AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.

OBJECTIVES: The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axial MRIs. The aim of this study is to develop and validate an AI-based model that automatically classifies LCCS using sagittal T2-weighted MRIs. METHODS: A pre-existing 3D AI algorithm was utilized to segment the spinal canal and intervertebral discs (IVDs), enabling quantitative measurements at each IVD level. Four musculoskeletal radiologists graded 683 IVD levels from 186 LCCS patients using the 4-class Lee grading system. A second consensus reading was conducted by readers 1 and 2, which, along with automatic measurements, formed the training dataset for a multiclass (grade 0-3) and binary (grade 0-1 vs. 2-3) random forest classifier with tenfold cross-validation. RESULTS: The multiclass model achieved a Cohen's weighted kappa of 0.86 (95% CI: 0.82-0.90), comparable to readers 3 and 4 with 0.85 (95% CI: 0.80-0.89) and 0.73 (95% CI: 0.68-0.79) respectively. The binary model demonstrated an AUC of 0.98 (95% CI: 0.97-0.99), sensitivity of 93% (95% CI: 91-96%), and specificity of 91% (95% CI: 87-95%). In comparison, readers 3 and 4 achieved a specificity of 98 and 99% and sensitivity of 74 and 54%, respectively. CONCLUSION: Both the multiclass and binary models, while only using sagittal MR images, perform on par with experienced radiologists who also had access to axial sequences. This underscores the potential of this novel algorithm in enhancing diagnostic accuracy and efficiency in medical imaging. KEY POINTS: Question How can the classification of lumbar central canal stenosis (LCCS) be made more efficient? Findings Multiclass and binary AI models, using only sagittal MR images, performed on par with experienced radiologists who also had access to axial sequences. Clinical relevance Our AI algorithm accurately classifies LCCS from sagittal MRI, matching experienced radiologists. This study offers a promising tool for automated LCCS assessment from sagittal T2 MRI, potentially reducing the reliance on additional axial imaging.

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.

Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022.

OBJECTIVES: To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022. MATERIALS AND METHODS: Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis. RESULTS: The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings. CONCLUSION: The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties. CLINICAL RELEVANCE STATEMENT: The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products. KEY POINTS: • The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence-based software; however, major obstacles to adoption remain the costs and IT-related difficulties.

Tourniquet Does Not Affect Long-term Outcomes in Minor Hand Surgery: A Randomized Controlled Trial.

Surgeons often prefer to use a tourniquet during minor procedures, such as carpal tunnel release (CTR) or trigger finger release (TFR). Besides the possible discomfort for the patient, the effect of tourniquet use on long-term results and complications is unknown. Our primary aim was to compare the patient-reported outcomes 1 year after CTR or TFR under local anesthesia with or without tourniquet. Secondary outcomes included satisfaction, sonographically estimated scar tissue thickness after CTR' and postoperative complications. Methods: Between May 2019 and May 2020, 163 patients planned for open CTR or TFR under local anesthesia were included. Before surgery, and at 3, 6, and 12 months postoperatively, Quick Disabilities of the Arm, Shoulder and Hand and Boston Carpal Tunnel questionnaires were administered, and complications were noted. At 6 months postoperatively, an ultrasound was conducted to determine the thickness of scar tissue in the region of median nerve. Results: A total of 142 patients (51 men [38%]) were included. The Quick Disabilities of the Arm, Shoulder and Hand questionnaire and Boston Carpal Tunnel Questionnaire scores improved significantly in both groups during follow-up, wherein most improvements were seen in the first 3 months. No difference in clinical outcome and scar tissue formation was found between the two groups after 12 months. The complication rate was comparable between both groups. Thirty-two (24%) patients had at least one complication, none needed surgical interventions, and no recurrent symptoms were seen. Conclusions: Our study shows similar long-term clinical outcomes, formation of scar tissue, and complication rates for patients undergoing CTR or TFR with or without a tourniquet. Tourniquet usage should be based on shared decision-making.

How does artificial intelligence in radiology improve efficiency and health outcomes?

Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement.

Complications of percutaneous transhepatic cholangiography and biliary drainage, a multicenter observational study.

OBJECTIVES: Over 2500 percutaneous transhepatic cholangiography and biliary drainage (PTCD) procedures are yearly performed in the Netherlands. Most interventions are performed for treatment of biliary obstruction following unsuccessful endoscopic biliary cannulation. Our aim was to evaluate complication rates and risk factors for complications in PTCD patients after failed ERCP. METHODS: We performed an observational study collecting data from a cohort that was subjected to PTCD during a 5-year period in one academic and four teaching hospitals. Primary objective was the development of infectious (sepsis, cholangitis, abscess, or cholecystitis) and non-infectious complications (bile leakage, severe hemorrhage, etc.) and mortality within 30 days of the procedure. Subsequently, risk factors for complications and mortality were analyzed with a multilevel logistic regression analysis. RESULTS: A total of 331 patients underwent PTCD of whom 205 (61.9%) developed PTCD-related complications. Of the 224 patients without a pre-existent infection, 91 (40.6%) developed infectious complications, i.e., cholangitis in 26.3%, sepsis in 24.6%, abscess formation in 2.7%, and cholecystitis in 1.3%. Non-infectious complications developed in 114 of 331 patients (34.4%). 30-day mortality was 17.2% (N = 57). Risk factors for infectious complications included internal drainage and drain obstruction, while multiple re-interventions were a risk factor for non-infectious complications. CONCLUSION: Both infectious and non-infectious complications are frequent after PTCD, most often due to biliary drain obstruction.

Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment.

BACKGROUND: Limited evidence is available on the clinical impact of artificial intelligence (AI) in radiology. Early health technology assessment (HTA) is a methodology to assess the potential value of an innovation at an early stage. We use early HTA to evaluate the potential value of AI software in radiology. As a use-case, we evaluate the cost-effectiveness of AI software aiding the detection of intracranial large vessel occlusions (LVO) in stroke in comparison to standard care. We used a Markov based model from a societal perspective of the United Kingdom predominantly using stroke registry data complemented with pooled outcome data from large, randomized trials. Different scenarios were explored by varying missed diagnoses of LVOs, AI costs and AI performance. Other input parameters were varied to demonstrate model robustness. Results were reported in expected incremental costs (IC) and effects (IE) expressed in quality adjusted life years (QALYs). RESULTS: Applying the base case assumptions (6% missed diagnoses of LVOs by clinicians, $40 per AI analysis, 50% reduction of missed LVOs by AI), resulted in cost-savings and incremental QALYs over the projected lifetime (IC: - $156, - 0.23%; IE: + 0.01 QALYs, + 0.07%) per suspected ischemic stroke patient. For each yearly cohort of patients in the UK this translates to a total cost saving of $11 million. CONCLUSIONS: AI tools for LVO detection in emergency care have the potential to improve healthcare outcomes and save costs. We demonstrate how early HTA may be applied for the evaluation of clinically applied AI software for radiology.

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence.

OBJECTIVES: Map the current landscape of commercially available artificial intelligence (AI) software for radiology and review the availability of their scientific evidence. METHODS: We created an online overview of CE-marked AI software products for clinical radiology based on vendor-supplied product specifications ( www.aiforradiology.com ). Characteristics such as modality, subspeciality, main task, regulatory information, deployment, and pricing model were retrieved. We conducted an extensive literature search on the available scientific evidence of these products. Articles were classified according to a hierarchical model of efficacy. RESULTS: The overview included 100 CE-marked AI products from 54 different vendors. For 64/100 products, there was no peer-reviewed evidence of its efficacy. We observed a large heterogeneity in deployment methods, pricing models, and regulatory classes. The evidence of the remaining 36/100 products comprised 237 papers that predominantly (65%) focused on diagnostic accuracy (efficacy level 2). From the 100 products, 18 had evidence that regarded level 3 or higher, validating the (potential) impact on diagnostic thinking, patient outcome, or costs. Half of the available evidence (116/237) were independent and not (co-)funded or (co-)authored by the vendor. CONCLUSIONS: Even though the commercial supply of AI software in radiology already holds 100 CE-marked products, we conclude that the sector is still in its infancy. For 64/100 products, peer-reviewed evidence on its efficacy is lacking. Only 18/100 AI products have demonstrated (potential) clinical impact. KEY POINTS: • Artificial intelligence in radiology is still in its infancy even though already 100 CE-marked AI products are commercially available. • Only 36 out of 100 products have peer-reviewed evidence of which most studies demonstrate lower levels of efficacy. • There is a wide variety in deployment strategies, pricing models, and CE marking class of AI products for radiology.

Trends in frequency and outcome of high-risk breast lesions at core needle biopsy in women recalled at biennial screening mammography, a multiinstitutional study.

Between January 1, 2011, and December 31, 2016, we studied the incidence, management and outcome of high-risk breast lesions in a consecutive series of 376,519 screens of women who received biennial screening mammography. During the 6-year period covered by the study, the proportion of women who underwent core needle biopsy (CNB) after recall remained fairly stable, ranging from 39.2% to 48.1% (mean: 44.2%, 5,212/11,783), whereas the proportion of high-risk lesions at CNB (i.e., flat epithelial atypia, atypical ductal hyperplasia, lobular carcinoma in situ and papillary lesions) gradually increased from 3.2% (25/775) in 2011 to 9.5% (86/901) in 2016 (p < 0.001). The mean proportion of high-risk lesions at CNB that were subsequently treated with diagnostic surgical excision was 51.4% (169/329) and varied between 41.0% and 64.3% through the years, but the excision rate for high-risk lesions per 1,000 screens and per 100 recalls increased from 0.25 (2011) to 0.70 (2016; p < 0.001) and from 0.81 (2011) to 2.50 (2016; p < 0.001), respectively. The proportion of all diagnostic surgical excisions showing in situ or invasive breast cancer was 29.0% (49/169) and varied from 22.2% (8/36) in 2014 to 38.5% (5/13) in 2011. In conclusion, the proportion of high-risk lesions at CNB tripled in a 6-year period, with a concomitant increased excision rate for these lesions. As the proportion of surgical excisions showing in situ or invasive breast cancer did not increase, a rising number of screened women underwent invasive surgical excision with benign outcome.

Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts.

OBJECTIVES: To determine the effect of computer-aided-detection (CAD) software for automated breast ultrasound (ABUS) on reading time (RT) and performance in screening for breast cancer. MATERIAL AND METHODS: Unilateral ABUS examinations of 120 women with dense breasts were randomly selected from a multi-institutional archive of cases including 30 malignant (20/30 mammography-occult), 30 benign, and 60 normal cases with histopathological verification or ≥ 2 years of negative follow-up. Eight radiologists read once with (CAD-ABUS) and once without CAD (ABUS) with > 8 weeks between reading sessions. Readers provided a BI-RADS score and a level of suspiciousness (0-100). RT, sensitivity, specificity, PPV and area under the curve (AUC) were compared. RESULTS: Average RT was significantly shorter using CAD-ABUS (133.4 s/case, 95% CI 129.2-137.6) compared with ABUS (158.3 s/case, 95% CI 153.0-163.3) (p < 0.001). Sensitivity was 0.84 for CAD-ABUS (95% CI 0.79-0.89) and ABUS (95% CI 0.78-0.88) (p = 0.90). Three out of eight readers showed significantly higher specificity using CAD. Pooled specificity (0.71, 95% CI 0.68-0.75 vs. 0.67, 95% CI 0.64-0.70, p = 0.08) and PPV (0.50, 95% CI 0.45-0.55 vs. 0.44, 95% CI 0.39-0.49, p = 0.07) were higher in CAD-ABUS vs. ABUS, respectively, albeit not significantly. Pooled AUC for CAD-ABUS was comparable with ABUS (0.82 vs. 0.83, p = 0.53, respectively). CONCLUSION: CAD software for ABUS may decrease the time needed to screen for breast cancer without compromising the screening performance of radiologists. KEY POINTS: • ABUS with CAD software may speed up reading time without compromising radiologists' accuracy. • CAD software for ABUS might prevent non-detection of malignant breast lesions by radiologists. • Radiologists reading ABUS with CAD software might improve their specificity without losing sensitivity.

Surveillance of Women with the BRCA1 or BRCA2 Mutation by Using Biannual Automated Breast US, MR Imaging, and Mammography.

Purpose To evaluate a multimodal surveillance regimen including yearly full-field digital (FFD) mammography, dynamic contrast agent-enhanced (DCE) magnetic resonance (MR) imaging, and biannual automated breast (AB) ultrasonography (US) in women with BRCA1 and BRCA2 mutations. Materials and Methods This prospective multicenter trial enrolled 296 carriers of the BRCA mutation (153 BRCA1 and 128 BRCA2 carriers, and 15 women with first-degree untested relatives) between September 2010 and November 2012, with follow-up until November 2015. Participants underwent 2 years of intensified surveillance including biannual AB US, and routine yearly DCE MR imaging and FFD mammography. The surveillance performance for each modality and possible combinations were determined. Results Breast cancer was screening-detected in 16 women (age range, 33-58 years). Three interval cancers were detected by self-examination, all in carriers of the BRCA1 mutation under age 43 years. One cancer was detected in a carrier of the BRCA1 mutation with a palpable abnormality in the contralateral breast. One incidental breast cancer was detected in a prophylactic mastectomy specimen. Respectively, sensitivity of DCE MR imaging, FFD mammography, and AB US was 68.1% (14 of 21; 95% confidence interval [CI]: 42.9%, 85.8%), 37.2% (eight of 21; 95% CI: 19.8%, 58.7%), and 32.1% (seven of 21; 95% CI: 16.1%, 53.8%); specificity was 95.0% (643 of 682; 95% CI: 92.7%, 96.5%), 98.1% (638 of 652; 95% CI: 96.7%, 98.9%), and 95.1% (1030 of 1088; 95% CI: 93.5%, 96.3%); cancer detection rate was 2.0% (14 of 702), 1.2% (eight of 671), and 1.0% (seven of 711) per 100 women-years; and positive predictive value was 25.2% (14 of 54), 33.7% (nine of 23), and 9.5% (seven of 68). DCE MR imaging and FFD mammography combined yielded the highest sensitivity of 76.3% (16 of 21; 95% CI: 53.8%, 89.9%) and specificity of 93.6% (643 of 691; 95% CI: 91.3%, 95.3%). AB US did not depict additional cancers. FFD mammography yielded no additional cancers in women younger than 43 years, the mean age at diagnosis. In carriers of the BRCA2 mutation, sensitivity of FFD mammography with DCE MR imaging surveillance was 90.9% (10 of 11; 95% CI: 72.7%, 100%) and 60.0% (six of 10; 95% CI: 30.0%, 90.0%) in carriers of the BRCA1 mutation because of the high interval cancer rate in carriers of the BRCA1 mutation. Conclusion AB US may not be of added value to yearly FFD mammography and DCE MR imaging surveillance of carriers of the BRCA mutation. Study results suggest that carriers of the BRCA mutation younger than 40 years may not benefit from FFD mammography surveillance in addition to DCE MR imaging. © RSNA, 2017 Online supplemental material is available for this article.

Trends and patterns of computed tomography scan use among children in The Netherlands: 1990-2012.

OBJECTIVE: To evaluate trends and patterns in CT usage among children (aged 0-17 years) in The Netherlands during the period 1990-2012. METHODS: Lists of electronically archived paediatric CT scans were requested from the Radiology Information Systems (RIS) of Dutch hospitals which reported >10 paediatric CT scans annually in a survey conducted in 2010. Data included patient identification, birth date, gender, scan date and body part scanned. For non-participating hospitals and for years prior to electronic archiving in some participating hospitals, data were imputed by calendar year and hospital type (academic, general with <500 beds, general with ≥ 500 beds). RESULTS: Based on 236,066 CT scans among 146,368 patients performed between 1990 and 2012, estimated annual numbers of paediatric CT scans in The Netherlands increased from 7,731 in 1990 to 26,023 in 2012. More than 70 % of all scans were of the head and neck. During the last decade, substantial increases of more than 5 % per year were observed in general hospitals with fewer than 500 beds and among children aged 10 years or older. CONCLUSION: The estimated number of paediatric CT scans has more than tripled in The Netherlands during the last two decades. KEY POINTS: • Paediatric CT in The Netherlands has tripled during the last two decades. • The number of paediatric CTs increased through 2012 in general hospitals. • Paediatric CTs continued to increase among children aged 10 years or older.

Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS).

BACKGROUND: Ultrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. METHODS: We propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. RESULTS: DSC values are 0.86 ± 0.06 and 0.86 ± 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. CONCLUSIONS: Evaluation metrics show that the algorithm accurately segments and reconstructs various lesions. Copyright © 2016 John Wiley & Sons, Ltd.

[Abdominal splenosis]. / Abdominale splenose.

Splenosis is a common benign finding that occurs after splenic trauma or after splenectomy. It is auto-transplantation of splenic tissue and can be seen intra-abdominally, intra-thoracically and even subcutaneously. Splenosis is usually found incidentally at laparoscopy, laparotomy or on radiological examination and is mostly asymptomatic. Treatment is only required if a patient complains of abdominal pain, obstruction or bleeding. On radiological examination splenosis can mimic a metastatic malignant disease. For this reason it is important to recognise splenosis and know the patient's medical history concerning splenic trauma or splenectomy, thus avoiding diagnostic laparoscopy or ultrasound guided biopsy. This paper presents two patients with splenosis. Additionally, we describe how to diagnose this entity by scintigraphy with (99m) Technetium-labelled heat-denatured erythrocytes.

Impact of the transition from screen-film to digital screening mammography on interval cancer characteristics and treatment - a population based study from the Netherlands.

INTRODUCTION: In most breast screening programmes screen-film mammography (SFM) has been replaced by full-field digital mammography (FFDM). We compared interval cancer characteristics at SFM and FFDM screening mammography. PATIENTS AND METHODS: We included all 297 screen-detected and 104 interval cancers in 60,770 SFM examinations and 427 screen-detected and 124 interval cancers in 63,182 FFDM examinations, in women screened in the period 2008-2010. Breast imaging reports, biopsy results and surgical reports of all cancers were collected. Two radiologists reviewed prior and diagnostic mammograms of all interval cancers. They determined breast density, described mammographic abnormalities and classified interval cancers as missed, showing a minimal sign abnormality or true negative. RESULTS: The referral rate and cancer detection at SFM were 1.5% and 4.9‰ respectively, compared to 3.0% (p<0.001) and 6.6‰ (p<0.001) at FFDM. Screening sensitivity was 74.1% at SFM (297/401, 95% confidence interval (CI)=69.8-78.4%) and 77.5% at FFDM (427/551, 95% CI=74.0-81.0%). Significantly more interval cancers were true negative at prior FFDM than at prior SFM screening mammography (65.3% (81/124) versus 47.1% (49/104), p=0.02). For interval cancers following SFM or FFDM screening mammography, no significant differences were observed in breast density or mammographic abnormalities at the prior screen, tumour size, lymph node status, receptor status, Nottingham tumour grade or surgical treatment (mastectomy versus breast conserving therapy). CONCLUSION: FFDM resulted in a significantly higher cancer detection rate, but sensitivity was similar for SFM and FFDM. Interval cancers are more likely to be true negative at prior FFDM than at prior SFM screening mammography, whereas their tumour characteristics and type of surgical treatment are comparable.

[Imaging in silicone breast implantation]. / Beeldvormende technieken bij siliconenborstprothesen.

Recently, there have been concerns regarding the use of breast implants from Poly Implant Prothèse (PIP, Seyne sur Mer, France) for breast augmentation due to their tendency to rupture and the possibility of having toxic contents. MRI using a specific silicone-sensitive sequence has proven to be the most sensitive and specific technique in the detection of intra- and extracapsular implant rupture. However, given its high costs, it is important that this technique is used sparingly. In this clinical lesson, we compare the sensitivity and specificity of mammography, ultrasound, CT and MRI for the detection of breast implant rupture. Based on two cases, a diagnostic approach is given in order to reduce health care costs.

Reduction of contrast medium volume in abdominal aorta CTA: multiphasic injection technique versus a test bolus volume.

OBJECTIVE: The purpose of this study is to reduce the administered contrast medium volume in abdominal CTA by using a test bolus injection, with the preservation of adequate quantitative and qualitative vessel enhancement. STUDY DESIGN: For this technical efficacy study 30 patients, who were referred for a CTA examination of the abdominal aorta, were included. Randomly 15 patients were assigned to undergo a multiphasic injection protocol and received 89 mL of contrast medium (Optiray 350) (protocol I). Fifteen patients were assigned to the test bolus injection protocol (protocol II), which implies injection of a 10 mL test bolus of Optiray 350 prior to performing CTA with a 40 mL of contrast medium. Quantitative assessment of vascular enhancement was performed by measuring the amount of Hounsfield Units in the aorta at 30 positions from the celiac trunk to the iliac arteries in both groups. Qualitative assessment was performed by three radiologists who scored the images at a 5-point scale. RESULTS: Quantitative assessment showed that there was no significant difference in vascular enhancement for patients between the two protocols, with mean attenuation values of 280.9 ± 50.84 HU and 258.60 ± 39.28 HU, respectively. The image quality of protocol I was rated 4.31 (range: 3.67/5.00) and of protocol II 4.11 (range: 2.67/5.00). These differences were not statistically significant. CONCLUSION: This study showed that by using a test bolus injection and the administration of 50 mL of contrast medium overall, CTA of the abdominal aorta can reliably be performed, with regard to quantitative and qualitative adequate vessel enhancement.

Reduced contrast medium in abdominal aorta CTA using a multiphasic injection technique.

PURPOSE: The purpose of this study was to determine if with a multiphasic injection technique the administered amount of contrast medium for abdominal computerized tomographic angiography (CTA) can be decreased, whilst improving CT image quality. MATERIALS AND METHODS: In 30 patients a multiphasic injection method was compared to the standard uniphasic contrast medium injection protocol. Fifteen patients underwent abdominal CTA with a standard uniphasic injection protocol (protocol I) receiving 100mL of a non-ionic radiopaque contrast agent (Ioversol). The second group of 15 patients underwent CTA with a multiphasic injection protocol (protocol II) receiving a total of 89 mL Ioversol. Vascular contrast enhancement and difference in enhancement uniformity were assessed quantitatively and image quality was assessed by three independent radiologists. RESULTS: Quantitative assessment of the vascular contrast enhancement showed that there was no significant difference in enhancement uniformity for patients between the protocols. The image quality was rated as being good to excellent in 81.8% and 88.0% of the scans, for protocol I and protocol II, respectively. However these differences were not statistically significant. CONCLUSION: By using a multiphasic injection technique with CTA of the abdominal aorta a reduction of 11 percent of contrast medium can be realized. Enhancement patterns are quantitatively as well as qualitatively comparable to the standard contrast medium injection protocol.

US-guided injection of the upper and lower extremity joints.

There is a growing interest in the application of ultrasound (US) guidance for diagnostic and therapeutic joint injections. US provides direct visualization of soft tissues and the outer borders of bony structures. With real-time needle guidance the success rate of intra-articular injections improves and iatrogenic damage to anatomic structures can be avoided. An US machine is more readily available, transferrable and more affordable than a fluoroscopy machine or CT scanner and lacks the risk of radiation. These factors make US a valuable alternative to procedures performed either blind or under fluoroscopic or CT guidance. This article focuses on the rationale for injections in the upper and lower extremity joints and describes and illustrates the different US-guided injection techniques.