Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction.

Background Multiple commercial artificial intelligence (AI) products exist for assessing radiographs; however, comparable performance data for these algorithms are limited. Purpose To perform an independent, stand-alone validation of commercially available AI products for bone age prediction based on hand radiographs and lung nodule detection on chest radiographs. Materials and Methods This retrospective study was carried out as part of Project AIR. Nine of 17 eligible AI products were validated on data from seven Dutch hospitals. For bone age prediction, the root mean square error (RMSE) and Pearson correlation coefficient were computed. The reference standard was set by three to five expert readers. For lung nodule detection, the area under the receiver operating characteristic curve (AUC) was computed. The reference standard was set by a chest radiologist based on CT. Randomized subsets of hand (n = 95) and chest (n = 140) radiographs were read by 14 and 17 human readers, respectively, with varying experience. Results Two bone age prediction algorithms were tested on hand radiographs (from January 2017 to January 2022) in 326 patients (mean age, 10 years ± 4 [SD]; 173 female patients) and correlated strongly with the reference standard (r = 0.99; P < .001 for both). No difference in RMSE was observed between algorithms (0.63 years [95% CI: 0.58, 0.69] and 0.57 years [95% CI: 0.52, 0.61]) and readers (0.68 years [95% CI: 0.64, 0.73]). Seven lung nodule detection algorithms were validated on chest radiographs (from January 2012 to May 2022) in 386 patients (mean age, 64 years ± 11; 223 male patients). Compared with readers (mean AUC, 0.81 [95% CI: 0.77, 0.85]), four algorithms performed better (AUC range, 0.86-0.93; P value range, <.001 to .04). Conclusions Compared with human readers, four AI algorithms for detecting lung nodules on chest radiographs showed improved performance, whereas the remaining algorithms tested showed no evidence of a difference in performance. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Omoumi and Richiardi in this issue.

Added value of an artificial intelligence algorithm in reducing the number of missed incidental acute pulmonary embolism in routine portal venous phase chest CT.

OBJECTIVES: The purpose of this study was to evaluate the incremental value of artificial intelligence (AI) compared to the diagnostic accuracy of radiologists alone in detecting incidental acute pulmonary embolism (PE) on routine portal venous contrast-enhanced chest computed tomography (CT). METHODS: CTs of 3089 consecutive patients referred to the radiology department for a routine contrast-enhanced chest CT between 27-5-2020 and 31-12-2020, were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The diagnostic performance of the AI was compared to the initial report. To determine the reference standard, discordant findings were independently evaluated by two readers. In case of disagreement, another experienced cardiothoracic radiologist with knowledge of the initial report and the AI output adjudicated. RESULTS: The prevalence of acute incidental PE in the reference standard was 2.2% (67 of 3089 patients). In 25 cases, AI detected initially unreported PE. This included three cases concerning central/lobar PE. Sensitivity of the AI algorithm was significantly higher than the outcome of the initial report (respectively 95.5% vs. 62.7%, p < 0.001), whereas specificity was very high for both (respectively 99.6% vs 99.9%, p = 0.012). The AI algorithm only showed a slightly higher amount of false-positive findings (11 vs. 2), resulting in a significantly lower PPV (85.3% vs. 95.5%, p = 0.047). CONCLUSION: The AI algorithm showed high diagnostic accuracy in diagnosing incidental PE, detecting an additional 25 cases of initially unreported PE, accounting for 37.3% of all positive cases. CLINICAL RELEVANCE STATEMENT: Radiologist support from AI algorithms in daily practice can prevent missed incidental acute PE on routine chest CT, without a high burden of false-positive cases. KEY POINTS: • Incidental pulmonary embolism is often missed by radiologists in non-diagnostic scans with suboptimal contrast opacification within the pulmonary trunk. • An artificial intelligence algorithm showed higher sensitivity detecting incidental pulmonary embolism on routine portal venous chest CT compared to the initial report. • Implementation of artificial intelligence support in routine daily practice will reduce the number of missed incidental pulmonary embolism.

An artificial intelligence algorithm for pulmonary embolism detection on polychromatic computed tomography: performance on virtual monochromatic images.

OBJECTIVES: Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to their different appearances, the performance of artificial intelligence (AI) trained on conventional CT images may worsen. The goal of this study was to assess the performance of an established AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPI) to detect pulmonary embolism (PE) on VMI. METHODS: Paired 60 kiloelectron volt (keV) VMI and CPI of 114 consecutive patients suspected of PE, obtained with a detector-based spectral CT scanner, were retrospectively analyzed by an established AI algorithm. The CT pulmonary angiography (CTPA) were classified as positive or negative for PE on a per-patient level. The reference standard was established using a comprehensive method that combined the evaluation of the attending radiologist and three experienced cardiothoracic radiologists aided by two different detection tools. Sensitivity, specificity, positive and negative predictive values and likelihood ratios of the algorithm on VMI and CPI were compared. RESULTS: The prevalence of PE according to the reference standard was 35.1% (40 patients). None of the diagnostic accuracy measures of the algorithm showed a significant difference between CPI and VMI. Sensitivity was 77.5% (95% confidence interval (CI) 64.6-90.4%) and 85.0% (73.9-96.1%) (p = 0.08) on CPI and VMI respectively and specificity 96.0% (91.4-100.0%) and 94.6% (89.4-99.7%) (p = 0.32). CONCLUSIONS: Diagnostic performance of the AI algorithm that was trained on CPI did not drop on VMI, which is reassuring for its use in clinical practice. CLINICAL RELEVANCE STATEMENT: A commercially available AI algorithm, trained on conventional polychromatic CTPA, could be safely used on virtual monochromatic images. This supports the sustainability of AI-aided detection of PE on CT despite ongoing technological advances in medical imaging, although monitoring in daily practice will remain important. KEY POINTS: • Diagnostic accuracy of an AI algorithm trained on conventional polychromatic images to detect PE did not drop on virtual monochromatic images. • Our results are reassuring as innovations in hardware and reconstruction in CT are continuing, whilst commercial AI algorithms that are trained on older generation data enter healthcare.

Diagnostic accuracy of an artificial intelligence algorithm versus radiologists for fracture detection on cervical spine CT.

OBJECTIVES: To compare diagnostic accuracy of a deep learning artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT to attending radiologists and assess which undetected fractures were injuries in need of stabilising therapy (IST). METHODS: This single-centre, retrospective diagnostic accuracy study included consecutive patients (age ≥18 years; 2007-2014) screened for C-spine fractures with CT. To validate ground truth, one radiologist and three neurosurgeons independently examined scans positive for fracture. Negative scans were followed up until 2022 through patient files and two radiologists reviewed negative scans that were flagged positive by AI. The neurosurgeons determined which fractures were ISTs. Diagnostic accuracy of AI and attending radiologists (index tests) were compared using McNemar. RESULTS: Of the 2368 scans (median age, 48, interquartile range 30-65; 1441 men) analysed, 221 (9.3%) scans contained C-spine fractures with 133 IST. AI detected 158/221 scans with fractures (sensitivity 71.5%, 95% CI 65.5-77.4%) and 2118/2147 scans without fractures (specificity 98.6%, 95% CI 98.2-99.1). In comparison, attending radiologists detected 195/221 scans with fractures (sensitivity 88.2%, 95% CI 84.0-92.5%, p < 0.001) and 2130/2147 scans without fracture (specificity 99.2%, 95% CI 98.8-99.6, p = 0.07). Of the fractures undetected by AI 30/63 were ISTs versus 4/26 for radiologists. AI detected 22/26 fractures undetected by the radiologists, including 3/4 undetected ISTs. CONCLUSION: Compared to attending radiologists, the artificial intelligence has a lower sensitivity and a higher miss rate of fractures in need of stabilising therapy; however, it detected most fractures undetected by the radiologists, including fractures in need of stabilising therapy. Clinical relevance statement The artificial intelligence algorithm missed more cervical spine fractures on CT than attending radiologists, but detected 84.6% of fractures undetected by radiologists, including fractures in need of stabilising therapy. KEY POINTS: The impact of artificial intelligence for cervical spine fracture detection on CT on fracture management is unknown. The algorithm detected less fractures than attending radiologists, but detected most fractures undetected by the radiologists including almost all in need of stabilising therapy. The artificial intelligence algorithm shows potential as a concurrent reader.

Intravoxel incoherent motion (IVIM)-derived perfusion fraction mapping for the visual evaluation of MR-guided high intensity focused ultrasound (MR-HIFU) ablation of uterine fibroids.

BACKGROUND: A method for periprocedural contrast agent-free visualization of uterine fibroid perfusion could potentially shorten magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) treatment times and improve outcomes. Our goal was to test feasibility of perfusion fraction mapping by intravoxel incoherent motion (IVIM) modeling using diffusion-weighted MRI as method for visual evaluation of MR-HIFU treatment progression. METHODS: Conventional and T2-corrected IVIM-derived perfusion fraction maps were retrospectively calculated by applying two fitting methods to diffusion-weighted MRI data (b = 0, 50, 100, 200, 400, 600 and 800 s/mm2 at 1.5 T) from forty-four premenopausal women who underwent MR-HIFU ablation treatment of uterine fibroids. Contrast in perfusion fraction maps between areas with low perfusion fraction and surrounding tissue in the target uterine fibroid immediately following MR-HIFU treatment was evaluated. Additionally, the Dice similarity coefficient (DSC) was calculated between delineated areas with low IVIM-derived perfusion fraction and hypoperfusion based on CE-T1w. RESULTS: Average perfusion fraction ranged between 0.068 and 0.083 in areas with low perfusion fraction based on visual assessment, and between 0.256 and 0.335 in surrounding tissues (all p < 0.001). DSCs ranged from 0.714 to 0.734 between areas with low perfusion fraction and the CE-T1w derived non-perfused areas, with excellent intraobserver reliability of the delineated areas (ICC 0.97). CONCLUSION: The MR-HIFU treatment effect in uterine fibroids can be visualized using IVIM perfusion fraction mapping, in moderate concordance with contrast enhanced MRI. IVIM perfusion fraction mapping has therefore the potential to serve as a contrast agent-free imaging method to visualize the MR-HIFU treatment progression in uterine fibroids.

Diffusion-weighted MRI with deep learning for visualizing treatment results of MR-guided HIFU ablation of uterine fibroids.

OBJECTIVES: No method is available to determine the non-perfused volume (NPV) repeatedly during magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU) ablations of uterine fibroids, as repeated acquisition of contrast-enhanced T1-weighted (CE-T1w) scans is inhibited by safety concerns. The objective of this study was to develop and test a deep learning-based method for translation of diffusion-weighted imaging (DWI) into synthetic CE-T1w scans, for monitoring MR-HIFU treatment progression. METHODS: The algorithm was retrospectively trained and validated on data from 33 and 20 patients respectively who underwent an MR-HIFU treatment of uterine fibroids between June 2017 and January 2019. Postablation synthetic CE-T1w images were generated by a deep learning network trained on paired DWI and reference CE-T1w scans acquired during the treatment procedure. Quantitative analysis included calculation of the Dice coefficient of NPVs delineated on synthetic and reference CE-T1w scans. Four MR-HIFU radiologists assessed the outcome of MR-HIFU treatments and NPV ratio based on the synthetic and reference CE-T1w scans. RESULTS: Dice coefficient of NPVs was 71% (± 22%). The mean difference in NPV ratio was 1.4% (± 22%) and not statistically significant (p = 0.79). Absolute agreement of the radiologists on technical treatment success on synthetic and reference CE-T1w scans was 83%. NPV ratio estimations on synthetic and reference CE-T1w scans were not significantly different (p = 0.27). CONCLUSIONS: Deep learning-based synthetic CE-T1w scans derived from intraprocedural DWI allow gadolinium-free visualization of the predicted NPV, and can potentially be used for repeated gadolinium-free monitoring of treatment progression during MR-HIFU therapy for uterine fibroids. KEY POINTS: • Synthetic CE-T1w scans can be derived from diffusion-weighted imaging using deep learning. • Synthetic CE-T1w scans may be used for visualization of the NPV without using a contrast agent directly after MR-HIFU ablations of uterine fibroids.

No Difference in Revision Rates and High Survival Rates in Large-head Metal-on-metal THA Versus Metal-on-polyethylene THA: Long-term Results of a Randomized Controlled Trial.

BACKGROUND: Pseudotumor formation is a well-known complication in metal-on-metal (MoM) THA. Pseudotumors combined with elevated serum ion levels and complaints from patients can lead to high revision rates. Long-term (> 10 years) results obtained from randomized trials comparing large-head MoM THA and conventional metal-on-polyethylene (MoP) THA are lacking regarding revision and survival rates, pseudotumor formation, functional outcomes, and serum ion levels. QUESTIONS/PURPOSES: At 10 years of follow-up, (1) what is the difference in survival and revision rates between large-head (38 to 60 mm) MoM THA and conventional 28-mm MoP THA? (2) What is the difference in pseudotumor formation between large-head MoM THA and MoP THA? (3) Is there a difference in functional outcome between large-head MoM THA and MoP THA? (4) What is the difference in serum ion levels between large-head MoM THA and MoP THA? METHODS: Between January 2006 and December 2008, 104 patients were randomized to receive either cementless MoM THA (50 patients) or cementless MoP THA (54 patients). In all, 78% (81 of 104) of patients completed the 10-year postoperative follow-up: 36 patients with MoM THA (72%; six patients lost to follow-up) and 45 with MoP THA (83%; four lost to follow-up). In the MoM group, 47% (17) were men, and the patients had a mean ± SD age of 60 ± 5 years. In the MoP group, 38% (17) were men, and the patients had a mean age of 61 ± 5 years. All baseline characteristics were similar between the groups. At 10 years of follow-up, all patient records were screened for revision surgery or complications, and the primary endpoint was survivorship free from revision for any cause at the 10-year follow-up interval, which we analyzed using a Kaplan-Meier survival analysis. All patients had a CT scan to determine the pseudotumor classification, which was reviewed by an independent radiologist. Functional outcome was measured using the patient-reported Oxford Hip Score and Harris Hip Score; the latter was assessed by a blinded nurse practitioner. Finally, serum ion cobalt and chrome concentrations were measured at 10 years postoperatively. Because the a priori sample size calculation for this randomized controlled trial was based on a different endpoint, a post hoc power analysis was performed for this long-term follow-up study, with survival as the primary outcome. It showed that considering the number of included patients, this study would have sufficient power (one-sided testing, alpha 0.05, power 80%) to discern a difference of 20% in the survival rate between the MoP and MoM groups (95% versus 75%). RESULTS: With the numbers available, there was no difference in survivorship free from revision for any cause between the MoP group and MoM group at 10 years (95% [95% CI 85% to 98%] versus 92% [95% CI 82% to 98%]; p = 0.6). A higher percentage of patients in the MoM group had pseudotumors on CT than those in the MoP group did, but pseudotumors were observed in both groups (56% [20 of 36] in the MoM group versus 22% [10 of 45] in the MoP group, relative risk 1.8 [95% CI 1.2 to 2.6]; p = 0.002). A higher proportion of elevated cobalt and chrome levels was found in the MoM group (19% and 14%, respectively) than in the MoP group (0% for both cobalt and chrome) (cobalt: RR 1.2 [95% CI 1.1 to 1.5]; p = 0.002; chrome: RR 1.2 [95% CI 1.0 to 1.3]; p = 0.01). In 25% of the patients with pseudotumors (5 of 20 patients), there were elevated serum cobalt levels. None of the 23 patients without pseudotumors had elevated cobalt levels (RR 1.3 [95% CI 1.0 to 1.7]; p = 0.01). There was no difference in functional outcome between study groups, nor a difference between patients with a pseudotumor and those without. CONCLUSION: This study showed that the survival of patients with large-head MoM THA was high and comparable to that of those with MoP THA, which contrasts with the high revision rates reported by others. Although some patients with MoP THAs experienced pseudotumors, the risk of a pseudotumor was much greater in MoM hips, and serum ion levels were higher in patients who received an MoM THA. For these reasons and unknown future complications, continued surveillance of patients with MoM THAs seems important. LEVEL OF EVIDENCE: Level I, therapeutic study.

Synthetic CT for the planning of MR-HIFU treatment of bone metastases in pelvic and femoral bones: a feasibility study.

OBJECTIVES: Visualization of the bone distribution is an important prerequisite for MRI-guided high-intensity focused ultrasound (MRI-HIFU) treatment planning of bone metastases. In this context, we evaluated MRI-based synthetic CT (sCT) imaging for the visualization of cortical bone. METHODS: MR and CT images of nine patients with pelvic and femoral metastases were retrospectively analyzed in this study. The metastatic lesions were osteolytic, osteoblastic or mixed. sCT were generated from pre-treatment or treatment MR images using a UNet-like neural network. sCT was qualitatively and quantitatively compared to CT in the bone (pelvis or femur) containing the metastasis and in a region of interest placed on the metastasis itself, through mean absolute difference (MAD), mean difference (MD), Dice similarity coefficient (DSC), and root mean square surface distance (RMSD). RESULTS: The dataset consisted of 3 osteolytic, 4 osteoblastic and 2 mixed metastases. For most patients, the general morphology of the bone was well represented in the sCT images and osteolytic, osteoblastic and mixed lesions could be discriminated. Despite an average timespan between MR and CT acquisitions of 61 days, in bone, the average (± standard deviation) MAD was 116 ± 26 HU, MD - 14 ± 66 HU, DSC 0.85 ± 0.05, and RMSD 2.05 ± 0.48 mm and, in the lesion, MAD was 132 ± 62 HU, MD - 31 ± 106 HU, DSC 0.75 ± 0.2, and RMSD 2.73 ± 2.28 mm. CONCLUSIONS: Synthetic CT images adequately depicted the cancellous and cortical bone distribution in the different lesion types, which shows its potential for MRI-HIFU treatment planning. KEY POINTS: • Synthetic computed tomography was able to depict bone distribution in metastatic lesions. • Synthetic computed tomography images intrinsically aligned with treatment MR images may have the potential to facilitate MR-HIFU treatment planning of bone metastases, by combining visualization of soft tissues and cancellous and cortical bone.

Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19.

Background The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, and the capacity of intensive care units was a limiting factor during the peak of the pandemic and is generally dependent on a country's clinical resources. Purpose To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. Materials and Methods In this retrospective study, which included patients from March 7, 2020, to April 24, 2020, a consecutive cohort of hospitalized patients with real-time reverse transcription polymerase chain reaction-confirmed COVID-19 from two large Dutch community hospitals was identified. After univariable analysis, a risk model to predict critical illness (ie, death and/or intensive care unit admission with invasive ventilation) was developed, using multivariable logistic regression including clinical, chest radiographic, and laboratory findings. Distribution and severity of lung involvement were visually assessed by using an eight-point scale (chest radiography score). Internal validation was performed by using bootstrapping. Performance is presented as an area under the receiver operating characteristic curve. Decision curve analysis was performed, and a risk calculator was derived. Results The cohort included 356 hospitalized patients (mean age, 69 years ± 12 [standard deviation]; 237 men) of whom 168 (47%) developed critical illness. The final risk model's variables included sex, chronic obstructive lung disease, symptom duration, neutrophil count, C-reactive protein level, lactate dehydrogenase level, distribution of lung disease, and chest radiography score at hospital presentation. The area under the receiver operating characteristic curve of the model was 0.77 (95% CI: 0.72, 0.81; P < .001). A risk calculator was derived for individual risk assessment: Dutch COVID-19 risk model. At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness, of which 59 (83%) would be true-positive results. Conclusion A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of intensive care unit beds or facilities. © RSNA, 2020 Online supplemental material is available for this article.

Development and clinical evaluation of a 3-step modified manipulation protocol for MRI-guided high-intensity focused ultrasound of uterine fibroids.

OBJECTIVES: The clinical applicability of magnetic resonance image-guided high-intensity focused ultrasound (MR-HIFU) treatment of uterine fibroids is often limited due to inaccessible fibroids or bowel interference. The aim of this study was to implement a newly developed 3-step modified manipulation protocol and to evaluate its influence on the number of eligible women and treatment failure rate. METHODS: From June 2016 to June 2018, 165 women underwent a screening MRI examination, 67 women of whom were consecutively treated with MR-HIFU at our institution. Group 1 (n = 20) was treated with the BRB manipulation protocol which consisted of sequential applications of urinary bladder filling, rectal filling, and urinary bladder emptying. Group 2 (n = 47) was treated using the 3-step modified manipulation protocol which included (1) the BRB maneuver with adjusted rectal filling by adding psyllium fibers to the solution; (2) Trendelenburg position combined with bowel massage; (3) the manual uterine manipulation (MUM) method for uterine repositioning. A comparison was made between the two manipulation protocols to evaluate differences in safety, the eligibility percentage, and treatment failure rate due to unsuccessful manipulation. RESULTS: After implementing the 3-step modified manipulation protocol, our ineligibility rate due to bowel interference or inaccessible fibroids decreased from 18% (16/88) to 0% (0/77). Our treatment failure rate due to unsuccessful manipulation decreased from 20% (4/20) to 2% (1/47). There were no thermal complications to the bowel or uterus. CONCLUSIONS: Implementation of the 3-step modified manipulation protocol during MR-HIFU therapy of uterine fibroids improved the eligibility percentage and reduced the treatment failure rate. TRIAL REGISTRATION: Registry number NL56182.075.16 KEY POINTS: • A newly developed 3-step modified manipulation protocol was successfully implemented without the occurrence of thermal complication to the bowel or uterus. • The 3-step modified manipulation protocol increased our eligibility percentage for MR-HIFU treatment of uterine fibroids. • The 3-step modified manipulation protocol reduced our treatment failure rate for MR-HIFU treatment of uterine fibroids.

The Focused Ultrasound Myoma Outcome Study (FUMOS); a retrospective cohort study on long-term outcomes of MR-HIFU therapy.

OBJECTIVES: Since 2004, uterine fibroids have been treated with MR-HIFU, but there are persevering doubts on long-term efficacy to date. In the Focused Ultrasound Myoma Outcome Study (FUMOS), we evaluated long-term outcomes after MR-HIFU therapy, primarily to assess the reintervention rate. METHODS: Data was retrospectively collected from 123 patients treated with MR-HIFU at our hospital from 2010 to 2017. Follow-up duration and baseline (MRI) characteristics were retrieved from medical records. Treatment failures, adverse events, and the nonperfused volume percentage (NPV%) were determined. Patients received a questionnaire about reinterventions, recovery time, satisfaction, and pregnancy outcomes. Restrictive treatment protocols were compared with unrestrictive (aiming for complete ablation) treatments. Subgroups were analyzed based on the achieved NPV < 50 or ≥ 50%. RESULTS: Treatment failures occurred in 12.1% and the number of adverse events was 13.7%. Implementation of an unrestrictive treatment protocol significantly (p = 0.006) increased the mean NPV% from 37.4% [24.3-53.0] to 57.4% [33.5-76.5]. At 63.5 ± 29.0 months follow-up, the overall reintervention rate was 33.3% (n = 87). All reinterventions were performed within 34 months follow-up, but within 21 months in the unrestrictive group. The reintervention rate significantly (p = 0.002) decreased from 48.8% in the restrictive group (n = 43; follow-up 87.5 ± 7.3 months) to 18.2% in the unrestrictive group (n = 44; follow-up 40.0 ± 22.1 months). The median recovery time was 2.0 [1.0-7.0] days. Treatment satisfaction rate was 72.4% and 4/11 women completed family planning after MR-HIFU. CONCLUSIONS: The unrestrictive treatment protocol significantly increased the NPV%. Unrestrictive MR-HIFU treatments led to acceptable reintervention rates comparable to other reimbursed uterine-sparing treatments, and no reinterventions were reported beyond 21 months follow-up. KEY POINTS: • All reinterventions were performed within 34 months follow-up, but in the unrestrictive treatment protocol group, no reinterventions were reported beyond 21 months follow-up. • The NPV% was negatively associated with the risk of reintervention; thus, operators should aim for complete ablation during MR-guided HIFU therapy of uterine fibroids. • Unrestrictive treatments have led to acceptable reintervention rates after MR-guided HIFU therapy compared to other reimbursed uterine-sparing treatments.

Use of multiparametric MRI to characterize uterine fibroid tissue types.

BACKGROUND: Although the biological characteristics of uterine fibroids (UF) have implications for therapy choice and effectiveness, there is limited MRI data about these characteristics. Currently, the Funaki classification and Scaled Signal Intensity (SSI) are used to predict treatment outcome but both screening-tools appear to be suboptimal. Therefore, multiparametric and quantitative MRI was studied to evaluate various biological characteristics of UF. METHODS: 87 patients with UF underwent an MRI-examination. Differences between UF tissues and myometrium were investigated using T2-mapping, Apparent Diffusion Coefficient (ADC) maps with different b-value combinations, contrast-enhanced T1-weighted and T2-weighted imaging. Additionally, the Funaki classification and SSI were calculated. RESULTS: Significant differences between myometrium and UF tissue in T2-mapping (p = 0.001), long-TE ADC low b-values (p = 0.002), ADC all b-values (p < 0.001) and high b-values (p < 0.001) were found. Significant differences between Funaki type 3 versus type 1 and 2 were observed in SSI (p < 0.001) and T2-values (p < 0.001). Significant correlations were found between SSI and T2-mapping (p < 0.001; ρs = 0.82), ADC all b-values (p = 0.004; ρs = 0.31), ADC high b-values (p < 0.001; ρs = 0.44) and long-TE ADC low b-values (p = 0.004; ρs = 0.31). CONCLUSIONS: Quantitative MR-data allowed us to distinguish UF tissue from myometrium and to discriminate different UF tissue types and may, therefore, be a useful tool to predict treatment outcome/determine optimal treatment modality.

How skin anatomy influences transcutaneous bilirubin determinations: an in vitro evaluation.

BACKGROUND: Transcutaneous bilirubinometry is an effective screening method for neonatal hyperbilirubinemia. Current transcutaneous bilirubin (TcB) meters are designed for the "standard" situation of TcB determinations on the forehead or sternum of term newborns. We hypothesize that skin anatomy can considerably influence TcB determinations in non-standard situations-e.g., on preterm newborns or alternative body locations. METHODS: A commercially available TcB meter (JM-105) was evaluated in vitro on phantoms that accurately mimic neonatal skin. We varied the mimicked cutaneous hemoglobin content (0-2.5 g/L), bone depth (0.26-5.26 mm), and skin maturity-related light scattering (1.36-2.27 mm-1) within the clinical range and investigated their influence on the TcB determination. To obtain a reference frame for bone depth at the forehead, magnetic resonance head scans of 46 newborns were evaluated. RESULTS: The TcB meter adequately corrected for mimicked hemoglobin content. However, TcB determinations were influenced considerably by clinically realistic variations in mimicked bone depth and light scattering (deviations up to 72 µmol/L). This greatly exceeds the specified accuracy of the device (±25.5 µmol/L). CONCLUSION: As bone depth and light scattering vary with gestational maturity and body location, caretakers should be cautious when interpreting TcB measurements on premature newborns and non-standard body locations.

CT radiation dose reduction in patients with total hip arthroplasties using model-based iterative reconstruction and orthopaedic metal artefact reduction.

OBJECTIVE: To evaluate the impact of radiation dose reduction on image quality in patients with metal-on-metal total hip arthroplasties (THAs) using model-based iterative reconstruction (MBIR) combined with orthopaedic metal artefact reduction (O-MAR). MATERIALS AND METHODS: Patients with metal-on-metal THAs received a pelvic CT with a full (FD) and a reduced radiation dose (RD) with -20%, -40%, -57%, or -80% CT radiation dose respectively, when assigned to group 1, 2, 3, or 4 respectively. FD acquisitions were reconstructed with iterative reconstruction, iDose4. RD acquisitions were additionally reconstructed with iterative model-based reconstruction (IMR) levels 1-3 with different levels of noise suppression. CT numbers, noise and contrast-to-noise ratios were measured in muscle, fat and bladder. Subjective image quality was evaluated on seven aspects including artefacts, osseous structures, prosthetic components and soft tissues. RESULTS: Seventy-six patients were randomly assigned to one of the four groups. While reducing radiation dose by 20%, 40%, 57%, or 80% in combination with IMR, CT numbers remained constant. Compared with iDose4, the noise decreased (p < 0.001) and contrast-to-noise ratios increased (p < 0.001) with IMR. O-MAR improved CT number accuracy in the bladder and reduced noise in the bladder, muscle and fat (p < 0.01). Subjective image quality was rated lower on RD IMR images than FD iDose4 images on all seven aspects (p < 0.05) and was not related to the applied radiation dose reduction. CONCLUSION: In RD IMR with O-MAR images, CT numbers remained constant, noise decreased and contrast-to-noise ratios between muscle and fat increased compared with FD iDose4 with O-MAR images in patients with metal-on-metal THAs. Subjective image quality reduced, regardless of the degree of radiation dose reduction.

Computed Tomography Imaging of a Hip Prosthesis Using Iterative Model-Based Reconstruction and Orthopaedic Metal Artefact Reduction: A Quantitative Analysis.

OBJECTIVES: To quantify the combined use of iterative model-based reconstruction (IMR) and orthopaedic metal artefact reduction (O-MAR) in reducing metal artefacts and improving image quality in a total hip arthroplasty phantom. METHODS: Scans acquired at several dose levels and kVps were reconstructed with filtered back-projection (FBP), iterative reconstruction (iDose) and IMR, with and without O-MAR. Computed tomography (CT) numbers, noise levels, signal-to-noise-ratios and contrast-to-noise-ratios were analysed. RESULTS: Iterative model-based reconstruction results in overall improved image quality compared to iDose and FBP (P < 0.001). Orthopaedic metal artefact reduction is most effective in reducing severe metal artefacts improving CT number accuracy by 50%, 60%, and 63% (P < 0.05) and reducing noise by 1%, 62%, and 85% (P < 0.001) whereas improving signal-to-noise-ratios by 27%, 47%, and 46% (P < 0.001) and contrast-to-noise-ratios by 16%, 25%, and 19% (P < 0.001) with FBP, iDose, and IMR, respectively. CONCLUSIONS: The combined use of IMR and O-MAR strongly improves overall image quality and strongly reduces metal artefacts in the CT imaging of a total hip arthroplasty phantom.

Imaging of the Postoperative Anterior Cruciate Ligament: Emphasis on New Surgical and Imaging Methods.

The aim of anterior cruciate ligament (ACL) reconstruction is to restore normal function of the knee, but unfortunately abnormal kinematics and a predisposition to knee osteoarthritis occur in a significant percentage of patients. So there is an ongoing need to improve treatment options and long-term outcome of patients with a ruptured ACL. With the recent advancements in the field of ACL tissue engineering, the focus of treatment for ACL injuries is changing from resection and reconstruction toward repair and regeneration. Several new ACL repair methods were recently introduced as an alternative to traditional ACL reconstructive procedures. Radiologists must become familiar with these new surgical methods to interpret their appearance correctly on postoperative imaging studies. This article provides an overview of the latest advancements in ACL surgical methods and discusses the role of imaging to assess the postoperative ACL including both standard and advanced imaging methods.

Ultrasound-Guided Tissue Core Biopsies in Supraclavicular Lymph Nodes in Patients with Suspected Thoracic Malignancies.

BACKGROUND: Supraclavicular (SC) lymph node metastases are important in the analysis of thoracic malignancies for staging as well as for diagnosing purposes. Ultrasound (US) guidance visualises lesions very precisely, enabling tissue biopsies in real-time mode. OBJECTIVES: To report on the diagnostic qualities of SC tissue core biopsies (TCB). METHODS: A retrospective database analysis was performed in TCB performed under US guidance in SC nodes in patients suspected of having a thoracic malignancy. Clinical characteristics and results of diagnostic evaluations were analysed. RESULTS: Between October 2008 and October 2014, 67 sessions for TCB in 65 patients were performed. The diagnostic accuracy of TCB for all diagnoses is 90%, with a sensitivity of 89%. For malignant diagnoses, the sensitivity of US-guided TCB is 93%. In 20 patients, molecular analysis for EGFR and KRAS was performed, with a diagnostic success rate of 95%. One patient suffered a moderate haemorrhage after TCB. CONCLUSION: TCB of SC nodes in the analysis of suspected thoracic malignancy is safe and has a high diagnostic accuracy in determining tumour subtype as well as molecular analysis.