Background: This study is to distinguish peripheral lung cancer and pulmonary inflammatory pseudotumor using CT-radiomics features extracted from PET/CT images.Methods: In this study, the standard 18F-fluorodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 21 patients with pulmonary inflammatory pseudotumor (PIPT) and 21 patients with peripheral lung cancer were retrospectively collected. The dataset was used to extract CT-radiomics features from regions of interest (ROI), The intra-class correlation coefficient (ICC) was used to screen the robust feature from all the radiomic features. Using, then, statistical methods to screen CT-radiomics features, which could distinguish peripheral lung cancer and PIPT. And the ability of radiomics features distinguished peripheral lung cancer and PIPT was estimated by receiver operating characteristic (ROC) curve and compared by the Delong test.Results: A total of 435 radiomics features were extracted, of which 361 features showed relatively good repeatability (ICC ≥ 0.6). 20 features showed the ability to distinguish peripheral lung cancer from PIPT. these features were seen in 14 of 330 Gray-Level Co-occurrence Matrix features, 1 of 49 Intensity Histogram features, 5 of 18 Shape features. The area under the curves (AUC) of these features were 0.731 ± 0.075, 0.717, 0.748 ± 0.038, respectively. ThePvalues of statistical differences among ROC were 0.0499 (F9, F20), 0.0472 (F10, F11) and 0.0145 (F11, Mean4). The discrimination ability of forming new features (Parent Features) after averaging the features extracted at different angles and distances was moderate compared to the previous features (Child features).Conclusion: Radiomics features extracted from non-contrast CT based on PET/CT images can help distinguish peripheral lung cancer and PIPT.

Background: Reliable and meaningful radiomic features is extremely crucial to characterize tumor phenotypes. This study was designed to experimentally evaluate the variability of radiomic features extracted from different b-values diffusion-weighted images (DWIs) in hepatocellular carcinoma (HCC). Methods: The research population was composed of 34 HCC patients and 12 healthy volunteers. At 3.0T MR scanner, with the identical imaging protocols, all cases underwent the following sequences at 10 b-values ranging from 0 to 1,500 s/mm²: T1WI, T2WI, multiple phases contrast-enhanced and intravoxel incoherent motion-DWI scans. For HCC trail, gross tumor volume (GTV) were manually delineated by an experienced radiologist at the b=0 s/mm² DWI sequence. For healthy volunteers trail, 3 cylindric regions of interest (ROIs) with 14 mm in height and approximately 20 mm in diameter were defined in parenchyma at II/III, V/VI and VII hepatic segments. Using 3D Slicer Radiomics software (www.slicer.org), we extracted 74 radiomic features, including 19 first-order statistical features and 55 texture features for each case sequence. Percentage coefficient of variation (%COV) was applied to evaluate the stability of each feature and %COV <30 was considered as low variation. Furthermore, to observe the trend for radiomic features value in various b-values DWIs, an exponential or polynomial model was used. Finally, concordance correlation coefficient (CCC) was applied to assess the reproducibility of radiomic features between different b-values DWIs. Results: The value of intensity histogram features and texture features derived from DWIs showed a dependency on the b-values in HCC. The low variations (%COV <30), moderate variations (30≤ %COV <50) and large variations (%COV ≥50) radiomic features accounted for about 26%, 28%, and 46%, respectively. The exponential and polynomial model indicated that about 70% radiomic features showed positive or negative dependence on b-values and about 4% radiomic features showed little dependence. We acquired a better fitting results in HCC group (the mean value and standard deviation of R-square were 0.958±0.096 and 0.896±0.071, P<0.05). Moreover, we found radiomic features extracted from nearby b-values (b=0, 20, 50, 100, 200 s/mm² and b=1,000 s/mm²) of DWIs showed a high reproducibility. Twelve radiomic features can be used to identify HCC and normal liver. Conclusions: Being influenced by different b-values, radiomic features tested here exist variability in HCC DWIs. Most features are unstable and extremely dependent on b-values in DWIs. Meanwhile, the research revealed that reproducible features can be extracted by nearby b-values DWIs.

Background: To assess the position accuracy of the six-degree-of-freedom (6-DoF) couch based on cone beam computed tomography (CBCT) and exploit the correlation of the six degrees errors. Methods: CT scans of an anthropomorphic phantom and patients were obtained at 3 mm slice thicknesses using a Philips scanner at the head, first supine and prostrate positions. An Eclipse Treatment Planning System was used to create a treatment plan. Different levels of known position errors were introduced to simulate patient position status for the anthropomorphic phantom. CBCT datasets for each position were acquired and registered to original CT datasets to evaluate the accuracy of the 6-DoF couch and determine the setup errors of patients. The setup errors of 200 CBCT datasets from 14 patients with pelvic tumors were analyzed. The correlations between six degrees position errors were finally extracted. Results: For the phantom study, the difference between known introduced errors and the setup errors were almost negligible. The deviation (mean ± one standard deviation) in registration methods were (0.01±0.02) cm, (0.04±0.075) cm, (0.02±0.004) cm, (0.01±0.04)°, (0.1±0.08)°, (0.03±0.05)° and (0.01±0.01) cm, (0.03±0.007) cm, (0.01±0.01) cm, (0.05±0.06)°, (0.08±0.08)°, (0.04±0.05)° for supine and prone position, respectively. The deviation in positions were (0.07±0.10) cm, (0.16±0.02) cm, (0.08±0.06) cm, (0.54±0.46)°, (0.24±0.16)°, (0.09±0.09)° and (0.06±0.09) cm, (0.19±0.09) cm, (0.09±0.07) cm, (0.49±0.49)°, (0.16±0.08)°, (0.1±0.13)° for bone and soft tissue registration methods, respectively. For patient data, the setup errors were (−0.07±0.22) cm, (0.14±0.35) cm, (−0.12±0.4) cm, (0.79±1.6)°, (0.41±0.71)°, (−0.03±0.8)° for supine position and (0.16±0.27) cm, (0.19±0.48) cm, (−0.05±0.34) cm, (1.1±1.49)°, (0.65±1.00)°, (−0.23±0.75)° for prone position, respectively. There is a significant moderate correlation between the longitudinal and pitch directions and between the vertical and pitch directions when the patient is in the supine position. Conclusions: The six-dimensional couch positioning verification system based on CBCT has high accuracy and can meet the requirements of precise radiotherapy for pelvic tumors. There is a certain correlation between translation direction and rotation direction.

Feasibility evaluation of kilovoltage cone-beam computed tomography dose calculation following scatter correction: investigations of phantom and representative tumor sites Huipeng Meng, Xiangjuan Meng, Qingtao Qiu, Yanlong Zhang, Xin Ming, Qifeng Li, Keqiang Wang, Ruohui Zhang, Jinghao Duan

Background and Purpose: Respiratory movement has an important impact on the radiotherapy for lung tumor. Respiratory gating technology is helpful to improve the accuracy of target delineation. This study investigated the value of prospective and retrospective respiratory gating simulations in target delineation and radiotherapy plan design for solitary pulmonary tumors (SPTs) in radiotherapy.Methods: The enrolled patients underwent CT simulation with three‐dimensional (3D) CT non gating, prospective respiratory gating, and retrospective respiratory gating simulation. The target volumes were delineated on three sets of CT images, and radiotherapy plans were prepared accordingly. Tumor displacements and movement information obtained using the two respiratory gating approaches, as well as the target volumes and dosimetry parameters in the radiotherapy plan were compared.Results: No significant difference was observed in tumor displacement measured using the two gating methods (p > 0.05). However, the internal gross tumor volumes (IGTVs), internal target volumes (ITVs), and planning target volumes (PTVs) based on the retrospective respiratory gating simulation were larger than those obtained using prospective gating (group A: p_IGTV = 0.041, p_ITV = 0.003, p_PTV = 0.008; group B: p_IGTV = 0.025, p_ITV = 0.039, p_PTV = 0.004). The two‐gating PTVs were both smaller than those delineated on 3D non gating images (p < 0.001). V_5Gy, V_10Gy, V_20Gy, V_30Gy, and mean lung dose in the two gated radiotherapy plans were lower than those in the 3D non gating plan (p < 0.001); however, no significant difference was observed between the two gating plans (p > 0.05).Conclusions: The application of respiratory gating could reduce the target volume and the radiation dose that the normal lung tissue received. Compared to prospective respiratory gating, the retrospective gating provides more information about tumor movement in PTV.