Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer.
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ABSTRACT:
SUBMITTER: Foley KG
PROVIDER: S-EPMC5717119 | biostudies-other | 2018 Jan
REPOSITORIES: biostudies-other
Publications
Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer.
Foley Kieran G KG Hills Robert K RK Berthon Beatrice B Marshall Christopher C Parkinson Craig C Lewis Wyn G WG Crosby Tom D L TDL Spezi Emiliano E Roberts Stuart Ashley SA
European radiology 20170802 1
<h4>Objectives</h4>This retrospective cohort study developed a prognostic model incorporating PET texture analysis in patients with oesophageal cancer (OC). Internal validation of the model was performed.<h4>Methods</h4>Consecutive OC patients (n = 403) were chronologically separated into development (n = 302, September 2010-September 2014, median age = 67.0, males = 227, adenocarcinomas = 237) and validation cohorts (n = 101, September 2014-July 2015, median age = 69.0, males = 78, adenocarcino ...[more]
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Project description:BackgroundPrognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n?=?427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with <?90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated.ResultsOut of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group.ConclusionPrognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.
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Project description:Response rates to the current gold standards of care for treating oesophageal adenocarcinoma (OAC) remain modest with 15-25% of patients achieving meaningful pathological responses, highlighting the need for novel therapeutic strategies. This study consists of immune, angiogenic, and inflammatory profiling of the tumour microenvironment (TME) and lymph node microenvironment (LNME) in OAC. The prognostic value of nodal involvement and clinicopathological features was compared using a retrospective cohort of OAC patients (n = 702). The expression of inhibitory immune checkpoints by T cells infiltrating tumour-draining lymph nodes (TDLNs) and tumour tissue post-chemo(radio)therapy at surgical resection was assessed by flow cytometry. Nodal metastases is of equal prognostic importance to clinical tumour stage and tumour regression grade (TRG) in OAC. The TME exhibited a greater immuno-suppressive phenotype than the LNME. Our data suggests that blockade of these checkpoints may have a therapeutic rationale for boosting response rates in OAC.
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Project description:PurposeIn this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics.MethodsIn cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach: a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing).ResultsThe model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training.ConclusionThe proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context.
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Project description:The ability of an individual to participate in courtroom proceedings is assessed by clinicians using legal 'fitness to plead' criteria. Findings of 'unfitness' are so rare that there is considerable professional unease concerning the utility of the current subjective assessment process. As a result, mentally disordered defendants may be subjected unfairly to criminal trials. The Law Commission in England and Wales has proposed legal reform, as well as the utilisation of a defined psychiatric instrument to assist in fitness to plead assessments. Similar legal reforms are occurring in other jurisdictions. Our objective was to produce and validate a standardised assessment instrument of fitness to plead employing a filmed vignette of criminal proceedings. The instrument was developed in consultation with legal and clinical professionals, and was refined using standard item reduction methods in two initial rounds of testing (n = 212). The factorial structure, test-retest reliability and convergent validity of the resultant instrument were assessed in a further round (n = 160). As a result of this iterative process a 25-item scale was produced, with an underlying two-factor structure representing the foundational and decision-making abilities underpinning fitness to plead. The sub-scales demonstrate good internal consistency (factor 1: 0·76; factor 2: 0·65) and test-retest stability (0·7) as well as excellent convergent validity with scores of intelligence, executive function and mentalising abilities (p?0·01 in all domains). Overall the standardised Fitness to Plead Assessment instrument has good psychometric properties. It has the potential to ensure that the significant numbers of mentally ill and cognitively impaired individuals who face trial are objectively assessed, and the courtroom process critically informed.
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Project description:OBJECTIVE:We aimed to improve prediction of outcome for patients with colorectal liver metastases, via prognostic models incorporating PET-derived measures, including radiomic features that move beyond conventional standard uptake value (SUV) measures. PATIENTS AND METHODS:A range of parameters including volumetric and heterogeneity measures were derived from FDG PET images of 52 patients with colorectal intrahepatic-only metastases (29 males and 23 females; mean age 62.9 years [SD 9.8; range 32-82]). The patients underwent PET/CT imaging as part of the clinical workup prior to final decision on treatment. Univariate and multivariate models were implemented, which included statistical considerations (to discourage false discovery and overfitting), to predict overall survival (OS), progression-free survival (PFS) and event-free survival (EFS). Kaplan-Meier survival analyses were performed, where the subjects were divided into high-risk and low-risk groups, from which the hazard ratios (HR) were computed via Cox proportional hazards regression. RESULTS:Commonly-invoked SUV metrics performed relatively poorly for different prediction tasks (SUVmax HR = 1.48, 0.83 and 1.16; SUVpeak HR = 2.05, 1.93, and 1.64, for OS, PFS and EFS, respectively). By contrast, the number of liver metastases and metabolic tumor volume (MTV) each performed well (with respective HR values of 2.71, 2.61 and 2.42, and 2.62, 1.96 and 2.29, for OS, PFS and EFS). Total lesion glycolysis (TLG) also resulted in similar performance as MTV. Multivariate prognostic modeling incorporating different features (including those quantifying intra-tumor heterogeneity) resulted in further enhanced prediction. Specifically, HR values of 4.29, 4.02 and 3.20 (p-values = 0.00004, 0.0019 and 0.0002) were obtained for OS, PFS and EFS, respectively. CONCLUSIONS:PET-derived measures beyond commonly invoked SUV parameters hold significant potential towards improved prediction of clinical outcome in patients with liver metastases, especially when utilizing multivariate models.
| S-EPMC7507885 | biostudies-literature
Project description:Dental microwear texture analysis (DMTA) quantifies microscopic scar or wear patterns left on teeth by different foods or extraneous ingested items such as grit. It can be a powerful tool for deducing the diets of extinct mammals. Here we investigate how intraspecific variation in the dental microwear of macropodids (kangaroos and their close relatives) can be used to maximize the dietary signal inferable from an inherently limited fossil record. We demonstrate significant intraspecific variation for every factor considered here for both scale-sensitive fractal analysis and International Organization for Standardization surface texture analysis variables. Intraspecific factors were then incorporated into interspecific (dietary) analyses through the use of Linear Mixed Effects modelling, incorporating Akaike's Information Criterion to compare models, and testing models through independent cross-validation. This revealed that for each DMTA variable only a small number of intraspecific factors need to be included to improve differentiation between species. Including specimen as a random factor accounted for stochastic inter-individual variation, and facet, incorporated effects of sampling location. Intraspecific effects of ecoregion, microscope, tooth position and wear were often but not universally important. We conclude that models of microwear data that include intraspecific variation can improve the resolution of dietary reconstructions.
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Project description:Measures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans are increasingly reported as potential biomarkers of non-small cell lung cancer (NSCLC) for classification and prognostication. Several segmentation algorithms have been used to delineate tumours, but their effects on the reproducibility and predictive and prognostic capability of derived parameters have not been evaluated. The purpose of our study was to retrospectively compare various segmentation algorithms in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from non-small cell lung cancer (NSCLC) 18F-FDG PET/CT images. Fifty three NSCLC patients (mean age 65.8 years; 31 males) underwent pre-chemoradiotherapy 18F-FDG PET/CT scans. Three readers segmented tumours using freehand (FH), 40% of maximum intensity threshold (40P), and fuzzy locally adaptive Bayesian (FLAB) algorithms. Intraclass correlation coefficient (ICC) was used to measure the inter-observer variability of the texture features derived by the three segmentation algorithms. Univariate cox regression was used on 12 commonly reported texture features to predict overall survival (OS) for each segmentation algorithm. Model quality was compared across segmentation algorithms using Akaike information criterion (AIC).40P was the most reproducible algorithm (median ICC 0.9; interquartile range [IQR] 0.85-0.92) compared with FLAB (median ICC 0.83; IQR 0.77-0.86) and FH (median ICC 0.77; IQR 0.7-0.85). On univariate cox regression analysis, 40P found 2 out of 12 variables, i.e. first-order entropy and grey-level co-occurence matrix (GLCM) entropy, to be significantly associated with OS; FH and FLAB found 1, i.e., first-order entropy. For each tested variable, survival models for all three segmentation algorithms were of similar quality, exhibiting comparable AIC values with overlapping 95% CIs.Compared with both FLAB and FH, segmentation with 40P yields superior inter-observer reproducibility of texture features. Survival models generated by all three segmentation algorithms are of at least equivalent utility. Our findings suggest that a segmentation algorithm using a 40% of maximum threshold is acceptable for texture analysis of 18F-FDG PET in NSCLC.
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Project description:BackgroundThe role of simulation training in paediatric surgery is expanding as more simulation devices are designed and validated. We aimed to conduct a training needs assessment of UK paediatric surgical trainees to prioritise procedures for simulation, and to validate a novel 3D-printed simulation model for oesophageal atresia and tracheo-oesophageal fistula (OA-TOF) repair.MethodsA questionnaire was sent to UK trainee paediatric surgeons surveying the availability and utility of simulation. The operation ranked as most useful to simulate was OA-TOF repair. 3D-printing techniques were used to build an OA-TOF model. Content, face and construct validity was assessed by 40 paediatric surgeons of varying experience.ResultsThirty-four paediatric surgeons completed the survey; 79% had access to surgical simulation at least monthly, and 47% had access to paediatric-specific resources. Perceived utility of simulation was 4.1/5. Validation of open OA-TOF repair was conducted by 40 surgeons. Participants rated the model as useful 4.9/5. Anatomical realism was scored 4.2/5 and surgical realism 3.9/5. The model was able to discriminate between experienced and inexperienced surgeons.ConclusionUK paediatric surgeons voted OA-TOF repair as the most useful procedure to simulate. In response we have developed and validated an affordable 3D-printed simulation model for open OA-TOF repair.
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Project description:Attention is important for selecting targets for action. Several studies have shown that attentional selection precedes eye movements to a target, and results in an enhanced sensitivity at the saccade goal. Typically these studies have used isolated targets on blank backgrounds, which are rare in real-world situations. Here, we examine the spatial profile of sensitivity around a saccade target on a textured background and how the influence of the surrounding context develops over time. We used two textured backgrounds: a uniform texture, and a concentric arrangement of an inner and an outer texture with orthogonal orientations. For comparison, we also measured sensitivity around the target on a blank background. The spatial profile of sensitivity was measured with a brief, dim, probe flashed around the saccade target. When the target was on a blank or a uniformly textured background, spatial sensitivity peaked near the target location around 350 ms after cue onset and declined with distance from the target. However, when the background was made up of an inner and outer texture, sensitivity to the inner texture was uniformly high, peaking at about 350 ms after cue onset, suggesting that the entire inner texture was selected along with the target. The enhancement of sensitivity on the inner texture was much smaller when observers attended the target covertly and performed the probe-detection task. Thus, our results suggest that the surface representation around the target is taken into account when an observer actively plans to interact with the target.
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Project description:PURPOSE:To develop and validate a set of atlases for auto-contouring cardiac substructures. METHODS:Eight radiation oncologists manually and independently delineated 15 cardiac substructures from noncontrast CT images of 6 patients by referring to their respective fused contrast CT images. Individual contours were fused together for each structure, edited by 2 physicians, and became atlases to delineate other 6 patients. The auto-delineated contours of the 6 additional patients became templates for manual contouring. These 12 patients with well-defined contours composed the final atlases for multi-atlas segmentation. RESULTS:The average time for manually contouring the 15 cardiac substructures was about 40min. Inter-observer variability was small for the heart, the chambers, and the aorta compared with that for other structures that were not clearly distinguishable in CT images. The mean dice similarity coefficient and mean surface distance of auto-segmented contours were within one standard deviation of expert contouring variability. Good agreement between auto-segmented and manual contours was observed for the heart, the chambers, and the great vessels. Independent validation on other 19 patients showed reasonable agreement for the heart chambers. CONCLUSIONS:A set of cardiac atlases was created for auto-contouring from noncontrast CT images. The accuracy of auto-contouring for the heart, chambers, and great vessels was validated for potential clinical use.
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