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.
Autologous fat transfer (AFT) is an upcoming technique for total breast reconstruction. Consequently, radiological imaging of women with an AFT reconstructed breast will increase in the coming years, ...yet radiological experience and evidence after AFT is limited.
The surgical procedure of AFT and follow-up with imaging modalities including mammography (MG), ultrasound (US), and MRI in patients with a total breast reconstruction with AFT are summarized to illustrate the radiological normal and suspicious findings for malignancy.
Imaging after a total breast reconstruction with AFT appears to be based mostly on benign imaging findings with an overall low biopsy rate. As higher volumes are injected in this technique, the risk for the onset of fat necrosis increases. Imaging findings most often are related to fat necrosis after AFT. On MG, fat necrosis can mostly be seen as oil cysts. The occurrence of a breast seroma after total breast reconstruction with AFT is an unfavourable outcome and may require special treatment. Fat deposition in the pectoral muscle is a previously unknown, but benign entity. Although fat necrosis is a benign entity, it can mimic breast cancer (recurrence).
In symptomatic women after total breast reconstruction with AFT, MG and US can be considered as first diagnostic modalities. Breast MRI can be used as a problem-solving tool during later stage. Future studies should investigate the most optimal follow-up strategy, including different imaging modalities, in patients treated with AFT for total breast reconstruction.
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.
"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.
Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded ...in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.
Abstract Objectives Toxicity-driven adaptive radiotherapy (RT) is enhanced by the superior soft tissue contrast of magnetic resonance (MR) imaging compared with conventional computed tomography (CT). ...However, in an MR-only RT pathway synthetic CTs (sCT) are required for dose calculation. This study evaluates 3 sCT approaches for accurate rectal toxicity prediction in prostate RT. Methods Thirty-six patients had MR (T2-weighted acquisition optimized for anatomical delineation, and T1-Dixon) with same day standard-of-care planning CT for prostate RT. Multiple sCT were created per patient using bulk density (BD), tissue stratification (TS, from T1-Dixon) and deep-learning (DL) artificial intelligence (AI) (from T2-weighted) approaches for dose distribution calculation and creation of rectal dose volume histograms (DVH) and dose surface maps (DSM) to assess grade-2 (G2) rectal bleeding risk. Results Maximum absolute errors using sCT for DVH-based G2 rectal bleeding risk (risk range 1.6% to 6.1%) were 0.6% (BD), 0.3% (TS) and 0.1% (DL). DSM-derived risk prediction errors followed a similar pattern. DL sCT has voxel-wise density generated from T2-weighted MR and improved accuracy for both risk-prediction methods. Conclusions DL improves dosimetric and predicted risk calculation accuracy. Both TS and DL methods are clinically suitable for sCT generation in toxicity-guided RT, however, DL offers increased accuracy and offers efficiencies by removing the need for T1-Dixon MR. Advances in knowledge This study demonstrates novel insights regarding the effect of sCT on predictive toxicity metrics, demonstrating clear accuracy improvement with increased sCT resolution. Accuracy of toxicity calculation in MR-only RT should be assessed for all treatment sites where dose to critical structures will guide adaptive-RT strategies. Clinical trial registration number Patient data were taken from an ethically approved (UK Health Research Authority) clinical trial run at Guy’s and St Thomas’ NHS Foundation Trust. Study Name: MR-simulation in Radiotherapy for Prostate Cancer. ClinicalTrials.gov Identifier: NCT03238170.
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.
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.
MRI is an emerging imaging modality to assess skeletal maturity. This study aimed to chart the learning curves of paediatric radiologists when using an unfamiliar MRI grading system of skeletal ...maturity and to assess the clinical feasibility of implementing said system.
958 healthy paediatric volunteers were prospectively included in a dual-facility study. Each subject underwent a conventional MRI scan at 1.5 T. To perform the image reading, the participants were grouped into five subsets (subsets 1-5) of equal size (
∼192) in chronological order for scan acquisition. Two paediatric radiologists (R1-2) with different levels of MRI experience, both of whom were previously unfamiliar with the study's MRI grading system, independently evaluated the subsets to assess skeletal maturity in five different growth plate locations. Congruent cases at blinded reading established the consensus reading. For discrepant cases, the consensus reading was obtained through an unblinded reading by a third paediatric radiologist (R3), also unfamiliar with the MRI grading system. Further, R1 performed a second blinded image reading for all included subjects with a memory wash-out of 180 days. Weighted Cohen kappa was used to assess interreader reliability (R1 vs consensus; R2 vs consensus) at non-cumulative and cumulative time points, as well as interreader (R1 vs R2) and intrareader (R1 vs R1) reliability at non-cumulative time points.
Mean weighted Cohen kappa values for each pair of blinded readers compared to consensus reading (interreader reliability, R1-2 vs consensus) were ≥0.85, showing a strong to almost perfect interreader agreement at both non-cumulative and cumulative time points and in all growth plate locations. Weighted Cohen kappa values for interreader (R1 vs R2) and intrareader reliability (R1 vs R1) were ≥0.72 at non-cumulative time points, with values ≥0.82 at subset 5.
Paediatric radiologists' clinical confidence when introduced to a new MRI grading system for skeletal maturity was high from the outset of their learning curve, despite the radiologists' varying levels of work experience with MRI assessment. The MRI grading system for skeletal maturity investigated in this study is a robust clinical method when used by paediatric radiologists and can be used in clinical practice.
Radiologists with fellowship training in paediatric radiology experienced no learning curve progress when introduced to a new MRI grading system for skeletal maturity and achieved desirable agreement from the first time point of the learning curve. The robustness of the investigated MRI grading system was not affected by the earlier different levels of MRI experience among the readers.
