Esophageal tumor segmentation in CT images using a Dilated Dense Attention Unet (DDAUnet)

Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim to investigate if and to what extent a simplified clinical workflow based on CT alone, allows one to automatically segment the esophageal tumor with sufficient quality. For this purpose, we present a fully automatic end-to-end esophageal tumor segmentation method based on convolutional neural networks (CNNs). The proposed network, called Dilated Dense Attention Unet (DDAUnet), leverages spatial and channel attention gates in each dense block to selectively concentrate on determinant feature maps and regions. Dilated convolutional layers are used to manage GPU memory and increase the network receptive field. We collected a dataset of 792 scans from 288 distinct patients including varying anatomies with air pockets, feeding tubes and proximal tumors. Repeatability and reproducibility studies were conducted for three distinct splits of training and validation sets. The proposed network achieved a DSC value of 0.79 +/- 0.20, a mean surface distance of 5.4 +/- 20.2mm and 95% Hausdorff distance of 14.7 +/- 25.0mm for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone. Our code is publicly available via https://github.com/yousefis/DenseUnet_Esophagus_Segmentation.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Appropriate use criteria for optical coherence tomography guidance in percutaneous coronary interventions: Recommendations of the working group of interventional cardiology of the Netherlands Society of Cardiology

Introduction: Optical coherence tomography (OCT) enables detailed imaging of the coronary wall, lumen and intracoronary implanted devices. Responding to the lack of specific appropriate use criteria (AUC) for this technique, we conducted a literature review and a procedure for appropriate use criteria. Methods: Twenty-one of all 184 members of the Dutch Working Group on Interventional Cardiology agreed to evaluate 49 pre-specified cases. During a meeting, factual indications were established whereupon members individually rated indications on a 9-point scale, with the opportunity to substantiate their scoring. Results: Twenty-six indications were rated ‘Appropriate’, eighteen indications ‘May be appropriate’, and five ‘Rarely appropriate’. Use of OCT was unanimously considered ‘Appropriate’ in stent thrombosis, and ‘Appropriate’ for guidance in PCI, especially in distal left main coronary artery and proximal left anterior descending coronary artery, unexplained angiographic abnormalities, and use of bioresorbable vascular scaffold (BVS). OCT was considered ‘Rarely Appropriate’ on top of fractional flow reserve (FFR) for treatment indication, assessment of strut coverage, bypass anastomoses or assessment of proximal left main coronary artery. Conclusions: The use of OCT in stent thrombosis is unanimously considered ‘Appropriate’ by these experts. Varying degrees of consensus exists on the appropriate use of OCT in other settings

Immobilization precision of a modified GTC frame

The purpose of this study was to evaluate and quantify the interfraction reproducibility and intrafraction immobilization precision of a modified GTC frame. The error of the patient alignment and imaging systems were measured using a cranial skull phantom, with simulated, predetermined shifts. The kV setup images were acquired with a room-mounted set of kV sources and panels. Calculated translations and rotations provided by the computer alignment software relying upon three implanted fiducials were compared to the known shifts, and the accuracy of the imaging and positioning systems was calculated. Orthogonal kV setup images for 45 proton SRT patients and 1002 fractions (average 22.3 fractions/patient) were analyzed for interfraction and intrafraction immobilization precision using a modified GTC frame. The modified frame employs a radiotransparent carbon cup and molded pillow to allow for more treatment angles from posterior directions for cranial lesions. Patients and the phantom were aligned with three 1.5 mm stainless steel fiducials implanted into the skull. The accuracy and variance of the patient positioning and imaging systems were measured to be 0.10 +/- 0.06 mm, with the maximum uncertainty of rotation being +/- 0.07 degrees. 957 pairs of interfraction image sets and 974 intrafraction image sets were analyzed. 3D translations and rotations were recorded. The 3D vector interfraction setup reproducibility was 0.13 mm +/- 1.8 mm for translations and the largest uncertainty of +/- 1.07 degrees for rotations. The intrafraction immobilization efficacy was 0.19 mm +/- 0.66 mm for translations and the largest uncertainty of +/- 0.50 degrees for rotations. The modified GTC frame provides reproducible setup and effective intrafraction immobilization, while allowing for the complete range of entrance angles from the posterior directio

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

Item does not contain fulltextIn our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade 2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC = 0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter

Data at Scale

Pre-requisites to better understand the chapter: basic knowledge of major sources of clinical data. Logical position of the chapter with respect to the previous chapter: in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular, we will learn how to distinguish and classify data according to its scale. Learning objectives: you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of 'big' clinical data; you will learn which are the major concerns limiting 'big' data exchange

Preparing Data for Predictive Modelling

This is the first chapter of five that cover an introduction to developing and validating models for predicting outcomes for the individual patient. Such prediction models can be used for predicting the occurrence or recurrence of an event, or of the most likely value on a continuous outcome. We will mainly focus on the prediction of binary outcomes, such as the occurrence of a complication, recurrence of disease, the presence of metastases, remission, survival, etc. This chapter deals with the selection of an appropriate study design for a study on prediction, and on methods to manipulate the data before the statistical modelling can begin

Prediction Modeling Methodology

In the previous chapter, you have learned how to prepare your data before you start the process of generating a predictive model. In this chapter, you will learn how to make a predictive model using very common regression techniques and how to evaluate the performance of a model. In the next chapter we will then look at more advanced machine learning techniques that have become increasingly popular in recent years

In vivo dosimetry in gynecological applications-A feasibility study

Item does not contain fulltextPURPOSE: To investigate the feasibility of in vivo dosimetry using microMOSFET dosimeters in patients treated with brachytherapy using two types of gynecological applicators. METHODS AND MATERIALS: In this study, a microMOSFET was placed in an empty needle of an Utrecht Interstitial Fletcher applicator or MUPIT (Martinez Universal Perineal Interstitial Template) applicator for independent verification of treatment delivery. Measurements were performed in 10 patients, with one to three microMOSFETs per applicator and repeated for one to four fractions, resulting in 50 in vivo measurements. Phantom measurements were used to determine characteristics of the microMOSFETs. RESULTS: Phantom measurements showed a linear relationship between dose and microMOSFET threshold voltage, and a calibration coefficient (mV/cGy) was determined. Reproducibility of repeated 50 cGy irradiations was 2% (1 standard deviation). Distance and angle dependencies were measured and correction factors were determined. Subsequently, three microMOSFETs were placed in a phantom to measure a validation plan. The difference between predicted and measured dose was less than the measurement uncertainty (+/-9%, 2 standard deviations). In vivo measurements were corrected for distance and angle dependencies. Differences between predicted and measured dose in the patients were smaller than the measurement uncertainty for the majority of the measurements. CONCLUSIONS: In vivo dosimetry using microMOSFETs in MUPIT and Utrecht Interstitial Fletcher applicators has proved to be feasible. Reimaging should be performed after detection of differences larger than 10% between predicted and measured dose to verify the applicator configuration. Movement of the applicator relative to the target or organs at risk is undetectable with this method