Objective: Previous studies have shown that increased cardiac uptake of 18F-fluorodeoxyglucose (FDG) from positron emission tomography (PET) may be an indicator of myocardial injury after radiotherapy. The primary objective of this study was to quantify cardiac subvolume dosimetry and 18F- FDG uptake in oncologic PET using a 17-segment model of the left ventricle (LV) and to identify dose limits related to changes in cardiac FDG uptake after radiotherapy (RT). Methods: Twenty-four esophageal cancer (EC) patients who underwent consecutive oncologic 18F-FDG PET/CT scans at baseline and post-RT were enrolled in this study. The radiation dose and the 18F-FDG uptake were quantitatively analyzed based on a 17-segment model. The 18F-FDG uptake and doses to the basal, mid and apical regions, and the changes in the 18F-FDG uptake for different dose ranges were analyzed. Results: A heterogeneous dose distribution was observed, and the basal region received a higher median mean dose (18.36 Gy) than the middle and apical regions (5.30 Gy and 2.21 Gy, respectively). Segments 1, 2, 3 and 4 received the highest doses, all of which were greater than 10 Gy. Three patterns were observed for the myocardial 18F-FDG uptake related to the radiation dose before and after RT: an increase (5 patients), a decrease (13 patients) and no change (6 patients). In the pairing analysis, the 18F-FDG uptake after RT decreased by 28.93% and 12.12% in the low-dose segments (0-10 Gy and 10-20 Gy, respectively) and increased by 7.24% in the high-dose segments (20-30 Gy). Conclusions: The RT dose varies substantially within LV segments in patients receiving thoracic EC RT. Increased 18F-FDG uptake in the myocardium after RT was observed for doses above 20 Gy.
Abstract Background Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3–10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC. Methods A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. Results Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developped by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI): 0.827(0.742–0.912)] than the clinical nomogram [AUC(95% CI): 0.731(0.626–0.836)] and radiomics predictive models [AUC(95% CI): 0.754(0.652–0.855), LR algorithms]. Caliberation and decision curve analyses revealed that the radiomics-clinical nomogram outperformed the other models. In comparison to the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI: 0.075–0.345), and its IDI was 0.071 (95% CI: 0.030–0.112), P = 0.001. Conclusion We developed and validated the first radiomics-clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.
Although the tumor-node-metastasis staging system is widely used for survival analysis of nasopharyngeal carcinoma (NPC), tumor heterogeneity limits its utility. In this study, we aimed to develop and validate a radiomics model, based on multiple-sequence magnetic resonance imaging (MRI), to estimate the probability of overall survival in patients diagnosed with NPC.Multiple-sequence MRIs, including T1-weighted, T1 contrast, and T2-weighted imaging, were collected from patients diagnosed with NPC. Radiomics features were extracted from the contoured gross tumor volume of three sequences from each patient using the least absolute shrinkage and selection operator with the Cox regression model. The optimal Rad score was determined using 12 of the 851 radiomics features derived from the multiple-sequence MRI and its discrimination power was compared in the training and validation cohorts. For better prediction performance, an optimal nomogram (radiomics nomogram-MS) that incorporated the optimal Rad score and clinical risk factors was developed, and a calibration curve and a decision curve were used to further evaluate the optimized discrimination power.A total of 504 patients diagnosed with NPC were included in this study. The optimal Rad score was significantly correlated with overall survival in both the training [C-index: 0.731, 95% confidence interval (CI): 0.709-0.753] and validation cohorts (C-index: 0.807, 95% CI: 0.782-0.832). Compared with the nomogram developed with only single-sequence MRI, the radiomics nomogram-MS had a higher discrimination power in both the training (C-index: 0.827, 95% CI: 0.809-0.845) and validation cohorts (C-index: 0.836, 95% CI: 0.815-0.857). Analysis of the calibration and decision curves confirmed the effectiveness and utility of the optimal radiomics nomogram-MS.The radiomics nomogram model that incorporates multiple-sequence MRI and clinical factors may be a useful tool for the early assessment of the long-term prognosis of patients diagnosed with NPC.
To develop and validate a radiomics model using computed tomography (CT) images acquired from the first diagnosis to estimate the status of occult brain metastases (BM) in patients with stage IV lung adenocarcinoma (LADC).One hundred and ninety-three patients who were first diagnosed with stage IV LADC were enrolled and divided into a training cohort (n=135) and a validation cohort (n=58). Then, 725 radiomic features were extracted from contoured primary tumor volumes of LADCs. Intra- and interobserver reliabilities were calculated, and the least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Subsequently, a radiomics signature (Rad-Score) was built. To improve performance, a nomogram incorporating a radiomics signature and an independent clinical predictor was developed. Finally, the established signature and nomogram were assessed using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Both empirical and α-binomial model-based ROCs and PRCs were plotted, and the area under the curve (AUC) and average precision (AP) of ROCs and PRCs were calculated and compared.A radiomics signature and Rad-Score were constructed using eight radiomic features, and these had significant correlations with occult BM status. A nomogram was developed by incorporating a Rad-Score and the primary tumor location. The nomogram yielded an optimal AUC of 0.911 [95% confidence interval (CI): 0.903-0.919] and an AP of 0.885 (95% CI: 0.876-0.894) in the training cohort, and an AUC of 0.873 (95% CI: 0.866-0.80) and an AP of 0.827 (95% CI: 0.820-0.834) in the validation cohort using α-binomial model-based method. The calibration curve demonstrated that the nomogram showed high agreement between the actual occult BM probability and predicted by the nomogram (P=0.427).The nomogram incorporating a radiomics signature and a clinical risk factor achieved optimal performance after holistic assessment using unbiased indexes for diagnosing occult BM of patients who were first diagnosed with stage IV LADC.
To study the feasibility of kilovoltage cone-beam computed tomography (KV-CBCT) dose calculation following scatter correction.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.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.The results suggest that CBCT has high accuracy in dose calculation via scatter correction and HU-RED calibration.
Abstract Background Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3–10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC. Methods A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. Results Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developped by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI): 0.827(0.742–0.912)] than the clinical nomogram [AUC(95% CI): 0.731(0.626–0.836)] and radiomics predictive models [AUC(95% CI): 0.754(0.652–0.855), LR algorithms]. Caliberation and decision curve analyses revealed that the radiomics-clinical nomogram outperformed the other models. In comparison to the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI: 0.075–0.345), and its IDI was 0.071 (95% CI: 0.030–0.112), P = 0.001. Conclusion We developed and validated the first radiomics-clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.
Abstract 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 (ICC³0.6). 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.
Hepatocellular carcinoma (HCC) is a cancer with a poor prognosis, and approximately 80% of HCC cases develop from cirrhosis. Imaging techniques in the clinic seem to be insufficient for revealing the microstructures of liver disease. In recent years, phase contrast imaging CT (PCI-CT) has opened new avenues for biomedical applications owing to its unprecedented spatial and contrast resolution. The aim of this study was to present three-dimensional (3D) visualization of human healthy liver, cirrhosis and HCC using a PCI-CT technique called in-line phase contrast imaging CT (ILPCI-CT) and to quantitatively evaluate the variations of these tissues, focusing on the liver parenchyma and microvasculature.Tissue samples from 9 surgical specimens of normal liver (n=3), cirrhotic liver (n=2), and HCC (n=4) were imaged using ILPCI-CT at the Shanghai Synchrotron Radiation Facility (SSRF) without contrast agents. 3D visualization of all ex vivo liver samples are presented. To quantitatively evaluate the vessel features, the vessel branch angles of each sample were clearly depicted. Additionally, radiomic features of the liver parenchyma extracted from the 3D images were measured. To evaluate the stability of the features, the percent coefficient of variation (%COV) was calculated for each radiomic feature. A %COV <30 was considered to be low variation. Finally, one-way ANOVA, followed by Tukey's test, was used to determine significant changes among the different liver specimens.ILPCI-CT allows for a clearer view of the architecture of the vessels and reveals more structural details than does conventional radiography. Combined with the 3D visualization technique, ILPCI-CT enables the acquisition of an accurate description of the 3D vessel morphology in liver samples. Qualitative descriptions and quantitative assessment of microvessels demonstrated clear differences among human healthy liver, cirrhotic liver and HCC. In total, 38 (approximately 51%) radiomic features had low variation, including 11 first-order features, 16 GLCM features, 6 GLRLM features and 5 GLSZM features. The differences in the mean vessel branch angles and 3 radiomic features (first-order entropy, GLCM-inverse variance and GLCM-sum entropy) were statistically significant among the three groups of samples.ILPCI-CT may allow for morphologic descriptions and quantitative evaluation of vessel microstructures and parenchyma in human healthy liver, cirrhotic liver and HCC. Vessel branch angles and radiomic features extracted from liver parenchyma images can be used to distinguish the three kinds of liver tissues.
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