Machine Learning for Prostate Histopathology Assessment

Pathology reporting on radical prostatectomy (RP) specimens is essential to post-surgery patient care. However, current pathology interpretation of RP sections is typically qualitative and subject to intra- and inter-observer variability, which challenges quantitative and repeatable reporting of lesion grade, size, location, and spread. Therefore, we developed and validated a software platform that can automatically detect and grade cancerous regions on whole slide images (WSIs) of whole-mount RP sections to support quantitative and visual reporting. Our study used hæmatoxylin- and eosin-stained WSIs from 299 whole-mount RP sections from 71 patients, comprising 1.2 million 480μm×480μm regions-of-interest (ROIs) covering benign and cancerous tissues which contain all clinically relevant grade groups. Each cancerous region was annotated and graded by an expert genitourinary pathologist. We used a machine learning approach with 7 different classifiers (3 non-deep learning and 4 deep learning) to classify: 1) each ROI as cancerous vs. non-cancerous, and 2) each cancerous ROI as high- vs. low-grade. Since recent studies found some subtypes beyond Gleason grade to have independent prognostic value, we also used one deep learning method to classify each cancerous ROI from 87 RP sections of 25 patients as each of eight subtypes to support further clinical pathology research on this topic. We cross-validated each system against the expert annotations. To compensate for the staining variability across different WSIs from different patients, we computed the tissue component map (TCM) using our proposed adaptive thresholding algorithm to label nucleus pixels, global thresholding to label lumen pixels, and assigning the rest as stroma/other. Fine-tuning AlexNet with ROIs of the TCM yielded the best results for prostate cancer (PCa) detection and grading, with areas under the receiver operating characteristic curve (AUCs) of 0.98 and 0.93, respectively, followed by fine-tuned AlexNet with ROIs of the raw image. For subtype grading, fine-tuning AlexNet with ROIs of the raw image yielded AUCs ≥ 0.7 for seven of eight subtypes. To conclude, deep learning approaches outperformed non-deep learning approaches for PCa detection and grading. The TCMs provided the primary cues for PCa detection and grading. Machine learning can be used for subtype grading beyond the Gleason grading system

Automated classification of cancer tissues using multispectral imagery

Automated classification of medical images for colorectal and prostate cancer diagnosis is a crucial tool for improving routine diagnosis decisions. Therefore, in the last few decades, there has been an increasing interest in refining and adapting machine learning algorithms to classify microscopic images of tumour biopsies. Recently, multispectral imagery has received a significant interest from the research community due to the fast-growing development of high-performance computers. This thesis investigates novel algorithms for automatic classification of colorectal and prostate cancer using multispectral imagery in order to propose a system outperforming the state-of-the-art techniques in the field. To achieve this objective, several feature extraction methods based on image texture have been investigated, analysed and evaluated. A novel texture feature for multispectral images is also constructed as an adaptation of the local binary pattern texture feature to multispectral images by expanding the pixels neighbourhood to the spectral dimension. It has the advantage of capturing the multispectral information with a limited feature vector size. This feature has demonstrated improved classification results when compared against traditional texture features. In order to further enhance the systems performance, advanced classification schemes such as bag-of-features - to better capture local information - and stacked generalisation - to select the most discriminative texture features - are explored and evaluated. Finally, the recent years have seen an accelerated and exponential rise of deep learning, boosted by the advances in hardware, and more specifically graphics processing units. Such models have demonstrated excellent results for supervised learning in multiple applications. This observation has motivated the employment in this thesis of deep neural network architectures, namely convolutional neural networks. Experiments were also carried out to evaluate and compare the performance obtained with the features extracted using convolutional neural networks with random initialisation against features extracted with pre-trained models on ImageNet dataset. The analysis of the classication accuracy achieved with deep learning models reveals that the latter outperforms the previously proposed texture extraction methods. In this thesis, the algorithms are assessed using two separate multiclass datasets: the first one consists of prostate tumour multispectral images, and the second contains multispectral images of colorectal tumours. The colorectal dataset was acquired on a wide domain of the light spectrum ranging from the visible to the infrared wavelengths. This dataset was used to demonstrate the improved results produced using infrared light as well as visible light

Malignitätsgrading des Prostatakarzinoms mittels morphometrischer Deskriptoren

Beim Prostatakarzinom handelt es sich, auf Deutschland bezogen, eindeutig um die häufigste Krebserkrankung und die dritthäufigste Krebstodesursache bei Männern. Neben diversen Umwelt- und Lifestyle-Faktoren konnte das Patientenalter als weiterer zentraler Risikofaktor identifiziert werden. Allerdings liegt als onkologische Besonderheit ein Großteil der Prostatakarzinome in einem klinisch inapparenten Stadium vor und hat daher keinen Einfluss auf die Lebensqualität und erwartung des betroffenen Mannes. Daraus leiten sich wiederum besondere Herausforderungen an mögliche Screeningprogramme und die verschiedenen Therapiekonzepte ab, da die Gefahr einer Überdiagnose und Übertherapie droht. Dabei reichen die momentan etablierten, kurativen und unter anderem vom Tumorstadium abhängigen Therapieoptionen von einer engmaschigen Befundkontrolle (Active Surveillance) bis hin zu einer radikalen Entfernung der Prostata. Grundlage für die Diagnosestellung und Therapieentscheidung bildet dabei das histologische Gleason Grading des Pathologen. Im Allgemeinen beruht das Gleason Grading auf einer Analyse der Mengenanteile verschiedener histologischer Muster und einer morphologischen Beschreibung der Drüsenarchitektur, wobei die Summe der beiden häufigsten Wachstumsformen einen Score ergibt. Unter anderem in Übereinstimmung mit dem rezidivfreien Überleben werden im aktuellen Gleason Grading die Kombinationen aus fünf histologischen Wachstumsmustern zu fünf Malignitätsgruppen zusammengefasst und unterschieden. Aufgrund der relativ subjektiven qualitativen und quantitativen Beurteilung durch den Pathologen, weist das Gleason Grading trotz aller Anpassungen und Verbesserungen teils noch erhebliche Defizite in der intra und interindividuellen Reproduzierbarkeit auf. Aufgrund der auf einer Beschreibung der Drüsenmorphologie beruhenden Beurteilung des Prostatakarzinoms kann die medizinische Bildverarbeitung nicht nur durch die immensen Möglichkeiten der Musterextraktionsalgorithmen den Pathologen in seinem Arbeitsprozess beziehungsweise seiner Entscheidungsfindung unterstützen und insbesondere zu einer Standardisierung und Verbesserung der Reproduzierbarkeit beitragen. In der vorliegenden Arbeit wird das Gleason Grading unter Anwendung expliziter histopathologischer Kenntnisse durch morphometrische Deskriptoren der medizinischen Bildverarbeitung abgebildet. Der hier etablierte Algorithmus gliedert sich in drei Abschnitte. Zunächst werden mit Hilfe diverser vorverarbeitender Bildbearbeitungsschritte die Drüsenepithelien aus den digitalisierten H&E gefärbten Schnitten von Nadelstanzbiopsien der Prostata extrahiert, um diese anschließend im zweiten Abschnitt der Tumordetektion zuführen zu können. Hierbei werden verschiedene Malignitätskriterien des Prostatakarzinoms unter bildmorphologischen Gesichtspunkten miteinander verknüpft und eine Identifikation der tumorösen Strukturen vorgenommen. Im dritten Abschnitt werden die gefundenen Tumorareale mittels zweier morphometrischer Deskriptoren verschiedenen Malignitätsstufen zugeordnet.:Bibliographische Beschreibung 3 Inhaltsverzeichnis 4 1 Einführung und Motivation zur Arbeit 6 1.1 Intention 6 1.2 Vorarbeiten 8 1.3 Forschungsstand 13 2 Pathologie des Prostatakarzinoms 18 2.1 Anatomie und Physiologie der Prostata 18 2.2 Ätiologie und Pathogenese 20 2.3 Diagnostik 22 2.4 Grading 27 2.5 Therapie 35 3 Methoden für ein formbasiertes Grading 41 3.1 Überblick 41 3.2 Vorverarbeitung 43 3.2.1 Skalierung 43 3.2.2 Farbseparierung 43 3.2.3 Histogrammanpassung 46 3.2.4 Kantenerhaltende Glättung 48 3.2.5 Binarisierung der Epithelien 50 3.3 Detektion 53 3.3.1 Wanddickenmessung 53 3.3.2 Formbetrachtung 60 3.3.3 Nachbarschaftsanalyse 61 3.4 Klassifikation 62 3.4.1 Inverse Kompaktheit 62 3.4.2 Lochanzahl 64 4 Material 68 4.1 Probenart und -gewinnung 68 4.2 Pilotdatensatz 70 4.3 Kohorte 70 5 Ergebnisse 73 5.1 Pilotdatensatz 76 5.2 Kohorte 81 6 Diskussion 85 7 Ausblick 92 8 Zusammenfassung 94 9 Literaturverzeichnis 97 Appendix 114 A.1 Abbildungsverzeichnis 114 A.2 Tabellenverzeichnis 117 A.3 Eigene Beiträge 118 A.4 Programmcode 119 Erklärung über die eigenständige Abfassung der Arbeit 124 Lebenslauf 125 Danksagung 12

Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography

Tesis por compendio[ES] Esta tesis presenta soluciones de vanguardia basadas en algoritmos de computer vision (CV) y machine learning (ML) para ayudar a los expertos en el diagnóstico clínico. Se centra en dos áreas relevantes en el campo de la imagen médica: la patología digital y la oftalmología. Este trabajo propone diferentes paradigmas de machine learning y deep learning para abordar diversos escenarios de supervisión en el estudio del cáncer de próstata, el cáncer de vejiga y el glaucoma. En particular, se consideran métodos supervisados convencionales para segmentar y clasificar estructuras específicas de la próstata en imágenes histológicas digitalizadas. Para el reconocimiento de patrones específicos de la vejiga, se llevan a cabo enfoques totalmente no supervisados basados en técnicas de deep-clustering. Con respecto a la detección del glaucoma, se aplican algoritmos de memoria a corto plazo (LSTMs) que permiten llevar a cabo un aprendizaje recurrente a partir de volúmenes de tomografía por coherencia óptica en el dominio espectral (SD-OCT). Finalmente, se propone el uso de redes neuronales prototípicas (PNN) en un marco de few-shot learning para determinar el nivel de gravedad del glaucoma a partir de imágenes OCT circumpapilares. Los métodos de inteligencia artificial (IA) que se detallan en esta tesis proporcionan una valiosa herramienta de ayuda al diagnóstico por imagen, ya sea para el diagnóstico histológico del cáncer de próstata y vejiga o para la evaluación del glaucoma a partir de datos de OCT.[CA] Aquesta tesi presenta solucions d'avantguarda basades en algorismes de *computer *vision (CV) i *machine *learning (ML) per a ajudar als experts en el diagnòstic clínic. Se centra en dues àrees rellevants en el camp de la imatge mèdica: la patologia digital i l'oftalmologia. Aquest treball proposa diferents paradigmes de *machine *learning i *deep *learning per a abordar diversos escenaris de supervisió en l'estudi del càncer de pròstata, el càncer de bufeta i el glaucoma. En particular, es consideren mètodes supervisats convencionals per a segmentar i classificar estructures específiques de la pròstata en imatges histològiques digitalitzades. Per al reconeixement de patrons específics de la bufeta, es duen a terme enfocaments totalment no supervisats basats en tècniques de *deep-*clustering. Respecte a la detecció del glaucoma, s'apliquen algorismes de memòria a curt termini (*LSTMs) que permeten dur a terme un aprenentatge recurrent a partir de volums de tomografia per coherència òptica en el domini espectral (SD-*OCT). Finalment, es proposa l'ús de xarxes neuronals *prototípicas (*PNN) en un marc de *few-*shot *learning per a determinar el nivell de gravetat del glaucoma a partir d'imatges *OCT *circumpapilares. Els mètodes d'intel·ligència artificial (*IA) que es detallen en aquesta tesi proporcionen una valuosa eina d'ajuda al diagnòstic per imatge, ja siga per al diagnòstic histològic del càncer de pròstata i bufeta o per a l'avaluació del glaucoma a partir de dades d'OCT.[EN] This thesis presents cutting-edge solutions based on computer vision (CV) and machine learning (ML) algorithms to assist experts in clinical diagnosis. It focuses on two relevant areas at the forefront of medical imaging: digital pathology and ophthalmology. This work proposes different machine learning and deep learning paradigms to address various supervisory scenarios in the study of prostate cancer, bladder cancer and glaucoma. In particular, conventional supervised methods are considered for segmenting and classifying prostate-specific structures in digitised histological images. For bladder-specific pattern recognition, fully unsupervised approaches based on deep-clustering techniques are carried out. Regarding glaucoma detection, long-short term memory algorithms (LSTMs) are applied to perform recurrent learning from spectral-domain optical coherence tomography (SD-OCT) volumes. Finally, the use of prototypical neural networks (PNNs) in a few-shot learning framework is proposed to determine the severity level of glaucoma from circumpapillary OCT images. The artificial intelligence (AI) methods detailed in this thesis provide a valuable tool to aid diagnostic imaging, whether for the histological diagnosis of prostate and bladder cancer or glaucoma assessment from OCT data.García Pardo, JG. (2022). Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182400Compendi

Meningioma classification using an adaptive discriminant wavelet packet transform

Meningioma subtypes classification is a real world problem from the domain of histological image analysis that requires new methods for its resolution. Computerised histopathology presents a whole new set of problems and introduces new challenges in image classification. High intra-class variation and low inter-class differences in textures is often an issue in histological image analysis problems such as Meningioma subtypes classification. In this thesis, we present an adaptive wavelets based technique that adapts to the variation in the texture of meningioma samples and provides high classification accuracy results. The technique provides a mechanism for attaining an image representation consisting of various spatial frequency resolutions that represent the image and are referred to as subbands. Each subband provides different information pertaining to the texture in the image sample. Our novel method, the Adaptive Discriminant Wavelet Packet Transform (ADWPT), provides a means for selecting the most useful subbands and hence, achieves feature selection. It also provides a mechanism for ranking features based upon the discrimination power of a subband. The more discriminant a subband, the better it is for classification. The results show that high classification accuracies are obtained by selecting subbands with high discrimination power. Moreover, subbands that are more stable i.e. have a higher probability of being selected provide better classification accuracies. Stability and discrimination power have been shown to have a direct relationship with classification accuracy. Hence, ADWPT acquires a subset of subbands that provide a highly discriminant and robust set of features for Meningioma subtype classification. Classification accuracies obtained are greater than 90% for most Meningioma subtypes. Consequently, ADWPT is a robust and adaptive technique which enables it to overcome the issue of high intra-class variation by statistically selecting the most useful subbands for meningioma subtype classification. It overcomes the issue of low inter-class variation by adapting to texture samples and extracting the subbands that are best for differentiating between the various meningioma subtype textures

Deep Learning in Medical Image Analysis

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

Integrated Graph Theoretic, Radiomics, and Deep Learning Framework for Personalized Clinical Diagnosis, Prognosis, and Treatment Response Assessment of Body Tumors

Purpose: A new paradigm is beginning to emerge in radiology with the advent of increased computational capabilities and algorithms. The future of radiological reading rooms is heading towards a unique collaboration between computer scientists and radiologists. The goal of computational radiology is to probe the underlying tissue using advanced algorithms and imaging parameters and produce a personalized diagnosis that can be correlated to pathology. This thesis presents a complete computational radiology framework (I GRAD) for personalized clinical diagnosis, prognosis and treatment planning using an integration of graph theory, radiomics, and deep learning. Methods: There are three major components of the I GRAD framework–image segmentation, feature extraction, and clinical decision support. Image Segmentation: I developed the multiparametric deep learning (MPDL) tissue signature model for segmentation of normal and abnormal tissue from multiparametric (mp) radiological images. The segmentation MPDL network was constructed from stacked sparse autoencoders (SSAE) with five hidden layers. The MPDL network parameters were optimized using k-fold cross-validation. In addition, the MPDL segmentation network was tested on an independent dataset. Feature Extraction: I developed the radiomic feature mapping (RFM) and contribution scattergram (CSg) methods for characterization of spatial and inter-parametric relationships in multiparametric imaging datasets. The radiomic feature maps were created by filtering radiological images with first and second order statistical texture filters followed by the development of standardized features for radiological correlation to biology and clinical decision support. The contribution scattergram was constructed to visualize and understand the inter-parametric relationships of the breast MRI as a complex network. This multiparametric imaging complex network was modeled using manifold learning and evaluated using graph theoretic analysis. Feature Integration: The different clinical and radiological features extracted from multiparametric radiological images and clinical records were integrated using a hybrid multiview manifold learning technique termed the Informatics Radiomics Integration System (IRIS). IRIS uses hierarchical clustering in combination with manifold learning to visualize the high-dimensional patient space on a two-dimensional heatmap. The heatmap highlights the similarity and dissimilarity between different patients and variables. Results: All the algorithms and techniques presented in this dissertation were developed and validated using breast cancer as a model for diagnosis and prognosis using multiparametric breast magnetic resonance imaging (MRI). The deep learning MPDL method demonstrated excellent dice similarity of 0.87±0.05 and 0.84±0.07 for segmentation of lesions on malignant and benign breast patients, respectively. Furthermore, each of the methods, MPDL, RFM, and CSg demonstrated excellent results for breast cancer diagnosis with area under the receiver (AUC) operating characteristic (ROC) curve of 0.85, 0.91, and 0.87, respectively. Furthermore, IRIS classified patients with low risk of breast cancer recurrence from patients with medium and high risk with an AUC of 0.93 compared to OncotypeDX, a 21 gene assay for breast cancer recurrence. Conclusion: By integrating advanced computer science methods into the radiological setting, the I-GRAD framework presented in this thesis can be used to model radiological imaging data in combination with clinical and histopathological data and produce new tools for personalized diagnosis, prognosis or treatment planning by physicians

Oncologic Thermoradiotherapy: Need for Evidence, Harmonisation, and Innovation

The road of acceptance of oncologic thermotherapy/hyperthermia as a synergistic modality in combination with standard oncologic therapies is still bumpy. This is partially due to the lack of level I evidence from international, multicentric, randomized clinical trials including large patient numbers and a long term follow-up. Therefore we need more level I EVIDENCE from clinical trials, we need HARMONISATION and global acceptance for existing technologies and a common language understood by all stakeholders and we need INNOVATION in the fields of biology, clinics and technology to move thermotherapy/hyperthermia forward. This is the main focus of this reprint. In this reprintyou find carefully selected and peer-reviewed contributions from Africa, America, Asia, and Europe. The published papers from leading scientists from all over the world covering a broad range of timely research topics might also help to strengthen thermotherapy on a global level