Impact of different respiratory gating methods on target delineation and a radiotherapy plan for solitary pulmonary tumors.

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: pIGTV = 0.041, pITV = 0.003, pPTV = 0.008; group B: pIGTV = 0.025, pITV = 0.039, pPTV = 0.004). The two-gating PTVs were both smaller than those delineated on 3D non gating images (p < 0.001). V5Gy, V10Gy, V20Gy, V30Gy, 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.

CT-based radiomics combined with hematologic parameters for survival prediction in locally advanced esophageal cancer patients receiving definitive chemoradiotherapy.

OBJECTIVES: The purpose of this study was to investigate the prognostic significance of radiomics in conjunction with hematological parameters in relation to the overall survival (OS) of individuals diagnosed with esophageal squamous cell carcinoma (ESCC) following definitive chemoradiotherapy (dCRT). METHODS: In this retrospective analysis, a total of 122 patients with locally advanced ESCC were included. These patients were randomly assigned to either the training cohort (n = 85) or the validation cohort (n = 37). In the training group, the least absolute shrinkage and selection operator (LASSO) regression was utilized to choose the best radiomic features for calculating the Rad-score. To develop a nomogram model, both univariate and multivariate analyses were conducted to identify the clinical factors and hematologic parameters that could predict the OS. The performance of the predictive model was evaluated using the C-index, while the accuracy was assessed through the calibration curve. RESULTS: The Rad-score was calculated by selecting 10 radiomic features through LASSO regression. OS was predicted independently by neutrophil-to-monocyte ratio (NMR) and Rad-score according to the results of multivariate analysis. Patients who had a Rad-score > 0.47 and an NMR > 9.76 were at a significant risk of mortality. A nomogram was constructed using the findings from the multivariate analysis. In the training cohort, the nomogram had a C-index of 0.619, while in the validation cohort, it was 0.573. The model's accuracy was demonstrated by the calibration curve, which was excellent. CONCLUSION: A prognostic model utilizing radiomics and hematologic parameters was developed, enabling the prediction of OS in patients with ESCC following dCRT. CRITICAL RELEVANCE STATEMENT: Patients with esophageal cancer who underwent definitive chemoradiotherapy may benefit from including CT radiomics in the nomogram model. KEY POINTS: • Predicting the prognosis of ESCC patients before treatment is particularly important. • Patients with a Rad-score > 0.47 and neutrophil-to-monocyte ratio > 9.76 had a high risk of mortality. • CT-based radiomics nomogram model could be used to predict the survival of patients.

Volumetric modulated arc therapy for hippocampal-sparing prophylactic cranial irradiation: Planning comparison of Halcyon and C-arm accelerators.

Background: The purpose of the study was to evaluate the dosimetry of the Halcyon in prophylactic cranial irradiation (PCI) with volumetric modulated arc therapy (VMAT) and hippocampal-sparing for small cell lung cancer (SCLC). Methods: Five VMAT plans were designed on CT images of 15 patients diagnosed with SCLC and received PCI. Three plans with two full arcs were generated on the Trilogy and the TrueBeam accelerators, and flattening filter (FF) and flattening filter free (FFF) modes were used on TrueBeam. Two Halcyon plans with two and three full arcs were generated, referred to as H-2A and H-3A, respectively. The prescription dose was 25 Gy in 2.5-Gy fractions. The dose limit for hippocampus were D100 ≤ 9Gy and Dmax ≤ 16Gy. The Wilcoxon matched-paired signed-rank test was used to evaluate the significance of the observed differences between the five plans. Results: H-2A plans significantly increased the D2 of PTV, and H-3A plans showed comparable or even better target dosimetry (better conformity) compared to the three plans on C-arm accelerators. Compared to T and TB plans, the two Halcyon plans significantly reduced the D100 and mean doses of bilateral hippocampus, the mean doses of eyeballs, and the maximum doses of lenses. D100 of hippocampus was reduced in TrueBeam plans comparing to Trilogy plans. The FFF plans on TrueBeam also represented advantages in Dmean and D100 of hippocampas, Dmean and Dmax of eyeballs, and the Dmax of lenses compared to FF plans. Halcyon plans and TrueBeam plans with FFF mode increased the MUs compared to FF plans. Comparing to H-2A, the H-3A plans exhibited additional dosimetric advantages, including D2, CI and HI of PTV, as well as the maximum and mean doses of hippocampus and eyeballs, and the maximum doses of optic nerves and brainstem. The two Halcyon plans significantly reduced the delivery time and showed the higher gamma passing rate than the three plans of C-arm accelerators. Conclusions: Compared with the C-arm accelerators, the dose of hippocampus and the delivery times on Halcyon are relatively significantly reduced for hippocampal-sparing PCI. Three arcs are recommended for VMAT plans with the Halcyon in hippocampal-sparing PCI.

Response Prediction Using 18F-FAPI-04 PET/CT in Patients with Esophageal Squamous Cell Carcinoma Treated with Concurrent Chemoradiotherapy.

This prospective study examined whether imaging results obtained using the tracer 18F-AlF-NOTA-fibroblast activation protein inhibitor (FAPI)-04 (denoted as 18F-FAPI-04) in PET/CT can predict the short-term outcome in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC) treated with concurrent chemoradiotherapy (CCRT). Methods: The 18 enrolled LA-ESCC patients underwent 18F-FAPI-04 PET/CT scanning before CCRT. The SUVmax, SUVmean, SUVpeak, metabolic tumor volume, and total lesion fibroblast activation protein expression of the primary tumor were recorded. Additionally, the SUVmax of the primary tumor and SUVmean of normal tissue (muscle and blood) were measured, and their ratios were denoted as target-to-background ratios (TBRmuscle and TBRblood). Patients were classified as responders or nonresponders according to RECIST (version 1.1), and variables were compared between the 2 groups. Results: The TBRblood, TBRmuscle, and SUVmean were significantly higher in nonresponders than in responders (all P < 0.05). Receiver-operating-characteristic curve analysis identified TBRblood (area under the curve [AUC], 0.883; P = 0.008), TBRmuscle (AUC, 0.896; P = 0.006) and SUVmean (AUC, 0.870; P = 0.010) as significant predictors of the response to CCRT, with cutoffs of 10.68, 10.95, and 6.88, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were also determined for TBRblood (100.0%, 72.7%, 66.7%, 88.9%, and 77.8%, respectively), TBRmuscle (100.0%, 72.7%, 66.7%, 88.9%, and 77.8%, respectively), and SUVmean (85.7%, 81.8%, 75.0%, 90.0%, and 83.3%, respectively). On univariate logistic regression analysis, TBRblood (P = 0.026), TBRmuscle (P = 0.036), SUVmean (P = 0.045), and tumor site (P = 0.032) were significantly correlated with the short-term outcome. On multivariable logistic regression analysis, TBRblood (P = 0.046) was an independent prognostic factor for short-term outcome. Conclusion: A higher baseline TBRblood on 18F-FAPI-04 PET/CT scans was associated with a poor response to CCRT in LA-ESCC patients, and thus, TBRblood may be useful for screening LA-ESCC patients before CCRT treatment.

Extended Maxillary Osteotomy Guide: A Design That Allows Manipulation of the Osteotomy Direction on the Posterior and Inner Walls of the Maxilla.

PURPOSE: Design an extended osteotomy guide (EOG) for Le Fort I osteotomy to improve the safety of surgery. MATERIALS AND METHODS: The digital Le Fort I osteotomy guide was designed in MIMICS 23.0. Twenty-eight patients were randomized into 2 groups. Patients in the experimental group used EOG, and patients in the control group used a traditional osteotomy guide (TOG). Virtual designs and actual postoperative outcomes were compared by cone-beam computed tomography. The safety of the operation was confirmed by the accuracy of the osteotomy direction and depth on the inner and posterior walls of the maxilla. RESULTS: All positioning deviations of both osteotomy guides were <0.3 mm (P>0.05). The osteotomy depths on the inner and posterior walls with the EOG and TOG deviated by 0.789±1.179 and 1.811±1.345 mm (P=0.004) and 0.648±0.999 and 1.262±0.942 mm (P=0.030), respectively. The angles of deviation of the osteotomy direction on the inner and posterior walls by the EOG and TOG were 2.025±2.434 and 5.069±2.391 degrees (P<0.001) and 2.772±2.979 and 8.653±4.690 degrees (P<0.001), respectively. CONCLUSIONS: The EOG was more accurate than TOG for manipulating osteotomy direction and depth on the inner and posterior maxillary walls. Thus, EOG could ensure higher surgical safety than TOG.

Diagnostic Value of 18F-NOTA-FAPI PET/CT in a Rat Model of Radiation-Induced Lung Damage.

In this study, we explore the diagnostic value of a novel PET/CT imaging tracer that specifically targets fibroblast activation protein (FAP), 18F-NOTA-FAPI, in a radiation induced lung damage (RILD) rat model. High focal radiation (40, 60, or 90 Gy) was administered to a 5-mm diameter area of the right lung in Wistar rats for evaluation of RILD induction. Lung tissues exposed to 90 Gy radiation were scanned with 18F-NOTA-FAPI PET/CT and with 18F-FDG. Dynamic 18F-NOTA-FAPI PET/CT scanning was performed on day 42 post-irradiation. After in vivo scanning, lung cryosections were prepared for autoradiography, hematoxylin and eosin (HE) and immunohistochemical (IHC) staining. An animal model of RILD was established and validated by histopathological analysis. On 18F-NOTA-FAPI PET/CT, RILD was first observed on days 42, 35 and 7 in the 40, 60 and 90 Gy groups, respectively. After treatment with 90 Gy, 18F-NOTA-FAPI uptake in an area of RILD emerged on day 7 (0.65 ± 0.05%ID/ml) and reappeared on day 28 (0.81 ± 0.09%ID/ml), remaining stable for 4-6 weeks. Autoradiography and HE staining IHC staining revealed that 18F-NOTA-FAPI accumulated mainly in the center of the irradiated area. IHC staining confirmed the presence of FAP+ macrophages in the RILD area, while FAP+ fibroblasts were observed in the peripheral area of irradiated lung tissue. 18F-NOTA-FAPI represents a promising radiotracer for in vivo imaging of RILD in a dose- and time-dependent manner. Noninvasive imaging of FAP may potentially aiding in the clinical management of radiotherapy patients.

Demonstration of quasi-real-time intrafractional motion in esophageal cancer radiotherapy provided by ExacTrac X-ray snap verification.

OBJECTIVE: To investigate the following hypotheses: (1) ExacTrac X-ray Snap Verification (ET-SV) is an alternative to CBCT for positioning patients with esophageal carcinoma (EC), (2) ET-SV can detect displacement in EC patients during radiotherapy (RT) and (3) EC patients can be feasibly monitored in quasi-real-time with ET-SV during RT. METHODS: Anthropomorphic phantoms and 13 patients were included in this study. CBCT and ET-SV were both implemented before treatment delivery to detect displacement, and their correction results were compared. For the patient tests, positional correction in 3 translational directions and the yaw direction were applied using the ET-SV correction results. The residual error was detected immediately using ET-SV. Finally, to acquire the intrafractional motion, ET-SV was implemented when the gantry was at 0°, 90°, 180° and 270°, respectively. RESULTS: In phantom tests, the maximum value of the difference in displacement between the CBCT and ET systems was 1.16 mm for translation and 0.31° for yaw. According to Bland-Altman analysis of the patient test results, 5% (5/98), 5% (5/98), 5% (5/98), and 4% (4/98) of points were beyond the upper and lower limits of agreement in the AP, SI, LR and yaw directions, respectively. The mean residual error was -0.482 mm, 1.215 mm, 1.0 mm, -0.487°, 0.105°, and 0.003° in the AP, SI, LR, pitch, roll and yaw directions, respectively. The intrafractional displacement ranged from -0.21 mm to 0 mm for translation and from -0.63° to 0.21° for rotation. The mean total translational error for intrafractional motion increased from 0.47 mm to 1.14 mm during the treatment. CONCLUSION: The accuracy of ET-SV for EC RT positional correction is comparable to that of CBCT. Thus, Quasi-real-time intrafractional monitoring can be used to detect EC patient displacement during radiotherapy.

Reproducibility for Hepatocellular Carcinoma CT Radiomic Features: Influence of Delineation Variability Based on 3D-CT, 4D-CT and Multiple-Parameter MR Images.

Purpose: Accurate lesion segmentation is a prerequisite for radiomic feature extraction. It helps to reduce the features variability so as to improve the reporting quality of radiomics study. In this research, we aimed to conduct a radiomic feature reproducibility test of inter-/intra-observer delineation variability in hepatocellular carcinoma using 3D-CT images, 4D-CT images and multiple-parameter MR images. Materials and Methods: For this retrospective study, 19 HCC patients undergoing 3D-CT, 4D-CT and multiple-parameter MR scans were included in this study. The gross tumor volume (GTV) was independently delineated twice by two observers based on contrast-enhanced computed tomography (CECT), maximum intensity projection (MIP), LAVA-Flex, T2W FRFSE and DWI-EPI images. We also delineated the peritumoral region, which was defined as 0 to 5 mm radius surrounding the GTV. 107 radiomic features were automatically extracted from CECT images using 3D-Slicer software. Quartile coefficient of dispersion (QCD) and intraclass correlation coefficient (ICC) were applied to assess the variability of each radiomic feature. QCD<10% and ICC≥0.75 were considered small variations and excellent reliability. Finally, the principal component analysis (PCA) was used to test the feasibility of dimensionality reduction. Results: For tumor tissues, the numbers of radiomic features with QCD<10% indicated no obvious inter-/intra-observer differences or discrepancies in 3D-CT, 4D-CT and multiple-parameter MR delineation. However, the number of radiomic features (mean 89) with ICC≥0.75 was the highest in the multiple-parameter MR group, followed by the 3DCT group (mean 77) and the MIP group (mean 73). The peritumor tissues also showed similar results. A total of 15 and 7 radiomic features presented excellent reproducibility and small variation in tumor and peritumoral tissues, respectively. Two robust features showed excellent reproducibility and small variation in tumor and peritumoral tissues. In addition, the values of the two features both represented statistically significant differences among tumor and peritumoral tissues (P<0.05). The PCA results indicated that the first seven principal components could preserve at least 90% of the variance of the original set of features. Conclusion: Delineation on multiple-parameter MR images could help to improve the reproducibility of the HCC CT radiomic features and weaken the inter-/intra-observer influence.

Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study.

BACKGROUND: Although surgical pathology or biopsy are considered the gold standard for glioma grading, these procedures have limitations. This study set out to evaluate and validate the predictive performance of a deep learning radiomics model based on contrast-enhanced T1-weighted multiplanar reconstruction images for grading gliomas. METHODS: Patients from three institutions who diagnosed with gliomas by surgical specimen and multiplanar reconstructed (MPR) images were enrolled in this study. The training cohort included 101 patients from institution 1, including 43 high-grade glioma (HGG) patients and 58 low-grade glioma (LGG) patients, while the test cohorts consisted of 50 patients from institutions 2 and 3 (25 HGG patients, 25 LGG patients). We then extracted radiomics features and deep learning features using six pretrained models from the MPR images. The Spearman correlation test and the recursive elimination feature selection method were used to reduce the redundancy and select most predictive features. Subsequently, three classifiers were used to construct classification models. The performance of the grading models was evaluated using the area under the receiver operating curve, sensitivity, specificity, accuracy, precision, and negative predictive value. Finally, the prediction performances of the test cohort were compared to determine the optimal classification model. RESULTS: For the training cohort, 62% (13 out of 21) of the classification models constructed with MPR images from multiple planes outperformed those constructed with single-plane MPR images, and 61% (11 out of 18) of classification models constructed with both radiomics features and deep learning features had higher area under the curve (AUC) values than those constructed with only radiomics or deep learning features. The optimal model was a random forest model that combined radiomic features and VGG16 deep learning features derived from MPR images, which achieved AUC of 0.847 in the training cohort and 0.898 in the test cohort. In the test cohort, the sensitivity, specificity, and accuracy of the optimal model were 0.840, 0.760, and 0.800, respectively. CONCLUSIONS: Multiplanar CE-T1W MPR imaging features are more effective than features from single planes when differentiating HGG and LGG. The combination of deep learning features and radiomics features can effectively grade glioma and assist clinical decision-making.

Feasibility evaluation of kilovoltage cone-beam computed tomography dose calculation following scatter correction: investigations of phantom and representative tumor sites.

BACKGROUND: To study the feasibility of kilovoltage cone-beam computed tomography (KV-CBCT) dose calculation following scatter correction. METHODS: CIRS 062 and Catphan 504 phantoms were used in this study, and 40 randomly selected subjects representing a variety of cases (ten head cancer cases, ten chest cancer cases, ten abdominal cancer cases and ten pelvic cavity cancer cases) were enrolled. We developed in-house software called the cone-beam CT imaging toolkit (CITK) to improve the quality of CBCT images. We first aligned each planning computed tomography (pCT) image with the corresponding CBCT image using rigid registration after scatter correction. Hounsfield unit-relative electron density (HU-RED) calibration was applied to the CBCT images. The pCT plan was then recalculated on CBCT images. Finally, the dosimetric differences between the two plans were evaluated. The dosimetric parameters included the D98, D2, Dmean, conformity index (CI), homogeneity index (HI) and other organ at risk (OAR) dose parameters of the planning target volume (PTV). The dose distribution index (DDI) and the gamma index were also assessed. Paired Student's t-tests or Wilcoxon rank tests were used to evaluate differences. P<0.05 was considered significant. RESULTS: In the phantom and patient cases, the average dosimetric difference was less than 1% in the PTV and OARs. There was no significant difference in the CI or HI between the two plans. The gamma pass rate of 2%/2 mm was greater than 95% in both plans. There was a significant difference in the DDI between the two plans in the chest group but not in the other groups. CONCLUSIONS: The results suggest that CBCT has high accuracy in dose calculation via scatter correction and HU-RED calibration.

Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis.

BACKGROUND: Liver fibrosis (LF) continues to develop and eventually progresses to cirrhosis. However, LF and early-stage cirrhosis (ESC) can be reversed in some cases, while advanced cirrhosis is almost impossible to cure. Advances in quantitative imaging techniques have made it possible to replace the gold standard biopsy method with non-invasive imaging, such as radiomics. Therefore, the purpose of this study is to develop a radiomics model to identify LF and ESC. METHODS: Patients with LF (n = 108) and ESC (n = 116) were enrolled in this study. As a control, patients with healthy livers were involved in the study (n = 145). Diffusion-weighted imaging (DWI) data sets with three b-values (0, 400, and 800 s/mm) of enrolled cases were collected in this study. Then, radiomics features were extracted from manually delineated volumes of interest. Two modeling strategies were performed after univariate analysis and feature selection. Finally, an optimal model was determined by the receiver operating characteristic area under the curve (AUC). RESULTS: The optimal models were built in plan 1. For model 1 in plan 1, the AUCs of the training and validation cohorts were 0.973 (95% confidence interval [CI] 0.946-1.000) and 0.948 (95% CI 0.903-0.993), respectively. For model 2 in plan 1, the AUCs of the training and validation cohorts were 0.944, 95% CI 0.905 to 0.983, and 0.968, 95% CI 0.940 to 0.996, respectively. CONCLUSIONS: Radiomics analysis of DWI images allows for accurate identification of LF and ESC, and the non-invasive biomarkers extracted from the functional DWI images can serve as a better alternative to biopsy.

Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery.

Background and purpose: Although patients with esophageal squamous cell carcinoma (ESCC) can achieve a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) followed by surgery, one-third of these patients with a pCR may still experience recurrence. The aim of this study is to develop and validate a predictive model to estimate recurrence-free survival (RFS) in those patients who achieved pCR. Materials and methods: Two hundred six patients with ESCC were enrolled and divided into a training cohort (n = 146) and a validation cohort (n = 60). Radiomic features were extracted from contrast-enhanced computed tomography (CT) images of each patient. Feature reduction was then implemented in two steps, including a multiple segmentation test and least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression method. A radiomics signature was subsequently constructed and evaluated. For better prediction performance, a clinical nomogram based on clinical risk factors and a nomogram incorporating the radiomics signature and clinical risk factors was built. Finally, the prediction models were further validated by calibration and the clinical usefulness was examined in the validation cohort to determine the optimal prediction model. Results: The radiomics signature was constructed using eight radiomic features and displayed a significant correlation with RFS. The nomogram incorporating the radiomics signature with clinical risk factors achieved optimal performance compared with the radiomics signature (P < 0.001) and clinical nomogram (P < 0.001) in both the training cohort [C-index (95% confidence interval [CI]), 0.746 (0.680-0.812) vs. 0.685 (0.620-0.750) vs. 0.614 (0.538-0.690), respectively] and validation cohort [C-index (95% CI), 0.724 (0.696-0.752) vs. 0.671 (0.624-0.718) vs. 0.629 (0.597-0.661), respectively]. The calibration curve and decision curve analysis revealed that the radiomics nomogram outperformed the other two models. Conclusions: A radiomics nomogram model incorporating radiomics features and clinical factors has been developed and has the improved ability to predict the postoperative recurrence risk in patients with ESCC who achieved pCR after nCRT followed by surgery.

Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor.

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. The P values 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.

Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging.

BACKGROUND: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC). METHODS: Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n = 163) and validation cohorts (n = 68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. CONCLUSIONS: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

Dosimetric study of three-dimensional static and dynamic SBRT radiotherapy for hepatocellular carcinoma based on 4DCT image deformable registration.

The purpose of this work was to determine the actual dose received by normal tissues during four-dimensional radiation therapy (4DRT) composed of ten phases of four-dimensional computer tomography (4DCT) images. The analysis was performed by tracking the hepatocellular carcinoma SBRT. Data were acquired from the tracking of each phase with the beam aperture for 28 hepatocellular carcinoma patients, and the data were used to generate a cumulative plan, which was compared to a three-dimensional (3D) plan formed from a merged target volume based on 4DCT images in a radiation treatment planning system (TPS). The change in normal tissue dose was evaluated in the plan using the parameters V5, V10, V15, V20, V25, V30, V35, and V40 (volumes receiving 5, 10, 15, 20, 25, 30, 35, and 40 Gy, respectively) in the dose-volume histogram for the liver; the mean dose was analyzed for the following tissues: liver, left kidney, and right kidney. The maximum dose was analyzed for the following tissues: bowel, duodenum, esophagus, stomach, and heart. There was a significant difference in the dose between the 4D planning target volume (PTV) (average 115.71 cm3 ) and ITV (169.86 cm3 ). The planning objective was for 95% of the volume of the PTV to be covered by the prescription dose, but the mean dose for the liver, left kidney and right kidney had an average decrease of 23.13%, 49.51%, and 54.38%, respectively. The maximum dose for the bowel, duodenum, esophagus, stomach, and heart had an average decrease of 16.77%, 28.07%, 24.28%, 4.89%, and 4.45%, respectively. Compared to 3D RT, the radiation volume for the liver V5, V10, V15, V20, V25, V30, V35, and V40 using the 4D plans had a significant decrease (P ï¹¤ 0.05). The 4D method creates plans that permit sparing of the normal tissues more than the commonly used ITV method, which delivers the same dosimetric effects to the target.

A New Proton Therapy Solution Provides Superior Cardiac Sparing Compared With Photon Therapy in Whole Lung Irradiation for Pediatric Tumor Patients.

OBJECTIVE: Whole lung irradiation (WLI) plays a crucial role in local control in pediatric patients with lung metastases and improves patient survival. The intention of this research was to explore the advantage of cardiac sparing between photons and protons during WLI. We also propose a new solution for cardiac sparing with proton techniques. METHODS: Eleven patients with pediatric tumors and pulmonary metastasis treated with 12 Gy WLI (all received volumetric-modulated arc therapy (VMAT)) in our institute between 2010 and 2019 were retrospectively selected. Each patient was replanned with intensity-modulated radiation therapy (IMRT), helical tomotherapy (HT), and two intensity-modulated proton radiotherapy (IMPT) plans (IMPT-1 and IMPT-2). IMPT-1 considered the whole lung as the planning target volume (PTV), utilizing the anteroposterior technique (0/180°). IMPT-2 was a new proton solution that we proposed in this research. This approach considered the unilateral lung as the PTV, and 3 ipsilateral fields were designed for each lung. Then, IMPT-2 was generated by summing two unilateral lung plans. The primary objective was to obtain adequate coverage (95% of the prescription dose to the PTV) while maximally sparing the dose to the heart. The PTV coverage, conformity index (CI), homogeneity index (HI), and dose-volume statistics of the heart and substructures were assessed by means of the averages of each comparison parameter. RESULTS: All treatment techniques achieved the target volume coverage required by clinical practice. HT yielded the best coverage and homogeneity for the target structure compared with other techniques. The CI from IMRT was excellent. For photon radiation therapy, the HT plan afforded superior dose sparing for the V5, V6, V7, V8, and Dmean of the heart and Dmean of the right ventricle (RV). IMRT displayed the most notable dose reductions in the V9, V10, V11, and V12 of the heart and Dmean of the right atrium (RA). The VMAT plan was the least effective on the heart and substructures. However, compared with photon radiation therapy, IMPT-1 did not show an advantage for heart protection. Interestingly, IMPT-2 provided significant superiority in cardiac sparing, including maximum dose sparing for the V5, V6, V7, V8, V9 and Dmean of the heart and Dmean of the RA, RV, left atrium (LA) and left ventricle (LV) compared to all other techniques. CONCLUSIONS: Considering the complex anatomical relation between target volumes and organs at risk (OARs), IMPT can provide a dose advantage for organs located outside of the target area rather than within or surrounding the area. It is hoped that advances in proton therapy (PT) plan design will lead to further improvements in radiotherapy approaches and provide the best treatment choice for individual patients.

Geometric accuracy evaluation of a six-degree-of-freedom (6-DoF) couch with cone beam computed tomography (CBCT) using a phantom and correlation study of the position errors in pelvic tumor radiotherapy.

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.

Injury-preconditioning secretome of umbilical cord mesenchymal stem cells amplified the neurogenesis and cognitive recovery after severe traumatic brain injury in rats.

Traumatic brain injury (TBI) is a dominant cause of death and permanent disability worldwide. Although TBI could significantly increase the proliferation of adult neural stem cells in the hippocampus, the survival and maturation of newborn cells is markedly low. Increasing evidence suggests that the secretome derived from mesenchymal stem cells (MSCs) would be an ideal alternative to MSC transplantation. The successive and microenvironmentally responsive secretion in MSCs may be critical for the functional benefits provided by transplanted MSCs after TBI. Therefore, it is reasonable to hypothesize that the signaling molecules secreted in response to local tissue damage can further facilitate the therapeutic effect of the MSC secretome. To simulate the complex microenvironment in the injured brain well, we used traumatically injured brain tissue extracts to pretreat umbilical cord mesenchymal stem cells (UCMSCs) in vitro and stereotaxically injected the secretome from traumatic injury-preconditioned UCMSCs into the dentate gyrus of the hippocampus in a rat severe TBI model. The results revealed that compared with the normal secretome, the traumatic injury-preconditioned secretome could significantly further promote the differentiation, migration, and maturation of newborn cells in the dentate gyrus and ultimately improve cognitive function after TBI. Cytokine antibody array suggested that the increased benefits of secretome administration were attributable to the newly produced proteins and up-regulated molecules from the MSC secretome preconditioned by a traumatically injured microenvironment. Our study utilized the traumatic injury-preconditioned secretome to amplify neurogenesis and improve cognitive recovery, suggesting this method may be a novel and safer candidate for nerve repair. Cover Image for this issue: doi: 10.1111/jnc.14741.