Identificación automática de marcadores patológicos en imágenes de histopatología

Abstract. The inter and intra subject variability is a common problem in several tasks associated to the examination of histopathological samples. This variability might hinder the evaluation of cancerous diseases. The development of automatic image analysis techniques and computerized aided diagnostic tools in pathology aims to reduce the impact of such variability by offering quantitative measurements and estimations. These measurements allow an accurate evaluation and classification of the diseases in virtual slide images. The main problem addressed in this thesis is evaluating the correlation of the automated identification of pathological markers with cancer malignancy and aggresivenes. Hence, a set of classifier models are trained to detect known pathological patterns. The classifiers are then used to quantify the presence of the pathological markers. Finally, the resulting measurements are correlated with the cancer risk recurrence. Results show that the automated detectors are able to quantify patterns that show differences across several cancer risk groups.La variabilidad inter e intra sujeto es un problema frecuente en muchas tareas asociadas al ex´amen de muestras histopatológicas. Esta variabilidad puede incidir negativamente en la evaluación de patologías relacionadas con el cáncer. El desarrollo de técnicas para el análisis automático de imágenes y de herramientas de soporte al diagnóstico en patología tiene como objetivo reducir el impacto de la variabilidad inter/intra sujeto mediante la obtención de medidas y estimaciones cuantitativas. Estas medidas permiten una evaluación y clasificación más precisa de las enfermedades observables en l´aminas virtuales. El principal problema abordado en esta tesis consiste en evaluar la correlación de la identificación automática de marcadores patológicos con la agresividad del cáncer. As´ı, un conjunto de clasificadores son entrenados para detectar marcadores patológicos conocidos. Los clasificadores son posteriormente usados para cuantificar la presencia de los marcadores patológicos. Finalmente, las mediciones resultantes son correlacionadas con el riesgo de recurrencia del cáncer. Los resultados muestran que los detectores automáticos son capaces de cuantificar los patrones que muestran diferencias entre diferentes grupos de riesgo.Doctorad

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer

Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy

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

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Artificial intelligence in histopathology image analysis for cancer precision medicine

In recent years, there have been rapid advancements in the field of computational pathology. This has been enabled through the adoption of digital pathology workflows that generate digital images of histopathological slides, the publication of large data sets of these images and improvements in computing infrastructure. Objectives in computational pathology can be subdivided into two categories, first the automation of routine workflows that would otherwise be performed by pathologists and second the addition of novel capabilities. This thesis focuses on the development, application, and evaluation of methods in this second category, specifically the prediction of gene expression from pathology images and the registration of pathology images among each other. In Study I, we developed a computationally efficient cluster-based technique to perform transcriptome-wide predictions of gene expression in prostate cancer from H&E-stained whole-slide-images (WSIs). The suggested method outperforms several baseline methods and is non-inferior to single-gene CNN predictions, while reducing the computational cost with a factor of approximately 300. We included 15,586 transcripts that encode proteins in the analysis and predicted their expression with different modelling approaches from the WSIs. In a cross-validation, 6,618 of these predictions were significantly associated with the RNA-seq expression estimates with FDR-adjusted p-values <0.001. Upon validation of these 6,618 expression predictions in a held-out test set, the association could be confirmed for 5,419 (81.9%). Furthermore, we demonstrated that it is feasible to predict the prognostic cell-cycle progression score with a Spearman correlation to the RNA-seq score of 0.527 [0.357, 0.665]. The objective of Study II is the investigation of attention layers in the context of multiple-instance-learning for regression tasks, exemplified by a simulation study and gene expression prediction. We find that for gene expression prediction, the compared methods are not distinguishable regarding their performance, which indicates that attention mechanisms may not be superior to weakly supervised learning in this context. Study III describes the results of the ACROBAT 2022 WSI registration challenge, which we organised in conjunction with the MICCAI 2022 conference. Participating teams were ranked on the median 90th percentile of distances between registered and annotated target landmarks. Median 90th percentiles for eight teams that were eligible for ranking in the test set consisting of 303 WSI pairs ranged from 60.1 µm to 15,938.0 µm. The best performing method therefore has a score slightly below the median 90th percentile of distances between first and second annotator of 67.0 µm. Study IV describes the data set that we published to facilitate the ACROBAT challenge. The data set is available publicly through the Swedish National Data Service SND and consists of 4,212 WSIs from 1,153 breast cancer patients. Study V is an example of the application of WSI registration for computational pathology. In this study, we investigate the possibility to register invasive cancer annotations from H&E to KI67 WSIs and then subsequently train cancer detection models. To this end, we compare the performance of models optimised with registered annotations to the performance of models that were optimised with annotations generated for the KI67 WSIs. The data set consists of 272 female breast cancer cases, including an internal test set of 54 cases. We find that in this test set, the performance of both models is not distinguishable regarding performance, while there are small differences in model calibration

Identification and characterization of new biomarkers in aggressive subtypes of breast cancer

En 2015, la récidive tumorale et les métastases du cancer du sein demeurent une cause importante de décès à travers le monde. Toutefois, ces cancers sont souvent hétérogènes car en dépit d’un phénotype similaire, l’évolution clinique et la réponse au traitement peuvent varier considérablement. Il y a donc un intérêt évident à identifier et à caractériser de nouveaux biomarqueurs pour permettre classer les tumeurs mammaires dans des sous-groupes plus homogènes. Notre hypothèse est que chaque cancer mammaire possède des caractéristiques distinctes au plan des altérations du génome et des profils d’expression géniques et que ces changements se traduisent cliniquement par une prédisposition à former des métastases ou à répondre ou non à la chimiothérapie et aux thérapies ciblées. Dans le cadre de nos travaux, nous nous sommes intéressés aux sous-types agressifs de tumeurs mammaires et notamment les cancers de type triple négatif. Nous avons aussi tenté d’identifier des marqueurs capables de distinguer l’une de l’autre les tumeurs de type luminal A et luminal B. Pour ce faire, nous avons d’abord utilisé une stratégie in silico à partir de données publiques (micro-puces d’ADN et séquençage de l’ARN). Nous avons ensuite construit sept micro-matrices tissulaires (TMA) provenant de tissus mammaires normaux et tumoraux fixés à la formaline et enrobés en paraffine. Ces outils nous ont permis d’évaluer par immunohistochimie les niveaux d’expression différentielle des marqueurs suivants : ANXA1, MMP-9, DP103 et MCM2. Ceux-ci ont été comparés aux marqueurs usuels du cancer du sein (ER, PR, HER2, CK5/6 et FOXA1) et corrélés aux données cliniques (survie globale et métastase). Nos résultats indiquent que ces nouveaux marqueurs jouent un rôle important dans l’évolution clinique défavorable des tumeurs de haut grade. Dans un premier article nous avons montré que l’expression d’ANXA1 est dérégulée dans les cancers de type triple-négatif et aussi, dans une certaine mesure, dans les tumeurs HER2+. Nous croyons qu’ANXA1 permet de mieux comprendre le processus d’hétérogénéité tumorale et facilite l’identification des tumeurs de haut grade. Nous proposons également qu’ d’ANXA1 stimule la transition épithélio-mésenchymateuse (EMT) et la formation des métastases. Dans un second temps, nous avons montré que les niveaux d’expression de MMP-9 reflètent la différenciation cellulaire et corrèlent avec les sous-types de cancers mammaires ayant un mauvais pronostic. Nous estimons que MMP-9 permet de mieux comprendre et d’identifier les tumeurs mammaires à haut risque. De fait, la surexpression de MMP-9 est associée à une augmentation des métastases, une récidive précoce et une diminution de la survie globale. Dans le cadre d’un troisième article, nous avons montré que la surexpression du marqueur de prolifération MCM2 s’observe dans les cancers triple-négatifs, HER2+ et Luminal B par comparaison aux cancers luminal A (p< 0.0001). Nos résultats suggèrent qu’en utilisant un seuil de 40% de noyaux marqués, nous pourrions distinguer l’une de l’autre les tumeurs de type luminal A et luminal B. Cela dit, avant de pouvoir envisager l’utilisation de ce marqueur en clinique, une étude de validation sur une nouvelle cohorte de patientes s’impose. En somme, les résultats de nos travaux suggèrent qu’ANXA1, MMP-9 et MCM2 sont des marqueurs intéressants pour mieux comprendre les mécanismes physiopathologiques impliqués dans la progression tumorale et le développement des métastases. À terme, ces nouveaux marqueurs pourraient être utilisés seuls ou en combinaison avec d’autres gènes candidats pour permettre le développement de trousses « multigènes » ou d’essais protéomiques multiplex pour prédire l’évolution clinique des cancers mammaires.In 2015, breast cancer remains a leading cause of death among women worldwide due to relapse and metastases. However, mammary tumors are known to be heterogeneous in terms of their clinical course and response to treatment, despite a seemingly similar phenotype. There is therefore an obvious need to identify and characterize new biomarkers of progression in breast cancers so that each tumor can be properly classified. Our hypothesis is that each breast cancer has its own set of genomic abnormalities or altered pattern of gene expression that can explain the aggressiveness of each tumor, its ability to metastasize and its response to chemotherapeutic agents or other forms of targeted therapies. In this study, our aim is to identify and characterize new biomarkers with prognostic value in aggressive subsets of breast cancer focusing primarily on triple-negative tumors and luminal B breast cancer. To achieve those aims, we conducted an in silico search from public databases of DNA microchip and RNA sequencing data. We next constructed seven tissue microarrays (TMA) using paraffin blocks from human breast cancer along with normal breast to examine the differential expression of new putative markers: ANXA1, MMP-9, DP103 and MCM2. Expression levels measured by immunohistochemistry were then compared to other conventional markers of breast cancer (ER, PR, HER2, Ki-67, CK 5/6, FOXA1) and correlated with clinical data (overall survival and metastasis). By comparing the relative expression of these markers in human breast tumors we were able to pinpoint the important role of ANXA1, MMP-9, DP103, and MCM2 in aggressive tumor subtypes recognized for their poor clinical course. Firstly, we have shown that ANXA1 expression is severely deregulated in high-grade breast cancers including triple-negative and, to some extent, HER2-positive breast cancers. In addition, our results also indicated a possible role of ANXA1 in regulating EMT and breast cancer cell metastasis. Secondly, expression of MMP-9 was found to mirror the degree of tumor differentiation and to correlate with breast cancers of unfavorable outcome. This implies that MMP-9 can help better characterize the biology of breast carcinoma and to identify subgroups of high-risk breast tumors. In fact, we found that high levels of MMP-9 in tumors were associated with increased metastatic dissemination, early relapse and reduced survival. Thirdly, we demonstrated that MCM2 is overexpressed in triple-negative, HER2 positive and luminal B breast cancer in comparison to luminal A breast cancer (p-value < 0.0001). Our findings support the notion that MCM2 can be used to distinguish luminal A from luminal B breast cancer based on a 40% index cut-point. However, an independent validation cohort is needed to confirm the clinical utility of MCM2. Lastly, our results suggest that ANXA1, MMP-9 and MCM2 are valuable genes/proteins candidate that can help better understand the mechanisms involved in tumor progression and metastasis. One may also envisage their use, alone or in combination with other genes, in the development of a multi-gene panel or multiplex proteomic assay to predict clinical outcome and guide therapeutic decisions

Automated Cancer Prognostic Modeling With Pathological Imaging Features

Objectives. To assess the reproducibility and overlapping of information in the automated pathological imaging features pipeline for lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and skin cutaneous melanoma (SKCM); and to construct an integrated prognostic model using these pathological imaging features for LUAD, LUSC and SKCM. Methods. 470 LUAD patients, 477 LUSC patients and 428 SKCM patients are included in the study. Whole-slide histopathology images as well as demographical and clinical data are obtained from the TCGA website. Histopathological images are processed using the Openslide Python library and chopped into twenty small 500*500- or 700*700-pixel subimages. CellProfiler software was used to extract imaging features and provide its statistical values. RV coefficients are computed to assess robustness and reproducibility of the pipeline. Cox Proportional Hazards Model with Principal Component Analysis, and Cox Proportional Hazards Model with elastic net regularization are used to construct the survival model. Concordance indexes are calculated to measure the predictivity of the models using random splitting-based evaluation.Results. The high RV coefficients indicate the robustness and reliability of the imaging feature pipeline in the context of extracting feature statistical values. By using the random splitting- based evaluation, we observe that pathological imaging data has moderate/weak predictive performance for LUAD, LUSC and SKCM, respectively. Conclusion. This study suggests that histopathological imaging features extracted from CellProfiler software may not be predictive for LUAD, LUSC and SKCM prognosis, but provide some broadly insight into cancer prognosis modeling and analysis. Future large-scale studies that incorporate other cell properties analysis software are needed