Abstract Objectives This study compared plans of high definition (HD), 2.5 mm width multi-leaf collimator (MLC), to standard, 5 mm width, isocentric linear accelerator (linacs), CyberKnife (CK) and ...Gamma Knife (GK) for stereotactic radiosurgery (SRS) techniques on multiple brain metastases. Methods Eleven patients undergoing SRS for multiple brain metastases were chosen. Targets and organs at risk (OARs) were delineated and optimised SRS plans were generated and compared. Results The linacs delivered similar conformity index (CI) values, but the gradient index (GI) for HD MLCs was significantly lower (p-value < 0.001). Half the OARs received significantly lower dose using HD MLCs. CK delivered a significantly lower CI than HD MLC linac (p-value < 0.001), but a significantly higher GI (p-value < 0.001). CI was significantly improved with the HD MLC linac compared to GK (p-value = 4.591 × 10−3), however, GK delivered a significantly lower GI (p-value < 0.001). OAR dose sparing was similar for the HD MLC TL, CK and GK. Conclusions Comparing linacs for SRS, the preferred choice is HD MLCs. Similar results were achieved with the HD MLC linac, CK or GK, with each delivering significant improvements in different aspects of plan quality. Advances in Knowledge This paper is the first to compare HD and standard width MLC linac plans using a combination of single isocentre volumetric modulated arc therapy and multi-isocentric dynamic conformal arc plans as required, which is a more clinically relevant assessment. Furthermore, it compares these plans with CK and GK, assessing the relative merits of each technique.
The aim of this study was to evaluate the length of time required to achieve full iodination using potassium tri-iodide as a contrast agent, prior to human fetal postmortem microfocus computed ...tomography (micro-CT) imaging.
Prospective assessment of optimal contrast iodination was conducted across 157 human fetuses (postmortem weight range 2-298 g; gestational age range 12-37 weeks), following micro-CT imaging. Simple linear regression was conducted to analyse which fetal demographic factors could produce the most accurate estimate for optimal iodination time.
Postmortem body weight (
= 0.6435) was better correlated with iodination time than gestational age (
= 0.1384), producing a line of best fit,
= 0.0304 × body weight (g) - 2.2103. This can be simplified for clinical use whereby immersion time (days) = 0.03 × body weight (g) - 2.2. Using this formula, for example, a 100-g fetus would take 5.2 days to reach optimal contrast enhancement.
The simplified equation can now be used to provide estimation times for fetal contrast preparation time prior to micro-CT imaging and can be used to manage service throughput and parental expectation for return of their fetus.
A simple equation from empirical data can now be used to estimate preparation time for human fetal postmortem micro-CT imaging.
To investigate differences in diffusion tensor imaging (DTI) parameters and proton density fat fraction (PDFF) in the spinal muscles of younger and older adult males.
Twelve younger (19-30 years) and ...12 older (61-81years) healthy, physically active male participants underwent T1
, T2
, Dixon and DTI of the lumbar spine. The eigenvalues (
,
, and
), fractional anisotropy (FA), and mean diffusivity (MD) from the DTI together with the PDFF were determined in the multifidus, medial and lateral erector spinae (ESmed, ESlat), and quadratus lumborum (QL) muscles. A two-way ANOVA was used to investigate differences with age and muscle and
-tests for differences in individual muscles with age.
The ANOVA gave significant differences with age for all DTI parameters and the PDFF (
< .01) and with muscle (
< .01) for all DTI parameters except for
and for the PDFF. The mean of the eigenvalues and MD were lower and the FA higher in the older age group with differences reaching statistical significance for all DTI measures for ESlat and QL (
< .01) but only in ESmed for
and MD (
< .05).
Differences in DTI parameters of muscle with age result from changes in both in the intra- and extra-cellular space and cannot be uniquely explained in terms of fibre length and diameter.
Previous studies looking at age have used small groups with uneven age spacing. Our study uses two well defined and separated age groups.
"How tall will I be?" Every paediatrician has been asked this during their career. The growth plate is the main site of longitudinal growth of the long bones. The chondrocytes in the growth plate ...have a columnar pattern detectable by diffusion tensor imaging (DTI). DTI shows the diffusion of water in a tissue and whether it is iso- or anisotropic. By detecting direction and magnitude of diffusion, DTI gives information about the microstructure of the tissue. DTI metrics include tract volume, length, and number, fractional anisotropy (FA), and mean diffusivity. DTI metrics, particularly tract volume, provide quantitative data regarding skeletal growth and, in conjunction with the fractional anisotropy, be used to determine whether a growth plate is normal. Tractography is a visual display of the diffusion, depicting its direction and amplitude. Tractography gives a more qualitative visualization of cellular orientation in a tissue and reflects the activity in the growth plate. These two components of DTI can be used to assess the growth plate without ionizing radiation or pain. Further refinements in DTI will improve prediction of post-imaging growth and growth plate closure, and assessment of the positive and negative effect of treatments like cis-retinoic acid and growth hormone administration.
In a clinical study, diffusion kurtosis imaging (DKI) has been used to visualize and distinguish white matter (WM) structures' details. The purpose of our study is to evaluate and compare the ...diffusion tensor imaging (DTI) and DKI parameter values to obtain WM structure differences of healthy subjects.
Thirteen healthy volunteers (mean age, 25.2 years) were examined in this study. On a 3-T MRI system, diffusion dataset for DKI was acquired using an echo-planner imaging sequence, and T
-weghted (T
w) images were acquired. Imaging analysis was performed using Functional MRI of the brain Software Library (FSL). First, registration analysis was performed using the T
w of each subject to MNI152. Second, DTI (eg, fractional anisotropy FA and each diffusivity) and DKI (eg, mean kurtosis MK, radial kurtosis RK, and axial kurtosis AK) datasets were applied to above computed spline coefficients and affine matrices. Each DTI and DKI parameter value for WM areas was compared. Finally, tract-based spatial statistics (TBSS) analysis was performed using each parameter.
The relationship between FA and kurtosis parameters (MK, RK, and AK) for WM areas had a strong positive correlation (FA-MK,
= 0.93; FA-RK,
= 0.89) and a strong negative correlation (FA-AK,
= 0.92). When comparing a TBSS connection, we found that this could be observed more clearly in MK than in RK and FA.
WM analysis with DKI enable us to obtain more detailed information for connectivity between nerve structures.
Quantitative indices of neurological diseases were determined using segmenting WM regions using voxel-based morphometry processing of DKI images.
Diffusion-weighted MRI (DWI) may provide biologically relevant target volumes for dose-escalated radiotherapy in locally advanced rectal cancer (LARC). This planning study assessed the dosimetric ...feasibility of delivering hypofractionated boost treatment to intra-tumoural regions of restricted diffusion prior to conventional long-course radiotherapy.
Ten patients previously treated with curative-intent standard long-course radiotherapy (50 Gy/25#) were re-planned. Boost target volumes (
) were delineated semi-automatically using 40th centile intra-tumoural apparent diffusion coefficient value with expansions (anteroposterior 11 mm, transverse 7 mm, craniocaudal 13 mm). Biased-dosed combined plans consisted of a single-fraction volumetric modulated arc therapy flattening-filter-free (VMAT-FFF) boost (phase 1) of 5, 7, or 10 Gy before long-course VMAT (phase 2). Phase 1 plans were assessed with reference to stereotactic conformality and deliverability measures. Combined plans were evaluated with reference to standard long-course therapy dose constraints.
Phase 1 BTV dose targets at 5/7/10 Gy were met in all instances. Conformality constraints were met with only 1 minor violation at 5 and 7 Gy. All phase 1 and combined phase 1 + 2 plans passed patient-specific quality assurance. Combined phase 1 + 2 plans generally met organ-at-risk dose constraints. Exceptions included high-dose spillage to bladder and large bowel, predominantly in cases where previously administered, clinically acceptable non-boosted plans also could not meet constraints.
Targeted upfront LARC radiotherapy dose escalation to DWI-defined is feasible with appropriate patient selection and preparation.
This is the first study to evaluate the feasibility of DWI-targeted upfront radiotherapy boost in LARC. This work will inform an upcoming clinical feasibility study.
Missed fractures are a costly healthcare issue, not only negatively impacting patient lives, leading to potential long-term disability and time off work, but also responsible for high medicolegal ...disbursements that could otherwise be used to improve other healthcare services. When fractures are overlooked in children, they are particularly concerning as opportunities for safeguarding may be missed. Assistance from artificial intelligence (AI) in interpreting medical images may offer a possible solution for improving patient care, and several commercial AI tools are now available for radiology workflow implementation. However, information regarding their development, evidence for performance and validation as well as the intended target population is not always clear, but vital when evaluating a potential AI solution for implementation. In this article, we review the range of available products utilizing AI for fracture detection (in both adults and children) and summarize the evidence, or lack thereof, behind their performance. This will allow others to make better informed decisions when deciding which product to procure for their specific clinical requirements.
This diagnostic study assessed the accuracy of radiologists retrospectively, using the deep learning and natural language processing chest algorithms implemented in Clinical Review version 3.2 for: ...pneumothorax, rib fractures in digital chest X-ray radiographs (CXR); aortic aneurysm, pulmonary nodules, emphysema, and pulmonary embolism in CT images.
The study design was double-blind (artificial intelligence AI algorithms and humans), retrospective, non-interventional, and at a single NHS Trust. Adult patients (≥18 years old) scheduled for CXR and CT were invited to enroll as participants through an opt-out process. Reports and images were de-identified, processed retrospectively, and AI-flagged discrepant findings were assigned to two lead radiologists, each blinded to patient identifiers and original radiologist. The radiologist's findings for each clinical condition were tallied as a verified discrepancy (true positive) or not (false positive).
The missed findings were: 0.02% rib fractures, 0.51% aortic aneurysm, 0.32% pulmonary nodules, 0.92% emphysema, and 0.28% pulmonary embolism. The positive predictive values (PPVs) were: pneumothorax (0%), rib fractures (5.6%), aortic dilatation (43.2%), pulmonary emphysema (46.0%), pulmonary embolus (11.5%), and pulmonary nodules (9.2%). The PPV for pneumothorax was nil owing to lack of available studies that were analysed for outpatient activity.
The number of missed findings was far less than generally predicted. The chest algorithms deployed retrospectively were a useful quality tool and AI augmented the radiologists' workflow.
The diagnostic accuracy of our radiologists generated missed findings of 0.02% for rib fractures CXR, 0.51% for aortic dilatation, 0.32% for pulmonary nodule, 0.92% for pulmonary emphysema, and 0.28% for pulmonary embolism for CT studies, all retrospectively evaluated with AI used as a quality tool to flag potential missed findings. It is important to account for prevalence of these chest conditions in clinical context and use appropriate clinical thresholds for decision-making, not relying solely on AI.
CT angiography (CTA)-based machine learning methods for infarct volume estimation have shown a tendency to overestimate infarct core and final infarct volumes (FIV). Our aim was to assess factors ...influencing the reliability of these methods.
The effect of collateral circulation on the correlation between convolutional neural network (CNN) estimations and FIV was assessed based on the Miteff system and hypoperfusion intensity ratio (HIR) in 121 patients with anterior circulation acute ischaemic stroke using Pearson correlation coefficients and median volumes. Correlation was also assessed between successful and futile thrombectomies. The timing of individual CTAs in relation to CTP studies was analysed.
The strength of correlation between CNN estimated volumes and FIV did not change significantly depending on collateral status as assessed with the Miteff system or HIR, being poor to moderate (
=
0.09-0.50). The strongest correlation was found in patients with futile thrombectomies (
=
0.61). Median CNN estimates showed a trend for overestimation compared to FIVs. CTA was acquired in the mid arterial phase in virtually all patients (120/121).
This study showed no effect of collateral status on the reliability of the CNN and best correlation was found in patients with futile thrombectomies. CTA timing in the mid arterial phase in virtually all patients can explain infarct volume overestimation.
CTA timing seems to be the most important factor influencing the reliability of current CTA-based machine learning methods, emphasizing the need for CTA protocol optimization for infarct core estimation.
Radiation therapy for lung cancer requires a gross tumour volume (GTV) to be carefully outlined by a skilled radiation oncologist (RO) to accurately pinpoint high radiation dose to a malignant mass ...while simultaneously minimizing radiation damage to adjacent normal tissues. This is manually intensive and tedious however, it is feasible to train a deep learning (DL) neural network that could assist ROs to delineate the GTV. However, DL trained on large openly accessible data sets might not perform well when applied to a superficially similar task but in a different clinical setting. In this work, we tested the performance of DL automatic lung GTV segmentation model trained on open-access Dutch data when used on Indian patients from a large public tertiary hospital, and hypothesized that
DL performance could be improved for a specific
clinical context, by means of modest transfer-learning on a small representative local subset.
X-ray computed tomography (CT) series in a public data set called "NSCLC-Radiomics" from The Cancer Imaging Archive was first used to train a DL-based lung GTV segmentation model (Model 1). Its performance was assessed using a different open access data set (Interobserver1) of Dutch subjects plus a private Indian data set from a local tertiary hospital (Test Set 2). Another Indian data set (Retrain Set 1) was used to fine-tune the former DL model using a transfer learning method. The Indian data sets were taken from CT of a hybrid scanner based in nuclear medicine, but the GTV was drawn by skilled Indian ROs. The final (after fine-tuning) model (Model 2) was then re-evaluated in "Interobserver1" and "Test Set 2." Dice similarity coefficient (DSC), precision, and recall were used as geometric segmentation performance metrics.
Model 1 trained exclusively on Dutch scans showed a significant fall in performance when tested on "Test Set 2." However, the DSC of Model 2 recovered by 14 percentage points when evaluated in the same test set. Precision and recall showed a similar rebound of performance after transfer learning, in spite of using a comparatively small sample size. The performance of both models, before and after the fine-tuning, did not significantly change the segmentation performance in "Interobserver1."
A large public open-access data set was used to train a generic DL model for lung GTV segmentation, but this did not perform well initially in the Indian clinical context. Using transfer learning methods, it was feasible to efficiently and easily fine-tune the generic model using only a small number of local examples from the Indian hospital. This led to a recovery of some of the geometric segmentation performance, but the tuning did not appear to affect the performance of the model in another open-access data set.
Caution is needed when using models trained on large volumes of international data in a local clinical setting, even when that training data set is of good quality. Minor differences in scan acquisition and clinician delineation preferences may result in an apparent drop in performance. However, DL models have the advantage of being efficiently "adapted" from a generic to a locally specific context, with only a small amount of fine-tuning by means of transfer learning on a small local institutional data set.
