Tumor Contour Research Articles

The contact length between the tumor contour and the lung on computed tomography is a risk factor for pleural recurrence after complete resection of thymoma.

Among the perioperative findings which included patient characteristics and imaging, surgical, and pathological findings of the tumor, the risk factors for pleural recurrence (PR) after complete resection of thymoma were explored. Fifty-three patients with Masaoka stage I to III thymoma who underwent complete resection, and five patients with stage IVa disease accompanied with pleural dissemination were analyzed. In these cohorts, the patients were divided into a non-recurrence (NR) group, PR group and stage IVa group. The maximum tumor diameter (TD) and the contact length between the tumor contour and the lung (CLTL) were measured at the maximum section of the tumor on axial computed tomography. A multivariate analysis including gender, age, TD, CLTL, tumor adhesion to the lung, Masaoka stage, and WHO histological type was performed to estimate risk factors for PR. The median follow-up period was 76.9 months. PR developed in six patients. The mean TDs were 44 mm in the NR group, 62 mm in the PR group and 73 mm in the stage IVa group, respectively. The mean CLTLs were 47, 90 and 96 mm, respectively. The CLTLs of the PR group were significantly longer than those of the NR group (p < 0.001), although the TDs were not significantly different between the two groups. The multivariate analysis indicated that CLTL was only a risk factor for PR (p = 0.046). The CLTL was significantly associated with the PR after complete resection of thymoma.

Dosimetric benefit of adaptive re-planning in pancreatic cancer stereotactic body radiotherapy

Stereotactic body radiotherapy (SBRT) shows promise in unresectable pancreatic cancer, though this treatment modality has high rates of normal tissue toxicity. This study explores the dosimetric utility of daily adaptive re-planning with pancreas SBRT. We used a previously developed supercomputing online re-planning environment (SCORE) to re-plan 10 patients with pancreas SBRT. Tumor and normal tissue contours were deformed from treatment planning computed tomographies (CTs) and transferred to daily cone-beam CT (CBCT) scans before re-optimizing each daily treatment plan. We compared the intended radiation dose, the actual radiation dose, and the optimized radiation dose for the pancreas tumor planning target volume (PTV) and the duodenum. Treatment re-optimization improved coverage of the PTV and reduced dose to the duodenum. Within the PTV, the actual hot spot (volume receiving 110% of the prescription dose) decreased from 4.5% to 0.5% after daily adaptive re-planning. Within the duodenum, the volume receiving the prescription dose decreased from 0.9% to 0.3% after re-planning. It is noteworthy that variation in the amount of air within a patient׳s stomach substantially changed dose to the PTV. Adaptive re-planning with pancreas SBRT has the ability to improve dose to the tumor and decrease dose to the nearby duodenum, thereby reducing the risk of toxicity.

A compilation on the contribution of the classic-curvature and the intensity-curvature functional to the study of healthy and pathological MRI of the human brain

This paper places the emphasis on two intensity-curvature measurement approaches called: 1) classic-curvature (CC); 2) intensity-curvature functional (ICF) and makes a summary on the contribution of the CC and the ICF to the study of magnetic resonance imaging (MRI) of the healthy human brain and also of human brain tumors. The summary is structured referencing earlier related works and also adding evidence of the improvements to MRI imaging: 1) the CC changes the gray scale of the MRI; 2) the ICF shows the visually perceptible third dimension which can identify the tumor contour line; 3) the CC and the ICF are indeed filter masks, which determine two additional domains of the MRI image. Thus, the evidence makes this piece of work different from related research and also indicates that there are four domains in addition to the MRI where is possible to study both healthy and pathological human brains.

The correlation of morphological features of chest computed tomographic scans with clinical characteristics of thymoma.

Chest computed tomography (CT) scanning has been widely utilized in thymoma identification and staging as well as in follow-up monitoring for recurrence. However, the relationship between some CT imaging features and pathological types, clinical stage, completeness of resection, or prognosis in thymoma has not been well explored. We retrospectively reviewed preoperative CT imaging for 238 thymoma patients, who had undergone thymectomy from October 2007 to December 2011. All CT parameters were assessed in each case based on clinical and pathological data. Survival analysis was performed by using the Kaplan-Meier and log-rank tests. Tumour contours (P = 0.008), homogeneity (P = 0.009), degree of enhancement (P = 0.013), fat plane obliteration with adjacent structures (P < 0.001), the presence of mediastinal lymphadenopathy (P = 0.010), irregular infiltration into the lung (P = 0.012) and tumour shape (P = 0.007) were associated with the World Health Organization (WHO) histological classification. Lobulated or irregular tumour contours (P < 0.001), presence of calcifications (P = 0.002), infiltration of surrounding fat (P < 0.001), irregular infiltration into the lung (P < 0.001), irregular infiltration into vascular (P < 0.001), more abutment of vessels (P < 0.001) and pulmonary changes adjacent to the tumour (P < 0.001) were associated with the more advanced Masaoka-Koga clinical stage. Tumour contours (P < 0.001), infiltration of surrounding fat (P = 0.008), irregular infiltration into the lung (P < 0.001) and degree of abutment of vessel circumference (P = 0.001) were associated with completeness of resection. With multivariate analysis, no CT image features could reliably predict on the overall or disease-free survival rate. CT imaging does have some features, which are significantly correlated with the WHO classification, the Masaoka-Koga clinical staging and the completeness of resection, although it has no definite role to evaluate preoperatively the survival rate of thymoma patients.

Extragastrointestinal stromal tumors: Computed tomography and magnetic resonance imaging findings

Extragastrointestinal stromal tumors (EGISTs) are rare mesenchymal tumors that originate outside the gastrointestinal tract. The aim of the present study was to investigate the computed tomography (CT) and magnetic resonance imaging (MRI) features of EGISTs and analyze the correlations between radiological findings and pathological features. CT and MRI images of 24 patients with EGISTs were reviewed retrospectively. Patient demographics and tumor characteristics, including localization, size, contours, borders, cystic-necrotic components, calcification, hemorrhage, tumor vessels, attenuation and intensity, degree and pattern of enhancement, ascites, tumor invasion, lymphadenopathy and distant metastasis were recorded. Statistical analyses to compare the radiological characteristics of low- and high-grade EGISTs were performed with χ2 or Fisher’s exact tests. The mean patient age at the time of presentation was 53 years. A total of 24 EGISTs were detected, originating in the omentum (n=4), mesentery (n=19) and retroperitoneum (n=1), respectively. The EGISTs displayed a predominantly spindle cell subtype (87.5%; 21/24). The majority of the EGISTs appeared as large (>10 cm; 70.8%; 17/24), round or oval (66.7%; 16/24), cystic-solid (87.5%; 21/24) and ill-defined (66.7%; 16/24) soft-tissue masses. The EGISTs were hypodense (69.6%; 16/23) or isodense (30.4%; 7/23) on CT images, hypointense (50%; 3/6), isointense (33.3%; 2/6) or hyperintense (16.7%; 1/6) on T1-weighted imaging (T1WI), and hyperintense on T2WI (100%; 6/6) and diffusion-WI (DWI; 100%; 6/6). A total of 54.2% (13/24) of the EGISTs displayed tumor vessels. Overall, 95.8% (23/24) of the masses showed marked enhancement and 87.5% (21/24) demonstrated heterogeneous enhancement. Calcification, hemorrhage, ascites and lymphadenopathy were rare characteristics in the EGISTs. Distant metastases were present in 10 patients (41.7%). The size, borders, tumor vessels and distant metastasis correlated with high-grade EGISTs [>5 mitoses/50 high-power fields (HPFs)] (P<0.05). The results of the present study indicated that clinical and radiological features, including advanced age, a large tumor size, cystic-necrotic components, rare lymphadenopathy, a heterogeneous enhancement pattern and hepatic metastasis may aid in the diagnosis of EGISTs. Radiological characteristics, such as a large size (>10 cm), ill-defined borders, tumor vessels and distant metastasis, can provide useful information in identifying the malignant behavior of EGISTs.

Gastric schwannoma: CT findings and clinicopathologic correlation.

The purpose of this study was to evaluate the computed tomography (CT) imaging characteristics of gastric schwannoma. Eight cases of gastric schwannomas confirmed by surgery and pathology were retrospectively analyzed by CT. We reviewed the CT findings of gastric schwannomas for the following characteristics: tumor location, size, contour, margin, growth pattern, enhancement pattern, the presence or absence of necrosis, and perigastric lymph nodes. The tumors were located in the lesser curvature of gastric body (n = 5) and greater curvature of the gastric antrum (n = 3) with a median size of 4.8 cm (range 1.7-11.4 cm). Gastric schwannomas appeared as submucosal tumors with CT features of ovoid (7/8 patients), well-defined (8/8) and exophytic (4/8) or mixed (3/8) growth patterns. On dynamic CT examination, the tumors displayed homogeneous enhancement in seven cases and heterogeneous enhancement in one case. Solid parts of eight tumors demonstrated mild enhancement during the arterial phase and strengthened progressive enhancement during the venous and delayed phases. Two cases had perigastric lymph nodes. Gastric schwannomas typically manifested as ovoid, well-defined, exophytic, or mixed growth pattern masses on CT. Homogeneous progressive enhancement on dynamic CT is a characteristic finding of gastric schwannoma.

Bayesian Multiscale Modeling of Closed Curves in Point Clouds

Modeling object boundaries based on image or point cloud data is frequently necessary in medical and scientific applications ranging from detecting tumor contours for targeted radiation therapy, to the classification of organisms based on their structural information. In low-contrast images or sparse and noisy point clouds, there is often insufficient data to recover local segments of the boundary in isolation. Thus, it becomes critical to model the entire boundary in the form of a closed curve. To achieve this, we develop a Bayesian hierarchical model that expresses highly diverse 2D objects in the form of closed curves. The model is based on a novel multiscale deformation process. By relating multiple objects through a hierarchical formulation, we can successfully recover missing boundaries by borrowing structural information from similar objects at the appropriate scale. Furthermore, the model’s latent parameters help interpret the population, indicating dimensions of significant structural variability and also specifying a “central curve” that summarizes the collection. Theoretical properties of our prior are studied in specific cases and efficient Markov chain Monte Carlo methods are developed, evaluated through simulation examples and applied to panorex teeth images for modeling teeth contours and also to a brain tumor contour detection problem.

Are there imaging characteristics associated with lung adenocarcinomas harboring ALK rearrangements?

Introduction5% of lung adenocarcinomas harbor rearrangements of the anaplastic lymphoma kinase (ALK) gene. This study compared computed tomography (CT) imaging features in patients with ALK rearrangements and those with EGFR mutations. Material/methods30 patients with ALK rearrangements were studied. 97 patients with epidermal growth factor receptor (EGFR) mutations were used as controls. Features assessed included size and location of thoracic lymphadenopathy, and the size, contour, consistency and location of the primary tumor. Results127 lung adenocarcinomas were examined. 30 (24%) tumors harbored ALK rearrangements, 97 (76%) tumors harbored EGFR mutations. ALK tumors had larger thoracic lymphadenopathy than the control group (p=0.005). Both readers identified 17 (57%) patients in the ALK group with lymph nodes >1.5cm. Reader 1 identified 19 (20%) patients in the EGFR group with lymph nodes >1.5cm, and reader 2 identified 18 (19%) (kappa 0.969). Patients with ALK rearrangements were more likely to have multifocal lymphadenopathy. Reader 1 identified 22 (73%) ALK patients versus 35 (36%) EGFR patients with multifocal thoracic nodal enlargement, while reader 2 identified 20 (67%) ALK patients versus 30 (31%) EGFR patients (kappa 0.953). 92% of ALK positive lesions were solid. ConclusionALK positive lung adenocarcinomas are more likely than EGFR mutant lung adenocarcinomas to be associated with larger volume, multifocal thoracic lymphadenopathy. While routine testing for ALK should be standard, the presence of such characteristics in a solid tumor should further prompt testing for ALK rearrangement.

Sonographic appearances of desmoid tumors.

The medical records of 16 patients (9 female and 7 male; age range, 5-66 years) with 24 lesions that had a histologic diagnosis of desmoid tumors were reviewed at our institution. Six cases were extra-abdominal, 4 intra-abdominal, and 6 in the abdominal wall. Lesions ranged from 1.5 to 18.0 cm in diameter (mean, 6.8 cm). All lesions were solid masses, which appeared hypoechoic, isoechoic, or hyperechoic with homogeneous or heterogeneous echogenicity. Posterior acoustic enhancement was seen in 18 lesions. No lesions showed central necrosis. Most lesions had substantial flow and high resistive index values (>0.70). Sonography can show a desmoid tumor's site, size, contour, margin, echogenicity, homogeneity, vascularity, and resistive index value in detail.

Pancreatic neuroendocrine tumours: correlation between MSCT features and pathological classification.

We aimed to evaluate the multi-slice computed tomography (MSCT) features of pancreatic neuroendocrine neoplasms (P-NENs) and analyse the correlation between the MSCT features and pathological classification of P-NENs. Forty-one patients, preoperatively investigated by MSCT and subsequently operated on with a histological diagnosis of P-NENs, were included. Various MSCT features of the primary tumour, lymph node, and distant metastasis were analysed. The relationship between MSCT features and pathologic classification of P-NENs was analysed with univariate and multivariate models. Contrast-enhanced images showed significant differences among the three grades of tumours in the absolute enhancement (P = 0.013) and relative enhancement (P = 0.025) at the arterial phase. Univariate analysis revealed statistically significant differences among the tumours of different grades (based on World Health Organization [WHO] 2010 classification) in tumour size (P = 0.001), tumour contour (P < 0.001), cystic necrosis (P = 0.001), tumour boundary (P = 0.003), dilatation of the main pancreatic duct (P = 0.001), peripancreatic tissue or vascular invasion (P < 0.001), lymphadenopathy (P = 0.011), and distant metastasis (P = 0.012). Multivariate analysis suggested that only peripancreatic tissue or vascular invasion (HR 3.934, 95 % CI, 0.426-7.442, P = 0.028) was significantly associated with WHO 2010 pathological classification. MSCT is helpful in evaluating the pathological classification of P-NENs. • P-NENs are potentially malignant, and classification of P-NENs carries important prognostic value. • MSCT plays an important role in the diagnosis and staging of P-NENs. • Correlations between classification of P-NENs and imaging findings have not been systematically evaluated. • MSCT could predict P-NENs classification and may be a useful prognostication tool.

Enhancing multimodality functional and molecular imaging using glucose-coated gold nanoparticles

To describe how pegylated glucose-coated gold nanoparticles (PEG-Glu-GNPs) can help improve computed tomography (CT) imaging. PEG-Glu-GNPs were designed for use as an imaging nanoprobe to act an effective contrast agent for both CT and PET scans. Twelve BALB/c mice were divided into two groups: mice with injected with PEG-Glu-GNPs and control mice. The mice were examined using high-resolution micro-CT at different time intervals (24 h, 7 days, and 15 days) after the injection of the particles. Greyscale density and CT attenuation values were determined to trace the excretion of the particles over time. Tumour contours were easily distinguished from surrounding tissue in mice injected with PEG-Glu-GNPs but not controls. This distinction was still visible at 7 days, but not at 15 days post-injection. Molecular imaging technology has enabled the development of a new generation of imaging probes. These sophisticated probes can visualize biological processes or enable early diagnosis of diseases in vivo. Compared to conventional CT images and PET scans, PEG-Glu-GNPs significantly improved image quality at the cellular and molecular level, which can significantly aid the early detection of cancer or cancer metastases.

Prior data assisted compressed sensing: A novel MR imaging strategy for real time tracking of lung tumors

Hybrid radiotherapy-MRI devices promise real time tracking of moving tumors to focus the radiation portals to the tumor during irradiation. This approach will benefit from the increased temporal resolution of MRI's data acquisition and reconstruction. In this work, the authors propose a novel spatial-temporal compressed sensing (CS) imaging strategy for the real time MRI--prior data assisted compressed sensing (PDACS), which aims to improve the image quality of the conventional CS without significantly increasing reconstruction times. Conventional 2D CS requires a random sampling of partial k-space data, as well as an iterative reconstruction that simultaneously enforces the image's sparsity in a transform domain as well as maintains the fidelity to the acquired k-space. PDACS method requires the additional acquisition of the prior data, and for reconstruction, it additionally enforces fidelity to the prior k-space domain similar to viewsharing. In this work, the authors evaluated the proposed PDACS method by comparing its results to those obtained from the 2D CS and viewsharing methods when performed individually. All three methods are used to reconstruct images from lung cancer patients whose tumors move and who are likely to benefit from lung tumor tracking. The patients are scanned, using a 3T MRI, under free breathing using the fully sampled k-space with 2D dynamic bSSFP sequence in a sagittal plane containing lung tumor. These images form a reference set for the evaluation of the partial k-space methods. To create partial k-space, the fully sampled k-space is retrospectively undersampled to obtain a range of acquisition acceleration factors, and reconstructed with 2D-CS, PDACS, and viewshare methods. For evaluation, metrics assessing global image artifacts as well as tumor contour shape fidelity are determined from the reconstructed images. These analyses are performed both for the original 3T images and those at a simulated 0.5T equivalent noise level. In the 3.0T images, the PDACS strategy is shown to give superior results compared to viewshare and conventional 2D CS using all metrics. The 2D-CS tends to perform better than viewshare at the low acceleration factors, while the opposite is true at the high acceleration factors. At simulated 0.5T images, PDACS method performs only marginally better than the viewsharing method, both of which are superior compared to 2D CS. The PDACS image reconstruction time (0.3 s/image) is similar to that of the conventional 2D CS. The PDACS method can potentially improve the real time tracking of moving tumors by significantly increasing MRI's data acquisition speeds. In 3T images, the PDACS method does provide a benefit over the other two methods in terms of both the overall image quality and the ability to accurately and automatically contour the tumor shape. MRI's data acquisition may be accelerated using the simpler viewsharing strategy at the lower, 0.5T magnetic field, as the marginal benefit of the PDACS method may not justify its additional reconstruction times.

Primary Lymphoepithelioma-like Carcinoma of the Lung

The aim of the study was to describe the clinical and computed tomography (CT) findings of primary lymphoepithelioma-like carcinoma (LELC) of the lung. The clinical data and CT findings of 35 patients with histologically proven primary LELC of the lung were retrospectively reviewed. The clinical data included age, sex, smoking history, signs and symptoms, tumor location, tumor size, stage, Epstein-Barr virus (EBV)-encoded small nuclear RNA status, treatment, and outcomes. Pretreatment CT scans of the thorax were used to access the tumor site, size, contour, density, vascular and/or bronchial involvement, relationship with pleura, and lymphadenopathy. The distinct clinical features of primary lung LELC include no significant predilection for sex, minimal association with a history of smoking, strong association with EBV in Asians, and a predilection for early or locally advanced stage of the disease. Most patients received complete resection and/or chemotherapy and radiotherapy. The 2- and 5-year overall survival rates for all patients were 81% and 51%, respectively. The common CT findings of primary lung LELC include peripheral nodules that have direct contact with the adjacent pleural surface, with a lesion density that is homogenous and sometimes heterogenous. Radiologists should consider the diagnosis of LELC when encountering solitary peripheral pulmonary nodules that have direct contact with the pleural surface in EBV endemic areas, and an EBV-encoded small nuclear RNA test should be performed on histologic specimens of patients meeting these clinical criteria.

Preoperative Computed Tomography Findings Predict Surgical Resectability of Thymoma

The aim of the study was to identify preoperative computed tomography (CT) imaging characteristics that correlated with surgical resectability. We retrospectively reviewed the CT scans of 133 patients who underwent surgical resection for thymoma at our institution between July 21, 1997, and September 22, 2010. Imaging characteristics recorded included tumor size, attenuation, contact of mediastinal vessels, tumor morphology, infiltration of surrounding fat, changes in the adjacent lung parenchyma, lymphadenopathy, and pleural involvement. The study group included 66 men and 67 women, aged 23-88 years (mean 58.8 years). Eighty patients (60.2%) were Masaoka stage I or II and 53 (39.8%) were Masaoka stage III or IV. Twenty-three patients (17.3%) had an incomplete surgical resection. Of these, 15 patients had microscopic residual disease (11.2%) and eight had gross residual disease (6.0%). The preoperative CT characteristics that correlated with an incomplete surgical resection included a lobulated tumor contour (p = 0.016), greater than or equal 50% abutment of the circumference of an adjacent vessel (p < 0.001), thoracic lymphadenopathy (p = 0.029), adjacent lung changes (p = 0.005) and pleural nodularity (p = 0.001). Tumor size was larger in the incompletely versus completely resected groups, with mean values of 9.7 and 6.9 cm (p value 0.013). On multivariate analysis, only degree of abutment of adjacent vessels and pleural nodularity were independent predictors of incomplete resection. Preoperative CT findings can predict the likelihood of successful surgical resection and could help to identify patients who might benefit from neoadjuvant chemotherapy.

Differential diagnosis of pancreatic serous oligocystic adenoma and mucinous cystic neoplasm with spectral CT imaging: Initial results

To investigate the imaging characteristics of pancreatic serous oligocystic adenoma (SOA) and mucinous cystic neoplasms (MCNs) using spectral computed tomography (CT) and to evaluate whether quantitative information derived from spectral imaging can improve the differential diagnosis of these diseases. From February 2010 to June 2013, 44 patients (24 SOAs and 20 MCNs) who underwent spectral CT imaging were included in the study. Conventional characteristics and quantitative parameters were compared between the two disease groups. Logistic regression was used for multiparametric analysis. The receiver-operating characteristic curve was used to evaluate the diagnostic performance of single parameter and multiparametric analysis. Two radiologists diagnosed the diseases blinded and independently, without and with the information of the statistical analysis. Tumour location, contour, size, and monochromatic CT values at 40 keV to 70 keV, iodine concentration, and effective atomic number (effective-Z) in the late arterial phase were the independent factors correlated with category. Multiparametric analysis with logistic regression showed that tumour size, location, and contour were the most effective variations, and obtained an area under the ROC curve (AUC) of 0.934. With the knowledge of statistical analysis, the accuracy of the first reader increased from 70.5% to 86.4%, and the accuracy of the second reader increased from 81.8% to 90.9%. Although CT spectral imaging provided additional information and multiparametric analysis obtained better performance than single-parameter analysis in differentiating MCNs from SOAs, multiparametric analysis with the combination of quantitative parameters derived from CT spectral imaging did not improve the diagnostic performance. Tumour size, location, and contour played an important role in differentiating MCNs from SOAs.

Applied Computational Engineering in Magnetic Resonance Imaging: A Tumor Case Study

Abstract — This paper solves the biomedical engineering problem of the extraction of complementary and/or additional information related to the depths of the anatomical structures of the human brain tumor imaged with Magnetic Resonance Imaging (MRI). The combined calculation of the signal resilient to interpolation and the Intensity-Curvature Functional provides with the complementary and/or additional information. The steps to undertake for the calculation of the signal resilient to interpolation are: (i) fitting a polynomial function to the signal, (ii) the calculation of the classic-curvature of the signal, (iii) the calculation of the Intensity-Curvature term before interpolation of the signal, (iv) the calculation of the Intensity-Curvature term after interpolation of the signal, (v) the solution of the equation of the two aforementioned Intensity-Curvature terms of the signal provides with the signal resilient to interpolation. The Intensity-Curvature Functional is the result of the ratio between the two Intensity-Curvature terms before and after interpolation. Because of the fact that the signal resilient to interpolation and the Intensity-Curvature Functional are derived through the process of re-sampling the original signal, it is possible to obtain an immense number of images from the original MRI signal. This paper shows the combined use of the signal resilient to interpolation and the Intensity-Curvature Functional in diagnostic settings when evaluating a tumor imaged with MRI. Additionally, the Intensity-Curvature Functional can identify the tumor contour line.

Relationship between CT findings and immunohistochemical types of gastrointestinal stromal tumors

To evaluate the relationship between CT findings and immunohistochemical types of gastrointestinal stromal tumors (GIST). CT imaging and clinicopathological data of 85 patients with GIST were analyzed retrospectively. The CT findings of GIST including lesion location, size, contour, boundary, tumor growth pattern, degree of enhancement, necrosis and calcification, were summarized and compared with the immunohistochemical types of the GIST. Of the 85 patients, smooth muscle differentiation was in 26 cases (30.6%), neural differentiation in 20 (23.5%) , both smooth muscle and neural differentiation in 10 (11.8%) and no obvious differentiation in 29 (34.1%) cases. GISTs occurred in the duodenum were more frequently seen in muscle type than in any other types, GIST with smooth muscle differentiation had higher prevalence of huge mass (larger than 10 cm), distinctive enhancement and extensive necrosis than other types (P < 0.05). There was no significant difference in the relationships of immunohistochemical types with tumor contour, boundary, growth pattern and calcification among the four groups of GIST (P > 0.05). CT scan is the most important and effective method for diagnosis of GIST. Analyzing CT signs has some potential value in judgmet of immunohistochemical types of GIST.

TH‐E‐17A‐11: Tracking Tumor’s Boundary in MV Image Sequences for Image‐Guided Radiation Therapy

Purpose:To develop a level set method (LSM)‐based algorithm to track the tumor’s boundary in MV image sequences for image‐guided radiation therapy (IGRT).Method:Four MV image sequences, each of which consists of 100 frames at frame rate of 7.5 Hz, are acquired by using the electronic portal imaging device (EPID) (Varian Medical Systems, Pal Alto, USA) during the treatment of lung cancer. In the first frame of each sequence, we roughly delineate an initial contour of the target tumor by hand. Using a LSM‐based algorithm, the initial contour can automatically shape itself to fit the tumor, and eventually detect the tumor’s boundary. We then employ the tumor’s boundary obtained from the previous frame as the initial contour in the subsequent frame, so that the LSM‐based method can drive this initial contour to the tumor’s boundary quickly and fulfill a tracking task.Results:The proposed method has been evaluated on four MV image sequences. The mean tracking errors were 0.23, 0.29, 0.37, and 1.18 mm, and their corresponding standard deviations were 0.97, 0.75, 1.2 and 1.48 mm, respectively.Conclusion:Compared with conventional tumor tracking techniques, the proposed system is capable not only of tracking the tumor’s position, but also of detecting the tumor’s boundary varying with the respiration during the treatment. Considering current radiation therapy technique, for example, dynamical multi‐leaf collimator (DMLC) has been widely applied in clinical treatment, this study indicates the potential for significant accuracy improvement in radiation therapy.This work was partially supported by a research grant from Varian Medical Systems (Palo Alto, California).

WE-B-19A-01: SRT II: Uncertainties in SRT

SRS delivery has undergone major technical changes in the last decade, transitioning from predominantly frame-based treatment delivery to imageguided, frameless SRS. It is important for medical physicists working in SRS to understand the magnitude and sources of uncertainty involved in delivering SRS treatments for a multitude of technologies (Gamma Knife, CyberKnife, linac-based SRS and protons). Sources of SRS planning and delivery uncertainty include dose calculation, dose fusion, and intra- and inter-fraction motion. Dose calculations for small fields are particularly difficult because of the lack of electronic equilibrium and greater effect of inhomogeneities within and near the PTV. Going frameless introduces greater setup uncertainties that allows for potentially increased intra- and interfraction motion, The increased use of multiple imaging modalities to determine the tumor volume, necessitates (deformable) image and contour fusion, and the resulting uncertainties introduced in the image registration process further contribute to overall treatment planning uncertainties. Each of these uncertainties must be quantified and their impact on treatment delivery accuracy understood. If necessary, the uncertainties may then be accounted for during treatment planning either through techniques to make the uncertainty explicit, or by the appropriate addition of PTV margins. Further complicating matters, the statistics of 1-5 fraction SRS treatments differ from traditional margin recipes relying on Poisson statistics. In this session, we will discuss uncertainties introduced during each step of the SRS treatment planning and delivery process and present margin recipes to appropriately account for such uncertainties. Learning Objectives: 1. To understand the major contributors to the total delivery uncertainty in SRS for Gamma Knife, CyberKnife, and linac-based SRS. 2. Learn the various uncertainties introduced by image fusion, deformable image registration, and contouring variation. 3. Learn a variety of strategies for dealing with uncertainty, including margin recipes and explicit visualization of uncertainty. Understand how the assessment of PTV margins differs from regular fractionation (van Herk recipe) for 1–5 fraction deliveries.

SU‐E‐J‐137: Incorporating Tumor Regression Into Robust Plan Optimization for Head and Neck Radiotherapy

Purpose:To develop a robust planning paradigm which incorporates a tumor regression model into the optimization process to ensure tumor coverage in head and neck radiotherapy.Methods:Simulation and weekly MR images were acquired for a group of head and neck patients to characterize tumor regression during radiotherapy. For each patient, the tumor and parotid glands were segmented on the MR images and the weekly changes were formulated with an affine transformation, where morphological shrinkage and positional changes are modeled by a scaling factor, and centroid shifts, respectively. The tumor and parotid contours were also transferred to the planning CT via rigid registration. To perform the robust planning, weekly predicted PTV and parotid structures were created by transforming the corresponding simulation structures according to the weekly affine transformation matrix averaged over patients other than him/herself. Next, robust PTV and parotid structures were generated as the union of the simulation and weekly prediction contours. In the subsequent robust optimization process, attainment of the clinical dose objectives was required for the robust PTV and parotids, as well as other organs at risk (OAR). The resulting robust plans were evaluated by looking at the weekly and total accumulated dose to the actual weekly PTV and parotid structures. The robust plan was compared with the original plan based on the planning CT to determine its potential clinical benefit.Results:For four patients, the average weekly change to tumor volume and position was −4% and 1.2 mm laterally‐posteriorly. Due to these temporal changes, the robust plans resulted in an accumulated PTV D95 that was, on average, 2.7 Gy higher than the plan created from the planning CT. OAR doses were similar.Conclusion:Integration of a tumor regression model into target delineation and plan robust optimization is feasible and may yield improved tumor coverage.Part of this research is supported by Varian Medical System