is introduced through five scientific publications that concentrate on the evolution and feasibility of the technology in tissue identification. The basis of the technology is the measurement of surgical smoke with differential mobility spectrometry (DMS). DMS is a measurement technology that provides information on the molecular content of a gaseous sample in atmospheric pressure by means of ionisation and subsequent differentiation of the ions in a high-strength asymmetric electric field. DMS is comparable to mass spectrometry (MS), and even though the analytical performance of MS is better, the reduced complexity, smaller size and lower cost of DMS make it an advantageous option. DMS has been used as a standalone measurement instrument in many types of general gas measurement applications and in some biomedical applications, such as breath analysis, but the context of use has always permitted a controlled environment and a relatively long measurement duration. Thus, the real-time application of surgical smoke measurement requires additional hardware and parameter optimisation. In addition, raw DMS measurement data do not provide directly quantifiable information on certain biomolecules, but rather a comprehensive spectrum of all contents in the sample combined. This means that the interpretation and identification of tissue type from the DMS output spectra is not trivial and involves a high number of dimensions that are most effectively analysed by means of machine learning. The interdisciplinary aspects of the system and their combined function and performance in tissue identification are the focus of this thesis. In the first three publications included in the thesis, the focus was on studying the overall feasibility of tissue identification and its possibilities with animal tissues and clinically relevant breast cancer samples. The results in laboratory conditions with controlled sampling were promising, and the diagnostic performance demonstrated the potential of the technology in tissue identification. In Publication IV, the system was modified to accommodate real-time measurements and to relay the classification information immediately after the measurement. The results demonstrated the feasibility of real-time tissue identification with the system, albeit in laboratory conditions and in a porcine model. In the final study, a prototype system was used intraoperatively during breast cancer surgeries. The results of this study were not comparable to the laboratory results in respect to diagnostic performance but indicated that the system can be adapted to the surgical workflow with minimal intrusiveness to provide information on the operated tissue. Overall, the results of this study indicate that a DMS-based tissue identification system has the potential to be used in real-time applications to identify tissue types with adequate diagnostic performance. With further development, the system presented in this thesis could fulfil the need for a surgical margin assessment device that would reduce avoidable reoperations of solid cancers and thus protect the well-being of cancer patients

Measuring Chemotherapy Response in Breast Cancer Using Optical and Ultrasound Spectroscopy

Purpose: This study comprises two subprojects. In subproject one, the study purpose was to evaluate response to neoadjuvant chemotherapy (NAC) using quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOS) in locally advanced breast cancer (LABC) during chemotherapy. In subproject two, DOS-based functional maps were analysed with texture-based image features to predict breast cancer response before the start of NAC. Patients and Measurements: The institution’s ethics review board approved this study. For subproject one, subjects (n=22) gave written consent before participating in the study. Participants underwent non-invasive, DOS and QUS imaging. Data were acquired at weeks 0 (i.e. baseline), 1, 4, 8 and before surgical removal of the tumour (mastectomy and/or lumpectomy); corresponding to chemotherapy schedules. QUS parameters including the midband fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were determined from tumour ultrasound data using spectral analysis. In the same patients, DOS was used to measure parameters relating to tumour haemoglobin and tissue composition such as %Water and %Lipids. Discriminant analysis and receiver-operating characteristic (ROC) analyses were used to correlate the measured imaging parameters to Miller-Payne pathological response during treatment. Additionally, multivariate analysis was carried out for pairwise DOS and QUS parameter combinations to determine if an increase in the classification accuracy could be obtained using combination DOS and QUS parametric models. For subproject two, 15 additional patients we recruited after first giving their written informed consent. A pooled analysis was completed for all DOS baseline data (subproject 1 and subproject 2; n=37 patients). LABC patients planned for NAC had functional DOS maps and associated textural features generated. A grey-level co-occurrence matrix (texture) analysis was completed for parameters associated with haemoglobin, tissue composition, and optical properties (deoxy-haemoglobin [Hb], oxy-haemoglobin [HbO2], total haemoglobin [HbT]), %Lipids, %Water, and scattering power [SP], scattering amplitude [SA]) prior to treatment. Textural features included contrast (con), vi correlation (cor), energy (ene), and homogeneity (hom). Patients were classified as ‘responders’ or ‘non-responders’ using Miller-Payne pathological response criteria after treatment completion. In order to test if baseline univariate texture features could predict treatment response, a receiver operating characteristic (ROC) analysis was performed, and the optimal sensitivity, specificity and area under the curve (AUC) was calculated using Youden’s index (Q-point) from the ROC. Multivariate analysis was conducted to test 40 DOS-texture features and all possible bivariate combinations using a naïve Bayes model, and k-nearest neighbour (k-NN) model classifiers were included in the analysis. Using these machine-learning algorithms, the pretreatment DOS-texture parameters underwent dataset training, testing, and validation and ROC analysis were performed to find the maximum sensitivity and specificity of bivariate DOS-texture features. Results: For subproject one, individual DOS and QUS parameters, including the spectral intercept (SI), oxy-haemoglobin (HbO2), and total haemoglobin (HbT) were significant markers for response outcome after one week of treatment (p<0.01). Multivariate (pairwise) combinations increased the sensitivity, specificity and AUC at this time; the SI+HbO2 showed a sensitivity/specificity of 100%, and an AUC of 1.0 after one week of treatment. For subproject two, the results indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) = 86.5 and 89.0%, respectively and an accuracy of 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn = 78.0, a %Sp = 81.0% and an accuracy of 79.5% using the naïve Bayes model. Conclusion: DOS and QUS demonstrated potential as coincident markers for treatment response and may potentially facilitate response-guided therapies. Also, the results of this study demonstrated that DOS-texture analysis can be used to predict breast cancer response groups prior to starting NAC using baseline DOS measurements

Systems biology of breast cancer

Breast cancer, with an alarming incidence rate throughout the globe, has attracted significant investigations to identify disease specific biomarkers. Among these, oestrogen receptor (ER) occupies a central role where overexpression is a prognostic indication for breast cancer. The cross-talk between the responsible contenders of ER-associated genes potentially play an important role in the disease aetiology. Investigation of such cross talk is the focus of this thesis. The development of high throughput technologies such as expression microarrays has paved the way for investigating thousands of genes at a time. Microarrays with their high data volume, multivariate nature and non-linearity pose challenges for analysing using conventional statistical approaches. To combat these challenges, computational researchers have developed machine learning approaches such as Artificial Neural Networks (ANNs). This thesis evaluates ANNs based methodologies and their application to the analysis of microarray data generated for breast cancer cases of differing oestrogen receptor status. Furthermore they are used for network inferencing to identify interactions between ER-associated markers and for the subsequent identification of putative pathway elements. The present thesis shows that it is possible to identify some ER-associated breast cancer relevant markers using ANNs. These have been subsequently validated on clinical breast tumour samples highlighting the promise of this approach