Portal Imaging Research Articles

Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients.

ObjectivesTo investigate the potential value of a contrast enhanced computed tomography (CECT)-based radiological-radiomics nomogram combining a lymph node (LN) radiomics signature and LNs’ radiological features for preoperative detection of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).Materials and methodsIn this retrospective study, 196 LNs in 61 PDAC patients were enrolled and divided into the training (137 LNs) and validation (59 LNs) cohorts. Radiomic features were extracted from portal venous phase images of LNs. The least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to select optimal features to determine the radiomics score (Rad-score). The radiological-radiomics nomogram was developed by using significant predictors of LN metastasis by multivariate logistic regression (LR) analysis in the training cohort and validated in the validation cohort independently. Its diagnostic performance was assessed by receiver operating characteristic curve (ROC), decision curve (DCA) and calibration curve analyses.ResultsThe radiological model, including LN size, and margin and enhancement pattern (three significant predictors), exhibited areas under the curves (AUCs) of 0.831 and 0.756 in the training and validation cohorts, respectively. Nine radiomic features were used to construct a radiomics model, which showed AUCs of 0.879 and 0.804 in the training and validation cohorts, respectively. The radiological-radiomics nomogram, which incorporated the LN Rad-score and the three LNs’ radiological features, performed better than the Rad-score and radiological models individually, with AUCs of 0.937 and 0.851 in the training and validation cohorts, respectively. Calibration curve analysis and DCA revealed that the radiological-radiomics nomogram showed satisfactory consistency and the highest net benefit for preoperative diagnosis of LN metastasis.ConclusionsThe CT-based LN radiological-radiomics nomogram may serve as a valid and convenient computer-aided tool for personalized risk assessment of LN metastasis and help clinicians make appropriate clinical decisions for PADC patients.

THE INFORMATIVE VALUE OF MAGNETIC RESONANCE IMAGING IN THE DIAGNOSIS OF RECTAL TUMOR INVOLVEMENT AGAINST THE BACKGROUND OF COMBINED TREATMENT

The aim of the study is to prove the informative value of magnetic resonance imaging in the diagnosis of rectal tumor involvement against the background of combined treatment for the patients (chemotherapy + radiation therapy) on the basis of the AI "Republican Oncological Dispensary", Cheboksary. All patients underwent magnetic resonance imaging on Elekta Synergy linear accelerator with a multi-leaf collimator, an integrated XVI X-ray volumetric imaging system and iViewGT portal imaging system, Mosaiq information and control system. Statistical processing was performed using standard methods of medical statistics using the SPSS Statistics package (version 17.0), arithmetic mean (M), standard deviation from the general mean age, mean value errors (m), Student's t-test were also calculated. Magnetic resonance imaging diagnostics made it possible to identify and dynamically control the change in the tumor involvement of the rectum against the background of the treatment: a change in the localization of the tumor in different parts of the rectum, a change in localization depending on the distance from the anal margin, a change in the thickness of the tumor wall, a change in the size and number of lymph nodes. Extramural venous invasion (EMVI), marginal resection boundary (CRM) of the tumor and invasion into surrounding tissues after surgical intervention were evaluated. This article examines the incidence of rectal cancer depending on age, gender, complaints, oncological history, risk factors contributing to the development of this pathology, as well as the presence of concomitant diseases that aggravate treatment. According to the results of the study, rectal cancer is revealed to be most often found in people aged 60–70, while men are most susceptible to the disease. According to histomorphological data, adenocarcinoma prevailed in 94 persons. Rectal cancer before and after chemoradiotherapy, according to magnetic resonance imaging (MRI) findings, prevailed in the rectal lower ampulla (before chemoradiotherapy – in 51 persons, after chemoradiotherapy – in 48), while the malignant neoplasm was at the distance of 21-30 mm from the anal margin in 27 persons, with the average wall thickness of 21.5 mm according to MRI findings before chemoradiotherapy and 13.9 mm after chemoradiotherapy. The difference in the average wall thickness according to the findings of two MRI studies was 7.61 mm (33.3%), which indicates the effectiveness of chemoradiotherapy.

Radiomics approach based on biphasic CT images well differentiate "early stage" of adrenal metastases from lipid-poor adenomas: A STARD compliant article.

The aim of the study was to develop an optimal radiomics model based on abdominal contrast-enhanced computed tomography (CECT) for pre-operative differentiation of “early stage” adrenal metastases from lipid-poor adenomas (LPAs). This retrospective study included 188 patients who underwent abdominal CECT (training cohort: LPAs, 68; metastases, 64; validation cohort: LPAs, 29; metastases, 27). Abdominal CECT included plain, arterial, portal, and venous imaging. Clinical and CECT radiological features were assessed and significant features were selected. Radiomic features of the adrenal lesions were extracted from four-phase CECT images. Significant radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression. The clinical-radiological, unenhanced radiomics, arterial radiomics, portal radiomics, venous radiomics, combined radiomics, and clinical-radiological-radiomics models were established using a support vector machine (SVM). The DeLong test was used to compare the areas under the receiver operating characteristic curves (AUCs) of all models. The AUCs of the unenhanced (0.913), arterial (0.845), portal (0.803), and venous (0.905) radiomics models were all higher than those of the clinical-radiological model (0.788) in the testing dataset. The AUC of the combined radiomics model (incorporating plain and venous radiomics features) was further improved to 0.953, which was significantly higher than portal radiomics model (P = .033) and clinical-radiological model (P = .009), with the highest accuracy (89.13%) and a relatively stable sensitivity (91.67%) and specificity (86.36%). As the optimal model, the combined radiomics model based on biphasic CT images is effective enough to differentiate “early stage” adrenal metastases from LPAs by reducing the radiation dose.

Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study.

To develop a radiomics-based hepatocellular carcinoma (HCC) grade classifier model based on data from gadoxetic acid-enhanced MRI. This retrospective study included 137 patients who underwent hepatectomy for a single HCC and gadoxetic acid-enhanced MRI within 60days before surgery. HCC grade was categorized as low or high (modified Edmondson-Steiner grade I-II vs. III-IV). We used the hepatobiliary phase (HBP), portal venous phase, T2-weighted image(T2WI), and T1-weighted image(T1WI). From the volume of interest in HCC, 833 radiomic features were extracted. Radiomic and clinical features were selected using a random forest regressor, and the classification model was trained and validated using a random forest classifier and tenfold stratified cross-validation. Eight models were developed using the radiomic features alone or by combining the radiomic and clinical features. Models were validated with internal enrolled data (internal validation) and a dataset (28 patients) at a separate institution (external validation). The area under the curve (AUC) of the validation results was compared using the DeLong test. In internal and external validation, the HBP radiomics-only model showed the highest AUC (internal 0.80 ± 0.09, external 0.70 ± 0.09). In external validation, all models showed lower AUC than those for internal validation, while the T2WI and T1WI models failed to predict the HCC grade (AUC 0.30-0.58) in contrast to the internal validation results (AUC 0.67-0.78). The radiomics-based machine learning model from gadoxetic acid-enhanced liver MRI could distinguish between low- and high-grade HCCs. The radiomics-only HBP model showed the best AUC among the eight models, good performance in internal validation, and fair performance in external validation.

Dose rate versus gantry speed performance evaluation for slow gantry speeds using DICOM RT plans.

PurposeVarian provides a DICOM RT Plan file that users can deliver to the electronic portal imaging device (EPID) panel to confirm the linear accelerator delivers consistent dose output across several regions of interest for varying dose rates and gantry speeds (DRGS). This work investigates if (a) the vendor‐provided DRGS DICOM RT Plan is valid within the gantry speed range of stereotactic body radiation therapy (SBRT) treatments, and (b) if output constancy is maintained at those gantry speeds on a TrueBeam.MethodsPython code was written to iterate through all control points in the DICOM RT Plan files for 21 SBRT patients and the MU per degree values were calculated for each control point. A histogram was generated to illustrate how MU per degree was distributed among the control points from the patient plans. Then, the total number of MUs was increased in the vendor‐provided DRGS DICOM file to make a “modified DRGS DICOM RT Plan,” which surpasses the maximum MU per degree value found in the patient plans, forcing the gantry to travel at slow speeds and deliver more MU per degree over the same arc length (representative of those during SBRT treatment delivery). The modified DRGS DICOM RT Plan file was then delivered on a TrueBeam to acquire EPID images of the dose distribution. The EPID images were analyzed with Pylinac, a Python library that analyzes DICOM RT images acquired during routine linac QA.ResultsOver 83% of patient DICOM RT Plan control points had MU per degree values greater than the MU per degree values in the vendor‐provided DRGS DICOM file. The Pylinac analysis of the EPID‐acquired images found a maximum deviation of 0.4% from machine baselines.ConclusionsThe modified DRGS DICOM file can be used to determine if a TrueBeam linac is operating within specifications even when very low gantry speeds are reached.

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study.

To investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to that of other reconstruction algorithms in a phantom experiment and an abdominal clinical study. An elliptical phantom with five different iodine concentrations (1-12 mgI/mL) was imaged five times with fast-kilovoltage-switching DECT for three target volume CT dose indexes. All images were reconstructed using filtered back-projection, iterative reconstruction (two levels), and DLIR algorithms. Measured and nominal iodine concentrations were compared among the algorithms. Contrast-enhanced CT of the abdomen with the same scanner was acquired in clinical patients. In arterial and portal venous phase images, iodine concentration, image noise, and coefficients of variation for four locations were retrospectively compared among the algorithms. One-way repeated-measures analyses of variance were used to evaluate differences in the iodine concentrations, standard deviations, coefficients of variation, and percentages of error among the algorithms. In the phantom study, the measured iodine concentrations were equivalent among the algorithms: within ± 8% of the nominal values, with root-mean-square deviations of 0.08-0.36 mgI/mL, regardless of radiation dose. In the clinical study (50 patients; 35 men; mean age, 68 ± 11 years), iodine concentrations were equivalent among the algorithms for each location (all p > .99). Image noise and coefficients of variation were lower with DLIR than with the other algorithms (all p < .01). The DLIR algorithm reduced image noise and variability of iodine concentration values compared with other reconstruction algorithms in the fast-kilovoltage-switching dual-energy CT. • In the phantom study, standard deviations and coefficients of variation in iodine quantification were lower on images with the deep learning image reconstruction algorithm than on those with other algorithms. • In the clinical study, iodine concentrations of measurement location in the upper abdomen were consistent across four reconstruction algorithms, while image noise and variability of iodine concentrations were lower on images with the deep learning image reconstruction algorithm.

Transit dosimetry of stereotactic body radiotherapy treatments with electronic portal dosimetry device in patient with spinal implant.

In recent years, the use of the Electronic Portal Imaging Device (EPID) as an in vivo dosimeter has become widespread. However, reports of EPID for stereotactic body radiotherapy (SBRT) applications is scarce. There is no data on this topic especially when there are high-density materials in the radiation field. In this study, we aimed to investigate the dose distributions of SBRT treatment plans in patients with spinal implants by transit EPID dosimetry. Implants were inserted in phantoms that mimic the vertebrae, and VMAT plans were created on the phantoms to deliver 16Gy radiation doses to the target in 1 fraction. Transit EPID measurements were performed for each irradiation. The results were compared with the treatment planning system using the gamma analysis method. According to the gamma analysis results, while the non-implant model met the acceptance criteria with a rate of 95.4%, the implanted models did not pass the test with results between the rates of 70% to 73%. In addition, while the dose difference in the isocenter was 1.3% for the non-implanted model, this difference was observed to be between 7 and 8% in the implanted models. Our study revealed that EPID can be used as transit dosimetry for the VMAT-SBRT applications. However, unacceptable dose differences were obtained by transit EPID dosimetry in the VMAT-SBRT applications of patients with an implant. In the treatment of such patients, alternative treatment methods should be preferred in which the interaction of the implants with radiation can be prevented.

Factors Affecting Isocenter Displacement and Planning Target Volume Margin for Patients With Rectal Cancer Receiving Radiation Therapy

PurposeSetup errors are inherent in the process of daily radiation therapy (RT) delivery. Pelvic RT for rectal cancer is one of the body sites associated with the largest shift among other body sites. This study aimed to evaluate interfraction random and systematic errors and hence propose the optimum planning target volume (PTV) in patients with rectal cancer. Methods and MaterialsTranslational and angular isocenter displacements were retrospectively collected for 189 patients. Random and systematic errors were determined, and then the PTV margin was computed. Effect of positioning, body mass index (BMI), and type of immobilization were studied. Portal images before and after online correction were used to define PTV for no-daily image-guided radiotherapy (IGRT) and daily IGRT respectively. ResultsBefore the online correction, the systematic errors were 2.5, 2.8, and 3.0 mm for superior-inferior (SI), right-left (RL), and anterior-posterior (AP) directions, respectively, compared with 2.1, 1.7, and 1.8 mm after online correction. The random errors were 6.2, 7.4, and 8.2 mm in SI, RL, and AP, respectively, before online correction, compared with 4, 4.2, and 4.5 mm after online correction. The recommended PTV margin was 0.7 and 1.0 cm for daily IGRT and no-daily IGRT, respectively. The prone position and BMI >30 kg/m2 warrant higher margins in no-daily IGRT cases, 1.2 and 1.4 cm, respectively. ConclusionsThe prone position, BMI >30 kg/m2, and belly board device are associated with larger daily setup errors warranting higher PTV margins for no-daily IGRT; however, that can be avoided by using daily IGRT.

Dose deviations induced by fractional image guidance system errors in intensity-modulated radiotherapy for brain tumor treatment

Background: Intensity-modulated radiotherapy (IMRT) techniques have a steep dose distribution, leading to large dosimetric errors caused by incorrect daily patient setup. Image-guided radiotherapy (IGRT) is an essential technique to verify accurate patient setup. The accuracy of verifying a patient’s position depends on many factors including the image guidance system and image registration software. Objectives: This study investigated the dosimetric and geometric differences between the original plan and simulated plans with setup errors associated with kilovoltage cone-beam computed tomography (kV-CBCT), kV, kV planar image, and electronic portal imaging detector (EPID) for brain tumor treatment using IMRT. Materials and methods: A PIXY Anthropomorphic Training/Teaching Phantom was used in this study. IMRT treatment plans with five co-planar fields from a 6 MV Elekta Versa HD linear accelerator were generated using the RayStation computer treatment planning system. Three image guidance systems, including kV-CBCT, kV planar imager, and EPID were used to perform image registration. To evaluate the efficiency of each image guidance system, a simulated setup error by couch was shifted 0, ±2, and ±4 mm in the lateral, longitudinal, and vertical planes. Errors in the image registration from the actual couch shift were collected. Measured errors were used to generate the treatment plans of 25 fractions using the IMRT technique by random shifts of the isocenter of each fraction while maintaining other planning parameters of the original plan. The dose deviation in planning target volume (PTV) and geometric deviation compared to original plan were recalculated and analyzed. Results: Accuracy of image registration in all image guidance systems indicated that the registration errors were less than 1.7, 2.0, and 1.0 mm for the kV-CBCT, kV planar, and EPID, respectively. The average PTV dosimetric deviation induced by the setup error ranged from -2 to 2 mm per fraction; this study showed dosimetric deviation at D98% approximately -6%, D95% approximately -4% and D2% below -1%, while the average of isodose distribution shift of -5% inside the PTV of the isocenter region were -1.58±0.67, -1.28±0.52 and -1.30±0.80 mm in the cerebella, parasagittal, and convex areas, respectively. An isodose shift of -10% was &lt;1 mm in all PTV locations. Conclusion: The efficiency of image guidance resulted in small errors within ±2 mm using kV-CBCT, kV planar, and EPID. The setup error influenced daily dose distribution, while the PTV recorded underdose of about 4% in the brain IMRT technique.

Radiomics in Addition to Computed Tomography-Based Body Composition Nomogram May Improve the Prediction of Postoperative Complications in Gastric Cancer Patients

Objectives: The study aimed to determine the impact of computed tomography (CT)-based body composition and radiomics nomogram on the prediction of postoperative complications in gastric cancer. Methods: The clinical data of 457 individuals with surgically confirmed gastric cancer, 320 patients in the training cohort (TC) and 137 patients in the validation cohort (VC), were retrospectively analyzed. Body composition data were measured using CT. Postoperative complications were graded using the Clavien-Dindo system. Dedicated radiomics prototype software was used to segment lesions and extract characteristics from preoperative portal venous-phase CT images. Clinical, radiomics, and combined models were developed using logistic regression analysis. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristic curve, and the prediction ability of the optimal model was evaluated using calibration curves and decision curve analysis (DCA). Results: Nutritional Risk Screening 2002 (NRS2002) score, sarcopenia, and blood loss were independent predictors of postoperative complications in gastric cancer. A radiomics signature was created using 19 conserved radiomics features. The nomogram based on both the clinical model and radiomics signature showed the greatest predictive performance, with AUCs of 0.763 (95% confidence interval [CI], 0.708–0.817) and 0.748 (95% CI: 0.667–0.818) in the TC and VC, respectively. The calibration curve and DCA revealed that the nomogram was beneficial in clinical practice for the preoperative prediction of postoperative complications. Conclusions: The combined model consisting of NRS2002 score, sarcopenia, blood loss, and a radiomics signature holds potential application value for the individualized prediction of postoperative complications in gastric cancer patients.

What is the optimal input information for deep learning-based pre-treatment error identification in radiotherapy?

Deep learning (DL) provides high sensitivity for detecting and identifying errors in pre-treatment radiotherapy quality assurance (QA). This work's objective was to systematically evaluate the impact of different dose comparison and image preprocessing methods on DL model performance for error identification in pre-treatment QA. For 53 volumetric modulated arc therapy (VMAT) and 69 stereotactic body radiotherapy (SBRT) treatment plans of lung cancer patients, mechanical errors were simulated (MLC leaf positions, monitor unit scaling, collimator rotation). Two classification levels were assessed: error type (Level 1) and error magnitude (Level 2). Portal dose images with and without errors were compared using standard (gamma analysis), simple (absolute/relative dose difference, ratio) and alternative (distance-to-agreement, structural similarity index, gradient) dose comparison methods. For preprocessing, different normalization methods (min/max and mean/standard deviation) and image resolutions (32×32, 64×64 and 128×128) were evaluated. All possible combinations of classification level, dose comparison, normalization method and image size resulted in 144 input datasets for DL networks for error identification. Average accuracy was highest for simple dose comparison methods (Level 1: 97.7%, Level 2: 78.1%) while alternative methods scored lowest (Level 1: 91.6%, Level 2: 71.2%). Mean/stdev normalization particularly improved Level 2 classification. Higher image resolution improved error identification, although for SBRT lower image resolution was also sufficient. The choice of dose comparison method has the largest impact on error identification for pre-treatment QA using DL, compared to image preprocessing. Model performance can improve by using simple dose comparison methods, mean/stdev normalization and high image resolution.

ExacTrac Dynamic workflow evaluation: Combined surface optical/thermal imaging and X-ray positioning.

In modern radiotherapy (RT), especially for stereotactic radiotherapy or stereotactic radiosurgery treatments, image guidance is essential. Recently, the ExacTrac Dynamic (EXTD) system, a new combined surface‐guided RT and image‐guided RT (IGRT) system for patient positioning, monitoring, and tumor targeting, was introduced in clinical practice. The purpose of this study was to provide more information about the geometric accuracy of EXTD and its workflow in a clinical environment. The surface optical/thermal‐ and the stereoscopic X‐ray imaging positioning systems of EXTD was evaluated and compared to cone‐beam computed tomography (CBCT). Additionally, the congruence with the radiation isocenter was tested. A Winston Lutz test was executed several times over 1 year, and repeated end‐to‐end positioning tests were performed. The magnitude of the displacements between all systems, CBCT, stereoscopic X‐ray, optical‐surface imaging, and MV portal imaging was within the submillimeter range, suggesting that the image guidance provided by EXTD is accurate at any couch angle. Additionally, results from the evaluation of 14 patients with intracranial tumors treated with open‐face masks are reported, and limited differences with a maximum of 0.02 mm between optical/thermal‐ and stereoscopic X‐ray imaging were found. As the optical/thermal positioning system showed a comparable accuracy to other IGRT systems, and due to its constant monitoring capability, it can be an efficient tool for detecting intra‐fractional motion and for real‐time tracking of the surface position during RT.

First clinical experience with real-time portal imaging-based breath-hold monitoring in tangential breast radiotherapy.

Background and purposeReal-time treatment monitoring with the electronic portal imaging device (EPID) can conceptually provide a more accurate assessment of the quality of deep inspiration breath-hold (DIBH) and patient movement during tangential breast radiotherapy (RT). A system was developed to measure two geometrical parameters, the lung depth (LD) and the irradiated width (named here skin distance, SD), along three user-selected lines in MV EPID images of breast tangents. The purpose of this study was to test the system during tangential breast RT with DIBH.Materials and methodsMeasurements of LDs and SDs were carried out in real time. DIBH was guided with a commercial system using a marker block. Results from 17 patients were assessed. Mean midline LDs, <mLDs>, per tangent were compared to the planned mLDs; differences between the largest and smallest observed <mLDs> (<mSDs>) per tangent were calculated.ResultsFor 56% (162/288) of the tangents tested, <mLDs> were outside the tolerance window. All but one patient had at least one fraction showing this behaviour. The largest difference found between an <mLD> and its planned mLD was −16.9 mm. The accuracy of patient positioning and the quality of marker-block-based DIBH guidance contributed to the differences. Fractions with patient position verification using a single EPID image taken before treatment showed a lower rate (34%), suggesting reassessment of setup procedures.ConclusionsReal-time treatment monitoring of the internal anatomy during DIBH delivery of tangential breast RT is feasible and useful. The new system requires no additional radiation for the patient.

Portal dosimetry correction method for validation of single isocenter VMAT plans for multiple brain metastases.

Portal dosimetry is one option for verification of volumetric‐modulated arc therapy (VMAT) planning for multiple brain metastases. However, due to the changing response of the portal imager with photon beam energy, the dose transmitted through closed multileaf collimator (MLC) leaves or narrow MLC gaps may be underestimated by the imager.We present a simple method for correcting for these effects that may be implemented within the Eclipse treatment planning system. We recalculated the predicted portal dose with and without this correction for 20 multiple brain met VMAT plans. Before the correction, 3/20 composite plan fields passed our standard quality assurance (QA) criteria (54/80 individual fields); the average gamma passing rate for the composite plans was 76.9 ± 16.6%, and the average gamma value across the composite plans was 0.67 ± 0.23. After correction, 20/20 composite plan fields passed the QA criteria (80/80 individual fields); the average gamma passing rate for composite plans was 99.2 ± 1.4%, the average gamma value across the composite plans was 0.33 ± 0.90. A measure of plan complexity, the average leaf pair opening could be correlated to the gamma analysis results for the uncorrected plans but not for the corrected plans.

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors.

ObjectivesThis study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.MethodsA total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.ResultsThe MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p<0.001).ConclusionsMachine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.

Transit-guided radiation therapy: proof of concept of an on-line technique for correcting position errors using transit portal images

Objective. Transit in vivo dosimetry methods monitor that the dose distribution is delivered as planned. However, they have a limited ability to identify and to quantify the cause of a given disagreement, especially those caused by position errors. This paper describes a proof of concept of a simple in vivo technique to infer a position error from a transit portal image (TPI). Approach. For a given treatment field, the impact of a position error is modeled as a perturbation of the corresponding reference (unperturbed) TPI. The perturbation model determines the patient translation, described by a shift vector, by comparing a given in vivo TPI to the corresponding reference TPI. Patient rotations can also be determined by applying this formalism to independent regions of interest over the patient. Eight treatment plans have been delivered to an anthropomorphic phantom under a large set of couch shifts (<15 mm) and rotations (<10°) to experimentally validate this technique, which we have named Transit-Guided Radiation Therapy (TGRT). Main results. The root mean squared error (RMSE) between the determined and the true shift magnitudes was 1.0/2.4/4.9 mm for true shifts ranging between 0–5/5–10/10–15 mm, respectively. The angular accuracy of the determined shift directions was 12° ± 14°. The RMSE between the determined and the true rotations was 0.5°. The TGRT technique decoupled translations and rotations satisfactorily. In 96% of the cases, the TGRT technique decreased the existing position error. The detection threshold of the TGRT technique was around 1 mm and it was nearly independent of the tumor site, delivery technique, beam energy or patient thickness. Significance. TGRT is a promising technique that not only provides reliable determinations of the position errors without increasing the required equipment, acquisition time or patient dose, but it also adds on-line correction capabilities to existing methods currently using TPIs.

Iodine Images in Dual-energy CT: Detection of Hepatic Steatosis by Quantitative Iodine Concentration Values

Hepatic steatosis is a common condition and an early manifestation of a systemic metabolic syndrome. As of today, there is no broadly accepted method for the diagnosis of hepatic steatosis in contrast-enhanced CT images. This retrospective study evaluates the potential of quantitative iodine values in portal venous phase iodine images in dual-energy CT (DECT) by measuring iodine concentrations in regions of interest (ROI) and analyzing the absolute iodine concentration of the liver parenchyma as well as three different blood-normalized iodine concentrations in a study cohort of 251 patients. An independent two sample t-test (p < 0.05) was used to compare the iodine concentrations of healthy and fatty liver. Diagnostic performance was assessed by ROC (receiver operating characteristic) curve analysis. The results showed significant differences between the average iodine concentration of healthy and fatty liver parenchyma for the absolute and for the blood-normalized iodine concentrations. The study concludes that the iodine uptake of the liver parenchyma is impaired by hepatic steatosis, and that the measurement of iodine concentration can provide a suitable method for the detection of hepatic steatosis in quantitative iodine images. Suitable thresholds of quantitative iodine concentration values for the diagnosis of hepatic steatosis are provided.

The fast calibration model for dosimetry with an electronic portal imaging device.

The aim of this study was to develop an algorithm that corrects the image of an electronic portal imaging device (EPID) of a linear accelerator so that it can be used for dosimetric purposes, such as in vivo dosimetry or quality assurance for photon radiotherapy. For that purpose, the impact of the field size, phantom thickness, and the varying spectral photon distribution within the irradiation field on the EPID image was investigated. The EPID measurements were verified using reference measurements with ionization chambers. Therefore, absolute dose measurements with an ionization chamber and relative dose measurements with a detector array were performed. An EPID calibration and correction algorithm was developed to convert the EPID image to a dose distribution. The algorithm was validated by irradiating inhomogeneous phantoms using square fields as well as irregular IMRT fields. It was possible to correct the influence of the field size, phantom thickness on the EPID signal as well as the homogenization of the image profile by several correction factors within 0.6%. A gamma index analysis (3%, 3mm) of IMRT fields showed a pass rate of above 99%, when comparing to the planning system. The developed algorithm enables an online dose measurement with the EPID during the radiation treatment. The algorithm is characterized by a robust, non-iterative, and thus real-time capable procedure with little measuring effort and does not depend on system-specific parameters. The EPID image is corrected by multiplying three independent correction factors. Therefore, it can easily be extent by further correction factors for other influencing variables, so it can be transferred to other linear accelerators and EPID configurations.

Wizja portalu w twórczości z gatunku horror, dark fantasy i science fiction (na przykładach tekstów „Nigdziebądź” Neila Gaimana, „Nocny patrol” Siergieja Łukjanienki, „Nie oglądaj się i milcz” Maksyma Kidruka, „Solaris” Stanisława Lema)

The article is devoted to the semantics of the portal image as an important component of fantasy and horror literature. The text focuses on the features of the representation of this artistic constant at the chronotype level, its changes resulting from the literary tradition and the author's individual intentions.The functions of the portal image in the artistic system of horror literary works were determined by the expression of space-time shifts and deformations in the artistic models of the world present in selected works and the combination of "real" and "unreal" chronotypes. In the article, particular attention is paid to the analysis of the organization of space-time characteristics of the author's images of the world, their correlation with the leading methods of psychological perception of extra-textual reality (top - bottom, mine - foreign). The portal's visions from selected works are presented in juxtaposition with the literary ideas of Stanisław Lem, preserved in the image of the planet Solaris.Because the functional component of the lead images is correlated with the portal constant, their integrity can be stated as determinants of genres such as: dark fantasy, horror and science fiction. The article is supplemented by a concise bibliography of thematic critical literary texts and source materials.

Investigation on the impact of the leaf trailing effect using the Halcyon integrated platform system.

The double-stacked design of the Halcyon multileaf collimator (MLC) presents new challenges for treatment planning systems (TPSs). The leaf trailing effect has recently been described as the result of the interplay between the fluence transmitted through the leaf tip ends of each MLC layer. This effect makes the dosimetric leaf gap (DLG) dependent on the distance between the leaves of different layers (trailing distance) and is not adequately modeled by the Eclipse TPS. The purpose of our study was to investigate and report the dose discrepancies produced by these limitations in clinical plans and to explore how these discrepancies can be mitigated and avoided. The integrated platform with the Halcyon v2 system, Eclipse and Aria v15.6, was used. The dose discrepancies were obtained with electronic portal imaging device (EPID) images and the portal dosimetry software and validated using radiochromic film dosimetry. The results for the AIDA commissioning test and for nine selected clinical beams with the sliding window intensity modulated radiotherapy (dIMRT) technique were thoroughly analyzed and presented. First, the digital imaging and communications in medicine radiotherapy (DICOM RT) plans were exported and the fluences were computed using different leaf tip models, and then were compared. Second, the detailed characteristics of the corresponding leaf sequences were investigated. Finally, modified DICOM RT plans were created in which the noncollimating (backup) leaves were retracted 2mm to increase the leaf trailing distance, the modified plans were imported back into the TPS and the measurements were repeated. Dedicated in-house tools were developed in Python to carry out allanalyses. Dose discrepancies greater than 10% and regions of gamma failure were found in both the AIDA test and clinical beams using static-gantry dIMRT. Fluence analysis highlighted that the discrepancies were due to limitations in the MLC model implemented in the TPS. Analysis of leaf sequences indicated that regions of failure were associated with very low leaf speeds and virtually motionless leaves within the beam aperture. Some of these discrepancies were mitigated by increasing the trailing distance of the noncollimating leaves without affecting the beam aperture, but this strategy was not possible in regions where the leaves from both layers actively defined the beamaperture. Current limitations of the MLC model in Eclipse produced discrepancies between calculated and delivered doses in clinical beams that caused plan-specific quality assurance failures and interruptions in the clinical workflow. Careful evaluation of the clinical plans produced by Eclipse for the Halcyon is recommended, especially for static gantry dIMRT treatments. Some characteristics of leaf sequences are problematic and should be avoided in clinical plans and, in general, a better leaf tip model is needed. This is particularly important in adaptive radiotherapy treatments, where the accuracy and reliability of TPS dose calculations are of the utmostimportance.