Chronic obstructive pulmonary disease: a complex comorbidity of lung cancer

Chronic obstructive pulmonary disease (COPD) is a major burden throughout the world. It is associated with a significantly increased incidence of lung cancer and may influence treatment options and outcome. Impaired lung function confirming COPD is an independent risk factor for lung cancer. Oxidative stress and inflammation may be a key link between COPD and lung cancer, with numerous molecular markers being analysed to attempt to understand the pathway of lung cancer development. COPD negatively influences the ability to deliver radical treatment options, so attempts must be made to look for alternative methods of treating lung cancer, while aiming to manage the underlying COPD. Detailed assessment and management plans utilizing the multidisciplinary team must be made for all lung cancer patients with COPD to provide the best care possible.Journal of Comorbidity 2011;1(1):45–5

Comorbidity in lung cancer: influence on treatment and survival

Lung cancer is the commonest cancer in Scotland and survival rates for patients in Scotland appear lower than in many other European countries. Although this variation in survival is usually interpreted as evidence of variation in facilities, access to care and clinical practice it is possible that the increased comorbidity and poor performance status of the Scottish population may contribute to the observed disparities in treatment and outcomes, although this has never been proven. The overall aim of the Thesis was to examine the impact of comorbidity in lung cancer, to attempt to quantify the extent and severity of comorbidity and to explore its relationship with treatment and survival. Between 2005 and 2008 all newly diagnosed lung cancer patients coming through the Multi-Disciplinary Teams (MDTs) in four Scottish Centres were included in the study. Patient demographics, World Health Organization/Eastern Cooperative Oncology Group performance status (PS), clinic-pathological features, stage, comorbidity, markers of systemic inflammation and proposed primary treatment modality were all recorded. Information on date of death was obtained via survival analysis undertaken by the Information Service Division (ISD) of NHS Scotland. Death records were complete until 1 June 2011, which served as the censor date for those alive. Chapter 4 examines the variations in demographics and baseline characteristics seen between the centres and reveals significant differences between the centres such as deprivation, stage at presentation, PS and treatments offered. Chapter 5 explores the relationship between comorbidity and the patient cohort. It shows that comorbidity can be quantified using a scoring index (the Scottish Comorbidity Scoring System (SCSS)) and that increasing comorbidity is associated with treatment centre and socio-economic status, with the most deprived patients having increased levels of co-morbidity. It also demonstrates that comorbidity appears to have an impact on treatment offered. Chapter 6 examines the relationship between systemic inflammation (utilizing the well established modified Glasgow Prognostic Score (mGPS)) and outcome in the patient cohort. It confirms previous work supporting the use of the mGPS in predicting lung cancer survival and also shows how it might be used to provide more objective risk stratification in patients diagnosed with lung cancer. Chapter 7 explores the relationship between a novel comorbidity scoring system (SCSS) and the already established Charlson Comorbidity Index (CCI) and the modified Glasgow Prognostic Score (mGPS). This study aimed to determine which of these factors provided the most accurate information on survival. The novel comorbidity scoring system, the SCSS compares very favourably with the more established CCI. In addition this study demonstrates clear differences between patients having potentially radically treatable disease (NSCLC stage I – IIIa) and disease which would generally be considered incurable (NSCLC IIIb/IV and SCLC). Chapter 8 examines the reasons for the clinician decision-making process and if these reasons do indeed mirror the individual patient’s demographics, fitness and stage. In the majority of patients, both in the early and advanced stage at presentation, the treatment decision appears to be appropriate given the recorded fitness, PS and comorbidity. However in a small but significant number of patients there did appear to be discrepancies between the clinician’s reasons for sub-optimal therapy and the recorded objective assessment of the patient in question. The work presented in this thesis has demonstrated the significant extent of comorbidity in lung cancer and the important role it appears to play (along with systemic inflammation) in determining treatment choice and survival

Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP). Magnetic resonance imaging (MRI) is being increasingly used in RTP due to its high resolution and excellent soft tissue contrast. Manually detecting larynx cancer from sequential MRI is time consuming and subjective. The large diversity of cancer in terms of geometry, non-distinct boundaries combined with the presence of normal anatomical regions close to the cancer regions necessitates the development of automatic and robust algorithms for this task. A new automatic algorithm for the detection of larynx cancer from 2D gadoliniumenhanced T1-weighted (T1+Gd) MRI to assist clinicians in RTP is presented. The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types. Furthermore, it utilizes the information of the expected cancerous location for cancer regions labeling. Comparison of this automatic detection system with manual clinical detection on real T1+Gd axial MRI slices of 2 patients (24 MRI slices) with visible larynx cancer yields an average dice similarity coefficient of 0.78±0.04 and average root mean square error of 1.82±0.28 mm. Preliminary results show that this fully automatic system can assist clinicians in RTP by obtaining quantifiable and non-subjective repeatable detection results in a particular time-efficient and unbiased fashion

Modified fuzzy c-means clustering for automatic tongue base tumour extraction from MRI data

Magnetic resonance imaging (MRI) is a widely used imaging modality to extract tumour regions to assist in radiotherapy and surgery planning. Extraction of a tongue base tumour from MRI is challenging due to variability in its shape, size, intensities and fuzzy boundaries. This paper presents a new automatic algorithm that is shown to be able to extract tongue base tumour from gadolinium-enhanced T1-weighted (T1+Gd) MRI slices. In this algorithm, knowledge of tumour location is added to the objective function of standard fuzzy c-means (FCM) to extract the tumour region. Experimental results on 9 real MRI slices demonstrate that there is good agreement between manual and automatic extraction results with dice similarity coefficient (DSC) of 0.77±0.08

Automatic pharynx and larynx cancer segmentation framework (PLCSF) on contrast enhanced MR images

A novel and effective pharynx and larynx cancer segmentation framework (PLCSF) is presented for automatic base of tongue and larynx cancer segmentation from gadolinium-enhanced T1-weighted magnetic resonance images (MRI). The aim of the proposed PLCSF is to assist clinicians in radiotherapy treatment planning. The initial processing of MRI data in PLCSF includes cropping of region of interest; reduction of artefacts and detection of the throat region for the location prior. Further, modified fuzzy c-means clustering is developed to robustly separate candidate cancer pixels from other tissue types. In addition, region-based level set method is evolved to ensure spatial smoothness for the final segmentation boundary after noise removal using non-linear and morphological filtering. Validation study of PLCSF on 102 axial MRI slices demonstrate mean dice similarity coefficient of 0.79 and mean modified Hausdorff distance of 2.2 mm when compared with manual segmentations. Comparison of PLCSF with other algorithms validates the robustness of the PLCSF. Inter- and intra-variability calculations from manual segmentations suggest that PLCSF can help to reduce the human subjectivity

3-dimensional throat region segmentation from MRI data based on fourier interpolation and 3-dimensional level set methods

A new algorithm for 3D throat region segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm initially pre-processes the MRI data to increase the contrast between the throat region and its surrounding tissues and also to reduce artifacts. Isotropic 3D volume is reconstructed using Fast Fourier Transform based interpolation. Furthermore, a cube encompassing the throat region is evolved using level set method to form a smooth 3D boundary of the throat region. The results of the proposed algorithm on real and synthetic MRI data are used to validate the robustness and accuracy of the algorithm

Simple and objective prediction of survival in patients with lung cancer: staging the host systemic inflammatory response

Background. Prediction of survival in patients diagnosed with lung cancer remains problematical. The aim of the present study was to examine the clinical utility of an established objective marker of the systemic inflammatory response, the Glasgow Prognostic Score, as the basis of risk stratification in patients with lung cancer. Methods. Between 2005 and 2008 all newly diagnosed lung cancer patients coming through the multidisciplinary meetings (MDTs) of four Scottish centres were included in the study. The details of 882 patients with a confirmed new diagnosis of any subtype or stage of lung cancer were collected prospectively. Results. The median survival was 5.6 months (IQR 4.8–6.5). Survival analysis was undertaken in three separate groups based on mGPS score. In the mGPS 0 group the most highly predictive factors were performance status, weight loss, stage of NSCLC, and palliative treatment offered. In the mGPS 1 group performance status, stage of NSCLC, and radical treatment offered were significant. In the mGPS 2 group only performance status and weight loss were statistically significant. Discussion. This present study confirms previous work supporting the use of mGPS in predicting cancer survival; however, it goes further by showing how it might be used to provide more objective risk stratification in patients diagnosed with lung cancer

TwoPath U-Net for automatic brain tumor segmentation from multimodal MRI data

A novel encoder-decoder deep learning network called TwoPath U-Net for multi-class automatic brain tumor segmentation task is presented. The network uses cascaded local and global feature extraction paths in the down-sampling path of the network which allows the network to learn different aspects of both the low-level feature and high-level features. The proposed network architecture using a full image and patches input technique was used on the BraTS2020 training dataset. We tested the network performance using the BraTS2019 validation dataset and obtained the mean dice score of 0.76, 0.64, and 0.58 and the Hausdorff distance 95% of 25.05, 32.83, and 37.57 for the whole tumor, tumor core and enhancing tumor regions

Automatic brain tumour regions segmentation using modified U-Net

Early diagnosis is an important key for brain tumour patients' survival. The segmentation of the tumour regions is done manually by the experts and it is time-consuming. In this work, we present a novel network architecture that automatically segments the whole tumour regions and intra-tumour structures (edema, enhancing tumour, necrotic and non-enhancing tumour). We evaluated the results using dice similarity coefficient and obtained promising results

Modified U-Net for automatic brain tumor regions segmentation

Novel deep learning based network architectures are investigated for advanced brain tumor image classification and segmentation. Variations in brain tumor characteristics together with limited labelled datasets represent significant challenges in automatic brain tumor segmentation. In this paper, we present a novel architecture based on the U-Net that incorporates both global and local feature extraction paths to improve the segmentation accuracy. The results included in the paper show superior performance of the novel segmentation for five tumor regions on the large BRATs 2018 dataset over other approaches