Deep multiple-instance learning for detecting multiple myeloma in CT scans of large bones

S nástupem moderních algoritmů strojového učení vzrostla popularita tématu automatické interpretace výstupů zobrazovacích metod v medicíně pomocí počítačů. Konvoluční neuronové sítě v současné době excelují v mnoha oblastech strojového vidění včetně rozpoznávání obrazu. V této diplomové práci zkoumáme možnosti využití konvolučních sítí jako diagnostického nástroje pro detekci abnormalit v CT snímcích stehenních kostí. Zaměřujeme se na diagnózu mnohočetného myelomu pro nějž jsou charakteristické viditelné léze v kostní dřeni, které lze pozorovat při vyšetření pomocí počítačové tomografie. Bylo otestováno několik různých přístupů včetně učení z více instancí. Náš klasifikátor podává spolehlivý výkon v experimentech s plně supervizovaným učením, vykazuje ovšem zásadní neschopnost konvergence při učení z více instancí. Předpokládáme, že náš navrhovaný neuronový model potřebuje ke konvergenci silnější chybovou odezvu a na toto téma navrhujeme budoucí možná vylepšení.The employment of computer aided diagnosis (CAD) systems for interpretation of medical images has become an increasingly popular topic with the arrival of modern machine learning algorithms. Convolutional neural networks perform exceptionally well nowadays in various pattern recognition tasks including image classification. In this thesis we examine the capabilities of a convolutional neural network binary classifier as a CAD system for detection of abnormalities in CT images of femurs. We focus on the diagnosis of multiple myeloma characterized by symptomatic bone marrow lesions commonly observable through computer tomography screening. Different approaches to the problem including multiple instance learning (MIL) were tested. The classifier showed a solid performance in our fully supervised experimental setting, it however exhibits a serious inability to learn from multiple instances. We conclude that the proposed neural model needs a stronger error signal in order to converge in the standard MIL setting and suggest potential improvements for further work in this area

Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma

Purpose: [18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients. Materials and methods: Whole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1–6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian 7 1.1 (axial skeleton), gluteal muscles SUVmedian 7 4 (extremities). Approach 2: liver SUVmedian 7 1.5 (axial skeleton), gluteal muscles SUVmedian 7 4 (extremities). Approach 3: liver SUVmedian 7 2 (axial skeleton), gluteal muscles SUVmedian 7 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients. Results: BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of β2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5. Conclusions: The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts

Implementation of Whole-Body MRI (MY-RADS) within the OPTIMUM/MUKnine multi-centre clinical trial for patients with myeloma.

BACKGROUND: Whole-body (WB) MRI, which includes diffusion-weighted imaging (DWI) and T1-w Dixon, permits sensitive detection of marrow disease in addition to qualitative and quantitative measurements of disease and response to treatment of bone marrow. We report on the first study to embed standardised WB-MRI within a prospective, multi-centre myeloma clinical trial (IMAGIMM trial, sub-study of OPTIMUM/MUKnine) to explore the use of WB-MRI to detect minimal residual disease after treatment. METHODS: The standardised MY-RADS WB-MRI protocol was set up on a local 1.5 T scanner. An imaging manual describing the MR protocol, quality assurance/control procedures and data transfer was produced and provided to sites. For non-identical scanners (different vendor or magnet strength), site visits from our physics team were organised to support protocol optimisation. The site qualification process included review of phantom and volunteer data acquired at each site and a teleconference to brief the multidisciplinary team. Image quality of initial patients at each site was assessed. RESULTS: WB-MRI was successfully set up at 12 UK sites involving 3 vendor systems and two field strengths. Four main protocols (1.5 T Siemens, 3 T Siemens, 1.5 T Philips and 3 T GE scanners) were generated. Scanner limitations (hardware and software) and scanning time constraint required protocol modifications for 4 sites. Nevertheless, shared methodology and imaging protocols enabled other centres to obtain images suitable for qualitative and quantitative analysis. CONCLUSIONS: Standardised WB-MRI protocols can be implemented and supported in prospective multi-centre clinical trials. Trial registration NCT03188172 clinicaltrials.gov; registration date 15th June 2017 https://clinicaltrials.gov/ct2/show/study/NCT03188172

Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study.

INTRODUCTION: Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods. METHODS AND ANALYSIS: This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment ('reference standard'). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response. ETHICS AND DISSEMINATION: MALIMAR has ethical approval from South Central-Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informed consent to participate in the study before taking part. MALIMAR is funded by National Institute for Healthcare Research Efficacy and Mechanism Evaluation funding (NIHR EME Project ID: 16/68/34). Findings will be made available through peer-reviewed publications and conference dissemination. TRIAL REGISTRATION NUMBER: NCT03574454

Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non- leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction

LABRAD : Vol 39, Issue 1 - September 2013

Immunophenotyping by Flowcytometry Chronic Lymphocytic Leukaemia: Diagnosis and Prognostic Factors Tumour Markers Role of Chemical Pathology in Screening and Diagnosis of Multiple Myeloma 1p/19q Deletion: Favourable Prognostic Marker for Oligodendroglioma EGFR Mutation Screening Test for Lung Cancer Patients Clinical Utility of BCR-ABL1 Kinase Domain Mutational Analysis Molecular Cytogenetic Testing for Acute Myeloid Leukaemia Diffuse Large B-Cell Lymphoma (DLBCL) Subgroups have Different Phenotypehttps://ecommons.aku.edu/labrad/1006/thumbnail.jp

Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks

Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow. © 2013 IEEE

Quantifying bone structure, micro-architecture, and pathophysiology with MRI

The radiology of bone has been transformed by magnetic resonance imaging, which has the ability to interrogate bone's complex architecture and physiology. New techniques provide information about both the macrostructure and microstructure of bone ranging from micrometre detail to the whole skeleton. Furthermore functional information about bone physiology can be used to detect disease early before structural changes occur. The future of bone imaging is in quantifying the anatomical and functional information to diagnose and monitor disease more precisely. This review explores the state of the art in quantitative MRI bone imaging

Investigating the pathological mechanism of neuropathy in POEMS syndrome

POEMS syndrome (Polyneuropathy, Organomegaly, Endocrinopathy, Monoclonal gammopathy, Skin disorder) is a rare disease characterised by an inflammatory polyneuropathy an a monoclonal plasma cell dyscrasia. POEMS syndrome causes some of the most significant disability and mortality of any inflammatory neuropathy. The pathophysiology is unknown but recognised to be cytokine mediated, notably driven by vascular endothelial growth factor, however little is known about the other mediators at play. This thesis collates clinical data from the largest POEMS cohorti in Europe in order to study the characteristic disease features, optimise therapies and identify factors that influence outcome. Utilising our POEMS sample biobank, we carry out highly sensitive immunoassays to study the cytokines released in POEMS syndrome, and whether they correlate with disease activity. We go on to study the proteome of POEMS syndrome through mass spectrometry, to uncover the biological pathway involved and identify a number of novel, potentially pathogenic molecules. Fluid biomarkers of neuropathy in POEMS syndrome and related neuropathies are additionally explored. The development and optimisation of a homebrew immunoassay for peripherin, a peripheral nerve specific biomarker is detailed. The potential clinical utility of this biomarker is compared against that of serum neurofilament light. Finally, we attempt to model the neuropathogenesis of POEMS neuropathy in vitro using a novel human induced pleuripotent stem cell derived neuronal culture system