Cost-effectiveness of Endovascular Treatment for Acute Stroke with Large Infarct: A United States Perspective.

Background The health economic benefit of endovascular treatment (EVT) in addition to best medical management for acute ischemic stroke with large ischemic core is uncertain. Purpose To assess the cost-effectiveness of EVT plus best medical management versus best medical management alone in treating acute ischemic stroke with large vessel occlusion and a baseline Alberta Stroke Program Early CT Score (ASPECTS) 3-5. Materials and Methods This is a secondary analysis of the randomized RESCUE-Japan LIMIT (Recovery by Endovascular Salvage for Cerebral Ultra-acute Embolism-Japan Large Ischemic Core Trial), with enrollment November 2018 to September 2021, in which the primary outcome was the modified Rankin Scale (mRS) score at 90 days. Participants with a baseline ASPECTS 3-5 (on the basis of noncontrast CT and diffusion-weighted imaging) were randomized 1:1 to receive EVT plus best medical management (n = 100) or best medical management alone (n = 102). The primary outcome of the current study was cost-effectiveness, determined according to the incremental cost-effectiveness ratio (ICER). A decision model consisting of a short-term component (cycle length of 3 months) and a long-term Markov state transition component (cycle length of 1 year) was used to estimate expected lifetime costs and quality-adjusted life-years (QALYs) from health care and societal perspectives in the United States. Upper and lower willingness-to-pay (WTP) thresholds were set at $100 000 and $50 000 per QALY, respectively. A deterministic one-way sensitivity analysis to determine the impact of participant age and a probabilistic sensitivity analysis to assess the impact of parameter uncertainty were conducted. Results A total of 202 participants were included in the study (mean age, 76 years ± 10 [SD]; 112 male). EVT plus best medical management resulted in ICERs of $15 743 (health care perspective) and $19 492 (societal perspective). At the lower and upper WTP thresholds, EVT was cost-effective up to 85 and 90 years (health care perspective) and 84 and 89 years (societal perspective) of age, respectively. When analyzing participants with the largest infarcts (ASPECTS 3) separately, EVT was not cost-effective (ICER, $337 072 [health care perspective] and $383 628 [societal perspective]). Conclusion EVT was cost-effective for participants with an ASPECTS 4-5, but not for those with an ASPECTS 3. ClinicalTrials.gov registration no. NCT03702413 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Widjaja in this issue.

Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer.

BACKGROUND: Bladder cancer (BC) treatment algorithms depend on accurate tumor staging. To date, computed tomography (CT) is recommended for assessment of lymph node (LN) metastatic spread in muscle-invasive and high-risk BC. However, the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited. OBJECTIVE: To evaluate the performance of quantitative radiomics signatures for detection of LN metastases in BC. DESIGN, SETTING, AND PARTICIPANTS: Of 1354 patients with BC who underwent radical cystectomy (RC) with lymphadenectomy who were screened, 391 with pathological nodal staging (pN0: n = 297; pN+: n = 94) were included and randomized into training (n = 274) and test (n = 117) cohorts. Pelvic LNs were segmented manually and automatically. A total of 1004 radiomics features were extracted from each LN and a machine learning model was trained to assess pN status using histopathology labels as the ground truth. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Radiologist assessment was compared to radiomics-based analysis using manual and automated LN segmentations for detection of LN metastases in BC. Statistical analysis was performed using the receiver operating characteristics curve method and evaluated in terms of sensitivity, specificity, and area under the curve (AUC). RESULTS AND LIMITATIONS: In total, 1845 LNs were manually segmented. Automated segmentation correctly located 361/557 LNs in the test cohort. Manual and automatic masks achieved an AUC of 0.80 (95% confidence interval [CI] 0.69-0.91; p = 0.64) and 0.70 (95% CI: 0.58-0.82; p = 0.17), respectively, in the test cohort compared to radiologist assessment, with an AUC of 0.78 (95% CI 0.67-0.89). A combined model of a manually segmented radiomics signature and radiologist assessment reached an AUC of 0.81 (95% CI 0.71-0.92; p = 0.63). CONCLUSIONS: A radiomics signature allowed discrimination of nodal status with high diagnostic accuracy. The model based on manual LN segmentation outperformed the fully automated approach. PATIENT SUMMARY: For patients with bladder cancer, evaluation of computed tomography (CT) scans before surgery using a computer-based method for image analysis, called radiomics, may help in standardizing and improving the accuracy of assessment of lymph nodes. This could be a valuable tool for optimizing treatment options.

Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy.

(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48−74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657−0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617−0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.

REDUCE - Indication catalogue based ordering of chest radiographs in intensive care units.

PURPOSE: To advance a transition towards an indication-based chest radiograph (CXR) ordering in intensive care units (ICUs) without compromising patient safety. MATERIALS AND METHODS: Single-center prospective cohort study with a retrospective reference group including 857 ICU patients. The routine group (n = 415) received CXRs at the discretion of the ICU physician, the restrictive group (n = 442) if specified by an indication catalogue. Documented data include number of CXRs per day and CXR radiation dose as primary outcomes, re-intubation and re-admission rates, hours of mechanical ventilation and ICU length of stay. RESULTS: CXR numbers were reduced in the restrictive group (964 CXRs in 2479 days vs. 1281 CXRs in 2318 days) and median radiation attributed to CXR per patient was significantly lowered in the restrictive group (0.068 vs. 0.076 Gy x cm2, P = 0.003). For patients staying ≥24 h, median number of CXRs per day was significantly reduced in the restrictive group (0.41 (IQR 0.21-0.61) vs. 0.55 (IQR 0.34-0.83), P < 0.001). Survival analysis proved non-inferiority. Secondary outcome parameters were not significantly different between the groups. CXR reduction was significant even for patients in most critical conditions. CONCLUSIONS: A substantial reduction of the number of CXRs on ICUs was feasible and safe using an indication catalogue thereby improving resource management. TRIAL REGISTRATION: DRKS00015621, German Clinical Trials Register.