618 research outputs found
Raman Spectroscopy Techniques for the Detection and Management of Breast Cancer
Breast cancer has recently become the most common cancer worldwide, and with increased incidence, there is increased pressure on health services to diagnose and treat many more patients. Mortality and survival rates for this particular disease are better than other cancer types, and part of this is due to the facilitation of early diagnosis provided by screening programmes, including the National Health Service breast screening programme in the UK. Despite the benefits of the programme, some patients undergo negative experiences in the form of false negative mammograms, overdiagnosis and subsequent overtreatment, and even a small number of cancers are induced by the use of ionising radiation. In addition to this, false positive mammograms cause a large number of unnecessary biopsies, which means significant costs, both financially and in terms of clinicians' time, and discourages patients from attending further screening. Improvement in areas of the treatment pathway is also needed. Surgery is usually the first line of treatment for early breast cancer, with breast conserving surgery being the preferred option compared to mastectomy. This type of operation achieves the same outcome as mastectomy - removal of the tumour - while allowing the patient to retain the majority of their normal breast tissue for improved aesthetic and psychological results. Yet, re-excision operations are often required when clear margins are not achieved, i.e. not all of the tumour is removed. This again has implications on cost and time, and increases the risk to the patient through additional surgery.
Currently lacking in both the screening and surgical contexts is the ability to discern specific chemicals present in the breast tissue being assessed/removed. Specifically relevant to mammography is the presence of calcifications, the chemistry of which holds information indicative of pathology that cannot be accessed through x-rays. In addition, the chemical composition of breast tumour tissue has been shown to be different to normal tissue in a variety of ways, with one particular difference being a significant increase in water content. Raman spectroscopy is a rapid, non-ionising, non-destructive technique based on light scattering. It has been proven to discern between chemical types of calcification and subtleties within their spectra that indicate the malignancy status of the surrounding tissue, and differentiate between cancerous and normal breast tissue based on the relative water contents.
Furthermore, this thesis presents work aimed at exploring deep Raman techniques to probe breast calcifications at depth within tissue, and using a high wavenumber Raman probe to discriminate tumour from normal tissue predominantly via changes in tissue water content. The ability of transmission Raman spectroscopy to detect different masses and distributions of calcified powder inclusions within tissue phantoms was tested, as well as elucidating a signal profile of a similar inclusion through a tissue phantom of clinically relevant thickness. The technique was then applied to the measurement of clinically active samples of bulk breast tissue from informed and consented patients to try to measure calcifications. Ex vivo specimens were also measured with a high wavenumber Raman probe, which found significant differences between tumour and normal tissue, largely due to water content, resulting in a classification model that achieved 77.1% sensitivity and 90.8% specificity. While calcifications were harder to detect in the ex vivo specimens, promising results were still achieved, potentially indicating a much more widespread influence of calcification in breast tissue, and to obtain useful signal from bulk human tissue is encouraging in itself. Consequently, this work demonstrates the potential value of both deep Raman techniques and high wavenumber Raman for future breast screening and tumour margin assessment methods
The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma
The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma
Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images
Graph convolutional neural networks have shown significant potential in
natural and histopathology images. However, their use has only been studied in
a single magnification or multi-magnification with late fusion. In order to
leverage the multi-magnification information and early fusion with graph
convolutional networks, we handle different embedding spaces at each
magnification by introducing the Multi-Scale Relational Graph Convolutional
Network (MS-RGCN) as a multiple instance learning method. We model
histopathology image patches and their relation with neighboring patches and
patches at other scales (i.e., magnifications) as a graph. To pass the
information between different magnification embedding spaces, we define
separate message-passing neural networks based on the node and edge type. We
experiment on prostate cancer histopathology images to predict the grade groups
based on the extracted features from patches. We also compare our MS-RGCN with
multiple state-of-the-art methods with evaluations on several source and
held-out datasets. Our method outperforms the state-of-the-art on all of the
datasets and image types consisting of tissue microarrays, whole-mount slide
regions, and whole-slide images. Through an ablation study, we test and show
the value of the pertinent design features of the MS-RGCN
Development and validation of novel and quantitative MRI methods for cancer evaluation
Quantitative imaging biomarkers (QIB) offer the opportunity to further the evaluation of cancer at presentation as well as predict response to anti-cancer therapies before and early during treatment with the ultimate goal of truly personalised medical care and the mitigation of futile, often detrimental, therapy. Few QIBs are successfully translated into clinical practice and there is increasing recognition that rigorous methodologies and standardisation of research pipelines and techniques are required to move a theoretically useful biomarker into the clinic.
To this end, I have aimed to give an overview of what I believe to be some of key elements within the research field beginning with the concept of imaging biomarkers, introducing concepts in development and validation, before providing a summary of the current and future utility of a range of quantitative MR imaging biomarkers techniques within the oncological imaging field.
The original, prospective, research moves from the technical and analytical validation of a novel QIB use (T1 mapping in cancer), first in vivo qualification of this biomarker in cancer patient response assessment and prediction (sarcoma and breast cancer as well as prostate cancer separately), and then moving on to application of more established QIBs in cancer evaluation (R2*/BOLD imaging in head and neck cancer) as well as how existing MR data can be post-processed to improved cancer evaluation (further metrics derived from diffusion weighted imaging in head and neck cancer and textural analysis of existing clinical MR images utility in prostate cancer detection)
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
Segmentation of Pathology Images: A Deep Learning Strategy with Annotated Data
Cancer has significantly threatened human life and health for many years. In the clinic, histopathology image segmentation is the golden stand for evaluating the prediction of patient prognosis and treatment outcome. Generally, manually labelling tumour regions in hundreds of high-resolution histopathological images is time-consuming and expensive for pathologists. Recently, the advancements in hardware and computer vision have allowed deep-learning-based methods to become mainstream to segment tumours automatically, significantly reducing the workload of pathologists. However, most current methods rely on large-scale labelled histopathological images. Therefore, this research studies label-effective tumour segmentation methods using deep-learning paradigms to relieve the annotation limitations. Chapter 3 proposes an ensemble framework for fully-supervised tumour segmentation. Usually, the performance of an individual-trained network is limited by significant morphological variances in histopathological images. We propose a fully-supervised learning ensemble fusion model that uses both shallow and deep U-Nets, trained with images of different resolutions and subsets of images, for robust predictions of tumour regions. Noise elimination is achieved with Convolutional Conditional Random Fields. Two open datasets are used to evaluate the proposed method: the ACDC@LungHP challenge at ISBI2019 and the DigestPath challenge at MICCAI2019. With a dice coefficient of 79.7 %, the proposed method takes third place in ACDC@LungHP. In DigestPath 2019, the proposed method achieves a dice coefficient 77.3 %. Well-annotated images are an indispensable part of training fully-supervised segmentation strategies. However, large-scale histopathology images are hardly annotated finely in clinical practice. It is common for labels to be of poor quality or for only a few images to be manually marked by experts. Consequently, fully-supervised methods cannot perform well in these cases. Chapter 4 proposes a self-supervised contrast learning for tumour segmentation. A self-supervised cancer segmentation framework is proposed to reduce label dependency. An innovative contrastive learning scheme is developed to represent tumour features based on unlabelled images. Unlike a normal U-Net, the backbone is a patch-based segmentation network. Additionally, data augmentation and contrastive losses are applied to improve the discriminability of tumour features. A convolutional Conditional Random Field is used to smooth and eliminate noise. Three labelled, and fourteen unlabelled images are collected from a private skin cancer dataset called BSS. Experimental results show that the proposed method achieves better tumour segmentation performance than other popular self-supervised methods. However, by evaluated on the same public dataset as chapter 3, the proposed self-supervised method is hard to handle fine-grained segmentation around tumour boundaries compared to the supervised method we proposed. Chapter 5 proposes a sketch-based weakly-supervised tumour segmentation method. To segment tumour regions precisely with coarse annotations, a sketch-supervised method is proposed, containing a dual CNN-Transformer network and a global normalised class activation map. CNN-Transformer networks simultaneously model global and local tumour features. With the global normalised class activation map, a gradient-based tumour representation can be obtained from the dual network predictions. We invited experts to mark fine and coarse annotations in the private BSS and the public PAIP2019 datasets to facilitate reproducible performance comparisons. Using the BSS dataset, the proposed method achieves 76.686 % IOU and 86.6 % Dice scores, outperforming state-of-the-art methods. Additionally, the proposed method achieves a Dice gain of 8.372 % compared with U-Net on the PAIP2019 dataset. The thesis presents three approaches to segmenting cancers from histology images: fully-supervised, unsupervised, and weakly supervised methods. This research effectively segments tumour regions based on histopathological annotations and well-designed modules. Our studies comprehensively demonstrate label-effective automatic histopathological image segmentation. Experimental results prove that our works achieve state-of-the-art segmentation performances on private and public datasets. In the future, we plan to integrate more tumour feature representation technologies with other medical modalities and apply them to clinical research
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Developing a machine learning model for tumor cell quantification in standard histology images of lung cancer
Summary
Background
Tumor purity estimation plays a crucial role in genomic profiling and is traditionally carried out manually by pathologists. This manual approach has several disadvantages, including potential inaccuracies due to human error, inconsistency in evaluation criteria among different pathologists, and the time-consuming nature of the process. These issues may be addressed by adopting a digital approach. In this thesis, we employ a machine learning (ML)-based, cell- based classifier to estimate tumor purity in lung cancer tissues.
Materials and methods
In this study, conducted as part of the subsequent clinical trial TNM-I, we incorporated 61 patients diagnosed with non-small cell lung cancer (NSCLC). Tumor purity was initially estimated manually by two pathologists. The digital estimation of tumor purity was executed using a ML-based classifier in QuPath. To determine the level of agreement and inter-rater reliability between the two pathologists, as well as between the manual and digital estimations, we computed Intraclass Correlation Coefficient (ICC) and Cohen’s Kappa using SPSS.
Results
The ICC coefficient when comparing the tumor purity estimations done by the two pathologists was 0.833, indicating good reliability. According to Cohen’s Kappa the inter- rater reliability between the pathologists was moderate with a value of 0.534. The ICC coefficient when comparing the manual and digital tumor purity estimation was 0.838, which indicates good reliability. When analyzing for Cohen’s Kappa we got a value of 0.563, indicating moderate inter-rater reliability between the tumor purity estimations done manually and digitally. All the results were statistically significant.
Conclusion
In summary, we have successfully developed a ML classifier that estimates tumor purity in lung cancer tissue. Our findings align with previous research and demonstrate strong correlation with traditional detection methods. These results underscore the importance of continuing research in enhancing ML-based strategies for tumor purity estimation
Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology
IntroductionAutomatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology.MethodsThe original U-Net model sometime has a caveat in exploring significant features. Herein, we present the Densely Convolutional Spatial Attention Network (DCSA-Net) model based on U-Net to perform the segmentation task. Furthermore, the developed model was tested on external multi-tissue dataset – MoNuSeg. To develop deep learning algorithms for well-segmenting nuclei, a large quantity of data are mandatory, which is expensive and less feasible. We collected hematoxylin and eosin–stained image data sets from two hospitals to train the model with a variety of nuclear appearances. Because of the limited number of annotated pathology images, we introduced a small publicly accessible data set of prostate cancer (PCa) with more than 16,000 labeled nuclei. Nevertheless, to construct our proposed model, we developed the DCSA module, an attention mechanism for capturing useful information from raw images. We also used several other artificial intelligence-based segmentation methods and tools to compare their results to our proposed technique.ResultsTo prioritize the performance of nuclei segmentation, we evaluated the model’s outputs based on the Accuracy, Dice coefficient (DC), and Jaccard coefficient (JC) scores. The proposed technique outperformed the other methods and achieved superior nuclei segmentation with accuracy, DC, and JC of 96.4% (95% confidence interval [CI]: 96.2 – 96.6), 81.8 (95% CI: 80.8 – 83.0), and 69.3 (95% CI: 68.2 – 70.0), respectively, on the internal test data set.ConclusionOur proposed method demonstrates superior performance in segmenting cell nuclei of histological images from internal and external datasets, and outperforms many standard segmentation algorithms used for comparative analysis
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