Discovery of novel prognostic tools to stratify high risk stage II colorectal cancer patients utilising digital pathology

Colorectal cancer (CRC) patients are stratified by the Tumour, Node and Metastasis (TNM) staging system for clinical decision making. Additional genomic markers have a limited utility in some cases where precise targeted therapy may be available. Thus, classical clinical pathological staging remains the mainstay of the assessment of this disease. Surgical resection is generally considered curative for Stage II patients, however 20-30% of these patients experience disease recurrence and disease specific death. It is imperative to identify these high risk patients in order to assess if further treatment or detailed follow up could be beneficial to their overall survival. The aim of the thesis was to categorise Stage II CRC patients into high and low risk of disease specific death through novel image based analysis algorithms. Firstly, an image analysis algorithm was developed to quantify and assess the prognostic value of three histopathological features through immuno-fluorescence: lymphatic vessel density (LVD), lymphatic vessel invasion (LVI) and tumour budding (TB). Image analysis provides the ability to standardise their quantification and negates observer variability. All three histopathological features were found to be predictors of CRC specific death within the training set (n=50); TB (HR =5.7; 95% CI, 2.38-13.8), LVD (HR =5.1; 95% CI, 2.04-12.99) and LVI (HR =9.9; 95% CI, 3.57- 27.98). Only TB (HR=2.49; 95% CI, 1.03-5.99) and LVI (HR =2.46; 95%CI, 1 - 6.05), however, were significant predictors of disease specific death in the validation set (n=134). Image analysis was further employed to characterise TB and quantify intra-tumoural heterogeneity. Tumour subpopulations within CRC tissue sections were segmented for the quantification of differential biomarker expression associated with epithelial mesenchymal transition and aggressive disease. Secondly, a novel histopathological feature ‘Sum Area Large Tumour Bud’ (ALTB) was identified through immunofluorescence coupled to a novel tissue phenomics approach. The tissue phenomics approach created a complex phenotypic fingerprint consisting of multiple parameters extracted from the unbiased segmentation of all objects within a digitised image. Data mining was employed to identify the significant parameters within the phenotypic fingerprint. ALTB was found to be a more significant predictor of disease specific death than LVI or TB in both the training set (HR = 20.2; 95% CI, 4.6 – 87.9) and the validation set (HR = 4; 95% CI, 1.5 – 11.1). Finally, ALTB was combined with two parameters, ‘differentiation’ and ‘pT stage’, which were exported from the original patient pathology report to form an integrative pathology score. The integrative pathology score was highly significant at predicting disease specific death within the validation set (HR = 7.5; 95% CI, 3 – 18.5). In conclusion, image analysis allows the standardised quantification of set histopathological features and the heterogeneous expression of biomarkers. A novel image based histopathological feature combined with classical pathology allows the highly significant stratification of Stage II CRC patients into high and low risk of disease specific death

Generative deep learning in digital pathology workflows

Funding: Supported by the Sir James Mackenzie Institute for Early Diagnosis, University of St Andrews and Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (grant number TS/S013121/1).Many modern histopathology laboratories are in the process of digitising their workflows. Once images of the tissue exist as digital data, it becomes feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on Deep Learning, promise systems that can identify pathologies in slide images with a high degree of accuracy. Generative modelling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology including the removal of color and intensity artefacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses some future directions for generative models within histopathology.PostprintPeer reviewe

The differential expression of micro-RNAs 21, 200c, 204, 205, and 211 in benign, dysplastic and malignant melanocytic lesions and critical evaluation of their role as diagnostic biomarkers

Overlapping histological features between benign and malignant lesions and a lack of firm diagnostic criteria for malignancy result in high rates of inter-observer variation in the diagnosis of melanocytic lesions. We aimed to investigate the differential expression of five miRNAs (21, 200c, 204, 205, and 211) in benign naevi (n = 42), dysplastic naevi (n = 41), melanoma in situ (n = 42), and melanoma (n = 42) and evaluate their potential as diagnostic biomarkers of melanocytic lesions. Real-time PCR showed differential miRNA expression profiles between benign naevi; dysplastic naevi and melanoma in situ; and invasive melanoma. We applied a random forest machine learning algorithm to classify cases based on their miRNA expression profiles, which resulted in a ROC curve analysis of 0.99 for malignant melanoma and greater than 0.9 for all other groups. This indicates an overall very high accuracy of our panel of miRNAs as a diagnostic biomarker of benign, dysplastic, and malignant melanocytic lesions. However, the impact of variable lesion percentage and spatial expression patterns of miRNAs on these real-time PCR results was also considered. In situ hybridisation confirmed the expression of miRNA 21 and 211 in melanocytes, while demonstrating expression of miRNA 205 only in keratinocytes, thus calling into question its value as a biomarker of melanocytic lesions. In conclusion, we have validated some miRNAs, including miRNA 21 and 211, as potential diagnostic biomarkers of benign, dysplastic, and malignant melanocytic lesions. However, we also highlight the crucial importance of considering tissue morphology and spatial expression patterns when using molecular techniques for the discovery and validation of new biomarkers.Publisher PDFPeer reviewe

Reproducibility of deep learning in digital pathology whole slide image analysis

Funding: This work is supported by the Industrial Centre for AI Research in digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690], and in part by Chief Scientist Office, Scotland.For a method to be widely adopted in medical research or clinical practice, it needs to be reproducible so that clinicians and regulators can have confidence in its use. Machine learning and deep learning have a particular set of challenges around reproducibility. Small differences in the settings or the data used for training a model can lead to large differences in the outcomes of experiments. In this work, three top-performing algorithms from the Camelyon grand challenges are reproduced using only information presented in the associated papers and the results are then compared to those reported. Seemingly minor details were found to be critical to performance and yet their importance is difficult to appreciate until the actual reproduction is attempted. We observed that authors generally describe the key technical aspects of their models well but fail to maintain the same reporting standards when it comes to data preprocessing which is essential to reproducibility. As an important contribution of the present study and its findings, we introduce a reproducibility checklist that tabulates information that needs to be reported in histopathology ML-based work in order to make it reproducible.Publisher PDFPeer reviewe

Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis

Diagnosis and prognosis of cancer is informed by the architecture inherent in cancer patient tissue sections. This architecture is typically identified by pathologists, yet advances in computational image analysis facilitate quantitative assessment of this structure. In this article we develop a spatial point process approach in order to describe patterns in cell distribution within tissue samples taken from colorectal cancer (CRC) patients. In particular, our approach is centered on the Palm intensity function. This leads to taking an approximate-likelihood technique in fitting point processes models. We consider two Neyman-Scott point processes and a void process, fitting these point process models to the CRC patient data. We find that the parameter estimates of these models may be used to quantify the spatial arrangement of cells. Importantly, we observe characteristic differences in the spatial arrangement of cells between patients who died from CRC and those alive at follow-up

A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis

Accurate prognosis is fundamental in planning an appropriate therapy for cancer patients. Consequent to the heterogeneity of the disease, intra- and inter-pathologist variability, and the inherent limitations of current pathological reporting systems, patient outcome varies considerably within similarly staged patient cohorts. This is particularly true when classifying stage IIcolorectal cancer patients using the current TNM guidelines. The aim of the present work is to address this problem through the use of machine learning. In particular, we introduce a novel, data driven framework which makes use of a large number of diverse types of features, readily collected from immunofluorescence imagery. Its outstanding performance in predictingmortality in stage II patients (AUROC= 0:94), exceeds that of current clinical guidelines such as pT stage (AUROC= 0:65), and is demonstrated on a cohort of 173 colorectal cancer patients.Publisher PDFPeer reviewe

Automated analysis of lymphocytic infiltration, tumor budding, and their spatial relationship improves prognostic accuracy in colorectal cancer

Funding: Medical Research Scotland, and Indica Labs, Inc. provided in-kind resource.Both immune profiling and tumor budding significantly correlate with colorectal cancer (CRC) patient outcome, but are traditionally reported independently. This study evaluated the association and interaction between lymphocytic infiltration and tumor budding, coregistered on a single slide, in order to determine a more precise prognostic algorithm for patients with stage II CRC. Multiplexed immunofluorescence and automated image analysis were used for the quantification of CD3+CD8+ T cells, and tumor buds (TBs), across whole slide images of three independent cohorts (training cohort: n = 114, validation cohort 1: n = 56, validation cohort 2: n = 62). Machine learning algorithms were used for feature selection and prognostic risk model development. High numbers of TBs (HR = 5.899, 95% CI, 1.875 - 18.55), low CD3+ 11 T cell density (HR = 9.964, 95% CI 3.156 - 31.46), and low mean number of CD3+CD8+ T cells within 50 μm of TBs (HR = 8.907, 95% CI 2.834 - 28.0) were associated with reduced disease-specific survival. A prognostic signature, derived from integrating TBs, lymphocyte infiltration, and their spatial relationship, reported a more significant cohort stratification (HR = 18.75, 95% CI 6.46–54.43), than TBs, Immunoscore, or pT stage. This was confirmed in two independent validation cohorts (HR = 12.27, 95% CI 3.524–42.73, HR = 15.61, 95% CI 4.692-51.91). The investigation of the spatial relationship between lymphocytes and TBs within the tumor microenvironment improves accuracy of prognosis of patients with stage II CRC through an automated image analysis and machine learning workflow.PostprintPeer reviewe

YAP translocation precedes cytoskeletal rearrangement in podocyte stress response : a podometric investigation of diabetic nephropathy

KH was funded by a University of St Andrews 600th Anniversary Ph.D. scholarship. ME and DH were supported by NHS Lothian.Podocyte loss plays a pivotal role in the pathogenesis of glomerular disease. However, the mechanisms underlying podocyte damage and loss remain poorly understood. Although detachment of viable cells has been documented in experimental Diabetic Nephropathy, correlations between reduced podocyte density and disease severity have not yet been established. YAP, a mechanosensing protein, has recently been shown to correlate with glomerular disease progression, however, the underlying mechanism has yet to be fully elucidated. In this study, we sought to document podocyte density in Diabetic Nephropathy using an amended podometric methodology, and to investigate the interplay between YAP and cytoskeletal integrity during podocyte injury. Podocyte density was quantified using TLE4 and GLEPP1 multiplexed immunofluorescence. Fourteen Diabetic Nephropathy cases were analyzed for both podocyte density and cytoplasmic translocation of YAP via automated image analysis. We demonstrate a significant decrease in podocyte density in Grade III/IV cases (124.5 per 106 μm3) relative to Grade I/II cases (226 per 106 μm3) (Student’s t-test, p<0.001), and further show that YAP translocation precedes cytoskeletal rearrangement following injury. Based on these findings we hypothesize that a significant decrease in podocyte density in late grade Diabetic Nephropathy may be explained by early cytoplasmic translocation of YAP.Publisher PDFPeer reviewe

Reception of the Herzog Stjepan Vukcic Kosaca in Herzegovina in the Second Half of the 20th Century

U radu se nastoji prikazati recepcija hercega Stjepana Vukčića Kosače u Hercegovini u drugoj polovici 20. stoljeća s posebnim naglaskom na razdoblje u kojemu je Bosna i Hercegovina bila jedna od republika bivše Jugoslavije. S obzirom na činjenicu da bi se barem početno znanje o hercegu Stjepanu trebalo steći tijekom osnovnoga i srednjoškolskoga obrazovanja, najprije je dat osvrt na nastavne planove i programe u kojima su, između ostaloga, naznačeni ciljevi (zadatci) nastave povijesti, a zatim se analiziraju tekstovi u udžbenicima povijesti koji su bili u uporabi u bh. školama (osnovnim i gimnazijama), u kojima se u određenim nastavnim jedinicama obrađuje djelovanje hercega Stjepana. Sudeći prema nastavnim planovima i programima, kao i sadržaju udžbenika iz druge polovice 20. stoljeća (barem nama dostupnima), mladež u Hercegovini u razmatranom razdoblju nije mogla mnogo saznati i naučiti o ovoj povijesnoj osobi, s čijom je titulom povezano ime prostora u kojemu žive. Na kraju istražuje se hercegova prisutnost u nazivima ulica, trgova i institucija u hercegovačkim gradovima i općinama. Istraživanje pokazuje da je tek u novije vrijeme evidentan interes nekih pojedinaca i institucija koje žele ovoj povijesnoj osobi dati značenje koje joj pripada. Naime, sve ulice u Hercegovini koje nose njegovo ime, kao i jedna institucija, imenovane su nakon 1990. godine.With the aim of presenting the reception of the Herzog Stjepan Vukcic Kosaca in Hercegovina in the second half of the 20th century, the paper analyzes the contents of history textbooks that write about the life and work of the Herzog Stjepan and his presence in the names of streets, squares and institutions in Herzegovinian towns and municipalities. Considering the second half of the 20th century, most attention is paid to the period in which Bosnia and Herzegovina was one of the republics of the former Yugoslavia. To illustrate the contents from medieval history, or the ones related to Stjepan, most important are school curricula, in which, apart from the teaching units and lessons, the teaching of history was particularly indicative, when during this period the constant task was "spreading fraternity and unity" or "developing patriotic awareness among students", the emphasis being on teaching history of the "newer period". Judging by the curricula and the content of textbooks from the second half of the 20th century (at least those available to us), the youth of Herzegovina in the mentioned period could not learn much about this historical person whose name is associated with the name of the area in which they live. Namely, by looking at the texts of the textbooks up to the 90s, it is evident that the texts in the textbooks differ, both in scope and emphasis on certain facts. What all the authors mention is taking of the \u27Herzog\u27 title in 1448 and the fall of Herzegovina under the Ottoman rule, whereas all the other facts about the Herzog Stjepan vary from one text to another. As there were no official textbooks in the region of Herzegovina in the 90s of the 20th century, the paper, as an example, presents the texts only from two textbooks (one for elementary school and the other for grammar school) used in some schools in Herzegovina which worked according to the Croatian curriculum, where we can find hardly any information about the Herzog Stjepan. Finally, the results of the research of the Herzog\u27s presence in the names of streets, squares and institutions of the Herzegovinian cities and municipalities show that the interest of some individuals and institutions, who want to give this historical person the significance he deserves, has only recently become evident. Namely, all the streets in Herzegovina named after the Herzog, as well as one institution, got this name after 1990