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OBJECTIVE: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up. MATERIALS AND METHODS: For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis. RESULTS: Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI. CONCLUSIONS: This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future. CLINICAL RELEVANCE STATEMENT: These results hint at AI's the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics. KEY POINTS: ⢠This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions. ⢠MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions. ⢠The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.
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BACKGROUND: Chest radiographs (CXRs) are still of crucial importance in primary diagnostics, but their interpretation poses difficulties at times. RESEARCH QUESTION: Can a convolutional neural network-based artificial intelligence (AI) system that interprets CXRs add value in an emergency unit setting? STUDY DESIGN AND METHODS: A total of 563 CXRs acquired in the emergency unit of a major university hospital were retrospectively assessed twice by three board-certified radiologists, three radiology residents, and three emergency unit-experienced nonradiology residents (NRRs). They used a two-step reading process: (1) without AI support; and (2) with AI support providing additional images with AI overlays. Suspicion of four suspected pathologies (pleural effusion, pneumothorax, consolidations suspicious for pneumonia, and nodules) was reported on a five-point confidence scale. Confidence scores of the board-certified radiologists were converted into four binary reference standards of different sensitivities. Performance by radiology residents and NRRs without AI support/with AI support were statistically compared by using receiver-operating characteristics (ROCs), Youden statistics, and operating point metrics derived from fitted ROC curves. RESULTS: NRRs could significantly improve performance, sensitivity, and accuracy with AI support in all four pathologies tested. In the most sensitive reference standard (reference standard IV), NRR consensus improved the area under the ROC curve (mean, 95% CI) in the detection of the time-critical pathology pneumothorax from 0.846 (0.785-0.907) without AI support to 0.974 (0.947-1.000) with AI support (P < .001), which represented a gain of 30% in sensitivity and 2% in accuracy (while maintaining an optimized specificity). The most pronounced effect was observed in nodule detection, with NRR with AI support improving sensitivity by 53% and accuracy by 7% (area under the ROC curve without AI support, 0.723 [0.661-0.785]; with AI support, 0.890 [0.848-0.931]; P < .001). Radiology residents had smaller, mostly nonsignificant gains in performance, sensitivity, and accuracy with AI support. INTERPRETATION: We found that in an emergency unit setting without 24/7 radiology coverage, the presented AI solution features an excellent clinical support tool to nonradiologists, similar to a second reader, and allows for a more accurate primary diagnosis and thus earlier therapy initiation.
Subject(s)
Artificial Intelligence , Emergency Service, Hospital , Radiography, Thoracic , Humans , Radiography, Thoracic/methods , Retrospective Studies , Male , Female , Clinical Competence , Middle Aged , ROC Curve , Adult , AgedABSTRACT
MR-guided high-intensity focused ultrasound (MR-HIFU) is an effective method for treating symptomatic uterine fibroids, especially solitary lesions. The aim of our study was to compare the clinical and morphological outcomes of patients who underwent MR-HIFU due to solitary fibroid (SF) or multiple fibroids (MFs) in a prospective clinical trial. We prospectively included 21 consecutive patients with SF (10) and MF (11) eligible for MR-guided HIFU. The morphological data were assessed using mint Lesion™ for MRI. The clinical data were determined using the Uterine Fibroid Symptom and Quality of Life (UFS-QOL) questionnaire before and 6 months after treatment. Unpaired and paired Wilcoxon-test and t-tests were applied, and Pearson's coefficient was used for correlation analysis. A p-value of 0.05 was considered statistically significant. The volume of treated fibroids significantly decreased in both the SF (mean baseline: 118.6 cm3; mean 6-month follow-up: 64.6 cm3) and MF (107.2 cm3; 55.1 cm3) groups. The UFS-QOL showed clinical symptoms significantly improved for patients in both the SF and MF groups regarding concern, activities, energy/mood, and control. The short-term outcome for the treatment of symptomatic fibroids in myomatous uterus by MR-guided HIFU is clinically similar to that of solitary fibroids.
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Artificial intelligence (AI) algorithms evaluating [supine] chest radiographs ([S]CXRs) have remarkably increased in number recently. Since training and validation are often performed on subsets of the same overall dataset, external validation is mandatory to reproduce results and reveal potential training errors. We applied a multicohort benchmarking to the publicly accessible (S)CXR analyzing AI algorithm CheXNet, comprising three clinically relevant study cohorts which differ in patient positioning ([S]CXRs), the applied reference standards (CT-/[S]CXR-based) and the possibility to also compare algorithm classification with different medical experts' reading performance. The study cohorts include [1] a cohort, characterized by 563 CXRs acquired in the emergency unit that were evaluated by 9 readers (radiologists and non-radiologists) in terms of 4 common pathologies, [2] a collection of 6,248 SCXRs annotated by radiologists in terms of pneumothorax presence, its size and presence of inserted thoracic tube material which allowed for subgroup and confounding bias analysis and [3] a cohort consisting of 166 patients with SCXRs that were evaluated by radiologists for underlying causes of basal lung opacities, all of those cases having been correlated to a timely acquired computed tomography scan (SCXR and CT within < 90 min). CheXNet non-significantly exceeded the radiology resident (RR) consensus in the detection of suspicious lung nodules (cohort [1], AUC AI/RR: 0.851/0.839, p = 0.793) and the radiological readers in the detection of basal pneumonia (cohort [3], AUC AI/reader consensus: 0.825/0.782, p = 0.390) and basal pleural effusion (cohort [3], AUC AI/reader consensus: 0.762/0.710, p = 0.336) in SCXR, partly with AUC values higher than originally published ("Nodule": 0.780, "Infiltration": 0.735, "Effusion": 0.864). The classifier "Infiltration" turned out to be very dependent on patient positioning (best in CXR, worst in SCXR). The pneumothorax SCXR cohort [2] revealed poor algorithm performance in CXRs without inserted thoracic material and in the detection of small pneumothoraces, which can be explained by a known systematic confounding error in the algorithm training process. The benefit of clinically relevant external validation is demonstrated by the differences in algorithm performance as compared to the original publication. Our multi-cohort benchmarking finally enables the consideration of confounders, different reference standards and patient positioning as well as the AI performance comparison with differentially qualified medical readers.
Subject(s)
Artificial Intelligence , Pneumothorax , Algorithms , Benchmarking , Humans , Pneumothorax/etiology , Radiography, Thoracic/methods , Retrospective StudiesABSTRACT
OBJECTIVES: Abbreviated breast MRI (AB-MRI) was introduced to reduce both examination and image reading times and to improve cost-effectiveness of breast cancer screening. The aim of this model-based economic study was to analyze the cost-effectiveness of full protocol breast MRI (FB-MRI) vs. AB-MRI in screening women with dense breast tissue for breast cancer. METHODS: Decision analysis and a Markov model were designed to model the cumulative costs and effects of biennial screening in terms of quality-adjusted life years (QALYs) from a US healthcare system perspective. Model input parameters for a cohort of women with dense breast tissue were adopted from recent literature. The impact of varying AB-MRI costs per examination as well as specificity on the resulting cost-effectiveness was modeled within deterministic sensitivity analyses. RESULTS: At an assumed cost per examination of $ 263 for AB-MRI (84% of the cost of a FB-MRI examination), the discounted cumulative costs of both MR-based strategies accounted comparably. Reducing the costs of AB-MRI below $ 259 (82% of the cost of a FB-MRI examination, respectively), the incremental cost-effectiveness ratio of FB-MRI exceeded the willingness to pay threshold and the AB-MRI-strategy should be considered preferable in terms of cost-effectiveness. CONCLUSIONS: Our preliminary findings indicate that AB-MRI may be considered cost-effective compared to FB-MRI for screening women with dense breast tissue for breast cancer, as long as the costs per examination do not exceed 82% of the cost of a FB-MRI examination. KEY POINTS: ⢠Cost-effectiveness of abbreviated breast MRI is affected by reductions in specificity and resulting false positive findings and increased recall rates. ⢠Abbreviated breast MRI may be cost-effective up to a cost per examination of 82% of the cost of a full protocol examination. ⢠Abbreviated breast MRI could be an economically preferable alternative to full protocol breast MRI in screening women with dense breast tissue.
Subject(s)
Breast Neoplasms , Female , Humans , Breast Neoplasms/diagnosis , Mammography/methods , Breast Density , Early Detection of Cancer/methods , Mass Screening , Magnetic Resonance Imaging/methods , Cost-Benefit Analysis , Quality-Adjusted Life YearsABSTRACT
PURPOSE: Comparison of puncture deviation and puncture duration between computed tomography (CT)- and C-arm CT (CACT)-guided puncture performed by residents in training (RiT). METHODS: In a cohort of 25 RiTs enrolled in a research training program either CT- or CACT-guided puncture was performed on a phantom. Prior to the experiments, the RiT's level of training, experience playing a musical instrument, video games, and ball sports, and self-assessed manual skills and spatial skills were recorded. Each RiT performed two punctures. The first puncture was performed with a transaxial or single angulated needle path and the second with a single or double angulated needle path. Puncture deviation and puncture duration were compared between the procedures and were correlated with the self-assessments. RESULTS: RiTs in both the CT guidance and CACT guidance groups did not differ with respect to radiologic experience (pâ=â1), angiographic experience (pâ=â0.415), and number of ultrasound-guided puncture procedures (pâ=â0.483), CT-guided puncture procedures (pâ=â0.934), and CACT-guided puncture procedures (pâ=â0.466). The puncture duration was significantly longer with CT guidance (without navigation tool) than with CACT guidance with navigation software (pâ<â0.001). There was no significant difference in the puncture duration between the first and second puncture using CT guidance (pâ=â0.719). However, in the case of CACT, the second puncture was significantly faster (pâ=â0.006). Puncture deviations were not different between CT-guided and CACT-guided puncture (pâ=â0.337) and between the first and second puncture of CT-guided and CACT-guided puncture (CT: pâ=â0.130; CACT: pâ=â0.391). The self-assessment of manual skills did not correlate with puncture deviation (pâ=â0.059) and puncture duration (pâ=â0.158). The self-assessed spatial skills correlated positively with puncture deviation (pâ=â0.011) but not with puncture duration (pâ=â0.541). CONCLUSION: The RiTs achieved a puncture deviation that was clinically adequate with respect to their level of training and did not differ between CT-guided and CACT-guided puncture. The puncture duration was shorter when using CACT. CACT guidance with navigation software support has a potentially steeper learning curve. Spatial skills might accelerate the learning of image-guided puncture. KEY POINTS: · The CT-guided and CACT-guided puncture experience of the RiTs selected as part of the program "Researchers for the Future" of the German Roentgen Society was adequate with respect to the level of training.. · Despite the lower collective experience of the RiTs with CACT-guided puncture with navigation software assistance, the learning curve regarding CACT-guided puncture may be faster compared to the CT-guided puncture technique.. · If the needle path is complex, CACT guidance with navigation software assistance might have an advantage over CT guidance.. CITATION FORMAT: · Meine TC, Hinrichs JB, Werncke T etâal. Phantom study for comparison between computed tomography- and C-Arm computed tomography-guided puncture applied by residents in radiology. Fortschr Röntgenstr 2022; 194: 272â-â280.
Subject(s)
Radiology , Tomography, X-Ray Computed , Humans , Phantoms, Imaging , Punctures/methods , Software , Tomography, X-Ray Computed/methodsABSTRACT
OBJECTIVES: Chest radiographs (CXRs) are commonly performed in emergency units (EUs), but the interpretation requires radiology experience. We developed an artificial intelligence (AI) system (precommercial) that aims to mimic board-certified radiologists' (BCRs') performance and can therefore support non-radiology residents (NRRs) in clinical settings lacking 24/7 radiology coverage. We validated by quantifying the clinical value of our AI system for radiology residents (RRs) and EU-experienced NRRs in a clinically representative EU setting. MATERIALS AND METHODS: A total of 563 EU CXRs were retrospectively assessed by 3 BCRs, 3 RRs, and 3 EU-experienced NRRs. Suspected pathologies (pleural effusion, pneumothorax, consolidations suspicious for pneumonia, lung lesions) were reported on a 5-step confidence scale (sum of 20,268 reported pathology suspicions [563 images × 9 readers × 4 pathologies]) separately by every involved reader. Board-certified radiologists' confidence scores were converted into 4 binary reference standards (RFSs) of different sensitivities. The RRs' and NRRs' performances were statistically compared with our AI system (trained on nonpublic data from different clinical sites) based on receiver operating characteristics (ROCs) and operating point metrics approximated to the maximum sum of sensitivity and specificity (Youden statistics). RESULTS: The NRRs lose diagnostic accuracy to RRs with increasingly sensitive BCRs' RFSs for all considered pathologies. Based on our external validation data set, the AI system/NRRs' consensus mimicked the most sensitive BCRs' RFSs with areas under ROC of 0.940/0.837 (pneumothorax), 0.953/0.823 (pleural effusion), and 0.883/0.747 (lung lesions), which were comparable to experienced RRs and significantly overcomes EU-experienced NRRs' diagnostic performance. For consolidation detection, the AI system performed on the NRRs' consensus level (and overcomes each individual NRR) with an area under ROC of 0.847 referenced to the BCRs' most sensitive RFS. CONCLUSIONS: Our AI system matched RRs' performance, meanwhile significantly outperformed NRRs' diagnostic accuracy for most of considered CXR pathologies (pneumothorax, pleural effusion, and lung lesions) and therefore might serve as clinical decision support for NRRs.
Subject(s)
Lung Diseases , Pleural Effusion , Pneumothorax , Radiology , Artificial Intelligence , Emergency Service, Hospital , Humans , Pleural Effusion/diagnostic imaging , Pneumothorax/diagnostic imaging , Radiography , Radiography, Thoracic/methods , Retrospective StudiesABSTRACT
BACKGROUND: The aim of this paper was to assess and compare the accuracy of common magnetic resonance imaging (MRI) pulse sequences in measuring the lesion sizes of hepatocellular carcinomas (HCCs) with respect to the Milan criteria and histopathology as a standard of reference. METHODS: We included 45 patients with known HCC who underwent contrast-enhanced MRI of the liver prior to liver transplantation or tumor resection. Tumor size was assessed pathologically for all patients. The MRI protocol contained axial T2-weighted images as well as T1-weighted imaging sequences before and after application of Gd-EOB-DTPA. Tumor diameters, the sharpness of lesions, and the presence of artifacts were evaluated visually on all available MRI sequences. MRI measurements and pathologically assessed tumor dimensions were correlated using Pearson's correlation coefficient and Bland-Altman plots. The rate of misclassifications following Milan criteria was assessed. RESULTS: The mean absolute error (in cm) of MRI size measurements in comparison to pathology was the smallest for the hepatobiliary phase T1-weighted acquisition (0.71 ± 0.70 cm, r = 0.96) and largest for the T2w turbo-spin-echo (TSE) sequence (0.85 ± 0.78 cm, r = 0.94). The misclassification rate regarding tumor size under the Milan criteria was lowest for the T2w half-Fourier acquisition single-shot turbo spin-echo sequence and the hepatobiliary phase T1w acquisition (each 8.6%). The highest rate of misclassification occurred in the portal venous phase T1w acquisition and T2w TSE sequence (each 14.3%). CONCLUSIONS: The hepatobiliary phase T1-weighted acquisition seems to be most accurate among commonly used MRI sequences for measuring HCC tumor size, resulting in low rates of misclassification with respect to the Milan criteria.
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(1) Background: Chest radiography (CXR) is still a key diagnostic component in the emergency department (ED). Correct interpretation is essential since some pathologies require urgent treatment. This study quantifies potential discrepancies in CXR analysis between radiologists and non-radiology physicians in training with ED experience. (2) Methods: Nine differently qualified physicians (three board-certified radiologists [BCR], three radiology residents [RR], and three non-radiology residents involved in ED [NRR]) evaluated a series of 563 posterior-anterior CXR images by quantifying suspicion for four relevant pathologies: pleural effusion, pneumothorax, pneumonia, and pulmonary nodules. Reading results were noted separately for each hemithorax on a Likert scale (0-4; 0: no suspicion of pathology, 4: safe existence of pathology) adding up to a total of 40,536 reported pathology suspicions. Interrater reliability/correlation and Kruskal-Wallis tests were performed for statistical analysis. (3) Results: While interrater reliability was good among radiologists, major discrepancies between radiologists' and non-radiologists' reading results could be observed in all pathologies. Highest overall interrater agreement was found for pneumothorax detection and lowest agreement in raising suspicion for malignancy suspicious nodules. Pleural effusion and pneumonia were often suspected with indifferent choices (1-3). In terms of pneumothorax detection, all readers mainly decided for a clear option (0 or 4). Interrater reliability was usually higher when evaluating the right hemithorax (all pathologies except pneumothorax). (4) Conclusions: Quantified CXR interrater reliability analysis displays a general uncertainty and strongly depends on medical training. NRR can benefit from radiology reporting in terms of time efficiency and diagnostic accuracy. CXR evaluation of long-time trained ED specialists has not been tested.
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BACKGROUND: Vesicoureteral reflux (VUR) represents a common pediatric anomaly in children with an upper urinary tract infection (UTI) and is defined as a retrograde flow of urine from the bladder into the upper urinary tract. There are many diagnostic options available, including voiding cystourethrography (VCUG) and contrasted-enhanced urosonography (ceVUS). ceVUS combines a diagnostic tool with a high sensitivity and specificity which, according to previous study results, was even shown to be superior to VCUG. Nevertheless, despite the recommendation of the EFSUMB, the ceVUS has not found a widespread use in clinical diagnostics in Europe yet. MATERIALS AND METHODS: Between 2016 and 2020, 49 patients with a marked female dominance (nâ=â37) were included. The youngest patient had an age of 5 months, the oldest patient 60 years. The contrast agent used in ceVUS was SonoVue®, a second-generation blood-pool agent. All examinations were performed and interpreted by a single experienced radiologist (EFSUMB Level 3). RESULTS: The 49 patients included in the study showed no adverse effects. 51% of patients (nâ=â26) were referred with the initial diagnosis of suspected VUR, while 49% of patients (nâ=â23) came for follow-up examination or to rule out recurrence of VUR. The vast majority had at least one febrile urinary tract infection in their recent medical history (nâ=â45; 91,8%). CONCLUSION: ceVUS is an examination method with a low risk profile which represents with its high sensitivity and specificity an excellent diagnostic tool in the evaluation of vesicoureteral reflux, especially in consideration of a generally very young patient cohort.
Subject(s)
Vesico-Ureteral Reflux , Child , Child, Preschool , Contrast Media , Europe , Female , Humans , Infant , Ultrasonography , Urination , Vesico-Ureteral Reflux/diagnostic imagingABSTRACT
BACKGROUND: In patients with liver cirrhosis, transjugular intrahepatic portosystemic shunt (TIPS) displays an effective method for treating portal hypertension. Main indications include refractory ascites and secondary prevention of esophageal bleeding. Color Doppler ultrasound (CDUS) plays a leading role in the follow-up management, whereas contrast-enhanced ultrasound (CEUS) is not routinely considered. We compared the efficacy of CEUS to CDUS and highlighted differences compared to findings of corresponding computed tomography (CT) and magnetic resonance imaging (MRI). (2) Methods: On a retrospective basis, 106 patients with CEUS examination after TIPS were included. The enrollment period was 12 years (between 2008 and 2020) and the age group ranged from 23.3 to 82.1 years. In addition, 92 CDUS, 43 CT and 58 MRI scans were evaluated for intermodal comparison. (3) Results: Intermodal analysis and comparison revealed a high level of concordance between CDUS, CT and MRI in the vast majority of cases. In comparison to CDUS, the correlation of the relevant findings was 92.5%, 95.3% for CT and 87.9% for MRI. In some cases, however, additional information was provided by CEUS (4) Conclusions: CEUS depicts a safe and effective imaging modality for follow-up after TIPS. In addition to CDUS, CEUS enables specific assessment of stent pathologies and stent dysfunction due to its capacity to dynamically visualize single microbubbles at high spatial and temporal resolution. Due to the low number of adverse events regarding the application of contrast agents, CEUS can be administered to a very broad patient population, thus avoiding additional radiation exposure compared to CT angiography in cases with divergent findings during follow-up.
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BACKGROUND: To evaluate the diagnostic accuracy of quantitative perfusion parameters in contrast-enhanced ultrasound to differentiate malignant from benign liver lesions. METHODS: In this retrospective study 134 patients with a total of 139 focal liver lesions were included who underwent contrast enhanced ultrasound (CEUS) between 2008 and 2018. All examinations were performed by a single radiologist with more than 15 years of experience using a second-generation blood pool contrast agent. The standard of reference was histopathology (n = 60), MRI or CT (n = 75) or long-term CEUS follow up (n = 4). For post processing regions of interests were drawn both inside of target lesions and the liver background. Time-intensity curves were fitted to the CEUS DICOM dataset and the rise time (RT) of contrast enhancement until peak enhancement, and a late-phase ratio (LPR) of signal intensities within the lesion and the background tissue, were calculated and compared between malignant and benign liver lesion using Student's t-test. Quantitative parameters were evaluated with respect to their diagnostic accuracy using receiver operator characteristic curves. Both features were then combined in a logistic regression model and the cumulated accuracy was assessed. RESULTS: RT of benign lesions (14.8 ± 13.8 s, p = 0.005), and in a subgroup analysis, particular hemangiomas (23.4 ± 16.2 s, p < 0.001) differed significantly to malignant lesions (9.3 ± 3.8 s). The LPR was significantly different between benign (1.59 ± 1.59, p < 0.001) and malignant lesions (0.38 ± 0.23). Logistic regression analysis with RT and LPR combined showed a high diagnostic accuracy of quantitative CEUS parameters with areas under the curve of 0.923 (benign vs. malignant) and 0.929 (hemangioma vs. malignant. CONCLUSIONS: Quantified CEUS parameters are helpful to differentiate malignant from benign liver lesions, in particular in case of atypical hemangiomas.
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BACKGROUND: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. METHODS: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. RESULTS: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. CONCLUSIONS: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.
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BACKGROUND: Radiology reporting of emergency whole-body computed tomography (CT) scans is time-critical and therefore involves a significant risk of pathology under-detection. We hypothesize a relevant number of initially missed secondary thoracic findings that would have been detected by an artificial intelligence (AI) software platform including several pathology-specific AI algorithms. METHODS: This retrospective proof-of-concept-study consecutively included 105 shock-room whole-body CT scans. Image data was analyzed by platform-bundled AI-algorithms, findings were reviewed by radiology experts and compared with the original radiologist's reports. We focused on secondary thoracic findings, such as cardiomegaly, coronary artery plaques, lung lesions, aortic aneurysms and vertebral fractures. RESULTS: We identified a relevant number of initially missed findings, with their quantification based on 105 analyzed CT scans as follows: up to 25 patients (23.8%) with cardiomegaly or borderline heart size, 17 patients (16.2%) with coronary plaques, 34 patients (32.4%) with aortic ectasia, 2 patients (1.9%) with lung lesions classified as "recommended to control" and 13 initially missed vertebral fractures (two with an acute traumatic origin). A high number of false positive or non-relevant AI-based findings remain problematic especially regarding lung lesions and vertebral fractures. CONCLUSIONS: We consider AI to be a promising approach to reduce the number of missed findings in clinical settings with a necessary time-critical radiological reporting. Nevertheless, algorithm improvement is necessary focusing on a reduction of "false positive" findings and on algorithm features assessing the finding relevance, e.g., fracture age or lung lesion malignancy.
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Background and Purpose: Basilar artery occlusion is associated with high morbidity and mortality. Optimal imaging and treatment strategy are still controversial and prognosis estimation challenging. We, therefore, aimed to determine the predictive value of computed tomography perfusion (CTP) parameters for functional outcome in patients with basilar artery occlusion in the context of endovascular treatment. Methods: Patients with basilar artery occlusion who underwent endovascular treatment were selected from a prospectively acquired cohort. Ischemic changes were assessed with the posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score on noncontrast computed tomography, computed tomography angiography (CTA) source images, and CTP maps. Basilar artery on CTA score, posterior-circulation CTA score, and posterior-circulation collateral score were evaluated on CTA. Perfusion deficit volumes were quantified on CTP maps. Good functional outcome was defined as modified Rankin Scale score ≤3 at 90 days. Statistical analysis included binary logistic regressions and receiver operating characteristics analyses. Results: Among 49 patients who matched the inclusion criteria, 24 (49.0%) achieved a good outcome. In univariate analysis, age, National Institutes of Health Stroke Scale score on admission, posterior cerebral artery involvement, absence of or hypoplastic posterior communicating arteries, basilar artery on CTA score, posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score, and perfusion deficit volumes on all CTP parameter maps presented significant association with functional outcome (P<0.05). In multivariate analyses, Basilar artery on CTA score, posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score (odds ratio range, 1.312.10 [95% CI, 1.007.24]), and perfusion deficit volumes on all CTP maps (odds ratio range, 0.770.98 [95% CI, 0.631.00]) remained as independent outcome predictors. Cerebral blood flow deficit volume yielded the best performance for the classification of good clinical outcome with an area under the curve of 0.92 (95% CI, 0.840.99). Age and admission National Institutes of Health Stroke Scale had lower discriminatory power (area under the curve, <0.7). Conclusions: CTP imaging parameters contain prognostic information for functional outcome in patients with stroke due to basilar artery occlusion and may identify patients with higher risk of disability at an early stage of hospitalization.
Subject(s)
Arterial Occlusive Diseases , Basilar Artery , Cerebral Blood Volume , Cerebrovascular Circulation , Computed Tomography Angiography , Stroke , Age Factors , Aged , Aged, 80 and over , Arterial Occlusive Diseases/diagnostic imaging , Arterial Occlusive Diseases/physiopathology , Arterial Occlusive Diseases/surgery , Basilar Artery/diagnostic imaging , Basilar Artery/physiopathology , Basilar Artery/surgery , Endovascular Procedures , Female , Humans , Male , Retrospective Studies , Risk Factors , Stroke/diagnostic imaging , Stroke/physiopathology , Stroke/surgeryABSTRACT
Neuroendocrine tumors (NETs) are relatively rare neoplasms arising from the hormone-producing neuroendocrine system that can occur in various organs such as pancreas, small bowel, stomach and lung. As the majority of these tumors express somatostatin receptors (SSR) on their cell membrane, utilization of SSR analogs in nuclear medicine is a promising, but relatively costly approach for detection and localization. The aim of this study was to analyze the cost-effectiveness of 68Ga-DOTA-TATE PET/CT (Gallium-68 DOTA-TATE Positron emission tomography/computed tomography) compared to 111In-pentetreotide SPECT/CT (Indium-111 pentetreotide Single Photon emission computed tomography/computed tomography) and to CT (computed tomography) alone in detection of NETs. A decision model on the basis of Markov simulations evaluated lifetime costs and quality-adjusted life years (QALYs) related to either a CT, SPECT/CT or PET/CT. Model input parameters were obtained from publicized research projects. The analysis is grounded on the US healthcare system. Deterministic sensitivity analysis of diagnostic parameters and probabilistic sensitivity analysis predicated on a Monte Carlo simulation with 30,000 reiterations was executed. The willingness-to-pay (WTP) was determined to be $ 100,000/QALY. In the base-case investigation, PET/CT ended up with total costs of $88,003.07 with an efficacy of 4.179, whereas CT ended up with total costs of $88,894.71 with an efficacy of 4.165. SPECT/CT ended up with total costs of $89,973.34 with an efficacy of 4.158. Therefore, the strategies CT and SPECT/CT were dominated by PET/CT in the base-case scenario. In the sensitivity analyses, PET/CT remained a cost-effective strategy. This result was due to reduced therapy costs of timely detection. The additional costs of 68Ga-DOTA-TATE PET/CT when compared to CT alone are justified in the light of potential savings in therapy costs and better outcomes.
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PURPOSE: Rectal cancer is one of the most frequent causes of cancer-related morbidity and mortality in the world. Correct identification of the TNM state in primary staging of rectal cancer has critical implications on patient management. Initial evaluations revealed a high sensitivity and specificity for whole-body PET/MRI in the detection of metastases allowing for metastasis-directed therapy regimens. Nevertheless, its cost-effectiveness compared with that of standard-of-care imaging (SCI) using pelvic MRI + chest and abdominopelvic CT is yet to be investigated. Therefore, the aim of this study was to analyze the cost-effectiveness of whole-body 18F FDG PET/MRI as an alternative imaging method to standard diagnostic workup for initial staging of rectal cancer. METHODS: For estimation of quality-adjusted life years (QALYs) and lifetime costs of diagnostic modalities, a decision model including whole-body 18F FDG PET/MRI with a hepatocyte-specific contrast agent and pelvic MRI + chest and abdominopelvic CT was created based on Markov simulations. For obtaining model input parameters, review of recent literature was performed. Willingness to pay (WTP) was set to $100,000/QALY. Deterministic sensitivity analysis of diagnostic parameters and costs was applied, and probabilistic sensitivity was determined using Monte Carlo modeling. RESULTS: In the base-case scenario, the strategy whole-body 18F FDG PET/MRI resulted in total costs of $52,186 whereas total costs of SCI were at $51,672. Whole-body 18F FDG PET/MRI resulted in an expected effectiveness of 3.542 QALYs versus 3.535 QALYs for SCI. This resulted in an incremental cost-effectiveness ratio of $70,291 per QALY for PET/MRI. Thus, from an economic point of view, whole-body 18F FDG PET/MRI was identified as an adequate diagnostic alternative to SCI with high robustness of results to variation of input parameters. CONCLUSION: Based on the results of the analysis, use of whole-body 18F FDG PET/MRI was identified as a feasible diagnostic strategy for initial staging of rectal cancer from a cost-effectiveness perspective.
Subject(s)
Fluorodeoxyglucose F18 , Rectal Neoplasms , Contrast Media , Cost-Benefit Analysis , Hepatocytes/pathology , Humans , Magnetic Resonance Imaging , Neoplasm Staging , Positron-Emission Tomography , Rectal Neoplasms/diagnostic imaging , Rectal Neoplasms/pathology , Sensitivity and Specificity , Tomography, X-Ray ComputedABSTRACT
BACKGROUND: This study aims to evaluate the potential benefits of structured reporting (SR) compared to conventional free-text reporting (FTR) in contrast-enhanced ultrasound (CEUS) of cystic renal lesions, based on the Bosniak classification. METHODS: Fifty patients with cystic renal lesions who underwent CEUS were included in this single-center study. FTR created in clinical routine were compared to SR retrospectively generated by using a structured reporting template. Two experienced urologists evaluated the reports regarding integrity, effort for information extraction, linguistic quality, and overall quality. RESULTS: The required information could easily be extracted by the reviewers in 100% of SR vs. 82% of FTR (p < 0.001). The reviewers trusted the information given by SR significantly more with a mean of 5.99 vs. 5.52 for FTR (p < 0.001). SR significantly improved the linguistic quality (6.0 for SR vs. 5.68 for FTR (p < 0.001)) and the overall report quality (5.98 for SR vs. 5.58 for FTR (p < 0.001)). CONCLUSIONS: SR significantly increases the quality of radiologic reports in CEUS examinations of cystic renal lesions compared to conventional FTR and represents a promising approach to facilitate interdisciplinary communication in the future.
ABSTRACT
BACKGROUND: Our retrospective single-center study aims to evaluate the impact of structured reporting (SR) using a CEUS LI-RADS template on report quality compared to conventional free-text reporting (FTR) in contrast-enhanced ultrasound (CEUS) for the diagnosis of hepatocellular carcinoma (HCC). METHODS: We included 50 patients who underwent CEUS for HCC staging. FTR created after these examinations were compared to SR retrospectively generated by using template-based online software with clickable decision trees. The reports were evaluated regarding report completeness, information extraction, linguistic quality and overall report quality by two readers specialized in internal medicine and visceral surgery. RESULTS: SR significantly increased report completeness with at least one key feature missing in 31% of FTR vs. 2% of SR (p < 0.001). Information extraction was considered easy in 98% of SR vs. 86% of FTR (p = 0.004). The trust of referring physicians in the report was significantly increased by SR with a mean of 5.68 for SR vs. 4.96 for FTR (p < 0.001). SR received significantly higher ratings regarding linguistic quality (5.79 for SR vs. 4.83 for FTR (p < 0.001)) and overall report quality (5.75 for SR vs. 5.01 for FTR (p < 0.001)). CONCLUSIONS: Using SR instead of conventional FTR increases the overall quality of reports in CEUS examinations of HCC patients and may represent a valuable tool to facilitate clinical decision-making and improve interdisciplinary communication in the future.
ABSTRACT
BACKGROUND: Characteristics of COVID-19 patients have mainly been reported within confirmed COVID-19 cohorts. By analyzing patients with respiratory infections in the emergency department during the first pandemic wave, we aim to assess differences in the characteristics of COVID-19 vs. Non-COVID-19 patients. This is particularly important regarding the second COVID-19 wave and the approaching influenza season. METHODS: We prospectively included 219 patients with suspected COVID-19 who received radiological imaging and RT-PCR for SARS-CoV-2. Demographic, clinical and laboratory parameters as well as RT-PCR results were used for subgroup analysis. Imaging data were reassessed using the following scoring system: 0 - not typical, 1 - possible, 2 - highly suspicious for COVID-19. RESULTS: COVID-19 was diagnosed in 72 (32,9%) patients. In three of them (4,2%) the initial RT-PCR was negative while initial CT scan revealed pneumonic findings. 111 (50,7%) patients, 61 of them (55,0%) COVID-19 positive, had evidence of pneumonia. Patients with COVID-19 pneumonia showed higher body temperature (37,7 ± 0,1 vs. 37,1 ± 0,1 °C; p = 0.0001) and LDH values (386,3 ± 27,1 vs. 310,4 ± 17,5 U/l; p = 0.012) as well as lower leukocytes (7,6 ± 0,5 vs. 10,1 ± 0,6G/l; p = 0.0003) than patients with other pneumonia. Among abnormal CT findings in COVID-19 patients, 57 (93,4%) were evaluated as highly suspicious or possible for COVID-19. In patients with negative RT-PCR and pneumonia, another third was evaluated as highly suspicious or possible for COVID-19 (14 out of 50; 28,0%). The sensitivity in the detection of patients requiring isolation was higher with initial chest CT than with initial RT-PCR (90,4% vs. 79,5%). CONCLUSIONS: COVID-19 patients show typical clinical, laboratory and imaging parameters which enable a sensitive detection of patients who demand isolation measures due to COVID-19.