Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Multimodal Multispectral Optical Endoscopic Imaging for Biomedical Applications

Optical imaging is an emerging field of clinical diagnostics that can address the growing medical need for early cancer detection and diagnosis. Various human cancers are amenable to better prognosis and patient survival if found and treated during early disease onset. Besides providing wide-field, macroscopic diagnostic information similar to existing clinical imaging techniques, optical imaging modalities have the added advantage of microscopic, high resolution cellular-level imaging from in vivo tissues in real time. This comprehensive imaging approach to cancer detection and the possibility of performing an ‘optical biopsy’ without tissue removal has led to growing interest in the field with numerous techniques under investigation. Three optical techniques are discussed in this thesis, namely multispectral fluorescence imaging (MFI), hyperspectral reflectance imaging (HRI) and fluorescence confocal endomicroscopy (FCE). MFI and HRI are novel endoscopic imaging-based extensions of single point detection techniques, such as laser induced fluorescence spectroscopy and diffuse reflectance spectroscopy. This results in the acquisition of spectral data in an intuitive imaging format that allows for quantitative evaluation of tissue disease states. We demonstrate MFI and HRI on fluorophores, tissue phantoms and ex vivo tissues and present the results as an RGB colour image for more intuitive assessment. This follows dimensionality reduction of the acquired spectral data with a fixed-reference isomap diagnostic algorithm to extract only the most meaningful data parameters. FCE is a probe-based point imaging technique offering confocal detection in vivo with almost histology-grade images. We perform FCE imaging on chemotherapy-treated in vitro human ovarian cancer cells, ex vivo human cancer tissues and photosensitiser-treated in vivo murine tumours to show the enhanced detection capabilities of the technique. Finally, the three modalities are applied in combination to demonstrate an optical viewfinder approach as a possible minimally-invasive imaging method for early cancer detection and diagnosis

Ovarian cancer molecular pathology.

Peer reviewe

Biological function and clinical implication of coagulation proteins during malignant transformation of pancreatic cells

The premalignant pancreatic cellular genotype can remain stable for years before rapid malignant transformation, often associated with inflammation. Tissue factor (TF) is an inflammatory modulator regulated by factor VIIa (fVIIa) for its levels and activity. The presence of TF in PDAC and its role in cell proliferation, angiogenesis, and metastasis suggests that TF may be a marker of the inflammatory microenvironment driving precursor lesions of pancreatic cancer. This study examined the in vitro influence of TF on pancreatic epithelial cells and its clinical value in detecting malignant transformation within pancreatic cyst fluid (PCyF). PCyF from 27 patients with pancreatic cystic lesions was analysed in a blinded fashion. TF and fVIIa levels were measured (ELISA), and the fVIIa:TF ratios were calculated. A cut-off value for TF concentration was determined and compared to the conventional assessment parameters (radiological features, CEA and amylase). Patients were categorised into four groups based on cytopathology and two groups based on indication for resection (‘resective’). Significant histological stage-dependent increases in TF levels were observed. Mean TF concentration was significantly higher (p=0.006) in the resective (high-grade dysplasia & malignant; 1.17 ng/ml, 95% CI 0.68, 1.67) vs non-resective group (benign & low-grade dysplasia; 0.27 ng/ml, 95% CI 0.1, 0.44), with a strong positive correlation (r= 0.746, p <0.001, TF cut-off 0.75 ng/ml, AUC 0.877, p=0.002). The fVIIa:TF ratio did not add further value. Incubation of pancreatic cells with recombinant TF resulted in increased expression of a marker of epithelial to mesenchymal transition (Vimentin). This influence was moderated by supplementation with fVIIa in benign (hTERT-HPNE) but not overtly malignant pancreatic cells (AsPC-1). Cyst-associated TF levels appear to correlate with cytological progression to the malignant phenotype and may allow better discrimination (specificity 94%) of the ‘resective’ lesion, reduce healthcare costs and offer a more nuanced tool for monitoring indeterminate cystic lesions

Improving biomarker assessment in breast pathology

The accuracy of prognostic and therapy-predictive biomarker assessment in breast tumours is crucial for management and therapy decision in patients with breast cancer. In this thesis, biomarkers used in clinical practice with emphasise on Ki67 and HER2 were studied using several methods including immunocytochemistry, in situ hybridisation, gene expression assays and digital image analysis, with the overall aim to improve routine biomarker evaluation and clarify the prognostic potential in early breast cancer. In paper I, we reported discordances in biomarker status from aspiration cytology and paired surgical specimens from breast tumours. The limited prognostic potential of immunocytochemistry-based Ki67 scoring demonstrated that immunohistochemistry on resected specimens is the superior method for Ki67 evaluation. In addition, neither of the methods were sufficient to predict molecular subtype. Following this in paper II, biomarker agreement between core needle biopsies and subsequent specimens was investigated, both in the adjuvant and neoadjuvant setting. Discordances in Ki67 and HER2 status between core biopsies and paired specimens suggested that these biomarkers should be re-tested on all surgical breast cancer specimens. In paper III, digital image analysis using a virtual double staining software was used to compare methods for assessment of proliferative activity, including mitotic counts, Ki67 and the alternative marker PHH3, in different tumour regions (hot spot, invasive edge and whole section). Digital image analysis using virtual double staining of hot spot Ki67 outperformed the alternative markers of proliferation, especially in discriminating luminal B from luminal A tumours. Replacing mitosis in histological grade with hot spot-scored Ki67 added significant prognostic information. Following these findings, the optimal definition of a hot spot for Ki67 scoring using virtual double staining in relation to molecular subtype and outcome was investigated in paper IV. With the growing evidence of global scoring as a superior method to improve reproducibility of Ki67 scoring, a different digital image analysis software (QuPath) was also used for comparison. Altogether, we found that automated global scoring of Ki67 using QuPath had independent prognostic potential compared to even the best virtual double staining hot spot algorithm, and is also a practical method for routine Ki67 scoring in breast pathology. In paper V, the clinical value of HER2 status was investigated in a unique trastuzumab-treated HER2-positive cohort, on the protein, mRNA and DNA levels. The results demonstrated that low levels of ERBB2 mRNA but neither HER2 copy numbers, HER2 ratio nor ER status, was associated with risk of recurrence among anti-HER2 treated breast cancer patients. In conclusion, we have identified important clinical aspects of Ki67 and HER2 evaluation and provided methods to improve the prognostic potential of Ki67 using digital image analysis. In addition to protein expression of routine biomarkers, mRNA levels by targeted gene expression assays may add further prognostic value in early breast cance

Near-Infrared Confocal Raman Spectroscopy for Real-Time Diagnosis of Cervical Precancer

Ph.DDOCTOR OF PHILOSOPH

Machine Learning Assisted Digital Pathology

Histopatologiset kudosnäytteet sisältävät valtavan määrän tietoa biologisista mekanismeista, jotka vaikuttavat monien tautien ilmenemiseen ja etenemiseen. Tästä syystä histopatologisten näytteiden arviointi on ollut perustana monien tautien diagnostiikassa vuosikymmenien ajan. Perinteinen histopatologinen arviointi on kuitenkin työläs tehtävä ja lisäksi erittäin altis inhimillisille virheille ja voi siten johtaa virheelliseen tai viivästyneeseen diagnoosiin. Viime vuosien teknologinen kehitys on tuonut patologien käyttöön lasiskannerit ja tiedonhallintajarjestelmät ja sitä myötä mahdollistaneet näytelasien digitoinnin ja käynnistäneet koko patologian työnkulun digitalisaation. Histopatologisten näytteiden saatavuus digitaalisina kuvina on puolestaan mahdollistanut älykkäiden algoritmien ja automatisoitujen laskennallisten kuva-analyysityökalujen kehittämisen diagnostiikan tueksi. Koneoppiminen on tekoälyn osa-alue, joka voidaan määritellä datasta oppimiseksi. Kuva-analyysin sovelluksissa, kuvan pikseliarvot muutetaan kvantitatiiviseksi piirre-esitykseksi jonka pohjalta kuva voidaan muuntaa merkitykselliseksi tiedoksi hyvödyntämällä koneoppimista. Vuosien saatossa koneoppimiseen perustuvan kuva-analyysin menetelmät ovat kehittyneet manuaalisesta piirteidenirroituksesta kohti viimevuosien vallitsevia syväoppimiseen pohjautuvia konvoluutioneuroverkkoja. Koneoppimisen hyödyt histopatologisessa arvioinnissa ovat huomattavat, sillä koneoppiminen mahdollistaa kuvien tulkinnan patologiin verrattavalla tarkkuudella ja siten pystyy merkittävästi parantamaan kliinisen patologian diagnostiikan tarkkuutta, toistettavuutta ja tehokkuutta. Tämä väitöstyö esittelee koneoppimiseen pohjautuvia menetelmiä jotka on kehitetty avustamaan kudosnäytteen histopatologista arviointia, vaihetta joka on merkityksellinen niin kliinisessä diagnostiikassa kuin prekliinisissä tutkimuksissa. Työssä esitellään piirteenirroituksen ja koneoppimisen tehokkuus histopatologiseen arviointiin liittyvissä kuva-analyysitehtävissä kuten kudoksen karakterisoinnissa, sekä rintasyövän etäpesäkkeiden, epiteelikudoksen ja tumien tunnistuksessa. Menetelmien lisäksi tässä väitöstyössä on käsitelty keskeisiä haasteita jotka on huomioitava integroitaessa koneoppimismenetelmiä kliiniseen käyttöön. Ennen kaikkea nämä tutkimukset ovat kuitenkin osoittaneet koneoppimisen mahdollisuudet tulevaisuudessa parantaa patologian kliinisten rutiinitehtävien tehokkuutta ja toistettavuutta sekä diagnostiikan laatua.Histopathological tissue samples contain a vast amount of information on underlying biological mechanisms that contribute to disease manifestation and progression. Therefore, diagnosis from histopathological tissue samples has been the gold standard for decades. However, traditional histopathological assessment is a laborious task and prone to human errors, thereby leading to misdiagnosis or delayed diagnosis. The development of whole slide scanners for digitization of tissue glass slides has initiated the transition to a fully digital pathology workflow that allows scanning, interpretation, and management of digital tissue slides. These advances have been the cornerstone for developing intelligent algorithms and automated computational approaches for histopathological assessment and clinical diagnostics. Machine learning is a subcategory of artificial intelligence and can be defined as a process of learning from data. In image analysis tasks, the raw pixel values are transformed into quantitative feature representations. Based on the image data representation, a machine learning model learns a set of rules that can be used to extract meaningful information and knowledge. Over the years, the field of machine learning based image analysis has developed from manually handcrafting complex features to the recent revolution of deep learning and convolutional neural networks. Histopathological assessment can benefit greatly from the ability of machine learning models to discover patterns and connections from the data. Therefore, machine learning holds great promise to improve the accuracy, reproducibility, and efficiency of clinical diagnostics in the field of digital pathology. This thesis is focused on developing machine learning based methods for assisting in the process of histopathological assessment, which is a significant step in clinical diagnostics as well as in preclinical studies. The studies presented in this thesis show the effectiveness of feature engineering and machine learning in histopathological assessment related tasks, such as; tissue characterisation, metastasis detection, epithelial tissue detection, and nuclei detection. Moreover, the studies presented in this thesis address the key challenges related to variation presented in histopathological data as well as the generalisation problem that need to be considered in order to integrate machine learning approaches into clinical practice. Overall, these studies have demonstrated the potential of machine learning for bringing standardization and reproducibility to the process of histopathological assessment

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Cell biomechanics and metastatic spreading: a study on human breast cancer cells

2010/2011Despite the intensive research of the past decades in oncology, cancer invasion and metastasis still represent the most important problem for treatment and the most common cause of death in cancer patients. Metastasis refers to the spread of malignant cells from a primary tumour to distant sites of the body and the adaptation of these cancer cells to a new and different tissue microenvironment. Usually, millions of cells can be released by a tumour into the circulation every day, but only a tiny minority of these cells are able to reach and colonize a distant organs: the utter inefficiency of the metastatic process implies that cells might strongly need biomechanical alterations that allow them to invade and colonize different tissues. The hypothesis that cellular biomechanics may play a significant role in tumour genesis and cancer invasion, gains every day more and more support: therefore characterizing these properties in connection with the membrane and cytoskeleton organization could be very important for understanding better the migration mechanisms and to develop new diagnostics and therapeutics tools. The goal of our study was the mechanical characterization of cell lines chosen as model of cancer progression using different biophysical techniques and the correlation of the mechanical properties with possible alterations of the cytoskeleton structure and plasma membrane composition. We used a custom built Optical Tweezers to extract the local viscoelastic properties of the cell plasma membrane, an Atomic Force Microscopy (AFM) to locally measure cell elasticity of cells, and a Microfluidic Optical Stretcher to measure the deformability of cells as whole bodies. We investigated then the actin organization of the cytoskeleton by STimulated Depletion and Emission (STED) and confocal microscopy. The lipid composition of cells was analysed by MALDI-mass spectroscopy in order to correlate the mechanical alterations of cells with alteration at the cytoskeleton and plasma membrane level. The cell lines analyzed derive from breast tissue and represent a model of human epithelial cells towards malignancy. In particular, two cell lines -MDA-MB-231 and MCF-7- provided by American Type Culture Collection (ATCC) were originally derived from breast cancers patients with different level of cancer aggressiveness. Cells were chosen according to the nowadays accepted classification of breast cancer based on gene expression pattern and proteomic expression, which divide breast cancers in subtypes that differ in terms of risk factor, distribution, prognosis, therapeutic treatment responsiveness, clinical outcomes and survival. The third cell line, HBL-100, is an immortalized but non-neoplastic cell line derived from the milk of a nursing mother with no evidence of breast lesions, representing a earlier stage of the cell transformation. A pulling membrane tether approach by means of Optical Tweezers has been chosen since it allows an accurate quantitative characterization of local viscoelastic properties of plasma membranes. Bovine Serum Albumine (BSA) coated silica beads were used to bind the plasma membrane and grab membrane tethers of several microns measuring the force exerted on the bead. By fitting with the Kelvin body model our force-elongation curves obtained by experimental data we extracted the parameters of interest: tether stiffness, membrane bending rigidity, and tether viscosity. We observed that lower values of tether stiffness and membrane bending rigidity corresponded to cells associated to a higher aggressive behaviour, while viscosity showed an inverse tendency. We also probed elasticity of the cells using by indentation experiments with AFM. We used a bead probe attached to the cantilever and measured the Young Modulus. The results obtained could not clearly discriminate the three cell types in terms of elasticity. Cell deformability was further investigated by means of Microfluidic Optical Stretcher. Cells in suspension were trapped by two counter propagating laser beams of low intensity from two optical fibers. Adjusting the intensity of the laser light, the forces acting on the cell surface increased, leading to a measurable elongation of the cell body along the laser beam axis. With MOS we were able to discriminate between cancer and control cells lines, while differences between the two cancer cell lines were not significant. However a trend could be observed: lower aggressive tumour cells were more resistant to deformation compared to the higher aggressive tumour cells. We investigated the cells cytoskeleton structure by STED and confocal microscopy confirming that malignancy involves cytoskeleton structure alterations. Differences in the organization. of the actin filaments and in the presence of actin drifts were observed. We peformed also a preliminary analysis of the cell lipid composition by MALDI MASS spectroscopy. We could observe that highly aggressive cells with softer membranes presented alterations at the level of Phosphatidylethanolamines (PEs) and Phosphatidylinositoles (PIs). The work of this thesis is partially published in the article “Custom Built Optical Tweezers for locally probing the viscoelastic properties of cancer cells” in the International Journal of Optomechatronics (June 2011). A second article including the comparative results of the biomechanical analysis on the breast cell lines is in preparation.XXIV Cicl