Purpose: The purpose of this study is to develop and validate a nomogram model combing radiomics features and clinical characteristics to preoperatively differentiate grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (pNET). Experimental Design: A total of 137 patients who underwent contrast-enhanced CT from two hospitals were included in this study. The patients from the second hospital (n ¼ 51) were selected as an independent validation set. The arterial phase in contrast-enhanced CT was selected for radiomics feature extraction. The Mann-Whitney U test and least absolute shrinkage and selection operator regression were applied for feature selection and radiomics signature construction. A combined nomogram model was developed by incorporating the radiomics signature with clinical factors. The association between the nomogram model and the Ki-67 index and rate of nuclear mitosis were also investigated respectively. The utility of the proposed model was evaluated using the ROC, area under ROC curve (AUC), calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was used for survival analysis. Results: An eight-feature-combined radiomics signature was constructed as a tumor grade predictor. The nomogram model combining the radiomics signature with clinical stage showed the best performance (training set: AUC ¼ 0.907; validation set: AUC ¼ 0.891). The calibration curve and DCA demonstrated the clinical usefulness of the proposed nomogram. A significant correlation was observed between the developed nomogram and Ki-67 index and rate of nuclear mitosis, respectively. The KM analysis showed a significant difference between the survival of predicted grade 1 and grade 2/3 groups (P ¼ 0.002). Conclusions: The combined nomogram model developed could be useful in differentiating grade 1 and grade 2/3 tumor in patients with pNETs.
Purpose To improve image quality and computed tomography (CT) number accuracy of daily cone beam CT (CBCT) through a deep learning methodology with generative adversarial network. Methods One hundred and fifty paired pelvic CT and CBCT scans were used for model training and validation. An unsupervised deep learning method, 2.5D pixel‐to‐pixel generative adversarial network (GAN) model with feature mapping was proposed. A total of 12 000 slice pairs of CT and CBCT were used for model training, while ten‐fold cross validation was applied to verify model robustness. Paired CT–CBCT scans from an additional 15 pelvic patients and 10 head‐and‐neck (HN) patients with CBCT images collected at a different machine were used for independent testing purpose. Besides the proposed method above, other network architectures were also tested as: 2D vs 2.5D; GAN model with vs without feature mapping; GAN model with vs without additional perceptual loss; and previously reported models as U‐net and cycleGAN with or without identity loss. Image quality of deep‐learning generated synthetic CT (sCT) images was quantitatively compared against the reference CT (rCT) image using mean absolute error (MAE) of Hounsfield units (HU) and peak signal‐to‐noise ratio (PSNR). The dosimetric calculation accuracy was further evaluated with both photon and proton beams. Results The deep‐learning generated sCTs showed improved image quality with reduced artifact distortion and improved soft tissue contrast. The proposed algorithm of 2.5 Pix2pix GAN with feature matching (FM) was shown to be the best model among all tested methods producing the highest PSNR and the lowest MAE to rCT. The dose distribution demonstrated a high accuracy in the scope of photon‐based planning, yet more work is needed for proton‐based treatment. Once the model was trained, it took 11–12 ms to process one slice, and could generate a 3D volume of dCBCT (80 slices) in less than a second using a NVIDIA GeForce GTX Titan X GPU (12 GB, Maxwell architecture). Conclusion The proposed deep learning algorithm is promising to improve CBCT image quality in an efficient way, thus has a potential to support online CBCT‐based adaptive radiotherapy.
Purpose:The feasibility of medical imaging using a medical linear accelerator to generate acoustic waves is investigated. This modality, x-ray acoustic computed tomography (XACT), has the potential to enable deeper tissue penetration in tissue than photoacoustic tomography via laser excitation. Methods: Short pulsed (μs-range) 10 MV x-ray beams with dose-rate of approximately 30 Gy/min were generated from a medical linear accelerator. The acoustic signals were collected with an ultrasound transducer (500 KHz central frequency) positioned around an object. The transducer, driven by a computer-controlled step motor to scan around the object, detected the resulting acoustic signals in the imaging plane at each scanning position. A pulse preamplifier, with a bandwidth of 20 KHz-2 MHz at −3 dB, and switchable gains of 40 and 60 dB, received the signals from the transducer and delivered the amplified signals to a secondary amplifier. The secondary amplifier had bandwidth of 20 KHz-30 MHz at −3 dB, and a gain range of 10-60 dB. Signals were recorded and averaged 128 times by an oscilloscope. A sampling rate of 100 MHz was used to record 2500 data points at each view angle. One set of data incorporated 200 positions as the receiver moved 360• . The x-ray generated acoustic image was then reconstructed with the filtered back projection algorithm. Results: The x-ray generated acoustic signals were detected from a lead rod embedded in a chicken breast tissue. The authors found that the acoustic signal was proportional to the x-ray dose deposition, with a correlation of 0.998. The two-dimensional XACT images of the lead rod embedded in chicken breast tissue were found to be in good agreement with the shape of the object. Conclusions:The first x-ray acoustic computed tomography image is presented. The new modality may be useful for a number of applications, such as providing the location of a fiducial, or monitoring x-ray dose distribution during radiation therapy. Although much work is needed to improve the image quality of XACT and to explore its performance in other irradiation energies, the benefits of this modality, as highlighted in this work, encourage further study.
Purpose Dual-energy CT (DECT) enhances tissue characterization because of its basis material decomposition capability. In addition to conventional two-material decomposition from DECT measurements, multi-material decomposition (MMD) is required in many clinical applications. To solve the ill-posed problem of reconstructing multiple-material images from dual-energy measurements, additional constraints are incorporated into the formulation, including volume and mass conservation and the assumptions that at most three materials in each pixel and various material types among pixels. The recently proposed flexible image-domain MMD method decomposes pixels sequentially into multiple basis materials using direct inversion scheme and leads to magnified noise in the material images. In this paper, we propose a statistical image-domain MMD method for DECT to suppress the noise. Methods The proposed method applies penalized weighted least-square (PWLS) reconstruction with a negative log-likelihood term and edge-preserving regularization for each material. The statistical weight is determined by a data-based method accounting for the noise variance of high- and low-energy CT images. We apply the optimization transfer principles to design a serial of pixel-wise separable quadratic surrogates (PWSQS) functions which monotonically decrease the cost function. The separability in each pixel enables simultaneous update of all pixels. Results The proposed method is evaluated on a digital phantom, Catphan©600 phantom and three patients (pelvis, head and thigh). We also implement the direct inversion and low-pass filtration methods for comparison purpose. Compared with the direct inversion method, the proposed method reduces noise standard deviation (STD) in soft-tissue by 95.35% in the digital phantom study, by 88.01% in the Catphan©600 phantom study, by 92.45% in the pelvis patient study, by 60.21% in the head patient study, and by 81.22% in the thigh patient study, respectively. The overall volume fraction accuracy is improved by around 6.85%. Compared with the low-pass filtration method, the root-mean-square percentage error (RMSE(%)) of electron densities in the Catphan©600 phantom is decreased by 20.89%. At modulation transfer function (MTF) magnitude decreased to 50%, the proposed method increases the spatial resolution by an overall factor of 1.64 on the digital phantom, and 2.16 on the Catphan©600 phantom. The overall volume fraction accuracy is increased by 6.15%. Conclusions We proposed a statistical image-domain MMD method using DECT measurements. The method successfully suppresses the magnified noise while faithfully retaining the quantification accuracy and anatomical structure in the decomposed material images. The proposed method is practical and promising for advanced clinical applications using DECT imaging.
Background Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Methods Independent patient cohorts from two hospitals were included for training ( n = 87) and validation ( n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. Findings The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. Interpretation The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. Fund This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.
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