Cost-Effectiveness Analysis of 177Lu-PSMA-617 Radioligand Therapy in Metastatic Castration-Resistant Prostate Cancer.

BACKGROUND: Metastatic castration-resistant prostate cancer poses a therapeutic challenge with poor prognosis. The VISION trial showed prolonged progression-free and overall survival in patients treated with lutetium Lu 177 vipivotide tetraxetan (177Lu-PSMA-617) radioligand therapy compared with using the standard of care (SoC) alone. The objective of this study was to determine the cost-effectiveness of 177Lu-PSMA-617 treatment compared with SoC therapy. METHODS: A partitioned survival model was developed using data from the VISION trial, which included overall and progression-free survival and treatment regimens for 177Lu-PSMA-617 and SoC. Treatment costs, utilities for health states, and adverse events were derived from public databases and the literature. Because 177Lu-PSMA-617 was only recently approved, costs for treatment were extrapolated from 177Lu-DOTATATE. Outcome measurements included the incremental cost, effectiveness, and cost-effectiveness ratio. The analysis was performed in a US setting from a healthcare system perspective over the lifetime horizon of 60 months. The willingness-to-pay threshold was set to $50,000, $100,000, and $200,000 per quality-adjusted life years (QALYs). RESULTS: The 177Lu-PSMA-617 group was estimated to gain 0.42 incremental QALYs. Treatment using 177Lu-PSMA-617 led to an increase in costs compared with SoC ($169,110 vs $85,398). The incremental cost, effectiveness, and cost-effectiveness ratio for 177Lu-PSMA-617 therapy was $200,708/QALYs. Sensitivity analysis showed robustness of the model regarding various parameters, which remained cost-effective at all lower and upper parameter bounds. In probabilistic sensitivity analysis using Monte Carlo simulation with 10,000 iterations, therapy using 177Lu-PSMA-617 was determined as the cost-effective strategy in 37.14% of all iterations at a willingness-to-pay threshold of $200,000/QALYs. CONCLUSIONS: Treatment using 177Lu-PSMA-617 was estimated to add a notable clinical benefit over SoC alone. Based on the model results, radioligand therapy represents a treatment strategy for patients with metastatic castration-resistant prostate cancer with cost-effectiveness in certain scenarios.

An anthropomorphic pelvis phantom for MR-guided prostate interventions.

PURPOSE: To design and manufacture a pelvis phantom for magnetic resonance (MR)-guided prostate interventions, such as MRGB (MR-guided biopsy) or brachytherapy seed placement. METHODS: The phantom was designed to mimic the human pelvis incorporating bones, bladder, prostate with four lesions, urethra, arteries, veins, and six lymph nodes embedded in ballistic gelatin. A hollow rectum enables transrectal access to the prostate. To demonstrate the feasibility of the phantom for minimal invasive MRI-guided interventions, a targeted inbore MRGB was performed. The needle probe was rectally inserted and guided using an MRI-compatible remote controlled manipulator (RCM). RESULTS: The presented pelvis phantom has realistic imaging properties for MR imaging (MRI), computed tomography (CT) and ultrasound (US). In the targeted inbore MRGB, a prostate lesion was successfully hit with an accuracy of 3.5 mm. The experiment demonstrates that the limited size of the rectum represents a realistic impairment for needle placements. CONCLUSION: The phantom provides a valuable platform for evaluating the performance of MRGB systems. Interventionalists can use the phantom to learn how to deal with challenging situations, without risking harm to patients.

A prospective study on inter-operator variability in semi-robotic software-based MRI/TRUS-fusion targeted prostate biopsies.

PURPOSE: Magnetic resonance imaging (MRI)/ultrasound-fusion prostate biopsy (FB) comprises multiple steps each of which can cause alterations in targeted biopsy (TB) accuracy leading to false-negative results. The aim was to assess the inter-operator variability of software-based fusion TB by targeting the same MRI-lesions by different urologists. METHODS: In this prospective study, 142 patients eligible for analysis underwent software-based FB. TB of all lesions (n = 172) were carried out by two different urologists per patient (n = 31 urologists). We analyzed the number of mismatches [overall prostate cancer (PCa), clinically significant PCa (csPCa) and non-significant PCa (nsPCa)] between both performed TB per patient. In addition we evaluated factors contributing to inter-operator variability by uni- and multivariable analyses. RESULTS: In 11.6% of all MRI-lesions (10.6% of all patients) there was a mismatch between TB1 and TB2 in terms of overall prostate cancer (PCa detection. Regarding csPCa, patient-based mismatch occurred in 14.8% (n = 21). Overall PCa and csPCa detection rate of TB1 and TB2 did not differ significantly on a per-patient and per-lesion level. Analyses revealed a smaller lesion size as predictive for mismatches (OR 9.19, 95% CI 2.02-41.83, p < 0.001). CONCLUSION: Reproducibility and precision of targeting particularly small lesions is still limited although using software-based FB. Further improvements in image-fusion, segmentation, needle-guidance, and automatization are necessary.

Additional MRI for initial M-staging in pancreatic cancer: a cost-effectiveness analysis.

OBJECTIVE: Pancreatic cancer is portrayed to become the second leading cause of cancer-related death within the next years. Potentially complicating surgical resection emphasizes the importance of an accurate TNM classification. In particular, the failure to detect features for non-resectability has profound consequences on patient outcomes and economic costs due to incorrect indication for resection. In the detection of liver metastases, contrast-enhanced MRI showed high sensitivity and specificity; however, the cost-effectiveness compared to the standard of care imaging remains unclear. The aim of this study was to analyze whether additional MRI of the liver is a cost-effective approach compared to routinely acquired contrast-enhanced computed tomography (CE-CT) in the initial staging of pancreatic cancer. METHODS: A decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Model input parameters were assessed based on evidence from recent literature. The willingness-to-pay (WTP) was set to $100,000/QALY. To evaluate model uncertainty, deterministic and probabilistic sensitivity analyses were performed. RESULTS: In the base-case analysis, the model yielded a total cost of $185,597 and an effectiveness of 2.347 QALYs for CE-MR/CT and $187,601 and 2.337 QALYs for CE-CT respectively. With a net monetary benefit (NMB) of $49,133, CE-MR/CT is shown to be dominant over CE-CT with a NMB of $46,117. Deterministic and probabilistic survival analysis showed model robustness for varying input parameters. CONCLUSION: Based on our results, combined CE-MR/CT can be regarded as a cost-effective imaging strategy for the staging of pancreatic cancer. KEY POINTS: • Additional MRI of the liver for initial staging of pancreatic cancer results in lower total costs and higher effectiveness. • The economic model showed high robustness for varying input parameters.

Economic potential of abbreviated breast MRI for screening women with dense breast tissue for breast cancer.

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.

End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT.

OBJECTIVES: To develop a pipeline for automated body composition analysis and skeletal muscle assessment with integrated quality control for large-scale application in opportunistic imaging. METHODS: First, a convolutional neural network for extraction of a single slice at the L3/L4 lumbar level was developed on CT scans of 240 patients applying the nnU-Net framework. Second, a 2D competitive dense fully convolutional U-Net for segmentation of visceral and subcutaneous adipose tissue (VAT, SAT), skeletal muscle (SM), and subsequent determination of fatty muscle fraction (FMF) was developed on single CT slices of 1143 patients. For both steps, automated quality control was integrated by a logistic regression model classifying the presence of L3/L4 and a linear regression model predicting the segmentation quality in terms of Dice score. To evaluate the performance of the entire pipeline end-to-end, body composition metrics, and FMF were compared to manual analyses including 364 patients from two centers. RESULTS: Excellent results were observed for slice extraction (z-deviation = 2.46 ± 6.20 mm) and segmentation (Dice score for SM = 0.95 ± 0.04, VAT = 0.98 ± 0.02, SAT = 0.97 ± 0.04) on the dual-center test set excluding cases with artifacts due to metallic implants. No data were excluded for end-to-end performance analyses. With a restrictive setting of the integrated segmentation quality control, 39 of 364 patients were excluded containing 8 cases with metallic implants. This setting ensured a high agreement between manual and fully automated analyses with mean relative area deviations of ΔSM = 3.3 ± 4.1%, ΔVAT = 3.0 ± 4.7%, ΔSAT = 2.7 ± 4.3%, and ΔFMF = 4.3 ± 4.4%. CONCLUSIONS: This study presents an end-to-end automated deep learning pipeline for large-scale opportunistic assessment of body composition metrics and sarcopenia biomarkers in clinical routine. KEY POINTS: • Body composition metrics and skeletal muscle quality can be opportunistically determined from routine abdominal CT scans. • A pipeline consisting of two convolutional neural networks allows an end-to-end automated analysis. • Machine-learning-based quality control ensures high agreement between manual and automatic analysis.

[MRI-guided minimally invasive treatment of prostate cancer]. / MRT-gestützte minimal-invasive Therapie des Prostatakarzinoms.

CLINICAL/METHODOLOGICAL ISSUE: Multiparametric magnetic resonance imaging (mpMRI) of the prostate plays a crucial role in the diagnosis and local staging of primary prostate cancer. STANDARD RADIOLOGICAL METHODS: Image-guided biopsy techniques such as MRI-ultrasound fusion not only allow guidance for targeted tissue sampling of index lesions for diagnostic confirmation, but also improve the detection of clinically significant prostate cancer. METHODOLOGICAL INNOVATIONS: Minimally invasive, focal therapies of localized prostate cancer complement the treatment spectrum, especially for low- and intermediate-risk patients. PERFORMANCE: In patients of low and intermediate risk, MR-guided, minimally invasive therapies could enable local tumor control, improved functional outcomes and possible subsequent therapy escalation. Further study results related to multimodal approaches and the application of artificial intelligence (AI) by machine and deep learning algorithms will help to leverage the full potential of focal therapies for prostate cancer in the upcoming era of precision medicine. ACHIEVEMENTS: Completion of ongoing randomized trials comparing each minimally invasive therapy approach with established whole-gland procedures is needed before minimally invasive therapies can be implemented into existing treatment guidelines. PRACTICAL RECOMMENDATIONS: This review article highlights minimally invasive therapies of prostate cancer and the key role of mpMRI for planning and conducting these therapies.

Comparing quantitative image parameters between animal and clinical CT-scanners: a translational phantom study analysis.

Purpose: This study compares phantom-based variability of extracted radiomics features from scans on a photon counting CT (PCCT) and an experimental animal PET/CT-scanner (Albira II) to investigate the potential of radiomics for translation from animal models to human scans. While oncological basic research in animal PET/CT has allowed an intrinsic comparison between PET and CT, but no 1:1 translation to a human CT scanner due to resolution and noise limitations, Radiomics as a statistical and thus scale-independent method can potentially close the critical gap. Methods: Two phantoms were scanned on a PCCT and animal PET/CT-scanner with different scan parameters and then the radiomics parameters were extracted. A Principal Component Analysis (PCA) was conducted. To overcome the limitation of a small dataset, a data augmentation technique was applied. A Ridge Classifier was trained and a Feature Importance- and Cluster analysis was performed. Results: PCA and Cluster Analysis shows a clear differentiation between phantom types while emphasizing the comparability of both scanners. The Ridge Classifier exhibited a strong training performance with 93% accuracy, but faced challenges in generalization with a test accuracy of 62%. Conclusion: These results show that radiomics has great potential as a translational tool between animal models and human routine diagnostics, especially using the novel photon counting technique. This is another crucial step towards integration of radiomics analysis into clinical practice.

Establishing an MSU service in a medium-sized German urban area-clinical and economic considerations.

Background and purpose: Mobile stroke units (MSU) have been demonstrated to improve prehospital stroke care in metropolitan and rural regions. Due to geographical, social and structural idiosyncrasies of the German city of Mannheim, concepts of established MSU services are not directly applicable to the Mannheim initiative. The aim of the present analysis was to identify major determinants that need to be considered when initially setting up a local MSU service. Methods: Local stroke statistics from 2015 to 2021 were analyzed and circadian distribution of strokes and local incidence rates were calculated. MSU patient numbers and total program costs were estimated for varying operating modes, daytime coverage models, staffing configurations which included several resource sharing models with the hospital. Additional case-number simulations for expanded catchment areas were performed. Results: Median time of symptom onset of ischemic stroke patients was 1:00 p.m. 54.3% of all stroke patients were admitted during a 10-h time window on weekdays. Assuming that MSU is able to reach 53% of stroke patients, the average expected number of ischemic stroke patients admitted to MSU would be 0.64 in a 10-h shift each day, which could potentially be increased by expanding the MSU catchment area. Total estimated MSU costs amounted to € 815,087 per annum. Teleneurological assessment reduced overall costs by 11.7%. Conclusion: This analysis provides a framework of determinants and considerations to be addressed during the design process of a novel MSU program in order to balance stroke care improvements with the sustainable use of scarce resources.

Automated prognosis of renal function decline in ADPKD patients using deep learning.

An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention. Current biomarkers used are height-adjusted total kidney volume (HtTKV), estimated glomerular filtration rate (eGFR), and patient age. However, manually measuring kidney volume is time-consuming and subject to observer variability. Additionally, incorporating automatically generated features from kidney MRI images, along with conventional biomarkers, can enhance prognostic improvement. To address these issues, we developed two deep-learning algorithms. Firstly, an automated kidney volume segmentation model accurately calculates HtTKV. Secondly, we utilize segmented kidney volumes, predicted HtTKV, age, and baseline eGFR to predict chronic kidney disease (CKD) stages >=3A, >=3B, and a 30% decline in eGFR after 8 years from the baseline visit. Our approach combines a convolutional neural network (CNN) and a multi-layer perceptron (MLP). Our study included 135 subjects and the AUC scores obtained were 0.96, 0.96, and 0.95 for CKD stages >=3A, >=3B, and a 30% decline in eGFR, respectively. Furthermore, our algorithm achieved a Pearson correlation coefficient of 0.81 between predicted and measured eGFR decline. We extended our approach to predict distinct CKD stages after eight years with an AUC of 0.97. The proposed approach has the potential to enhance monitoring and facilitate prognosis in ADPKD patients, even in the early disease stages.

Economic evaluation of breast MRI in screening - a systematic review and basic approach to cost-effectiveness analyses.

Background: Economic evaluations have become an accepted methodology for decision makers to allocate resources in healthcare systems. Particularly in screening, where short-term costs are associated with long-term benefits, and adverse effects of screening intermingle, cost-effectiveness analyses provide a means to estimate the economic value of screening. Purpose: To introduce the methodology of economic evaluations and to review the existing evidence on cost-effectiveness of MR-based breast cancer screening. Materials and methods: The various concepts and techniques of economic evaluations critical to the interpretation of cost-effectiveness analyses are briefly introduced. In a systematic review of the literature, economic evaluations from the years 2000-2022 are reviewed. Results: Despite a considerable heterogeneity in the reported input variables, outcome categories and methodological approaches, cost-effectiveness analyses report favorably on the economic value of breast MRI screening for different risk groups, including both short- and long-term costs and outcomes. Conclusion: Economic evaluations indicate a strongly favorable economic value of breast MRI screening for women at high risk and for women with dense breast tissue.

Is There Still a Role for Two-Phase Contrast-Enhanced CT and Virtual Monoenergetic Images in the Era of Photon-Counting Detector CT?

BACKGROUND: To compare the diagnostic characteristics between arterial phase imaging versus portal venous phase imaging, applying polychromatic T3D images and low keV virtual monochromatic images using a 1st generation photon-counting CT detector, of CT in patients with hepatocellular carcinoma (HCC). METHODS: Consecutive patients with HCC, with a clinical indication for CT imaging, were prospectively enrolled. Virtual monoenergetic images (VMI) were reconstructed at 40 to 70 keV for the PCD-CT. Two independent, blinded radiologists counted all hepatic lesions and quantified their size. The lesion-to-background ratio was quantified for both phases. SNR and CNR were determined for T3D and low VMI images; non-parametric statistics were used. RESULTS: Among 49 oncologic patients (mean age 66.9 ± 11.2 years, eight females), HCC was detected in both arterial and portal venous scans. The signal-to-noise ratio, the CNR liver-to-muscle, the CNR tumor-to-liver, and CNR tumor-to-muscle were 6.58 ± 2.86, 1.40 ± 0.42, 1.13 ± 0.49, and 1.53 ± 0.76 in the arterial phase and 5.93 ± 2.97, 1.73 ± 0.38, 0.79 ± 0.30, and 1.36 ± 0.60 in the portal venous phase with PCD-CT, respectively. There was no significant difference in SNR between the arterial and portal venous phases, including between "T3D" and low keV images (p > 0.05). CNRtumor-to-liver differed significantly between arterial and portal venous contrast phases (p < 0.005) for both "T3D" and all reconstructed keV levels. CNRliver-to-muscle and CNRtumor-to-muscle did not differ in either the arterial or portal venous contrast phases. CNRtumor-to-liver increased in the arterial contrast phase with lower keV in addition to SD. In the portal venous contrast phase, CNRtumor-to-liver decreased with lower keV; whereas, CNRtumor-to-muscle increased with lower keV in both arterial and portal venous contrast phases. CTDI and DLP mean values for the arterial upper abdomen phase were 9.03 ± 3.59 and 275 ± 133, respectively. CTDI and DLP mean values for the abdominal portal venous phase were 8.75 ± 2.99 and 448 ± 157 with PCD-CT, respectively. No statistically significant differences were found concerning the inter-reader agreement for any of the (calculated) keV levels in either the arterial or portal-venous contrast phases. CONCLUSIONS: The arterial contrast phase imaging provides higher lesion-to-background ratios of HCC lesions using a PCD-CT; especially, at 40 keV. However, the difference was not subjectively perceived as significant.

Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.

OBJECTIVES: The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions. METHODS: In this retrospective, IRB-approved study, 31 pancreatic cancer patients with 861 lesions (median age [IQR]: 65.39 [56.87, 75.08], 48.4% male) and 47 colorectal cancer patients with 435 lesions (median age [IQR]: 65.79 [56.99, 74.62], 63.8% male) were enrolled. A pretrained nnU-Net performed automated segmentation of 1296 liver lesions. Radiomics features for each lesion were extracted using pyradiomics. The performance of several radiomics-based machine-learning classifiers was investigated for the lesions and compared to an image-based deep-learning approach using a DenseNet-121. The performance was evaluated by AUC/ROC analysis. RESULTS: The radiomics-based K-nearest neighbor classifier showed the best performance on an independent test set with AUC values of 0.87 and an accuracy of 0.67. In comparison, the image-based DenseNet-121-classifier reached an AUC of 0.80 and an accuracy of 0.83. CONCLUSIONS: CT-based radiomics and deep learning can distinguish the etiology of liver metastases from gastrointestinal primary tumors. Compared to deep learning, radiomics based models showed a varying generalizability in distinguishing liver metastases from colorectal cancer and pancreatic adenocarcinoma.

Cost-effectiveness of opportunistic QCT-based osteoporosis screening for the prediction of incident vertebral fractures.

Objectives: Opportunistic quantitative computed tomography (oQCT) derived from non-dedicated routine CT has demonstrated high accuracy in diagnosing osteoporosis and predicting incident vertebral fractures (VFs). We aimed to investigate the cost-effectiveness of oQCT screening compared to dual-energy X-ray absorptiometry (DXA) as the standard of care for osteoporosis screening. Methods: Three screening strategies ("no osteoporosis screening", "oQCT screening", and "DXA screening") after routine CT were simulated in a state-transition model for hypothetical cohorts of 1,000 patients (women and men aged 65 years) over a follow-up period of 5 years (base case). The primary outcomes were the cumulative costs and the quality-adjusted life years (QALYs) estimated from a U.S. health care perspective for the year 2022. Cost-effectiveness was assessed based on a willingness-to-pay (WTP) threshold of $70,249 per QALY. The secondary outcome was the number of prevented VFs. Deterministic and probabilistic sensitivity analyses were conducted to test the models' robustness. Results: Compared to DXA screening, oQCT screening increased QALYs in both sexes (additional 2.40 per 1,000 women and 1.44 per 1,000 men) and resulted in total costs of $3,199,016 and $950,359 vs. $3,262,934 and $933,077 for women and men, respectively. As a secondary outcome, oQCT screening prevented 2.6 and 2.0 additional VFs per 1,000 women and men, respectively. In the probabilistic sensitivity analysis, oQCT screening remained cost-effective in 88.3% (women) and 90.0% (men) of iterations. Conclusion: oQCT screening is a cost-effective ancillary approach for osteoporosis screening and has the potential to prevent a substantial number of VFs if considered in daily clinical practice.

Deep Learning-Based Total Kidney Volume Segmentation in Autosomal Dominant Polycystic Kidney Disease Using Attention, Cosine Loss, and Sharpness Aware Minimization.

Early detection of the autosomal dominant polycystic kidney disease (ADPKD) is crucial as it is one of the most common causes of end-stage renal disease (ESRD) and kidney failure. The total kidney volume (TKV) can be used as a biomarker to quantify disease progression. The TKV calculation requires accurate delineation of kidney volumes, which is usually performed manually by an expert physician. However, this is time-consuming and automated segmentation is warranted. Furthermore, the scarcity of large annotated datasets hinders the development of deep learning solutions. In this work, we address this problem by implementing three attention mechanisms into the U-Net to improve TKV estimation. Additionally, we implement a cosine loss function that works well on image classification tasks with small datasets. Lastly, we apply a technique called sharpness aware minimization (SAM) that helps improve the generalizability of networks. Our results show significant improvements (p-value < 0.05) over the reference kidney segmentation U-Net. We show that the attention mechanisms and/or the cosine loss with SAM can achieve a dice score (DSC) of 0.918, a mean symmetric surface distance (MSSD) of 1.20 mm with the mean TKV difference of −1.72%, and R2 of 0.96 while using only 100 MRI datasets for training and testing. Furthermore, we tested four ensembles and obtained improvements over the best individual network, achieving a DSC and MSSD of 0.922 and 1.09 mm, respectively.

Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity.

(1) Background: Tumoral heterogeneity (TH) is a major challenge in the treatment of metastatic colorectal cancer (mCRC) and is associated with inferior response. Therefore, the identification of TH would be beneficial for treatment planning. TH can be assessed by identifying genetic alterations. In this work, a radiomics-based approach for assessment of TH in colorectal liver metastases (CRLM) in CT scans is demonstrated. (2) Methods: In this retrospective study, CRLM of mCRC were segmented and radiomics features extracted using pyradiomics. Unsupervised k-means clustering was applied to features and lesions. Feature redundancy was evaluated by principal component analysis and reduced by Pearson correlation coefficient cutoff. Feature selection was conducted by LASSO regression and visual analysis of the clusters by radiologists. (3) Results: A total of 47 patients' (36% female, median age 64) CTs with 261 lesions were included. Five clusters were identified, and the categories small disseminated (n = 31), heterogeneous (n = 105), homogeneous (n = 64), mixed (n = 59), and very large type (n = 2) were assigned based on visual characteristics. Further statistical analysis showed correlation (p < 0.01) of clusters with sex, primary location, T- and N-status, and mutational status. Feature reduction and selection resulted in the identification of four features as a final set for cluster definition. (4) Conclusions: Radiomics features can characterize TH in liver metastases of mCRC in CT scans, and may be suitable for a better pretherapeutic classification of liver lesion phenotypes.

Lifetime economic potential of mobile stroke units in acute stroke care: A model-based analysis of the drivers of cost-effectiveness.

BACKGROUND AND PURPOSE: To simulate patient-level costs, analyze the economic potential of telemedicine-based mobile stroke units for acute prehospital stroke care, and identify major determinants of cost-effectiveness, based on two recent prospective trials from the United States and Germany. METHODS: A Markov decision model was developed to simulate lifetime costs and outcomes of mobile stroke unit. The model compares diagnostic and therapeutic pathways of ischemic stroke, hemorrhagic stroke, and stroke mimic patients by conventional care or by mobile stroke units. The treatment outcomes were derived from the B_PROUD and the BEST-mobile stroke unit trials and further input parameters were derived from recent literature. Uncertainty was addressed by deterministic and probabilistic sensitivity analyses. A lifetime horizon based on the US healthcare system was adopted to evaluate different cost thresholds for mobile stroke unit and the resulting cost-effectiveness. Willingness-to-pay thresholds were set at 1x and 3x gross domestic product per capita, as recommended by the World Health Organization. RESULTS: In the base case scenario, mobile stroke unit care yielded an incremental gain of 0.591 quality-adjusted life years per dispatch. Mobile stroke unit was highly cost-effective up to a maximum average cost of 43,067 US dollars per patient. Sensitivity analyses revealed that MSU cost-effectiveness is mainly affected by reduction of long-term disability costs. Also, among other parameters, the rate of stroke mimics patients diagnosed by MSU plays an important role. CONCLUSION: This study demonstrated that mobile stroke unit can possibly be operated on an excellent level of cost-effectiveness in urban areas in North America with number of stroke mimic patients and long-term stroke survivor costs as major determinants of lifetime cost-effectiveness.

Cost-effectiveness analysis in radiology: methods, results and implications. / Kosteneffektivitätsanalysen in der Radiologie: Methoden, Ergebnisse, Implikationen.

BACKGROUND: Diagnostic radiological examinations as well as interventional radiological therapies are performed at a steadily increasing rate amidst increasingly limited resources in healthcare systems. Given their potential to contribute decisively to optimized therapy, in most cases associated short-term direct costs can be well justified from a clinical perspective. However, to realize their clinical benefits, they must also succeed in justifying them to payers and policymakers. Therefore, the aim of this work is to present suitable methods for economic analysis of radiological precedures and to elaborate their relevance for radiology. METHODOLOGY: Methods and metrics of cost-effectiveness analysis are presented and then exemplified using the example cases of MR mammography and interventional treatment of oligometastatic tumor disease of the liver. RESULTS: Cost-effectiveness considerations, taking into account long-term gains in lifespan and quality of life, as well as potential savings through improved treatment planning, do often objectively and credibly justify short-term additional costs. CONCLUSIONS: Cost-effectiveness analyses performed with radiological and health economic expertise can support the establishment of new radiological technologies in diagnostics and therapy. KEY POINTS: · When radiological procedures are employed, short-term costs are often offset by significant long-term benefits.. · Radiological examinations and therapies must be justified in the context of limited economic resources.. · Economic methodologies can be used to quantify the quality and cost-effectiveness of radiological methods.. · Such analyses as well as targeted training should be encouraged to provide greater transparency.. CITATION FORMAT: · Froelich MF, Kunz WG, Tollens F et al. Cost-effectiveness analysis in radiology: methods, results and implications. Fortschr Röntgenstr 2022; 194: 29 - 38.

Diagnostic Workup for Patients with Solid Renal Masses: A Cost-Effectiveness Analysis.

BACKGROUND: For patients with solid renal masses, a precise differentiation between malignant and benign tumors is crucial for forward treatment management. Even though MRI and CT are often deemed as the gold standard in the diagnosis of solid renal masses, CEUS may also offer very high sensitivity in detection. The aim of this study therefore was to evaluate the effectiveness of CEUS from an economical point of view. METHODS: A decision-making model based on a Markov model assessed expenses and utilities (in QALYs) associated with CEUS, MRI and CT. The utilized parameters were acquired from published research. Further, a Monte Carlo simulation-based deterministic sensitivity analysis of utilized variables with 30,000 repetitions was executed. The willingness-to-pay (WTP) is at USD 100,000/QALY. RESULTS: In the baseline, CT caused overall expenses of USD 10,285.58 and an efficacy of 11.95 QALYs, whereas MRI caused overall expenses of USD 7407.70 and an efficacy of 12.25. Further, CEUS caused overall expenses of USD 5539.78, with an efficacy of 12.44. Consequently, CT and MRI were dominated by CEUS, and CEUS remained cost-effective in the sensitivity analyses. CONCLUSIONS: CEUS should be considered as a cost-effective imaging strategy for the initial diagnostic workup and assessment of solid renal masses compared to CT and MRI.

Impact of specificity on cost-effectiveness of screening women at high risk of breast cancer with magnetic resonance imaging, mammography and ultrasound.

PURPOSE: Aim of this study was to analyze the comparative cost-effectiveness of MR-mammography vs conventional imaging in a screening setting for women with high risk of breast cancer, with particular focus on the impact of specificity of MRM. METHOD: Decision analytic modelling and Markov Modelling were applied to evaluate cumulative costs of each screening modality and their subsequent treatments as well as cumulative outcomes in quality adjusted life years (QALYs). For the selected time horizon of 30 years, false positive and false negative results were included. Model input parameters for women with high risk of breast cancer were estimated based on published data from a US healthcare system perspective. Major influence factors were identified and evaluated in a deterministic sensitivity analysis. Based on current recommendations for economic evaluations, a probabilistic sensitivity analysis was conducted to test the model stability. RESULTS: In a base-case analysis, screening with XM vs. MRM and treatment resulted in overall costs of $36,201.57 vs. $39,050.97 and a cumulative effectiveness of 19.53 QALYs vs. 19.59 QALYs. This led to an incremental cost-effectiveness ratio (ICER) of $ 45,373.94 per QALY for MRM. US and XM + US resulted in ICER values higher than the willingness to pay (WTP). In the sensitivity analyses, MRM remained a cost-effective strategy for screening high-risk patients as long as the specificity of MRM did not drop below 86.7 %. CONCLUSION: In high-risk breast cancer patients, MRM can be regarded as a cost-effective alternative to XM in a yearly screening setting. Specificity may be an important cost driver in settings with yearly screening intervals.