Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph

The present work aimed to evaluate the reproducibility of radiomics features derived from manual delineation and semiautomatic segmentation after enhancement using the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE) techniques on a benign tumor of two-dimensional (2D) mammography images. Thirty mammogram images with known benign tumors were obtained from The Cancer Imaging Archive (TCIA) datasets and were randomly selected as subjects. The samples were enhanced for semiautomatic segmentation sets using the Active Contour Model in MATLAB 2019a before analysis by two independent observers. Meanwhile, the images without any enhancement were segmented manually. The samples were divided into three categories: (1) CLAHE images, (2) AHE images, and (3) manual segmented images. Radiomics features were extracted using algorithms provided by MATLAB 2019a software and were assessed with a reliable intra-class correlation coefficient (ICC) score. Radiomics features for the CLAHE group (ICC = 0.890 ± 0.554, p 0.05). Features in all three categories were more robust for the CLAHE compared to the AHE and manual groups. This study shows the existence in variation for the radiomics features extracted from tumor region that are segmented using various image enhancement techniques. Semiautomatic segmentation with image enhancement using CLAHE algorithm gave the best result and was a better alternative than manual delineation as the first two techniques yielded reproducible descriptors. This method should be applicable for predicting outcomes in patient with breast cancer

Quantitative Modeling of Tissue Activity Curves of 64Cu-ATSM and Delineation of Tumour Sub-volumes in Treatment Planning

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Quantitive Modelling of Tissue Activity Curves of 64Cu-ATSM and Delineation of Tumour Sub-Volumes in Treatment Planning.

The majority of solid tumours develop hypoxia because oxygen demand exceeds oxygen supply. In association with this is the poor prognosis and response to therapy. In oncology, it is well appreciated that merging anatomical and functional information has a significant impact on the extent of disease and target volume delineation. Commercial image-fusion software packages are becoming available but require comprehensive evaluation to ensure reliability of image-fusion and the underpinning registration algorithms, particularly for radiotherapy. The present work seeks to assess such accuracy for a number of available registration methods provided by the commercial package ProSoma™. The molecular imaging modality of Positron Emission Tomography (PET), in conjunction with radio-labelled molecules that undergo chemical changes inside tumours as a result of the presence or absence of oxygen, has became a promising technique for the non-invasive quantification of tumour hypoxia. Herein the relationship between tumour hypoxia and vasculature geometry is considered using a novel mathematical approach, likewise the spatiotemporal distribution of a hypoxia PET sensitive tracer is determined. Representation of the oxygen distribution in 2-D vascular architecture using a reaction diffusion model enables quantitative relationships to be obtained, specifically between tissue diffusivity, tissue metabolism, anatomical structure of blood vessels and oxygen gradients. Similarly, tissue activity curves (TAC) are a potential key in providing information on cellular perfusion and limited-diffusion. In this thesis a development to the work of Kelly and Brady (2006) is described and verified, with a particular interest in simulating TACs of the most promising hypoxia PET sensitive tracer, 64Cu-ATSM

Quantitive Modelling of Tissue Activity Curves of 64Cu-ATSM and Delineation of Tumour Sub-Volumes in Treatment Planning.

The majority of solid tumours develop hypoxia because oxygen demand exceeds oxygen supply. In association with this is the poor prognosis and response to therapy. In oncology, it is well appreciated that merging anatomical and functional information has a significant impact on the extent of disease and target volume delineation. Commercial image-fusion software packages are becoming available but require comprehensive evaluation to ensure reliability of image-fusion and the underpinning registration algorithms, particularly for radiotherapy. The present work seeks to assess such accuracy for a number of available registration methods provided by the commercial package ProSoma™. The molecular imaging modality of Positron Emission Tomography (PET), in conjunction with radio-labelled molecules that undergo chemical changes inside tumours as a result of the presence or absence of oxygen, has became a promising technique for the non-invasive quantification of tumour hypoxia. Herein the relationship between tumour hypoxia and vasculature geometry is considered using a novel mathematical approach, likewise the spatiotemporal distribution of a hypoxia PET sensitive tracer is determined. Representation of the oxygen distribution in 2-D vascular architecture using a reaction diffusion model enables quantitative relationships to be obtained, specifically between tissue diffusivity, tissue metabolism, anatomical structure of blood vessels and oxygen gradients. Similarly, tissue activity curves (TAC) are a potential key in providing information on cellular perfusion and limited-diffusion. In this thesis a development to the work of Kelly and Brady (2006) is described and verified, with a particular interest in simulating TACs of the most promising hypoxia PET sensitive tracer, 64Cu-ATSM

Regression Analysis between the Different Breast Dose Quantities Reported in Digital Mammography and Patient Age, Breast Thickness, and Acquisition Parameters

Breast cancer is the leading cause of cancer death among women worldwide. Screening mammography is considered the primary imaging modality for the early detection of breast cancer. The radiation dose from mammography increases the patients’ risk of radiation-induced cancer. The mean glandular dose (MGD), or the average glandular dose (AGD), provides an estimate of the absorbed dose of radiation by the glandular tissues of a breast. In this paper, MGD is estimated for the craniocaudal (CC) and mediolateral–oblique (MLO) views using entrance skin dose (ESD), X-ray spectrum information, patient age, breast glandularity, and breast thickness. Moreover, a regression analysis is performed to evaluate the impact of mammography acquisition parameters, age, and breast thickness on the estimated MGD and other machine-produced dose quantities, namely, ESD and organ dose (OD). Furthermore, a correlation study is conducted to evaluate the correlation between the ESD and OD, and the estimated MGD per image view. This retrospective study was applied to a dataset of 2035 mammograms corresponding to a cohort of 486 subjects with an age range of 28–86 years who underwent screening mammography examinations. Linear regression metrics were calculated to evaluate the strength of the correlations. The mean (and range) MGD for the CC view was 0.832 (0.110–3.491) mGy and for the MLO view was 0.995 (0.256–2.949) mGy. All the mammography dose quantities strongly correlated with tube exposure (mAs): ESD (R2 = 0.938 for the CC view and R2 = 0.945 for the MLO view), OD (R2 = 0.969 for the CC view and R2 = 0.983 for the MLO view), and MGD (R2 = 0.980 for the CC view and R2 = 0.972 for the MLO view). Breast thickness showed a better correlation with all the mammography dose quantities than patient age, which showed a poor correlation. Moreover, a strong correlation was found between the calculated MGD and both the ESD (R2 = 0.929 for the CC view and R2 = 0.914 for the MLO view) and OD (R2 = 0.971 for the CC view and R2 = 0.972 for the MLO view). Furthermore, it was found that the MLO scan views yield a slightly higher dose compared to CC scan views. It was also found that the glandular absorbed dose is more dependent on glandularity than size. Despite being more reflective of the dose absorbed by the glandular tissue than OD and ESD, MGD is considered labor-intensive and time-consuming to estimate

PET-based Treatment Response Assessment for Neoadjuvant Chemoradiation in Pancreatic Adenocarcinoma: An Exploratory Study

PURPOSE: Performance of anatomical metrics of Response Evaluation Criteria in Solid Tumors (RECIST1.1) versus Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST1.0) for neoadjuvant chemoradiation (nCR) of pancreatic adenocarcinoma was evaluated based on the pathological treatment response (PTR) data. METHODS AND MATERIALS: The pre- and post-nCR CT and PET data for 14 patients with resectable or borderline resectable pancreatic head adenocarcinoma treated with nCR followed by surgery were retrospectively analyzed. These data were compared with the PTR which were graded according to tumor cell destruction (cellularity), with Grade 0, 1, 2 or 3 (G0, G1, G2 or G3) for complete, moderate, minimal and poor responses, respectively. Maximum standardized uptake value (SUVmax) was defined using body-weight (SUVbw). PERCIST1.0 was defined using lean-body mass normalized SUV (SUVlb or SUL). RECIST1.1 was defined by contouring the whole pancreas head on the CT image. Pre- and post-SUL-peak and SUVmax, RECIST1.1 and PETRECIST1.0 were correlated with PTR using Pearson’s correlation coefficient test. RESULTS: The average mean and SD in SUL-peak for all patients analyzed were lower in post-nCR (3.63±1.06) compared to those at pre-nCR (4.29±0.89). Using PERCIST1.0, 62% of patients showed stable metabolic disease (SMD), 23% partial metabolic response (PMR), and 15% progressive metabolic disease (PMD). Using RECIST1.1, 85% of patients showed stable disease (SD), 8% partial response (PR), and 7% progressive diseases (PD). A poor insignificant correlation was established between PRT and PERECIST1.0 (r=0.121), whereas no correlation was seen with RECIST1.1. CONCLUSIONS: PERCIST1.0 appears to increase the chance of detecting patients with progressive disease compared to the conventional anatomical-based assessment of RECIST1.1. The integration of these additional radiographic metrics in assessing treatment response to nCR for pancreatic adenocarcinoma may provide a promising strategy to better select patients that are most suitable for therapeutic intensification

Correlation of ADC With Pathological Treatment Response for Radiation Therapy of Pancreatic Cancer

PURPOSE: To investigate the feasibility of using apparent diffusion coefficient (ADC) to assesspathological treatment response in pancreatic ductal adenocarcinoma (PDAC) following neoadjuvant chemoradiation (nCR). MATERIALS/METHODS: MRI and pathological data collected for 25patients with resectable and borderline resectable PDAC following nCR were retrospectively analyzed. Pre- and post-nCR mean ADC values in the tumors were compared using Wilcoxon matched pairs test. Correlation of pathological treatment response and ADC values was assessed using Pearson’s correlation coefficient test and receiver-operating-curve (ROC) analysis. RESULTS: The average mean and standard deviation (SD) of the ADC values for all the patients analyzed were significantly higher in post-nCR (1.667±0.161×10-3) compared with those prior to nCR (1.395±0.136×10-3 mm2/sec), (P<0.05). The mean ADC values after nCR were significantly correlated with the pathological responses (r=-0.5172); P=0.02. The area under the curve (AUC) of the ADC values for differentiating G1, G2 and G3 pathological responses, using ROC analysis, was found to be 0.6310 and P=0.03. CONCLUSION: Changes of pre- and post-treatment ADC values significantly correlated with pathological treatment response for PDAC patients treated with chemoradiation therapy, indicating that the ADC could be used to assesstreatment response for PDAC

Challenges Associated with Effective Implementation of CT Dose Check Standards and Radiation Monitoring Index in Computed Tomography: Healthcare Sector Experience

Computed tomography (CT) radiation dose management tools should be used whenever possible, particularly with the increasing demand for acquiring CT studies. Herein, we aim to assess the advantages and challenges faced with implementing two CT dose management tools. A second aim was to highlight CT examinations exceeding dose notification values (NVs) and define the common set of causes. A total of 13,037 CT examinations collected over a six-month period, were evaluated, using two independent CT dose management tools, a CT Dose Notification prospective-view tool (PVT) following CT Dose Check standards and a retrospective statistical-based view tool (RSVT). Dose NVs were set to twice the Local Diagnostic Reference Levels. There was a significant discrepancy between dose NV counts registered with prospective (4.15%) and retrospective (7.98%) tools using T-Test. A core difference is the dose configuration setup, with PVT and RSVT being dose per series and whole study, respectively. Both prospective and retrospective dose management tools were equally useful despite their technical difference. Configuring the CT prospective dose notification check tool using NVs that is based on DRLs has limitations, and one needs to establish dose NVs per series to overcome this technical hurdle. Technical challenges make the implementation of CT Dose Check standards puzzling