Feasibility of determining external beam radiotherapy dose using LuSy dosimeter.

INTRODUCTION: Radiation dose measurement is an essential part of radiotherapy to verify the correct delivery of doses to patients and ensure patient safety. Recent advancements in radiotherapy technology have highlighted the need for fast and precise dosimeters. Technologies like FLASH radiotherapy and magnetic-resonance linear accelerators (MR-LINAC) demand dosimeters that can meet their unique requirements. One promising solution is the plastic scintillator-based dosimeter with high spatial resolution and real-time dose output. This study explores the feasibility of using the LuSy dosimeter, an in-house developed plastic scintillator dosimeter for dose verification across various radiotherapy techniques, including conformal radiotherapy (CRT), intensity-modulated radiation therapy (IMRT), volumetric-modulated arc therapy (VMAT), and stereotactic radiosurgery (SRS). MATERIALS AND METHODS: A new dosimetry system, comprising a new plastic scintillator as the sensing material, was developed and characterized for radiotherapy beams. Treatment plans were created for conformal radiotherapy, IMRT, VMAT, and SRS and delivered to a phantom. LuSy dosimeter was used to measure the delivered dose for each plan on the surface of the phantom and inside the target volumes. Then, LuSy measurements were compared against an ionization chamber, MOSFET dosimeter, radiochromic films, and dose calculated using the treatment planning system (TPS). RESULTS: For CRT, surface dose measurement by LuSy dosimeter showed a deviation of -5.5% and -5.4% for breast and abdomen treatment from the TPS, respectively. When measuring inside the target volume for IMRT, VMAT, and SRS, the LuSy dosimeter produced a mean deviation of -3.0% from the TPS. Surface dose measurement resulted in higher TPS discrepancies where the deviations for IMRT, VMAT, and SRS were -2.0%, -19.5%, and 16.1%, respectively. CONCLUSION: The LuSy dosimeter was feasible for measuring radiotherapy doses for various treatment techniques. Treatment delivery verification enables early error detection, allowing for safe treatment delivery for radiotherapy patients.

Addressing challenges in diagnostic X-ray dosimetry: uncertainties and corrections for Al2O3:C-based optically stimulated luminescent dosimeters.

The use of Al2O3:C-based optically stimulated luminescent dosimeters (OSLDs) in diagnostic X-ray is a challenge because of their energy dependence (ED) and variability of element sensitivity factors (ESFs). This study aims to develop a method to determine ED and ESFs of Landauer nanoDot™ OSLDs for clinical X-ray and investigate the uncertainties associated with ESF and ED correction factors. An area of 2 × 2 cm2 at the central axis of the X-ray field was used to establish the ESFs. A total of 80 OSLDs were categorized into "controlled" (n = 40) and "less-controlled" groups (n = 40). The ESFs of the OSLDs were determined using an 80 kVp X-ray beam quality in free-air geometry. The OSLDs were cross-calibrated with an ion chamber to establish the average calibration coefficient and ESFs. The OSLDs were then irradiated at tube potentials ranging from 50 to 150 kVp to determine their ED. The uniformity of the X-ray field was ± 1.5% at 100 cm source-to-surface distance. The batch homogeneities of user-defined ESFs were 2.4% and 8.7% for controlled and less-controlled OSLDs, respectively. The ED of OSLDs ranged from 1.125 to 0.812 as tube potential increased from 50 kVp to 150 kVp. The total uncertainty of OSLDs, without ED correction, could be as high as 16%. After applying ESF and ED correction, the total uncertainties were reduced to 6.3% in controlled OLSDs and 11.6% in less-controlled ones. OSLDs corrected with user-defined ESF and ED can reduce the uncertainty of dose measurements in diagnostic X-rays, particularly in managing less-controlled OSLDs.

Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification.

Objective.Generally, due to a lack of explainability, radiomics based on deep learning has been perceived as a black-box solution for radiologists. Automatic generation of diagnostic reports is a semantic approach to enhance the explanation of deep learning radiomics (DLR).Approach.In this paper, we propose a novel model called radiomics-reporting network (Radioport), which incorporates text attention. This model aims to improve the interpretability of DLR in mammographic calcification diagnosis. Firstly, it employs convolutional neural networks to extract visual features as radiomics for multi-category classification based on breast imaging reporting and data system. Then, it builds a mapping between these visual features and textual features to generate diagnostic reports, incorporating an attention module for improved clarity.Main results.To demonstrate the effectiveness of our proposed model, we conducted experiments on a breast calcification dataset comprising mammograms and diagnostic reports. The results demonstrate that our model can: (i) semantically enhance the interpretability of DLR; and, (ii) improve the readability of generated medical reports.Significance.Our interpretable textual model can explicitly simulate the mammographic calcification diagnosis process.

Customised weight-based volume contrast media protocol for multiphase abdominal computed tomography.

INTRODUCTION: Multiphase computed tomography (CT) using fixed volume contrast media may lead to high radiation exposure and toxicity in patients with low body weight. We evaluated a customised weight-based protocol for multiphase CT in terms of radiation exposure, image quality and cost savings. METHODS: A total of 224 patients were recruited. An optimised CT protocol was applied using 100 kV and 1 mL/kg of contrast media dosing. The image quality and radiation dose exposure of this CT protocol were compared to those of a standard 120 kV, 80 mL fixed volume protocol. The radiation dose information and CT Hounsfield units were recorded. The signal-to-noise ratio, contrast-to-noise ratio (CNR) and figure of merit (FOM) were used as comparison metrics. The images were assessed for contrast opacification and visual quality by two radiologists. The renal function, contrast media volume and cost were also evaluated. RESULTS: The median effective dose was lowered by 16% in the optimised protocol, while the arterial phase images achieved significantly higher CNR and FOM. The radiologists' evaluation showed more than 97% absolute agreement with no significant differences in image quality. No significant differences were found in the pre- and post-CT estimated glomerular filtration rate. However, contrast media usage was significantly reduced by 1,680 mL, with an overall cost savings of USD 421 in the optimised protocol. CONCLUSION: The optimised weight-based protocol is cost-efficient and lowers radiation dose while maintaining overall contrast enhancement and image quality.

2D shear wave elastography for the assessment of quadriceps entheses-a methodological study.

OBJECTIVE: To establish the scanning protocol for 2-dimensional shear wave elastography (SWE) on normal entheses by investigating the possible confounding factors that may increase the variability of measured elasticity. MATERIAL AND METHODS: 30 normal quadriceps entheses were scanned using SWE to compare the stiffness and coefficient variation by changing the ultrasonic coupling gel thickness, knee position, region of interest size, and scanning plane. RESULTS: No significant difference in median shear wave velocity (SWV) was observed in different coupling gel thicknesses. The median SWV was higher in the knee flexion position than in the extended position (p < 0.001). Increased knee flexion led to stiffer quadriceps enthesis and higher SWV (ρ = 0.8, p < 0.001). The median SWV was higher when the diameter region of interest was 4.0 mm than 2.0 mm (p = 0.001). The median SWV was higher in the transverse plane than in the longitudinal plane (p < 0.001). Strong correlation was found between SWV and the degree of the shear wave to muscle fiber direction (ρ = 0.8, p < 0.001). The coefficient variation was lower in a gel thickness of 2.5 cm, with an extended knee, a region of interest of 2.0 mm, and a longitudinal plane (p > 0.05). For interobserver reliability for the proposed protocol, the intraclass correlation coefficients was 0.763. CONCLUSION: In this study, we determined supine position with the knee extended; using 2.0 mm diameter region of interest and image acquisition at the longitudinal plane with thicker layer coupling gel seems most appropriate to reliably image healthy quadriceps entheses with SWE.

Shear wave elastography in chronic kidney disease - the physics and clinical application.

Chronic kidney disease is a leading public health problem worldwide. The global prevalence of chronic kidney disease is nearly five hundred million people, with almost one million deaths worldwide. Estimated glomerular filtration rate, imaging such as conventional ultrasound, and histopathological findings are necessary as each technique provides specific information which, when taken together, may help to detect and arrest the development of chronic kidney disease, besides managing its adverse outcomes. However, estimated glomerular filtration rate measurements are hampered by substantial error margins while conventional ultrasound involves subjective assessment. Although histopathological assessment is the best tool for evaluating the severity of the renal pathology, it may lead to renal insufficiency and haemorrhage if complications occurred. Ultrasound shear wave elastography, an emerging imaging that quantifies tissue stiffness non-invasively has gained interest recently. This method applies acoustic force pulses to generate shear wave within the tissue that propagate perpendicular to the main ultrasound beam. By measuring the speed of shear wave propagation, the tissue stiffness is estimated. This paper reviews the literature and presents our combined experience and knowledge in renal shear wave elastography research. It discusses and highlights the confounding factors on shear wave elastography, current and future possibilities in ultrasound renal imaging and is not limited to new sophisticated techniques.

Development and characterisation of a plastic scintillator dosemeter in high-energy photon beams.

The radioluminescent (RL) dosemeter is excellent for real-time radiation measurement and can be used in various applications. A plastic scintillator is often the choice sensor because of its size and tissue equivalency. This study aims to characterise a novel plastic scintillator irradiated with high-energy photon beams. An RL dosimetry system was developed using the plastic scintillator. The RL dosimetry system was irradiated using a linear accelerator to characterise the dose linearity, dose rate, energy dependency and depth dose. The developed system showed a linear response toward the dose and dose rate. An energy dependency factor of 1.06 was observed. Depth dose measurement showed a mean deviation of 1.21% from the treatment planning system. The response and characteristics of the plastic scintillator show that it may be used as an alternative in an RL dosimetry system.

A Radiomics Study: Classification of Breast Lesions by Textural Features from Mammography Images.

This study investigates the feasibility of using texture radiomics features extracted from mammography images to distinguish between benign and malignant breast lesions and to classify benign lesions into different categories and determine the best machine learning (ML) model to perform the tasks. Six hundred and twenty-two breast lesions from 200 retrospective patient data were segmented and analysed. Three hundred fifty radiomics features were extracted using the Standardized Environment for Radiomics Analysis (SERA) library, one of the radiomics implementations endorsed by the Image Biomarker Standardisation Initiative (IBSI). The radiomics features and selected patient characteristics were used to train selected machine learning models to classify the breast lesions. A fivefold cross-validation was used to evaluate the performance of the ML models and the top 10 most important features were identified. The random forest (RF) ensemble gave the highest accuracy (89.3%) and positive predictive value (66%) and likelihood ratio of 13.5 in categorising benign and malignant lesions. For the classification of benign lesions, the RF model again gave the highest likelihood ratio of 3.4 compared to the other models. Morphological and textural radiomics features were identified as the top 10 most important features from the random forest models. Patient age was also identified as one of the significant features in the RF model. We concluded that machine learning models trained against texture-based radiomics features and patient features give reasonable performance in differentiating benign versus malignant breast lesions. Our study also demonstrated that the radiomics-based machine learning models were able to emulate the visual assessment of mammography lesions, typically used by radiologists, leading to a better understanding of how the machine learning model arrive at their decision.

The role of shear viscosity as a biomarker for improving chronic kidney disease detection using shear wave elastography: A computational study using a validated finite element model.

The application of ultrasound shear wave elastography for detecting chronic kidney disease, namely renal fibrosis, has been widely studied. A good correlation between tissue Young's modulus and the degree of renal impairment has been established. However, the current limitation of this imaging modality pertains to the linear elastic assumption used in quantifying the stiffness of renal tissue in commercial shear wave elastography systems. As such, when underlying medical conditions such as acquired cystic kidney disease, which may potentially influence the viscous component of renal tissue, is present concurrently with renal fibrosis, the accuracy of the imaging modality in detecting chronic kidney disease may be affected. The findings in this study demonstrate that quantifying the stiffness of linear viscoelastic tissue using an approach similar to those implemented in commercial shear wave elastography systems led to percentage errors as high as 87%. The findings presented indicate that use of shear viscosity to detect changes in renal impairment led to a reduction in percentage error to values as low as 0.3%. For cases in which renal tissue was affected by multiple medical conditions, shear viscosity was found to be a good indicator in gauging the reliability of the Young's modulus (quantified through a shear wave dispersion analysis) in detecting chronic kidney disease. The findings show that percentage error in stiffness quantification can be reduced to as low as 0.6%. The present study demonstrates the potential use of renal shear viscosity as a biomarker to improve the detection of chronic kidney disease.

Automatic slice thickness measurement on three types of Catphan CT phantoms.

Objective. To develop an algorithm to measure slice thickness running on three types of Catphan phantoms with the ability to adapt to any misalignment and rotation of the phantoms.Method. Images of Catphan 500, 504, and 604 phantoms were examined. In addition, images with various slice thicknesses ranging from 1.5 to 10.0 mm, distance to the iso-center and phantom rotations were also examined. The automatic slice thickness algorithm was carried out by processing only objects within a circle having a diameter of half the diameter of the phantom. A segmentation was performed within an inner circle with dynamic thresholds to produce binary images with wire and bead objects within it. Region properties were used to distinguish wire ramps and bead objects. At each identified wire ramp, the angle was detected using the Hough transform. Profile lines were then placed on each ramp based on the centroid coordinates and detected angles, and the full-width at half maximum (FWHM) was determined for the average profile. The slice thickness was obtained by multiplying the FWHM by the tangent of the ramp angle (23°).Results. Automatic measurements work well and have only a small difference (<0.5 mm) from manual measurements. For slice thickness variation, automatic measurement successfully performs segmentation and correctly locates the profile line on all wire ramps. The results show measured slice thicknesses that are close (<3 mm) to the nominal thickness at thin slices, but slightly deviated for thicker slices. There is a strong correlation (R2= 0.873) between automatic and manual measurements. Testing the algorithm at various distances from the iso-center and phantom rotation angle also produced accurate results.Conclusion. An automated algorithm for measuring slice thickness on three types of Catphan CT phantom images has been developed. The algorithm works well on various thicknesses, distances from the iso-center, and phantom rotations.

Ultrasound shear wave elastography for the evaluation of renal pathological changes in adult patients.

OBJECTIVE: Many studies have conflicting findings in using shear wave elastography (SWE) to assess renal fibrosis. This study reviews the use of SWE to evaluate pathological changes in native kidneys and renal allografts. It also tries to elucidate the confounding factors and care taken to ensure the results are consistent and reliable. METHODS: The review was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Literature search was conducted in Pubmed, Web of Science and Scopus database up to 23 October 2021. To evaluate risk and bias applicability, the Cochrane risk-of bias tool and GRADE was used. The review was registered under PROSPERO CRD42021265303. RESULTS: A total of 2921 articles were identified. 104 full texts were examined and 26 studies included in systematic review. 11 studies performed on native kidneys and 15 studies on transplanted kidney. A wide range of impact factors was found that affect the accuracy of SWE of renal fibrosis in adult patients. CONCLUSIONS: Compared to point SWE, two-dimensional SWE with elastogram could enable better selection of the region of interest in kidneys, leading to reproducible results. Tracking waves were attenuated as the depth from skin to region of interest increased, therefore, SWE is not recommended for overweight or obese patients. Variable transducer forces might also affect SWE reproducibility, thus, training of operators to ensure consistent operator-dependent transducer forces may be helpful. ADVANCES IN KNOWLEDGE: This review provides a holistic insight on the efficiency of using SWE in evaluating pathological changes in native and transplanted kidneys, thereby contributing to the knowledge of its utilisation in clinical practice.

Preliminary study of longitudinal changes in the pituitary and brain of children on growth hormone therapy.

BACKGROUND: In subjects with isolated growth hormone deficiency (IGHD), recombinant human growth hormone (rhGH) is an approved method to achieve potential mid-parental height. However, data reporting rhGH treatment response in terms of brain structure volumes were scarce. We report the volumetric changes of the pituitary gland, basal ganglia, corpus callosum, thalamus, hippocampus and amygdala in these subjects post rhGH treatment. MATERIALS AND METHODS: This was a longitudinal study of eight IGHD subjects (2 males, 6 females) with a mean age of 11.1 ± 0.8 years and age-matched control groups. The pituitary gland, basal ganglia and limbic structures volumes were obtained using 3T MRI voxel-based morphology. The left-hand bone age was assessed using the Tanner-Whitehouse method. Follow-up imaging was performed after an average of 1.8 ± 0.4 years on rhGH. RESULTS: Subjects with IGHD had a smaller mean volume of the pituitary gland, right thalamus, hippocampus, and amygdala than the controls. After rhGH therapy, these volumes normalized to the age-matched controls. Corpus callosum of IGHD subjects had a larger mean volume than the controls and did not show much volume changes in response to rhGH therapy. There were changes towards normalization of bone age deficit of IGHD in response to rhGH therapy. CONCLUSION: The pituitary gland, hippocampus, and amygdala volumes in IGHD subjects were smaller than age-matched controls and showed the most response to rhGH therapy. Semi-automated volumetric assessment of pituitary gland, hippocampus, and amygdala using MRI may provide an objective assessment of response to rhGH therapy.

Lipid Alterations in Glioma: A Systematic Review.

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review.

OBJECTIVE: Magnetic resonance imaging (MRI) is the most sensitive imaging modality in detecting breast cancer. The purpose of this systematic review is to investigate the role of human extracted MRI phenotypes in classifying molecular subtypes of breast cancer. METHODS: We performed a literature search of published articles on the application of MRI phenotypic features in invasive breast cancer molecular subtype classifications by radiologists' interpretation on Medline Complete, Pubmed, and Google scholar from 1st January 2000 to 31st March 2021. Of the 1453 literature identified, 42 fulfilled the inclusion criteria. RESULTS: All studies were case-controlled, retrospective study and research-based. The majority of the studies assessed the MRI features using American College of Radiology- Breast Imaging Reporting and Data System (ACR-BIRADS) classification and using dynamic contrast-enhanced (DCE) kinetic features, Apparent Diffusion Coefficient (ADC) values, and T2 sequence. Most studies divided invasive breast cancer into 4 main subtypes, luminal A, luminal B, HER2, and triple-negative (TN) cancers, and used 2 readers. We present a summary of the radiologists' extracted breast MRI phenotypical features and their correlating breast cancer subtypes classifications. The characteristic features are morphology, enhancement kinetics, and T2 signal intensity. We found that the TN subtype has the most distinctive MRI features compared to the other subtypes and luminal A and B have many similar features. CONCLUSION: The MRI features which are predictive of each subtype are the morphology, internal enhancement features, and T2 signal intensity, predominantly between TN and the rest. Radiologists' visual interpretation of some of MRI features may offer insight into the respective invasive breast cancer molecular subtype. However, current evidence are still limited to "suggestive" features instead of a diagnostic standard.  Further research is recommended to explore this potential application, for example, by augmentation of radiologists' visual interpretation by artificial intelligence.

The perceptions of medical physicists towards relevance and impact of artificial intelligence.

Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practices, the possibility of AI threatening/disrupting the medical physicists' practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists' practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control (QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists' practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality.

Evaluation of skin dose and skin toxicity in patients undergoing intraoperative radiotherapy for early breast cancer.

PURPOSE: This study aims to evaluate in vivo skin dose delivered by intraoperative radiotherapy (IORT) and determine the factors associated with an increased risk of radiation-induced skin toxicity. METHODOLOGY: A total of 21 breast cancer patients who underwent breast-conserving surgery and IORT, either as IORT alone or IORT boost plus external beam radiotherapy (EBRT), were recruited in this prospective study. EBT3 film was calibrated in water and used to measure skin dose during IORT at concentric circles of 5 mm and 40 mm away from the applicator. For patients who also had EBRT, the maximum skin dose was estimated using the radiotherapy treatment planning system. Mid-term skin toxicities were evaluated at 3 and 6 months post-IORT. RESULTS: The average skin dose at 5 mm and 40 mm away from the applicator was 3.07 ± 0.82 Gy and 0.99 ± 0.28 Gy, respectively. Patients treated with IORT boost plus EBRT received an additional skin dose of 41.07 ± 1.57 Gy from the EBRT component. At 3 months post-IORT, 86% of patients showed no evidence of skin toxicity. However, the number of patients suffering from skin toxicity increased from 15% to 38% at 6 months post-IORT. We found no association between the IORT alone or with the IORT boost plus EBRT and skin toxicity. Older age was associated with increased risk of skin toxicities. A mathematical model was derived to predict skin dose. CONCLUSION: EBT3 film is a suitable dosimeter for in vivo skin dosimetry in IORT, providing patient-specific skin doses. Both IORT alone and IORT boost techniques resulted in similar skin toxicity rates.

Shear Wave Elastography: A Review on the Confounding Factors and Their Potential Mitigation in Detecting Chronic Kidney Disease.

Early detection of chronic kidney disease is important to prevent progression of irreversible kidney damage, reducing the need for renal transplantation. Shear wave elastography is ideal as a quantitative imaging modality to detect chronic kidney disease because of its non-invasive nature, low cost and portability, making it highly accessible. However, the complexity of the kidney architecture and its tissue properties give rise to various confounding factors that affect the reliability of shear wave elastography in detecting chronic kidney disease, thus limiting its application to clinical trials. The objective of this review is to highlight the confounding factors presented by the complex properties of the kidney, in addition to outlining potential mitigation strategies, along with the prospect of increasing the versatility and reliability of shear wave elastography in detecting chronic kidney disease.

Monte Carlo-based patient internal dosimetry in fluoroscopy-guided interventional procedures: A review.

PURPOSE: This systematic review aims to understand the dose estimation approaches and their major challenges. Specifically, we focused on state-of-the-art Monte Carlo (MC) methods in fluoroscopy-guided interventional procedures. METHODS: All relevant studies were identified through keyword searches in electronic databases from inception until September 2020. The searched publications were reviewed, categorised and analysed based on their respective methodology. RESULTS: Hundred and one publications were identified which utilised existing MC-based applications/programs or customised MC simulations. Two outstanding challenges were identified that contribute to uncertainties in the virtual simulation reconstruction. The first challenge involves the use of anatomical models to represent individuals. Currently, phantom libraries best balance the needs of clinical practicality with those of specificity. However, mismatches of anatomical variations including body size and organ shape can create significant discrepancies in dose estimations. The second challenge is that the exact positioning of the patient relative to the beam is generally unknown. Most dose prediction models assume the patient is located centrally on the examination couch, which can lead to significant errors. CONCLUSION: The continuing rise of computing power suggests a near future where MC methods become practical for routine clinical dosimetry. Dynamic, deformable phantoms help to improve patient specificity, but at present are only limited to adjustment of gross body volume. Dynamic internal organ displacement or reshaping is likely the next logical frontier. Image-based alignment is probably the most promising solution to enable this, but it must be automated to be clinically practical.

Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification.

BACKGROUND AND OBJECTIVE: The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound. Recently, generative adversarial network (GAN) has aided in tackling a general issue in DLR, i.e., obtaining a sufficient number of medical images. However, GAN methods require a pair of input and labeled images, which require an exhaustive human annotation process that is very time-consuming. The aim of this paper is to develop a radiomics model based on a semi-supervised GAN method to perform data augmentation in breast ultrasound images. METHODS: A total of 1447 ultrasound images, including 767 benign masses and 680 malignant masses were acquired from a tertiary hospital. A semi-supervised GAN model was developed to augment the breast ultrasound images. The synthesized images were subsequently used to classify breast masses using a convolutional neural network (CNN). The model was validated using a 5-fold cross-validation method. RESULTS: The proposed GAN architecture generated high-quality breast ultrasound images, verified by two experienced radiologists. The improved performance of semi-supervised learning increased the quality of the synthetic data produced in comparison to the baseline method. We achieved more accurate breast mass classification results (accuracy 90.41%, sensitivity 87.94%, specificity 85.86%) with our synthetic data augmentation compared to other state-of-the-art methods. CONCLUSION: The proposed radiomics model has demonstrated a promising potential to synthesize and classify breast masses on ultrasound in a semi-supervised manner.