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How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days. [ABSTRACT FROM AUTHOR]

Since absorption of X-ray radiation has the possibility of inducing cancerous, genetic and other diseases to patients, researches usually attempt to reduce the radiation dose. However, reduction of the radiation dose associated with CT scans will unavoidably increase the severity of noise and artifacts, which can seriously affect diagnostic confidence. Due to the outstanding performance of deep neural networks in image processing, in this paper, we proposed a Stacked Competitive Network (SCN) approach to noise reduction, which stacks several successive Competitive Blocks (CB). The carefully handcrafted design of the competitive blocks was inspired by the idea of multi-scale processing and improvement the network’s capacity. Qualitative and quantitative evaluations demonstrate the competitive performance of the proposed method in noise suppression, structural preservation, and lesion detection. [ABSTRACT FROM AUTHOR]

Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results. [ABSTRACT FROM AUTHOR]

Background: Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment. Methods: The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called ‘cohort differences model’ approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets. Results: The individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets. Conclusion: The results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets. [ABSTRACT FROM AUTHOR]

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images. [ABSTRACT FROM AUTHOR]

Background: Surgical indications for rectal neuroendocrine tumors with potential lymph node metastasis remain controversial. Although accurate preoperative diagnosis of nodal status may be helpful for treatment strategy, scant data about clinical values of lymph node size have been reported. The aim of this retrospective study was to investigate the relationship between lymph node size and lymph node metastasis. Methods: Participants comprised 102 patients who underwent rectal resection with total mesenteric excision or tumor-specific mesenteric excision and in some cases additional lateral pelvic lymph node dissection for rectal neuroendocrine tumor between June 2005 and September 2016. All lymph nodes from specimens were checked and measured. Results: Pathological lymph node metastasis was confirmed in 37 patients (36%), including 6 patients (5.8%) with lateral pelvic lymph node metastasis. A total of 1169 lymph nodes in the mesorectum were retrieved from all specimens, with 78 lymph nodes (6.7%) showing metastasis. Mean length (long-axis diameter) of metastatic lymph nodes in the mesorectum was 4.31 mm, significantly larger than that of non-metastatic lymph nodes (2.39 mm, P<0.01). The optimal cut-off of major axis length for predicting mesorectal lymph node metastasis was 3 mm. We could predict lymph node metastasis in only 7 patients (21%) from preoperative multidetector-row computed tomography. Conclusions: Metastatic lymph nodes were small, so predicting lymph node metastasis from preoperative computed tomography is difficult. Alternative modalities with a scan width less than 3 mm may be needed to predict lymph node metastasis of rectal NET with low cost and labour requirements. [ABSTRACT FROM AUTHOR]

Background: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.Methods and Findings: We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks.Conclusions: Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data. [ABSTRACT FROM AUTHOR]

Background: Four-dimensional computed tomography (4D-CT) ventilation is an emerging imaging modality. Functional avoidance of regions according to 4D-CT ventilation may reduce lung toxicity after radiation therapy. This study evaluated associations between 4D-CT ventilation-based dosimetric parameters and clinical outcomes. Methods: Pre-treatment 4D-CT data were used to retrospectively construct ventilation images for 40 thoracic cancer patients retrospectively. Fifteen patients were treated with conventional radiation therapy, 6 patients with hyperfractionated radiation therapy and 19 patients with stereotactic body radiation therapy (SBRT). Ventilation images were calculated from 4D-CT data using a deformable image registration and Jacobian-based algorithm. Each ventilation map was normalized by converting it to percentile images. Ventilation-based dosimetric parameters (Mean Dose, V5 [percent lung volume receiving ≥5 Gy], and V20 [percent lung volume receiving ≥20 Gy]) were calculated for highly and poorly ventilated regions. To test whether the ventilation-based dosimetric parameters could be used predict radiation pneumonitis of ≥Grade 2, the area under the curve (AUC) was determined from the receiver operating characteristic analysis. Results: For Mean Dose, poorly ventilated lung regions in the 0–30% range showed the highest AUC value (0.809; 95% confidence interval [CI], 0.663–0.955). For V20, poorly ventilated lung regions in the 0–20% range had the highest AUC value (0.774; 95% [CI], 0.598–0.915), and for V5, poorly ventilated lung regions in the 0–30% range had the highest AUC value (0.843; 95% [CI], 0.732–0.954). The highest AUC values for Mean Dose, V20, and V5 were obtained in poorly ventilated regions. There were significant differences in all dosimetric parameters between radiation pneumonitis of Grade 1 and Grade ≥2. Conclusions: Poorly ventilated lung regions identified on 4D-CT had higher AUC values than highly ventilated regions, suggesting that functional planning based on poorly ventilated regions may reduce the risk of lung toxicity in radiation therapy. [ABSTRACT FROM AUTHOR]

Background: As Medicare expands the use of computed tomography (CT) for diagnosing lung cancer, there is increased opportunity to diagnose lung cancer in asymptomatic patients. This descriptive study characterizes the disease-specific diagnostic and treatment services that patients with a positive diagnosis following CT received, stratified by presentation at CT. Methods: Patients who were diagnosed with lung cancer following CT in 2013, had no history of lung cancer, survived at least 1 year, were aged 55–80 years, and had Medicare Advantage insurance were included. Patients were grouped based upon presentation at CT: morbidities unrelated to lung cancer, classic lung cancer symptoms, and cancer syndromes. Patients with none of these factors were categorized into a no diagnoses/symptoms group. The type and intensity of services used in the year following the CT was reported for each group. Results: 1,261 patients were included. Early treatment services were most common in the group with morbidities unrelated to lung cancer (13.7%) and least common in the cancer syndromes group (6.6%). Advanced treatment services were used by 47.3% of the cancer syndromes group versus 23.5% of the no diagnoses/symptoms group. Conclusions: The intensity of disease-specific diagnostic and treatment services varied by presentation at CT. Patients with no symptoms or morbidities at the time of CT less frequently received advanced interventions. Learning about the utilization patterns of others with a similar presentation at CT may help patients with positive lung cancer diagnoses engage in shared decision making and in norming their experiences against those of other similarly-situated patients. [ABSTRACT FROM AUTHOR]

Development and comparison of spine-shaped phantoms generated by two different 3D-printing technologies, digital light processing (DLP) and Polyjet has been purposed to utilize in patient-specific quality assurance (QA) of stereotactic body radiation treatment. The developed 3D-printed spine QA phantom consisted of an acrylic body phantom and a 3D-printed spine shaped object. DLP and Polyjet 3D printers using a high-density acrylic polymer were employed to produce spine-shaped phantoms based on CT images. Image fusion was performed to evaluate the reproducibility of our phantom, and the Hounsfield units (HUs) were measured based on each CT image. Two different intensity-modulated radiotherapy plans based on both CT phantom image sets from the two printed spine-shaped phantoms with acrylic body phantoms were designed to deliver 16 Gy dose to the planning target volume (PTV) and were compared for target coverage and normal organ-sparing. Image fusion demonstrated good reproducibility of the developed phantom. The HU values of the DLP- and Polyjet-printed spine vertebrae differed by 54.3 on average. The PTV Dmax dose for the DLP-generated phantom was about 1.488 Gy higher than that for the Polyjet-generated phantom. The organs at risk received a lower dose for the 3D printed spine-shaped phantom image using the DLP technique than for the phantom image using the Polyjet technique. Despite using the same material for printing the spine-shaped phantom, these phantoms generated by different 3D printing techniques, DLP and Polyjet, showed different HU values and these differently appearing HU values according to the printing technique could be an extra consideration for developing the 3D printed spine-shaped phantom depending on the patient’s age and the density of the spinal bone. Therefore, the 3D printing technique and materials should be carefully chosen by taking into account the condition of the patient in order to accurately produce 3D printed patient-specific QA phantom. [ABSTRACT FROM AUTHOR]

Background: The National Lung Screening Trial (NLST) results indicate that computed tomography (CT) lung cancer screening for current and former smokers with three annual screens can be cost-effective in a trial setting. However, the cost-effectiveness in a population-based setting with >3 screening rounds is uncertain. Therefore, the objective of this study was to estimate the cost-effectiveness of lung cancer screening in a population-based setting in Ontario, Canada, and evaluate the effects of screening eligibility criteria.Methods and Findings: This study used microsimulation modeling informed by various data sources, including the Ontario Health Insurance Plan (OHIP), Ontario Cancer Registry, smoking behavior surveys, and the NLST. Persons, born between 1940 and 1969, were examined from a third-party health care payer perspective across a lifetime horizon. Starting in 2015, 576 CT screening scenarios were examined, varying by age to start and end screening, smoking eligibility criteria, and screening interval. Among the examined outcome measures were lung cancer deaths averted, life-years gained, percentage ever screened, costs (in 2015 Canadian dollars), and overdiagnosis. The results of the base-case analysis indicated that annual screening was more cost-effective than biennial screening. Scenarios with eligibility criteria that required as few as 20 pack-years were dominated by scenarios that required higher numbers of accumulated pack-years. In general, scenarios that applied stringent smoking eligibility criteria (i.e., requiring higher levels of accumulated smoking exposure) were more cost-effective than scenarios with less stringent smoking eligibility criteria, with modest differences in life-years gained. Annual screening between ages 55-75 for persons who smoked ≥40 pack-years and who currently smoke or quit ≤10 y ago yielded an incremental cost-effectiveness ratio of $41,136 Canadian dollars ($33,825 in May 1, 2015, United States dollars) per life-year gained (compared to annual screening between ages 60-75 for persons who smoked ≥40 pack-years and who currently smoke or quit ≤10 y ago), which was considered optimal at a cost-effectiveness threshold of $50,000 Canadian dollars ($41,114 May 1, 2015, US dollars). If 50% lower or higher attributable costs were assumed, the incremental cost-effectiveness ratio of this scenario was estimated to be $38,240 ($31,444 May 1, 2015, US dollars) or $48,525 ($39,901 May 1, 2015, US dollars), respectively. If 50% lower or higher costs for CT examinations were assumed, the incremental cost-effectiveness ratio of this scenario was estimated to be $28,630 ($23,542 May 1, 2015, US dollars) or $73,507 ($60,443 May 1, 2015, US dollars), respectively. This scenario would screen 9.56% (499,261 individuals) of the total population (ever- and never-smokers) at least once, which would require 4,788,523 CT examinations, and reduce lung cancer mortality in the total population by 9.05% (preventing 13,108 lung cancer deaths), while 12.53% of screen-detected cancers would be overdiagnosed (4,282 overdiagnosed cases). Sensitivity analyses indicated that the overall results were most sensitive to variations in CT examination costs. Quality of life was not incorporated in the analyses, and assumptions for follow-up procedures were based on data from the NLST, which may not be generalizable to a population-based setting.Conclusions: Lung cancer screening with stringent smoking eligibility criteria can be cost-effective in a population-based setting. [ABSTRACT FROM AUTHOR]

CT-guided percutaneous ablation for liver cancer treatment is a relevant technique for patients not eligible for surgery and with tumors that are inconspicuous on US imaging. The lack of real-time imaging and the use of a limited amount of CT contrast agent make targeting the tumor with the needle challenging. In this study, we evaluate a registration framework that allows the integration of diagnostic pre-operative contrast enhanced CT images and intra-operative non-contrast enhanced CT images to improve image guidance in the intervention. The liver and tumor are segmented in the pre-operative contrast enhanced CT images. Next, the contrast enhanced image is registered to the intra-operative CT images in a two-stage approach. First, the contrast-enhanced diagnostic image is non-rigidly registered to a non-contrast enhanced image that is conventionally acquired at the start of the intervention. In case the initial registration is not sufficiently accurate, a refinement step is applied using non-rigid registration method with a local rigidity term. In the second stage, the intra-operative CT-images that are used to check the needle position, which often consist of only a few slices, are registered rigidly to the intra-operative image that was acquired at the start of the intervention. Subsequently, the diagnostic image is registered to the current intra-operative image, using both transformations, this allows the visualization of the tumor region extracted from pre-operative data in the intra-operative CT images containing needle. The method is evaluated on imaging data of 19 patients at the Erasmus MC. Quantitative evaluation is performed using the Dice metric, mean surface distance of the liver border and corresponding landmarks in the diagnostic and the intra-operative images. The registration of the diagnostic CT image to the initial intra-operative CT image did not require a refinement step in 13 cases. For those cases, the resulting registration had a Dice coefficient for the livers of 91.4%, a mean surface distance of 4.4 mm and a mean distance between corresponding landmarks of 4.7 mm. For the three cases with a refinement step, the registration result significantly improved (p<0.05) compared to the result of the initial non rigid registration method (DICE of 90.3% vs 71.3% and mean surface distance of 5.1 mm vs 11.3 mm and mean distance between corresponding landmark of 6.4 mm vs 10.2 mm). The registration of the preoperative data with the needle image in 16 cases yielded a DICE of 90.1% and a mean surface distance of 5.2 mm. The remaining three cases with DICE smaller than 80% were classified as unsuccessful registration. The results show that this is promising tool for liver image registration in interventional radiology. [ABSTRACT FROM AUTHOR]

This study aimed to identify the computed tomography characteristics of treatment-naïve patients with lung adenocarcinoma and known driver mutations in EGFR, KRAS, or ALK. Patients with advanced lung adenocarcinoma (stage IIIB–IV) and known mutations in EGFR, KRAS, or ALK were assessed. The radiological findings for the main tumor and intra-thoracic status were retrospectively analyzed in each group, and the groups’ characteristics were compared. We identified 265 treatment-naïve patients with non-small-cell carcinoma, who had EGFR mutations (n = 159), KRAS mutations (n = 55), or ALK rearrangements (n = 51). Among the three groups, we evaluated only patients with stage IIIB–IV lung adenocarcinoma who had EGFR mutations (n = 126), KRAS mutations (n = 35), or ALK rearrangements (n = 47). We found that ground-glass opacity at the main tumor was significantly more common among EGFR-positive patients, compared to ALK-positive patients (p = 0.009). Lymphadenopathy was significantly more common among ALK-positive patients, compared to EGFR-positive patients (p = 0.003). Extranodal invasion was significantly more common among ALK-positive patients, compared to EGFR-positive patients and KRAS-positive patients (p = 0.001 and p = 0.049, respectively). Lymphangitis was significantly more common among ALK-positive patients, compared to EGFR-positive patients (p = 0.049). Pleural effusion was significantly less common among KRAS-positive patients, compared to EGFR-positive patients and ALK-positive patients (p = 0.046 and p = 0.026, respectively). Lung metastases were significantly more common among EGFR-positive patients, compared to KRAS-positive patients and ALK-positive patients (p = 0.007 and p = 0.04, respectively). In conclusion, EGFR mutations were associated with ground-glass opacity, KRAS-positive tumors were generally solid and less likely to metastasize to the lung and pleura, and ALK-positive tumors tended to present with lymphadenopathy, extranodal invasion, and lymphangitis. These mutation-specific imaging characteristics may be related to the biological differences between these cancers. [ABSTRACT FROM AUTHOR]

Background: Image texture analysis is a noninvasive technique for quantifying intratumoral heterogeneity, with derived texture features reported to be closely related to the treatment outcome of tumors. Gastric cancer is one of the most common tumors and the third leading cause of cancer-related deaths worldwide. Although trastuzumab is associated with a survival gain among patients with human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer, optimal patient selection is challenging. The purpose of this study was to determine whether CT texture features of HER2-positive gastric cancer were related to the survival rate after trastuzumab treatment. Methods and Findings: Patients diagnosed with HER2-positive advanced gastric cancer from February 2007 to August 2014 were retrospectively selected. Using in-house built software, histogram features (kurtosis and skewness) and gray-level co-occurrence matrices (GLCM) features (angular second moment [ASM], contrast, entropy, variance, and correlation) were derived from the CT images of HER2-positive advanced gastric cancer in 26 patients. All the patients were followed up for more than 6 months, with no confirmed deaths. The patients were dichotomized into a good and poor survival group based on cutoff points of overall survival of 12 months. A receiver-operating characteristics (ROC) analysis was performed to test the ability of each texture parameter to identify the good survival group. Kaplan–Meier curves for patients above and below each threshold were constructed. Using a threshold of >265.8480 for contrast, >488.3150 for variance, and ≤0.1319×10−3. for correlation, all of the area under the ROC curves showed fair accuracy (>0.7). Kaplan–Meier analysis showed statistically significant survival difference between two groups according to optimal cutoff values of contrast, variance, correlation and ASM. However, as this study had a small number of patients, a further study with a larger population will be needed to validate the results. Conclusions: Heterogeneous texture features on CT images were associated with better survival in patients with HER2-positive advanced gastric cancer who received trastuzumab-based treatment. Therefore, texture analysis shows potential to be a clinically useful imaging biomarker providing additional prognostic information for patient selection. [ABSTRACT FROM AUTHOR]

Introduction: For accurate dose calculations, it is necessary to provide a correct relationship between the CT numbers and electron density in radiotherapy treatment planning systems (TPSs). The purpose of this study was to investigate the energy dependence of measured CT numbers on substituted materials used for CT number calibration of radiotherapy TPSs and the resulting errors in the treatment planning calculation doses. Materials and Methods: In this study, we designed a cylindrical water phantom with different materials used as tissue equivalent materials for the simulation of tissues and obtaining the related CT numbers. For evaluating the effect of CT number variations of substituted materials due to energy changing of scanner (kVp) on the dose calculation of TPS, the slices of the scanned phantom at three kVp's were imported into the desired TPSs (MIRS and CorePLAN). Dose calculations were performed on two TPSs. Results: The mean absolute percentage differences between the CT numbers of CT scanner and two treatment planning systems for all the samples were 3.22%±2.57% for CorePLAN and 2.88%±2.11% for MIRS. It was also found that the maximum absolute percentage difference between all of the calculated doses from each photon beam of linac (6 and 15 MV) at three kVp's was less than 1.2%. Discussion: The present study revealed that, for the materials with effective low atomic number, the mean CT number increased with increasing energy, which was opposite for the materials with an effective high atomic number. We concluded that the tissue substitute materials had a different behavior in the energy ranges from 80 to 130 kVp. So, it is necessary to consider the energy dependence of the substitute materials used for the measurement or calibration of CT number for radiotherapy treatment planning systems. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to improve the image quality of cone-beam computed tomography (CBCT) mounted on the gantry of a linear accelerator used in radiation therapy based on the image information provided by planning multi-detector CT (MDCT). Methods: MDCT-based shading correction for CBCT and virtual monochromatic CT (VMCT) synthesized using the dual-energy method were performed. In VMCT, the high-energy data were obtained from CBCT, while the low-energy data were obtained from MDCT. An electron density phantom was used to investigate the efficacy of shading correction and VMCT on improving the target detectability, Hounsfield unit (HU) accuracy and variation, which were quantified by calculating the contrast-to-noise ratio (CNR), the percent difference (%Diff) and the standard deviation of the CT numbers for tissue equivalent background material, respectively. Treatment plan studies for a chest phantom were conducted to investigate the effects of image quality improvement on dose planning. Results: For the electron density phantom, the mean value of CNR was 17.84, 26.78 and 34.31 in CBCT, shading-corrected CBCT and VMCT, respectively. The mean value of %Diff was 152.67%, 11.93% and 7.66% in CBCT, shading-corrected CBCT and VMCT, respectively. The standard deviation within a uniform background of CBCT, shading-corrected CBCT and VMCT was 85, 23 and 15 HU, respectively. With regards to the chest phantom, the monitor unit (MU) difference between the treatment plan calculated using MDCT and those based on CBCT, shading corrected CBCT and VMCT was 6.32%, 1.05% and 0.94%, respectively. Conclusions: Enhancement of image quality in on-board CBCT can contribute to daily patient setup and adaptive dose delivery, thus enabling higher confidence in patient treatment accuracy in radiation therapy. Based on our results, VMCT has the highest image quality, followed by the shading corrected CBCT and the original CBCT. The research results presented in this study should be able to provide a route to reach a high level of image quality for CBCT imaging in radiation oncology. [ABSTRACT FROM AUTHOR]

Background: Tumor hypoxia is associated with treatment resistance to cancer therapies. Hypoxia can be investigated by immunohistopathologic methods but such procedure is invasive. A non-invasive method to interrogate tumor hypoxia is an attractive option as such method can provide information before, during, and after treatment for personalized therapies. Our study evaluated the correlations between computed tomography (CT) perfusion parameters and immunohistopathologic measurement of tumor hypoxia. Methods: Wistar rats, 18 controls and 19 treated with stereotactic radiosurgery (SRS), implanted with the C6 glioma tumor were imaged using CT perfusion on average every five days to monitor tumor growth. A final CT perfusion scan and the brain were obtained on average 14 days (8–22 days) after tumor implantation. Tumor hypoxia was detected immunohistopathologically with pimonidazole. The tumor, necrotic, and pimonidazole-positive areas on histology samples were measured. Percent necrotic area and percent hypoxic areas were calculated. Tumor volume (TV), blood flow (BF), blood volume (BV), and permeability-surface area product (PS) were obtained from the CT perfusion studies. Correlations between CT perfusion parameters and histological parameters were assessed by Spearman’s ρ correlation. A Bonferroni-corrected P value < 0.05 was considered significant. Results: BF and BV showed significant correlations with percent hypoxic area ρ = -0.88, P < 0.001 and ρ = -0.81, P < 0.001, respectively, for control animals and ρ = -0.7, P < 0.001 and ρ = -0.6, P = 0.003, respectively, for all animals, while TV and BV were correlated (ρ = -0.64, P = 0.01 and ρ = -0.43, P = 0.043, respectively) with percent necrotic area. PS was not correlated with either percent necrotic or percent hypoxic areas. Conclusions: Percent hypoxic area provided significant correlations with BF and BV, suggesting that CT perfusion parameters are potential non-invasive imaging biomarkers of tumor hypoxia. [ABSTRACT FROM AUTHOR]

Introduction: Patient management following elective cranial surgery often includes routine postoperative computed tomography (CT). We analyzed whether a regime of early extubation and close neurological monitoring without routine CT is safe, and compared the rate of postoperative emergency neurosurgical intervention with published data. Methods: Four hundred ninety-two patients were prospectively analyzed; 360 had supra- and 132 had infratentorial lesions. Extubation within one hour after skin closure was aimed for in all cases. CT was performed within 48 hours only in cases of unexpected neurological findings. Results: Four-hundred sixty-nine of the 492 patients (95.3%) were extubated within one hour, 20 (4.1%) within 3 hours, and three (0.6%) within 3 to 10 hours. Emergency CT within 48 hours was performed for 43/492 (8.7%) cases. Rate of recraniotomy within 48 hours for patients with postoperative hemorrhage was 0.8% (n = 4), and 0.8% (n = 4) required placement of an external ventricular drain (EVD). Of 469 patients extubated within one hour, 3 required recraniotomy and 2 required EVD placements. Of 23 patients with delayed extubation, 1 recraniotomy and 2 EVDs were required. Failure to extubate within one hour was associated with a significantly higher risk of surgical intervention within 48 hours (rate 13.0%, p = 0.004, odds ratio 13.9, 95% confidence interval [3.11–62.37]). Discussion: Early extubation combined with close neurological monitoring is safe and omits the need for routine postoperative CT. Patients not extubated within one hour do need early CT, since they had a significantly increased risk of requiring emergency neurosurgical intervention. Trial Registration: ClinicalTrials.gov [ABSTRACT FROM AUTHOR]

Objective: We evaluated postoperative changes in nasal cavity volume and their effects on nasal function and symptoms after endoscopic endonasal transsphenoidal approach for antero-central skull base surgery. Study Design: Retrospective chart review at a tertiary referral center. Methods: We studied 92 patients who underwent binostril, four-hand, endoscopic endonasal transsphenoidal approach surgery using the bilateral modified nasoseptal rescue flap technique. Pre- and postoperative paranasal computed tomography and the Mimics® program were used to assess nasal cavity volume changes at three sections. We also performed several pre- and postoperative tests, including the Connecticut Chemosensory Clinical Research Center test, Cross-Cultural Smell Identification Test, Nasal Obstruction Symptoms Evaluation, and Sino-Nasal Outcome Test-20. In addition, a visual analog scale was used to record subjective symptoms. We compared these data with the pre- and postoperative nasal cavity volumes. Results: Three-dimensional, objective increases in nasal passage volumes were evident between the inferior and middle turbinates (p<0.001) and between the superior turbinate and choana (p = 0.006) postoperatively. However, these did not correlate with subjectively assessed symptoms (NOSE, SNOT-20 and VAS; all nasal cavity areas; p≥0.05) or olfactory dysfunction (CCCRC and CCSIT test; all nasal cavity areas; p≥0.05). Conclusion: Skull base tumor surgery via an endoscopic endonasal transsphenoidal approach altered the patients’ nasal anatomy, but the changes in nasal cavity volumes did not affect nasal function or symptoms. These results will help surgeons to appropriately expose the surgical field during an endoscopic endonasal transsphenoidal approach. [ABSTRACT FROM AUTHOR]

With advances in therapeutic instruments and techniques, three-dimensional dose delivery has been widely used in radiotherapy. The verification of dose distribution in a small field becomes critical because of the obvious dose gradient within the field. The study investigates the dose distributions of various field sizes by using NIPAM polymer gel dosimeter. The dosimeter consists of 5% gelatin, 5% monomers, 3% cross linkers, and 5 mM THPC. After irradiation, a 24 to 96 hour delay was applied, and the gel dosimeters were read by a cone beam optical computed tomography (optical CT) scanner. The dose distributions measured by the NIPAM gel dosimeter were compared to the outputs of the treatment planning system using gamma evaluation. For the criteria of 3%/3 mm, the pass rates for 5 × 5, 3 × 3, 2 × 2, 1 × 1, and 0.5 × 0.5 cm2 were as high as 91.7%, 90.7%, 88.2%, 74.8%, and 37.3%, respectively. For the criteria of 5%/5 mm, the gamma pass rates of the 5 × 5, 3 × 3, and 2 × 2 cm2 fields were over 99%. The NIPAM gel dosimeter provides high chemical stability. With cone-beam optical CT readouts, the NIPAM polymer gel dosimeter has potential for clinical dose verification of small-field irradiation. [ABSTRACT FROM AUTHOR]

Background: Perihematomal edema contributes to secondary brain injury in the course of intracerebral hemorrhage. The effect of decompressive surgery on perihematomal edema after intracerebral hemorrhage is unknown. This study analyzed the course of PHE in patients who were or were not treated with decompressive craniectomy. Methods: More than 100 computed tomography images from our published cohort of 25 patients were evaluated retrospectively at two university hospitals in Switzerland. Computed tomography scans covered the time from admission until day 100. Eleven patients were treated by decompressive craniectomy and 14 were treated conservatively. Absolute edema and hematoma volumes were assessed using 3-dimensional volumetric measurements. Relative edema volumes were calculated based on maximal hematoma volume. Results: Absolute perihematomal edema increased from 42.9 ml to 125.6 ml (192.8%) after 21 days in the decompressive craniectomy group, versus 50.4 ml to 67.2 ml (33.3%) in the control group (Δ at day 21 = 58.4 ml, p = 0.031). Peak edema developed on days 25 and 35 in patients with decompressive craniectomy and controls respectively, and it took about 60 days for the edema to decline to baseline in both groups. Eight patients (73%) in the decompressive craniectomy group and 6 patients (43%) in the control group had a good outcome (modified Rankin Scale score 0 to 4) at 6 months (P = 0.23). Conclusions: Decompressive craniectomy is associated with a significant increase in perihematomal edema compared to patients who have been treated conservatively. Perihematomal edema itself lasts about 60 days if it is not treated, but decompressive craniectomy ameliorates the mass effect exerted by the intracerebral hemorrhage plus the perihematomal edema, as reflected by the reduced midline shift. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate whether early reductions in CT perfusion parameters predict response to pre-operative chemotherapy prior to surgery for gastroesophageal junction (GEJ) and gastric cancer. Materials and Methods: Twenty-eight patients with adenocarcinoma of the gastro-esophageal junction (GEJ) and stomach were included. Patients received three series of chemotherapy before surgery, each consisting of a 3-week cycle of intravenous epirubicin, cisplatin or oxaliplatin, concomitant with capecitabine peroral. The patients were evaluated with a CT perfusion scan prior to, after the first series of, and after three series of chemotherapy. The CT perfusion scans were performed using a 320-detector row scanner. Tumour volume and perfusion parameters (arterial flow, blood volume and permeability) were computed on a dedicated workstation with a consensus between two radiologists. Response to chemotherapy was evaluated by two measures. Clinical response was defined as a tumour size reduction of more than 50%. Histological response was evaluated based on residual tumour cells in the surgical specimen using the standardized Mandard Score 1 to 5, in which values of 1 and 2 were classified as responders, and 3 to 5 were classified as nonresponders. Results: A decrease in tumour permeability after one series of chemotherapy was positively correlated with clinical response after three series of chemotherapy. Significant changes in permeability and tumour volume were apparent after three series of chemotherapy in both clinical and histological responders. A cut-off value of more than 25% reduction in tumour permeability yielded a sensitivity of 69% and a specificity of 58% for predicting clinical response. Conclusion: Early decrease in permeability is correlated with the likelihood of clinical response to pre-operative chemotherapy in GEJ and gastric cancer. As a single diagnostic test, CT Perfusion only has moderate sensitivity and specificity in response assessment of pre-operative chemotherapy making it insufficient for clinical decision purposes. [ABSTRACT FROM AUTHOR]