Purpose: This study investigated whether a radiomic analysis of pretreatment ¹⁸F-FDG PET can predict prognosis in patients with Hodgkin lymphoma (HL). Methods: Forty-two patients who were diagnosed as having HL and underwent pretreatment ¹⁸F-FDG PET scans were retrospectively enrolled. For each patient, we extracted 450 radiomic features from PET images. The prognostic significance of the clinical and radiomic features was assessed in relation to progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic curve, Cox proportional hazards regression, and Kaplan-Meier analyses were performed to examine the potential independent predictors and to evaluate the predictive value. Results: Intensity nonuniformity extracted from a gray-level run-length matrix and the Ann Arbor stage were independently associated with PFS (hazard ratio [HR] = 22.8, P < 0.001; HR = 7.6, P = 0.024) and OS (HR = 14.5, P = 0.012; HR = 8.5, P = 0.048), respectively. In addition, SUV kurtosis was an independent prognosticator for PFS (HR = 6.6, P = 0.026). We devised a prognostic scoring system based on these 3 risk predictors. The proposed scoring system further improved the risk stratification of the current staging classification (P < 0.001). Conclusions: The radiomic feature intensity nonuniformity is an independent prognostic predictor of PFS and OS in patients with HL. We devised a prognostic scoring system, which may be more beneficial for patient risk stratification in guiding therapy compared with the current Ann Arbor staging system.

This study aimed to compare different types of right breast cancer radiotherapy planning techniques and to estimate the whole-body effective doses and the critical organ absorbed doses. The three planning techniques are intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT; two methods) and hybrid 3D-CRT/IMRT (three-dimensional conformal radiotherapy/intensity-modulated radiotherapy). The VMAT technique includes two methods to deliver a dose: non-continuous partial arc and continuous partial arc. A thermoluminescent dosimeter (TLD) is placed in the RANDO phantom to estimate the organ absorbed dose. Each planning technique applies 50.4 Gy prescription dose and treats critical organs, including the lung and heart. Dose-volume histogram was used to show the planning target volume (V95%), homogeneity index (HI), conformity index (CI), and other optimized indices. The estimation of whole-body effective dose was based on the International Commission on Radiation Protection (ICRP) Publication 60 and 103. The results were as follows: Continuous partial arc and non-continuous partial arc showed the best CI and HI. The heart absorbed doses in the continuous partial arc and hybrid 3D-CRT/IMRT were 0.07 ± 0.01% and 0% (V5% and V10%, respectively). The mean dose of the heart was lowest in hybrid 3D-CRT/IMRT (1.47 Gy ± 0.02). The dose in the left contralateral lung (V5%) was lowest in continuous partial arc (0%). The right ipsilateral lung average dose and V20% are lowest in continuous partial arc. Hybrid 3D-CRT/IMRT has the lowest mean dose to contralateral breast (organs at risk). The whole-body effective doses for ICRP-60 and ICRP-103 were highest in continuous partial arc (2.01 Sv ± 0.23 and 2.89 Sv ± 0.15, respectively). In conclusion, the use of VMAT with continuous arc has a lower risk of radiation pneumonia, while hybrid 3D-CRT/IMRT attain lower secondary malignancy risk and cardiovascular complications.

Multiple exposure speckle imaging has demonstrated its improved accuracy compared to single exposure speckle imaging for relative quantitation of blood flow in vivo. However, the calculation of blood flow maps relies on a pixelwise non-linear fit of a multi-parametric model to the speckle contrasts. This approach has two major drawbacks. First, it is computer-intensive and prevents real time imaging and, second, the mathematical model is not universal and should in principle be adapted to the type of blood vessels. We evaluated a model-free machine learning approach based on a convolutional neural network as an alternative to the non-linear fit approach. A network was designed and trained with annotated speckle contrast data from microfluidic experiments. The neural network performances are then compared to the non-linear fit approach applied to in vitro and in vivo data. The study demonstrates the potential of convolutional networks to provide relative blood flow maps from multiple exposure speckle data in real time.

Digital radiography (DR) is a mature technology and has been broadly used in medical diagnosis. Currently, it’s also used for fruit quality inspection in the market. This purpose of the study is to conduct non-destructive experiments for visual comparisons of digital radiography images, further construct visualized grayscale image analysis technology, and analyze the changes in lemon quality and ripening using quantitative statistical methods. The materials used for the experiments were three lemons of different ripening. A general medical X-ray DR system for was used in this study for 2D digital radiography. The medical X-ray DR images were created based on the Digital Imaging and Communications in Medicine (DICOM) standard. Photometric interpretation of monochrome was applied to create multi-layered grayscale images. Then quantitative analyses and comparisons were performed with image matrix structures and grayscale pixel values in the tissues using visualization techniques and statistical methods. After layer segmentation on the radiological images, the correlations between the lemon structures and tissue changes were assessed by using the Kruskal–Wallis test. The results showed that the p values for lemon, fiber, and pulp were all under 0.05, while the peel layer did not exhibit significant change. The pulp layer is the best region for statistical analyses to determine the lemon ripening. In conclusion, this study can provide a solid reference for future quality classification in the agricultural market. The research findings can be referenced for developing computing techniques applied to agricultural inspection, expanding the scope of application of the medical DR technology.

Time-of-flight dual photon emission computed tomography (TOF-DuPECT) is an imaging system that can obtain radionuclide distributions using time information recorded from two cascade-decay photons. The potential decay locations in the image space, a hyperbolic response curve, can be determined via time-difference-of-arrival (TDOA) estimations from two instantaneous coincidence photons. In this feasibility study, Monte Carlo simulations were performed to generate list-mode coincidence data. A full-ring positron emission tomography-like detection system geometry was built in the simulation environment. A contrast phantom and a Jaszczak-like phantom filled with Selenium-75 (Se-75) were used to evaluate the image quality. A TOF-DuPECT system with varying coincidence time resolution (CTR) was then evaluated. We used the stochastic origin ensemble (SOE) algorithm to reconstruct images from the recorded list-mode data. The results indicate that the SOE method can be successfully employed for the TOF-DuPECT system and can achieve acceptable image quality when the CTR is less than 100 ps. Therefore, the TOF-DuPECT imaging system is feasible. With the improvement of the detector with time, future implementations and applications of TOF-DuPECT are promising. Further quantitative imaging techniques such as attenuation and scatter corrections for the TOF-DuPECT system will be developed in future.