Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Texture analysis of aggressive and nonaggressive lung tumor CE CT images

This paper presents the potential for fractal analysis of time sequence contrast-enhanced (CE) computed tomography (CT) images to differentiate between aggressive and nonaggressive malignant lung tumors (i.e., high and low metabolic tumors). The aim is to enhance CT tumor staging prediction accuracy through identifying malignant aggressiveness of lung tumors. As branching of blood vessels can be considered a fractal process, the research examines vascularized tumor regions that exhibit strong fractal characteristics. The analysis is performed after injecting 15 patients with a contrast agent and transforming at least 11 time sequence CE CT images from each patient to the fractal dimension and determining corresponding lacunarity. The fractal texture features were averaged over the tumor region and quantitative classification showed up to 83.3% accuracy in distinction between advanced (aggressive) and early-stage (nonaggressive) malignant tumors. Also, it showed strong correlation with corresponding lung tumor stage and standardized tumor uptake value of fluoro deoxyglucose as determined by positron emission tomography. These results indicate that fractal analysis of time sequence CE CT images of malignant lung tumors could provide additional information about likely tumor aggression that could potentially impact on clinical management decisions in choosing the appropriate treatment procedure

The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer

Objectives: Measuring tumour heterogeneity by textural analysis in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) provides predictive and prognostic information but technical aspects of image processing can influence parameter measurements. We therefore tested effects of image smoothing, segmentation and quantisation on the precision of heterogeneity measurements. Methods: Sixty-four 18F-FDG PET/CT images of oesophageal cancer were processed using different Gaussian smoothing levels (2.0, 2.5, 3.0, 3.5, 4.0 mm), maximum standardised uptake value (SUVmax) segmentation thresholds (45 %, 50 %, 55 %, 60 %) and quantisation (8, 16, 32, 64, 128 bin widths). Heterogeneity parameters included grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRL), neighbourhood grey-tone difference matrix (NGTDM), grey-level size zone matrix (GLSZM) and fractal analysis methods. The concordance correlation coefficient (CCC) for the three processing variables was calculated for each heterogeneity parameter. Results: Most parameters showed poor agreement between different bin widths (CCC median 0.08, range 0.004–0.99). Segmentation and smoothing showed smaller effects on precision (segmentation: CCC median 0.82, range 0.33–0.97; smoothing: CCC median 0.99, range 0.58–0.99). Conclusions: Smoothing and segmentation have only a small effect on the precision of heterogeneity measurements in 18F-FDG PET data. However, quantisation often has larger effects, highlighting a need for further evaluation and standardisation of parameters for multicentre studies. Key points: • Heterogeneity measurement precision in 18 F-FDG PET is influenced by image processing methods. • Quantisation shows large effects on precision of heterogeneity parameters in 18 F-FDG PET/CT. • Smoothing and segmentation show comparatively smaller effects on precision of heterogeneity parameters

Challenges and Promises of Radiomics for Rectal Cancer

Moreira, J. M., Santiago, I., Santinha, J., Figueiredo, N., Marias, K., Figueiredo, M., ... Papanikolaou, N. (2019). Challenges and Promises of Radiomics for Rectal Cancer. Current Colorectal Cancer Reports, 15(6), 175-180. https://doi.org/10.1007/s11888-019-00446-yPurpose of Review: This literature review aims to gather the relevant works published on the topic of Radiomics in Rectal Cancer. Research on this topic has focused on finding predictors of rectal cancer staging and chemoradiation treatment response from medical images. The methods presented may, in principle, aid clinicians with the appropriate treatment planning options. Finding appropriate automatic tools to help in this task is very important, since rectal cancer has been considered one of the most challenging oncological pathologies in recent years. Recent Findings: Radiomics is a class of methods based on the extraction of mineable, high-dimensional data/features from the routine, standard-of-care medical imaging. This data is then fed to machine learning algorithms, with the goal of automatically obtaining predictions regarding disease stage and therapeutic response. Summary: The literature reviewed suggests that Radiomics will continue to be a part of the body of research in oncology in the upcoming years. However, and excluding very few studies, proper validation on the performance of the methods (mainly with external datasets) is still one of the main limitations of the field, which strongly limits their clinical applicability. Progress will only occur if the community opens itself to collaborate with different groups, as data availability and limited shareability continues to be the barrier for its development. Nowadays, Radiomics is used for nearly every type of cancer. In particular, for rectal cancer, the need for predicting treatment response will continue to demand and boost research in this field.authorsversionpublishe

Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis

OBJECTIVES: There are individual variations in neo-adjuvant chemoradiation therapy (nCRT) in patients with locally advanced rectal cancer (LARC). No reliable modality currently exists that can predict the efficacy of nCRT. The purpose of this study is to assess if CT-based fractal dimension and filtration-histogram texture analysis can predict therapeutic response to nCRT in patients with LARC. METHODS: In this retrospective study, 215 patients (average age: 57 years (18-87 years)) who received nCRT for LARC between June 2005 and December 2016 and underwent a staging diagnostic portal venous phase CT were identified. The patients were randomly divided into two datasets: a training set (n = 170), and a validation set (n = 45). Tumor heterogeneity was assessed on the CT images using fractal dimension (FD) and filtration-histogram texture analysis. In the training set, the patients with pCR and non-pCR were compared in univariate analysis. Logistic regression analysis was applied to identify the predictive value of efficacy of nCRT and receiver operating characteristic analysis determined optimal cutoff value. Subsequently, the most significant parameter was assessed in the validation set. RESULTS: Out of the 215 patients evaluated, pCR was reached in 20.9% (n = 45/215) patients. In the training set, 7 out of 37 texture parameters showed significant difference comparing between the pCR and non-pCR groups and logistic multivariable regression analysis incorporating clinical and 7 texture parameters showed that only FD was associated with pCR (p = 0.001). The area under the curve of FD was 0.76. In the validation set, we applied FD for predicting pCR and sensitivity, specificity, and accuracy were 60%, 89%, and 82%, respectively. CONCLUSION: FD on pretreatment CT is a promising parameter for predicting pCR to nCRT in patients with LARC and could be used to help make treatment decisions. KEY POINTS: • Fractal dimension analysis on pretreatment CT was associated with response to neo-adjuvant chemoradiation in patients with locally advanced rectal cancer. • Fractal dimension is a promising biomarker for predicting pCR to nCRT and may potentially select patients for individualized therapy

Role of computed tomography in quantitative assessment of emphysema

Pulmonary emphysema, together with chronic bronchitis is a part of chronic obstructive pulmonary disease (COPD), which is one of the leading causes of death in the United States and worldwide. There are many methods to diagnose emphysema. Unfortunately many of them, for example pulmonary function tests (PFTs), clinical signs and conventional radiology are able to detect emphysema usually in its late stages when a great portion of lung parenchyma has been already destroyed by the disease. Computed tomography (CT) allows for early detection of emphysema. CT also makes it possible to quantify the total amount of emphysema in the lungs which is important in order to precisely estimate the severity of the disease. Those abilities of CT are important in monitoring the course of the disease and in attempts to prevent its further progression. In this review we discuss currently available methods for imaging emphysema with emphasis on the quantitative assessment of emphysema. To date, quantitative methods have not been widely used clinically, however, the initial results of several research studies regarding this subject are very encouraging