393 research outputs found

    An end-to-end deep learning histochemical scoring system for breast cancer TMA

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    One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists

    Improving biomarker assessment in breast pathology

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    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

    Automated segmentation of tissue images for computerized IHC analysis

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    This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

    QuantISH : RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability

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    RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.Peer reviewe

    Noise reduction and mammography image segmentation optimization with novel QIMFT-SSA method

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    Breast cancer is one of the most dreaded diseases that affects women worldwide and has led to many deaths. Early detection of breast masses prolongs life expectancy in women and hence the development of an automated system for breast masses supports radiologists for accurate diagnosis. In fact, providing an optimal approach with the highest speed and more accuracy is an approach provided by computer-aided design techniques to determine the exact area of breast tumors to use a decision support management system as an assistant to physicians. This study proposes an optimal approach to noise reduction in mammographic images and to identify salt and pepper, Gaussian, Poisson and impact noises to determine the exact mass detection operation after these noise reduction. It therefore offers a method for noise reduction operations called Quantum Inverse MFT Filtering and a method for precision mass segmentation called the Optimal Social Spider Algorithm (SSA) in mammographic images. The hybrid approach called QIMFT-SSA is evaluated in terms of criteria compared to previous methods such as peak Signal-to-Noise Ratio (PSNR) and Mean-Squared Error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison to state-of-arts methods. supported the work

    QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability

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    RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology

    Artificial intelligence for breast cancer precision pathology

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    Breast cancer is the most common cancer type in women globally but is associated with a continuous decline in mortality rates. The improved prognosis can be partially attributed to effective treatments developed for subgroups of patients. However, nowadays, it remains challenging to optimise treatment plans for each individual. To improve disease outcome and to decrease the burden associated with unnecessary treatment and adverse drug effects, the current thesis aimed to develop artificial intelligence based tools to improve individualised medicine for breast cancer patients. In study I, we developed a deep learning based model (DeepGrade) to stratify patients that were associated with intermediate risks. The model was optimised with haematoxylin and eosin (HE) stained whole slide images (WSIs) with grade 1 and 3 tumours and applied to stratify grade 2 tumours into grade 1-like (DG2-low) and grade 3-like (DG2-high) subgroups. The efficacy of the DeepGrade model was validated using recurrence free survival where the dichotomised groups exhibited an adjusted hazard ratio (HR) of 2.94 (95% confidence interval [CI] 1.24-6.97, P = 0.015). The observation was further confirmed in the external test cohort with an adjusted HR of 1.91 (95% CI: 1.11-3.29, P = 0.019). In study II, we investigated whether deep learning models were capable of predicting gene expression levels using the morphological patterns from tumours. We optimised convolutional neural networks (CNNs) to predict mRNA expression for 17,695 genes using HE stained WSIs from the training set. An initial evaluation on the validation set showed that a significant correlation between the RNA-seq measurements and model predictions was observed for 52.75% of the genes. The models were further tested in the internal and external test sets. Besides, we compared the model's efficacy in predicting RNA-seq based proliferation scores. Lastly, the ability of capturing spatial gene expression variations for the optimised CNNs was evaluated and confirmed using spatial transcriptomics profiling. In study III, we investigated the relationship between intra-tumour gene expression heterogeneity and patient survival outcomes. Deep learning models optimised from study II were applied to generate spatial gene expression predictions for the PAM50 gene panel. A set of 11 texture based features and one slide average gene expression feature per gene were extracted as input to train a Cox proportional hazards regression model with elastic net regularisation to predict patient risk of recurrence. Through nested cross-validation, the model dichotomised the training cohort into low and high risk groups with an adjusted HR of 2.1 (95% CI: 1.30-3.30, P = 0.002). The model was further validated on two external cohorts. In study IV, we investigated the agreement between the Stratipath Breast, which is the modified, commercialised DeepGrade model developed in study I, and the Prosigna® test. Both tests sought to stratify patients with distinct prognosis. The outputs from Stratipath Breast comprise a risk score and a two-level risk stratification whereas the outputs from Prosigna® include the risk of recurrence score and a three-tier risk stratification. By comparing the number of patients assigned to ‘low’ or ‘high’ risk groups, we found an overall moderate agreement (76.09%) between the two tests. Besides, the risk scores by two tests also revealed a good correlation (Spearman's rho = 0.59, P = 1.16E-08). In addition, a good correlation was observed between the risk score from each test and the Ki67 index. The comparison was also carried out in the subgroup of patients with grade 2 tumours where similar but slightly dropped correlations were found

    On the histopathological growth patterns of colorectal liver metastasis:a Study of Histology, Immunology, Genetics, and Prognosis

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    This thesis aims to validate and establish the histopathological growth patterns of colorectal cancer liver metastasis as a relevant biomarker, and to evaluate immunity and genetics as potential underlying biological mechanisms
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