Data-driven Representation Learning from Histopathology Image Databases to Support Digital Pathology Analysis

Cancer research is a major public health priority in the world due to its high incidence, diversity and mortality. Despite great advances in this area during recent decades, the high incidence and lack of specialists have proven that one of the major challenges is to achieve early diagnosis. Improved early diagnosis, especially in developing countries, plays a crucial role in timely treatment and patient survival. Recent advances in scanner technology for the digitization of pathology slides and the growth of global initiatives to build databases for cancer research have enabled the emergence of digital pathology as a new approach to support pathology workflows. This has led to the development of many computational methods for automatic histopathology image analysis, which in turn has raised new computational challenges due to the high visual variability of histopathology slides, the difficulty in assessing the effectiveness of methods (considering the lack of annotated data from different pathologists and institutions), and the need of interpretable, efficient and feasible methods for practical use. On the other hand, machine learning techniques have focused on exploiting large databases to automatically extract and induce information and knowledge, in the form of patterns and rules, that allow to connect low-level content with its high-level meaning. Several approaches have emerged as opposed to traditional schemes based on handcrafted features for data representation, which nowadays are known as representation learning. The objective of this thesis is the exploration, development and validation of precise, interpretable and efficient computational machine learning methods for automatic representation learning from histopathology image databases to support diagnosis tasks of different types of cancer. The validation of the proposed methods during the thesis development allowed to corroborate their capability in several histopathology image analysis tasks of different types of cancer. These methods achieve good results in terms of accuracy, robustness, reproducibility, interpretability and feasibility suggesting their potential practical application towards translational and personalized medicine.Resumen. La investigación en cáncer es una de las principales prioridades de salud pública en el mundo debido a su alta incidencia, diversidad y mortalidad. A pesar de los grandes avances en el área en las últimas décadas, la alta incidencia y la falta de especialistas ha llevado a que una de las principales problemáticas sea lograr su detección temprana, en especial en países en vías de desarrollo, como quiera a que de ello depende las posibilidades de un tratamiento oportuno y las oportunidades de supervivencia de los pacientes. Los recientes avances en tecnología de escáneres para digitalización de láminas de patología y el crecimiento de iniciativas mundiales para la construcción de bases de datos para la investigación en cáncer, han permitido el surgimiento de la patología digital como un nuevo enfoque para soportar los flujos de trabajo en patología. Esto ha llevado al desarrollo de una gran variedad de métodos computacionales para el análisis automático de imágenes de histopatología, lo cual ha planteado nuevos desafíos computacionales debido a la alta variabilidad visual de las láminas de histopatología; la dificultad para evaluar la efectividad de los métodos por la falta de datos de diferentes instituciones que cuenten con anotaciones por parte de los patólogos, y la necesidad de métodos interpretables, eficientes y factibles para su uso práctico. Por otro lado, el aprendizaje de máquina se ha enfocado en explotar las grandes bases de datos para extraer e inducir de manera automática información y conocimiento, en forma de patrones y reglas, que permita conectar el contenido de bajo nivel con su significado. Diferentes técnicas han surgido en contraposición a los esquemas tradicionales basados en diseño manual de la representación de los datos, en lo que se conoce como aprendizaje de la representación. El propósito de esta tesis fue la exploración, desarrollo y validación de métodos computacionales de aprendizaje de máquina precisos, interpretables y eficientes a partir de bases de datos de imágenes de histopatología para el aprendizaje automático de la representación en tareas de apoyo al diagnóstico de distintos tipos de cáncer. La validación de los distintos métodos propuestos durante el desarrollo de la tesis permitieron corroborar la capacidad de cada uno de ellos en distintivas tareas de análisis de imágenes de histopatología, en diferentes tipos de cáncer, con buenos resultados en términos de exactitud, robustez, reproducibilidad, interpretabilidad y factibilidad, lo cual sugiere su potencial aplicación práctica hacia la medicina traslacional y personalizada.Doctorad

A Validation Employing Convolutional Neural Network for the Radiographic Detection of Absence or Presence of Teeth

Dental radiography plays an important role in clinical diagnosis, treatment and making decisions. In recent years, efforts have been made on developing techniques to detect objects in images. The aim of this study was to detect the absence or presence of teeth using an effective convolutional neural network, which reduces calculation times and has success rates greater than 95%. A total of 8000 dental panoramic images were collected. Each image and each tooth was categorized, independently and manually, by two experts with more than three years of experience in general dentistry. The neural network used consists of two main layers: object detection and classification, which is the support of the previous one. A Matterport Mask RCNN was employed in the object detection. A ResNet (Atrous Convolution) was employed in the classification layer. The neural model achieved a total loss of 0.76% (accuracy of 99.24%). The architecture used in the present study returned an almost perfect accuracy in detecting teeth on images from different devices and different pathologies and ages

MULTIPLE CLASSIFICATION OF BRAIN TUMORS FOR EARLY DETECTION USING A NOVEL CONVOLUTIONAL NEURAL NETWORK MODEL

Brain tumors can have very dangerous and fatal effects if not diagnosed early. These are diagnosed by specialized doctors using biopsy samples taken from the brain. This process is exhausting and wastes doctors' time too much. Researchers have been working to develop a quick and accurate way for identifying and classifying brain tumors in order to overcome these drawbacks. Computer-assisted technologies are utilized to support doctors and specialists in making more efficient and accurate decisions. Deep learning-based methods are one of these technologies and have been used extensively in recent years. However, there is still a need to explore architectures with higher accuracy performance. For this purpose, in this paper proposed a novel convolutional neural network (CNN) which has twenty-four layers to multi-classify brain tumors from brain MRI images for early diagnosis. In order to demonstrate the effectiveness of the proposed model, various comparisons and tests were carried out. Three different state-of-the-art CNN models were used in the comparison: AlexNet, ShuffleNet and SqueezeNet. At the end of the training, proposed model is achieved highest accuracy of 92.82% and lowest loss of 0.2481. In addition, ShuflleNet determines the second highest accuracy at 90.17%. AlexNet has the lowest accuracy at 80.5% with 0.4679 of loss. These results demonstrate that the proposed CNN model provides greater precision and accuracy than the state-of-art CNN models

Molecular mechanisms of ovarian cancer prevention

Combined oral contraceptive pill (COCP) lowers the risk of ovarian cancer. The risk reduction has been reported as between 33 - 41%. The duration of use affects the risk reduction. The mechanism of risk reduction has been theorised. According to the incessant ovulation theory, repeated ovulation causes inflammation, which causes mutations and subsequent carcinogenesis. COCP prevents ovulation, reducing cancer risk. It was previously shown that COCP decreased genome-wide CpG methylation, suggesting that COCP could regulate gene expression through epigenetic mechanisms. This study hypothesised that COCP intake would affect CpG methylation depending on tissues. It was hypothesised that differential CpG methylation was linked to differential gene expression, which could in turn affect tissue organisation. Reduced representation bisulphite sequencing (RRBS) and pyrosequencing were used to measure the methylation in mice. RNA sequencing (RNAseq) and quantitative reverse transcription PCR (RT-qPCR) were used to study gene expression. Haematoxylin & eosin (H&E) staining and light microscopy were used to detect changes in tissue organisation. Hypomethylation was observed across the genome. CpGs were differentially methylated in COCP treated mice compared to untreated mice. RNAseq revealed that COCP treatment for 5 weeks altered gene expression. In several genes, epigenetic changes were linked to altered gene expression. Subsequent RT-qPCR validated these findings, shown by up-regulation of Kallikrein 1 (KLK1) and fibroblast growth factor (FGF7). There were alterations in the number of ovarian follicles and tissue structures, with a significant change in antral follicle count. In conclusion, COCP changed CpG methylation and gene expression in mice. This was the first study which investigated the effect of COCP in the fallopian tubes. Future studies may build on the fallopian tube specific gene expression and investigate what roles they play in protection against ovarian cancer.Open Acces

Microscopy and Analysis

Microscopes represent tools of the utmost importance for a wide range of disciplines. Without them, it would have been impossible to stand where we stand today in terms of understanding the structure and functions of organelles and cells, tissue composition and metabolism, or the causes behind various pathologies and their progression. Our knowledge on basic and advanced materials is also intimately intertwined to the realm of microscopy, and progress in key fields of micro- and nanotechnologies critically depends on high-resolution imaging systems. This volume includes a series of chapters that address highly significant scientific subjects from diverse areas of microscopy and analysis. Authoritative voices in their fields present in this volume their work or review recent trends, concepts, and applications, in a manner that is accessible to a broad readership audience from both within and outside their specialist area

Risk factors and biomarkers for metastatic cutaneous squamous cell carcinoma

The incidence of cutaneous squamous cell carcinoma (cSCC), the most common skin cancer with metastatic potential, continues to increase. Although proportion of cSCCs metastasize and cause mortality, sufficient means to identify the metastasis-prone tumors are not available. In this thesis the metastatic cSCCs from the area served by Turku University Hospital were identified and characterized revealing that the rate of metastasis in the study region was 2.3%. Further, it was discovered that metastasis occurs rapidly and that there was no history of cSCC in 85% of patients with metastatic cSCC. Invasion depth, tumor diameter, age and location on lower lip or forehead were associated with increased risk of metastasis. On the other hand, usage of isosorbide mono-/dinitrate and aspirin as well as comorbidity with premalignant lesions or basal cell carcinoma were associated with lower risk of metastasis. With multiplexed immunohistochemistry, it was demonstrated that the activity and phenotype of cancer-associated fibroblasts (CAFs) evolve during the progression of cSCC. Elevation of α-smooth muscle actin (αSMA), secreted protein acidic and rich in cysteine (SPARC) and fibroblast activating protein (FAP) expression was associated with invasion and expression of FAP and platelet-derived growth factor receptor-β (PDGFRβ) with metastasis. High expression of stromal PDGFRβ and periostin were associated with worse prognosis. CAF107 (PDGFRα-/PDGFRβ+/FAP+) subset was associated with invasion and metastasis, and predicted poor prognosis of cSCC. A deep learning algorithm was harnessed to distinguish primary tumors that metastasize rapidly from non-metastatic cSCCs with slide level area under the receiver operating characteristic curve (AUROC) of 0.747 on whole slide images representing primary cSCCs. Furthermore, a risk factor model, that utilized prediction by AI, was created and provided staging systems and comparative risk factor models surpassing classification and prognostivity. These results characterize features associated with the metastasis risk of cSCC and indicate that CAF-markers and AI could provide clinical tools for the metastasis risk assessment and thus improve the prognosis of patient with metastatic cSCC.Etäpesäkkeitä lähettävän okasolusyövän riskitekijät ja biomarkkerit Yleisimmän etäpesäkkeitä lähettävän ihosyövän, okasolusyövän, ilmaantuvuus jatkaa kasvuaan. Vaikka osa okasolusyövistä lähettää etäpesäkkeitä ja aiheuttaa kuolleisuutta, ei etäpesäkkeitä lähettämään tulevien okasolusyöpien tunnistamiseksi ole toistaiseksi riittäviä keinoja. Tässä väitöskirjassa karakterisoitiin Turun yliopistollisen keskussairaalan vastuualueen metastasoituneet okasolusyövät ja osoitettiin että tutkimusalueen okasolusyövistä 2.3% etenee etäpesäkkeitä lähettäväksi. Metastasoituminen tapahtui nopeasti ja valtaosassa tapauksista (85%) etäpesäkkeen lähetti ensimmäinen potilaalla todettu okasolusyöpä. Ikä, kasvaimen invaasiosyvyys, halkaisija ja sijainti alahuulessa tai otsalla yhdistyivät kohonneeseen metastaasiriskiin. Isosorbidinitraatin ja aspiriinin käyttö sekä esiasteiden ja tyvisolusyövän esiintyminen taas liittyivät alentuneeseen metastaasiriskiin. Multiplex-immunohistokemiaa hyödyntäen osoitettin, että syöpään liittyvien fibroblastien (CAF) aktiviteetti ja ilmiasu muuttuu okasolusyövän edetessä. Kohonnut sileälihasaktiini alfan (αSMA), osteonektiinin ja fibroblastia aktivoivan proteiinin (FAP) ilmentyminen liittyi invaasioon ja FAP:n sekä verihiutaleista johdetun kasvutekijäreseptori β:n (PDGFRβ) etäpesäkkeiden lähettämiseen. PDGFRβ:n ja periostiinin ilmentyminen taas yhdistyi huonoon ennusteeseen. CAF107 (PDGFRα-/PDGFRβ+/FAP+) alatyyppi liittyi invaasioon, metastasointiin ja huonoon ennusteeeseen. Etäpesäkkeitä lähettämään tulevien okasolusyöpien tunnistamiseen valjastettu syväoppimisalgoritmi erotti okasolusyöpiä edustavista digitalisoiduista mikroskopiakuvista nopeasti etäpesäkkeitä lähettävät okasolusyövät okasolusyövistä, jotka eivät lähetä etäpesäkkeitä, leiketason AUROC-arvolla 0.747. Tekoälyarviota hyödyntävä riskitekijämalli voitti luokittelujärjestelmät ja kilpailevat riskitekijämallit okasolusyöpien luokittelussa ja ennusteen arvioinnissa. Tulokset antavat lisätietoa metastasoituvan okasolusyövän luonteesta ja osoittavat CAF-markkereiden sekä tekoälyn voivan tarjota kliinisiä työkaluja okasolusyövän metastaasiriskin arviointiin ja täten voivan parantaa etäpesäkkeitä lähettävän okasolusyöpäpotilaan ennustetta tulevaisuudessa

Evaluation of PD-L1 expression in various formalin-fixed paraffin embedded tumour tissue samples using SP263, SP142 and QR1 antibody clones

Background & objectives: Cancer cells can avoid immune destruction through the inhibitory ligand PD-L1. PD-1 is a surface cell receptor, part of the immunoglobulin family. Its ligand PD-L1 is expressed by tumour cells and stromal tumour infltrating lymphocytes (TIL). Methods: Forty-four cancer cases were included in this study (24 triple-negative breast cancers (TNBC), 10 non-small cell lung cancer (NSCLC) and 10 malignant melanoma cases). Three clones of monoclonal primary antibodies were compared: QR1 (Quartett), SP 142 and SP263 (Ventana). For visualization, ultraView Universal DAB Detection Kit from Ventana was used on an automated platform for immunohistochemical staining Ventana BenchMark GX. Results: Comparing the sensitivity of two different clones on same tissue samples from TNBC, we found that the QR1 clone gave higher percentage of positive cells than clone SP142, but there was no statistically significant difference. Comparing the sensitivity of two different clones on same tissue samples from malignant melanoma, the SP263 clone gave higher percentage of positive cells than the QR1 clone, but again the difference was not statistically significant. Comparing the sensitivity of two different clones on same tissue samples from NSCLC, we found higher percentage of positive cells using the QR1 clone in comparison with the SP142 clone, but once again, the difference was not statistically significant. Conclusion: The three different antibody clones from two manufacturers Ventana and Quartett, gave comparable results with no statistically significant difference in staining intensity/ percentage of positive tumour and/or immune cells. Therefore, different PD-L1 clones from different manufacturers can potentially be used to evaluate the PD- L1 status in different tumour tissues. Due to the serious implications of the PD-L1 analysis in further treatment decisions for cancer patients, every antibody clone, staining protocol and evaluation process should be carefully and meticulously validated

Urological Cancer 2020

This Urological Cancer 2020 collection contains a set of multidisciplinary contributions to the extraordinary heterogeneity of tumor mechanisms, diagnostic approaches, and therapies of the renal, urinary tract, and prostate cancers, with the intention of offering to interested readers a representative snapshot of the status of urological research