Automatic detection of midfacial fractures in facial bone CT images using deep learning-based object detection models.

BACKGROUND: Midfacial fractures are among the most frequent facial fractures. Surgery is recommended within 2 weeks of injury, but this time frame is often extended because the fracture is missed on diagnostic imaging in the busy emergency medicine setting. Using deep learning technology, which has progressed markedly in various fields, we attempted to develop a system for the automatic detection of midfacial fractures. The purpose of this study was to use this system to diagnose fractures accurately and rapidly, with the intention of benefiting both patients and emergency room physicians. METHODS: One hundred computed tomography images that included midfacial fractures (e.g., maxillary, zygomatic, nasal, and orbital fractures) were prepared. In each axial image, the fracture area was surrounded by a rectangular region to create the annotation data. Eighty images were randomly classified as the training dataset (3736 slices) and 20 as the validation dataset (883 slices). Training and validation were performed using Single Shot MultiBox Detector (SSD) and version 8 of You Only Look Once (YOLOv8), which are object detection algorithms. RESULTS: The performance indicators for SSD and YOLOv8 were respectively: precision, 0.872 and 0.871; recall, 0.823 and 0.775; F1 score, 0.846 and 0.82; average precision, 0.899 and 0.769. CONCLUSIONS: The use of deep learning techniques allowed the automatic detection of midfacial fractures with good accuracy and high speed. The system developed in this study is promising for automated detection of midfacial fractures and may provide a quick and accurate solution for emergency medical care and other settings.

Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs.

PURPOSE: Progression of hip osteoarthritis (hip OA) leads to pain and disability, likely leading to surgical treatment such as hip arthroplasty at the terminal stage. The severity of hip OA is often classified using the Crowe and Kellgren-Lawrence (KL) classifications. However, as the classification is subjective, we aimed to develop an automated approach to classify the disease severity based on the two grades using digitally-reconstructed radiographs from CT images. METHODS: Automatic grading of the hip OA severity was performed using deep learning-based models. The models were trained to predict the disease grade using two grading schemes, i.e., predicting the Crowe and KL grades separately, and predicting a new ordinal label combining both grades and representing the disease progression of hip OA. The models were trained in classification and regression settings. In addition, the model uncertainty was estimated and validated as a predictor of classification accuracy. The models were trained and validated on a database of 197 hip OA patients, and externally validated on 52 patients. The model accuracy was evaluated using exact class accuracy (ECA), one-neighbor class accuracy (ONCA), and balanced accuracy. RESULTS: The deep learning models produced a comparable accuracy of approximately 0.65 (ECA) and 0.95 (ONCA) in the classification and regression settings. The model uncertainty was significantly larger in cases with large classification errors ( P < 6 e - 3 ). CONCLUSIONS: In this study, an automatic approach for grading hip OA severity from CT images was developed. The models have shown comparable performance with high ONCA, which facilitates automated grading in large-scale CT databases and indicates the potential for further disease progression analysis. Classification accuracy was correlated with the model uncertainty, which would allow for the prediction of classification errors. The code will be made publicly available at https://github.com/NAIST-ICB/HipOA-Grading .

Hybrid representation-enhanced sampling for Bayesian active learning in musculoskeletal segmentation of lower extremities.

PURPOSE: Manual annotations for training deep learning models in auto-segmentation are time-intensive. This study introduces a hybrid representation-enhanced sampling strategy that integrates both density and diversity criteria within an uncertainty-based Bayesian active learning (BAL) framework to reduce annotation efforts by selecting the most informative training samples. METHODS: The experiments are performed on two lower extremity datasets of MRI and CT images, focusing on the segmentation of the femur, pelvis, sacrum, quadriceps femoris, hamstrings, adductors, sartorius, and iliopsoas, utilizing a U-net-based BAL framework. Our method selects uncertain samples with high density and diversity for manual revision, optimizing for maximal similarity to unlabeled instances and minimal similarity to existing training data. We assess the accuracy and efficiency using dice and a proposed metric called reduced annotation cost (RAC), respectively. We further evaluate the impact of various acquisition rules on BAL performance and design an ablation study for effectiveness estimation. RESULTS: In MRI and CT datasets, our method was superior or comparable to existing ones, achieving a 0.8% dice and 1.0% RAC increase in CT (statistically significant), and a 0.8% dice and 1.1% RAC increase in MRI (not statistically significant) in volume-wise acquisition. Our ablation study indicates that combining density and diversity criteria enhances the efficiency of BAL in musculoskeletal segmentation compared to using either criterion alone. CONCLUSION: Our sampling method is proven efficient in reducing annotation costs in image segmentation tasks. The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets.

Automatic orbital segmentation using deep learning-based 2D U-net and accuracy evaluation: A retrospective study.

The purpose of this study was to verify whether the accuracy of automatic segmentation (AS) of computed tomography (CT) images of fractured orbits using deep learning (DL) is sufficient for clinical application. In the surgery of orbital fractures, many methods have been reported to create a 3D anatomical model for use as a reference. However, because the orbit bone is thin and complex, creating a segmentation model for 3D printing is complicated and time-consuming. Here, the training of DL was performed using U-Net as the DL model, and the AS output was validated with Dice coefficients and average symmetry surface distance (ASSD). In addition, the AS output was 3D printed and evaluated for accuracy by four surgeons, each with over 15 years of clinical experience. One hundred twenty-five CT images were prepared, and manual orbital segmentation was performed in all cases. Ten orbital fracture cases were randomly selected as validation data, and the remaining 115 were set as training data. AS was successful in all cases, with good accuracy: Dice, 0.860 ± 0.033 (mean ± SD); ASSD, 0.713 ± 0.212 mm. In evaluating AS accuracy, the expert surgeons generally considered that it could be used for surgical support without further modification. The orbital AS algorithm developed using DL in this study is extremely accurate and can create 3D models rapidly at low cost, potentially enabling safer and more accurate surgeries.

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography.

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.

4D-foot analysis on effect of arch support on ankle, subtalar, and talonavicular joint kinematics.

BACKGROUND: It has been difficult to study the effects of arch support on multiple joints simultaneously. Herein, we evaluated foot and ankle kinematics using a fully automated analysis system, "4D-Foot," consisting of a biplane X-ray imager and two-dimensional‒three-dimensional registration, with automated image segmentation and landmark detection tools. METHODS: We evaluated the effect of arch support on ankle, subtalar, and talonavicular joint kinematics in five healthy female volunteers without a clinical history of foot and ankle disorders. Computed tomography images of the foot and ankle and X-ray videos of walking barefoot and with arch support were acquired. A kinematic analysis using the "4D-Foot" system was performed. The ankle, subtalar, and talonavicular joint kinematics were quantified from heel-strike to foot-off, with and without arch support. RESULTS: For the ankle joint, significant differences were observed in dorsi/plantarflexion, inversion/eversion, and internal/external rotation in the late midstance phase. The dorsi/plantarflexion and inversion/eversion motions were smaller with arch support. For the subtalar joint, a significant difference was observed in all the dynamic motions in the heel-strike and late midstance phases. For the talonavicular joint, significant differences were observed in inversion/eversion and internal/external rotation in heel-strike and the late midstance phases. For the subtalar and talonavicular joints, the motion was larger with arch support. An extremely strong correlation was observed when the motion of the subtalar and talonavicular joints was compared for each condition and motion. CONCLUSIONS: The results indicated that the arch support decreased the ankle motion and increased the subtalar and talonavicular joint motions. Additionally, our study demonstrated that the in vivo subtalar and talonavicular joints revealed a strong correlation, suggesting that the navicular and calcaneal bones were moving similarly to the talus and that the arch support stabilizes the ankle joint and compensatively increases the subtalar and talonavicular joint motions.

Artificial intelligence-based volumetric analysis of muscle atrophy and fatty degeneration in patients with hip osteoarthritis and its correlation with health-related quality of life.

PURPOSE: Artificial intelligence (AI) technologies have enabled precise three-dimensional analysis of individual muscles on computed tomography (CT) or magnetic resonance images via automatic segmentation. This study aimed to perform three-dimensional assessments of pelvic and thigh muscle atrophy and fatty degeneration in patients with unilateral hip osteoarthritis using CT and to evaluate the correlation with health-related quality of life (HRQoL). METHODS: The study included one man and 43 women. Six muscle groups were segmented, and the muscle atrophy ratio was calculated volumetrically. The degree of fatty degeneration was defined as the difference between the mean CT values (Hounsfield units [HU]) of the healthy and affected sides. HRQoL was evaluated using the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index and the Japanese Orthopaedic Association Hip Disease Evaluation Questionnaire (JHEQ). RESULTS: The mean muscle atrophy rate was 16.3%, and the mean degree of muscle fatty degeneration was 7.9 HU. Multivariate correlation analysis revealed that the WOMAC stiffness subscale was significantly related to fatty degeneration of the hamstrings, the WOMAC physical function subscale was significantly related to fatty degeneration of the iliopsoas muscle, and the JHEQ movement subscale was significantly related to fatty degeneration of the hip adductors. CONCLUSION: We found that fatty degeneration of the hamstrings, iliopsoas, and hip adductor muscles was significantly related to HRQoL in patients with hip osteoarthritis. These findings suggest that these muscles should be targeted during conservative rehabilitation for HOA and perioperative rehabilitation for THA.

Rotational-motion measurement of the sacroiliac joint using upright MRI scanning and intensity-based registration: is there a sex difference?

PURPOSE: The sacroiliac joint (SIJ) has attracted increasing attention as a source of low back and groin pain, but the kinematics of SIJ against standing load and its sex difference remain unclear due to the difficulty of in vivo load study. An upright magnetic resonance imaging (MRI) system can provide in vivo imaging both in the supine and standing positions. The reliability of the mobility of SIJ against the standing load was evaluated and its sex difference was examined in healthy young volunteers using an upright MRI. METHOD: Static (reliability) and kinematic studies were performed. In the static study, a dry bone of pelvic ring embedded in gel form and frozen in the plastic box was used. In the kinematic study, 19 volunteers (10 males, 9 females) with a mean age of 23.9 years were included. The ilium positions for the sacrum in supine and standing positions were measured against the pelvic coordinates to evaluate the mobility of the SIJ. RESULTS: In the static study, the residual error of the rotation of the SIJ study was < 0.2°. In the kinematic study, the mean values of SIJ sagittal rotation from supine to standing position in males and females were - 0.9° ± 0.7° (mean ± standard deviation) and - 1.7° ± 0.8°, respectively. The sex difference was statistically significant (p = 0.04). The sagittal rotation of the SIJ showed a significant correlation with the sacral slope. CONCLUSION: The residual error for measuring the SIJ rotation using the upright MRI was < 0.2°. The young healthy participants showed sex differences in the sagittal rotation of the SIJ against the standing load and the females showed a larger posterior rotation of the ilium against the sacrum from the supine to standing position than the males. Therefore, upright MRI is useful to investigate SIJ motion.

Why upright standing men urinate more efficiently than in supine position: A morphological analysis with real-time magnetic resonance imaging.

PURPOSE: Few studies have examined the effects of body position on urination efficiency morphologically. We aimed to dissect out the anatomical changes of pelvic organs during urination in the upright and supine positions by a real-time magnetic resonance imaging (rtMRI) system. METHODS: Thirteen healthy male volunteers aged 26-60 years were included in the study. The sagittal real-time two-dimensional images were taken to evaluate urinary efficiency, along with change in six morphological indices at the time of storage and the beginning of voiding, in both upright ant supine positions. RESULTS: Urination was more efficient in upright position than in supine position, as expressed by higher average rate of bladder emptying (9.9 ± 4.2 vs. 6.8 ± 2.9 ml/s, p < 0.05) and also by fewer participants showing significant residual urine (1/13 vs. 7/13, p < 0.05). At the onset of voiding in standing position, the levator ani (LA) muscle moves downward and backward followed by descent of the bladder neck and rotation of the prostate around the symphysis. Such changes were expressed by two morphological indices. One was posterior vesicourethral angle at the start of voiding, 152 ± 7 versus 140 ± 1 in upright and supine position (p < 0.05). The other index was the change in angle between the LA line and pubo-coccygeal line in upright and supine position, 9.4 ± 9.9 versus 1.6 ± 7.9 before voiding (p < 0.05) and 30.2 ± 14.0 versus 17.3 ± 12.9 after the start of voiding (p < 0.05). CONCLUSION: The dynamic relaxation of LA seemed to be a key movement that enables more efficient urination in standing position than in supine position.

Development of an open-source measurement system to assess the areal bone mineral density of the proximal femur from clinical CT images.

Commercial software is generally needed to measure the areal bone mineral density (aBMD) of the proximal femur from clinical computed tomography (CT) images. This study developed and verified an open-source reproducible system to quantify CT-aBMD to screen osteoporosis using clinical CT images. PURPOSE: For existing CT images acquired for various reasons other than osteoporosis, it might be beneficial to estimate areal BMD as assessed by dual-energy X-ray absorptiometry (DXA-based BMD) to ascertain the bone status based on DXA. In this study, we aimed to (1) develop an open-source reproducible measurement system to quantify DXA-based BMD from CT images and (2) validate its accuracy. METHODS: This study analyzed 75 pairs of hip CT and DXA images of women that were acquired for the preoperative assessment of total hip arthroplasty. From the CT images, the femur and a calibration phantom were automatically segmented using pre-trained codes/models available at https://github.com/keisuke-uemura . The proximal femoral region was isolated by manually selected landmarks and was projected onto the coronal plane to measure the areal density (CT-aHU). The calibration phantom was employed to convert the CT-aHU into CT-aBMD. Each parameter was correlated with DXA-based BMD, and the residual errors of CT images to estimate the T-scores in DXA were calculated using the standard error of estimate (SEE). RESULTS: The correlation coefficients of DXA-based BMD with CT-aHU and CT-aBMD were 0.947 and 0.950, respectively (both p < 0.001). The SEE for quantifying the T-scores in DXA were 0.51 and 0.50 for CT-aHU and CT-aBMD, respectively. CONCLUSION: With the method developed herein, CT permits estimation of the DXA-based BMD of the proximal femur within the standard DXA total hip region of interest with an SEE of 0.5 in T-scores. The radiation dose for CT acquisition needs consideration; therefore, our data do not provide a rationale for performing CT for screening osteoporosis. However, on CT images already acquired for clinical indications other than osteoporosis, researchers may use this open-source system to investigate osteoporosis status through the estimated DXA-based BMD of the proximal femur.

Translation of Cellular Protein Localization Using Convolutional Networks.

Protein localization in cells has been analyzed by fluorescent labeling using indirect immunofluorescence and fluorescent protein tagging. However, the relationships between the localization of different proteins had not been analyzed using artificial intelligence. Here, we applied convolutional networks for the prediction of localization of the cytoskeletal proteins from the localization of the other proteins. Lamellipodia are one of the actin-dependent subcellular structures involved in cell migration and are mainly generated by the Wiskott-Aldrich syndrome protein (WASP)-family verprolin homologous protein 2 (WAVE2) and the membrane remodeling I-BAR domain protein IRSp53. Focal adhesion is another actin-based structure that contains vinculin protein and promotes lamellipodia formation and cell migration. In contrast, microtubules are not directly related to actin filaments. The convolutional network was trained using images of actin filaments paired with WAVE2, IRSp53, vinculin, and microtubules. The generated images of WAVE2, IRSp53, and vinculin were highly similar to their real images. In contrast, the microtubule images generated from actin filament images were inferior without the generation of filamentous structures, suggesting that microscopic images of actin filaments provide more information about actin-related protein localization. Collectively, this study suggests that image translation by the convolutional network can predict the localization of functionally related proteins, and the convolutional network might be used to describe the relationships between the proteins by their localization.

Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network.

PURPOSE: In quantitative computed tomography (CT), manual selection of the intensity calibration phantom's region of interest is necessary for calculating density (mg/cm3) from the radiodensity values (Hounsfield units: HU). However, as this manual process requires effort and time, the purposes of this study were to develop a system that applies a convolutional neural network (CNN) to automatically segment intensity calibration phantom regions in CT images and to test the system in a large cohort to evaluate its robustness. METHODS: This cross-sectional, retrospective study included 1040 cases (520 each from two institutions) in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used. A training dataset was created by manually segmenting the phantom regions for 40 cases (20 cases for each institution). The CNN model's segmentation accuracy was assessed with the Dice coefficient, and the average symmetric surface distance was assessed through fourfold cross-validation. Further, absolute difference of HU was compared between manually and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate the correlation coefficients between HU and phantom density. RESULTS: The source code and the model used for phantom segmentation can be accessed at https://github.com/keisuke-uemura/CT-Intensity-Calibration-Phantom-Segmentation . The median Dice coefficient was 0.977, and the median average symmetric surface distance was 0.116 mm. The median absolute difference of the segmented regions between manual and automated segmentation was 0.114 HU. For the test cases, the median correlation coefficients were 0.9998 and 0.999 for the two institutions, with a minimum value of 0.9863. CONCLUSION: The proposed CNN model successfully segmented the calibration phantom regions in CT images with excellent accuracy.

Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis.

PURPOSE: Liver shape variations have been considered as feasible indicators of liver fibrosis. However, current statistical shape models (SSM) based on principal component analysis represent gross shape variations without considering the association with the fibrosis stage. Therefore, we aimed at the application of a statistical shape modelling approach using partial least squares regression (PLSR), which explicitly uses the stage as supervised information, for understanding the shape variations associated with the stage as well as predicting it in contrast-enhanced MR images. METHODS: Contrast-enhanced MR images of 51 patients with fibrosis stages F0/1 (n = 18), F2 (n = 15), F3 (n = 7) and F4 (n = 11) were used. The livers were manually segmented from the images. An SSM was constructed using PLSR, by which shape variation modes (scores) that were explicitly associated with the reference pathological fibrosis stage were derived. The stage was predicted using a support vector machine (SVM) based on the PLSR scores. The performance was assessed using the area under receiver operating characteristic curve (AUC). RESULTS: In addition to commonly known shape variations, such as enlargement of left lobe and shrinkage of right lobe, our model represented detailed variations, such as enlargement of caudate lobe and the posterior part of right lobe, and shrinkage in the anterior part of right lobe. These variations qualitatively agreed with localized volumetric variations reported in clinical studies. The accuracy (AUC) at classifications F0/1 versus F2‒4 (significant fibrosis), F0‒2 versus F3‒4 and F0‒3 versus F4 (cirrhosis) were 0.90 ± 0.03, 0.80 ± 0.05 and 0.82 ± 0.05, respectively. CONCLUSIONS: The proposed approach offered an explicit representation of commonly known as well as detailed shape variations associated with liver fibrosis stage. Thus, the application of PLSR-based SSM is feasible for understanding the shape variations associated with the liver fibrosis stage and predicting it.

Radiomics with artificial intelligence for precision medicine in radiation therapy.

Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.

Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis.

Computer-aided diagnosis (CAD) is a field that is essentially based on pattern recognition that improves the accuracy of a diagnosis made by a physician who takes into account the computer's "opinion" derived from the quantitative analysis of radiological images. Radiomics is a field based on data science that massively and comprehensively analyzes a large number of medical images to extract a large number of phenotypic features reflecting disease traits, and explores the associations between the features and patients' prognoses for precision medicine. According to the definitions for both, you may think that radiomics is not a paraphrase of CAD, but you may also think that these definitions are "image manipulation". However, there are common and different features between the two fields. This review paper elaborates on these common and different features and introduces the potential of radiomics for cancer diagnosis and treatment by comparing it with CAD.

Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features.

PURPOSE: To identify the optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based (WDB) radiomic features in CT images. MATERIALS AND METHODS: The CT images of patients with histologically confirmed nonsmall cell lung carcinomas (NSCLCs) in training (Dataset T; n = 162) and validation (Dataset V; n = 143) datasets were analyzed for this study. The optimal mother wavelets were identified based on the impacts of the WDB radiomic features on the patient survival times. Four hundred and thirty-two three-dimensional WDB radiomic features were calculated from regions of interest (ROI) of 162 tumor contours. A Coxnet algorithm was used to select a subset of radiomic features (signature) based on the prediction of survival times with a fivefold cross validation. The impacts of the radiomic features on the patients' survival times were assessed by using a multivariate Cox proportional hazard regression (MCPHR) model. The major contribution of this study was to identify optimal mother wavelets based on a maximization of a novel ranking index (RI) incorporating the Coxnet cross-validated partial log-likelihood and the summation of the P-values of the radiomic features in the MCPHR model on Dataset T. The prognostic performance of the optimal mother wavelets was validated based on the concordance index (CI) of the MCPHR models when applied to Dataset V. The proposed approach was tested by using 31 mother wavelets from 6 wavelet families that were available in a commercially available software (Matlab® 2016b). RESULTS: The optimal mother wavelets were Symlet 5 and Biorthogonal 2.6 at 128 requantization levels, which yielded RIs of 4.27 ± 0.29 (3 features) and 6.50 ± 0.50 (5 features), respectively. The CIs of the MCPHR models of Symlet 5 were 0.66 ± 0.03 (Dataset T) and 0.64 ± 0.00 (Dataset V), whereas those of Biorthogonal 2.6 were 0.68 ± 0.03 (Dataset T) and 0.62 ± 0.02 (Dataset V). The radiomic signatures included the GLRLM-based LHL gray level nonuniformity feature that demonstrated statistically significant differences in stratifying patients with better and worse prognoses in Datasets T and V. CONCLUSION: This study has revealed the potential of Symlet and Biorthogonal mother wavelets in  the survival prediction of lung cancer patients by using WDB radiomic features in CT images.

Platelet rich plasma injections for lateral epicondylitis of the elbow reduce the need for surgical intervention.

OBJECTIVE: We aimed to assess the effectiveness of PRP injections in lateral epicondylitis of the elbow as it was felt after PRP introduction the numbers of patients requiring surgery for had reduced. METHODS: We conducted a retrospective review of cases from the 1st January 2008 to 31st December 2015. The numbers of patients undergoing surgical release and the numbers of patients requiring PRP injections were recorded each year and the numbers of patients requiring surgery was compared pre and post PRP injection to ascertain if PRP introduction reduced surgical intervention. RESULTS: Prior to PRP, a yearly mean of 12.75 patients underwent surgery, since PRP this reduced to 4.25 patients, P < 0.001. This leads to an absolute risk reduction of 0.773 and number needed to treat of 1.3. PRP injection successfully reduced symptoms in 56/64 (87.5%) patients in our study. CONCLUSION: We consider PRP injection, for intractable lateral epicondylitis of the elbow, not only a safe but also very effective tool in reducing symptoms and have shown it has reduced the need for surgical intervention in this difficult cohort of patients.

Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images.

PURPOSE: We aimed to explore the temporal stability of radiomic features in the presence of tumor motion and the prognostic powers of temporally stable features. METHODS: We selected single fraction dynamic electronic portal imaging device (EPID) (n = 275 frames) and static digitally reconstructed radiographs (DRRs) of 11 lung cancer patients, who received stereotactic body radiation therapy (SBRT) under free breathing. Forty-seven statistical radiomic features, which consisted of 14 histogram-based features and 33 texture features derived from the graylevel co-occurrence and graylevel run-length matrices, were computed. The temporal stability was assessed by using a multiplication of the intra-class correlation coefficients (ICCs) between features derived from the EPID and DRR images at three quantization levels. The prognostic powers of the features were investigated using a different database of lung cancer patients (n = 221) based on a Kaplan-Meier survival analysis. RESULTS: Fifteen radiomic features were found to be temporally stable for various quantization levels. Among these features, seven features have shown potentials for prognostic prediction in lung cancer patients. CONCLUSIONS: This study suggests a novel approach to select temporally stable radiomic features, which could hold prognostic powers in lung cancer patients.

Multidimensional Image Analysis for High Precision Radiation Therapy.

High precision radiation therapy (HPRT) has been improved by utilizing conventional image engineering technologies. However, different frameworks are necessary for further improvement of HPRT. This review paper attempted to define the multidimensional image and what multidimensional image analysis is, which may be feasible for increasing the accuracy of HPRT. A number of researches in radiation therapy field have been introduced to understand the multidimensional image analysis. Multidimensional image analysis could greatly assist clinical staffs in radiation therapy planning, treatment, and prediction of treatment outcomes.

Feasibility of differential geometry-based features in detection of anatomical feature points on patient surfaces in range image-guided radiation therapy.

PURPOSE: To investigate the feasibility of differential geometry features in the detection of anatomical feature points on a patient surface in infrared-ray-based range images in image-guided radiation therapy. METHODS: The key technology was to reconstruct the patient surface in the range image, i.e., point distribution with three-dimensional coordinates, and characterize the geometrical shape at every point based on curvature features. The region of interest on the range image was extracted by using a template matching technique, and the range image was processed for reducing temporal and spatial noise. Next, a mathematical smooth surface of the patient was reconstructed from the range image by using a non-uniform rational B-splines model. The feature points were detected based on curvature features computed on the reconstructed surface. The framework was tested on range images acquired by a time-of-flight (TOF) camera and a Kinect sensor for two surface (texture) types of head phantoms A and B that had different anatomical geometries. The detection accuracy was evaluated by measuring the residual error, i.e., the mean of minimum Euclidean distances (MMED) between reference (ground truth) and detected feature points on convex and concave regions. RESULTS: The MMEDs obtained using convex feature points for range images of the translated and rotated phantom A were [Formula: see text] and [Formula: see text], respectively, using the TOF camera. For the phantom B, the MMEDs of the convex and concave feature points were [Formula: see text] and [Formula: see text] mm, respectively, using the Kinect sensor. There was a statistically significant difference in the decreased MMED for convex feature points compared with concave feature points [Formula: see text]. CONCLUSIONS: The proposed framework has demonstrated the feasibility of differential geometry features for the detection of anatomical feature points on a patient surface in range image-guided radiation therapy.