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AimTo assess, from a United States (US) payer's perspective, the cost-effectiveness of gels designed to separate the endometrial surfaces (intrauterine spacers) placed following intrauterine surgery.Materials and methodsA decision tree model was developed to estimate the cost-effectiveness of intrauterine spacers used to facilitate endometrial repair and prevent the formation (primary prevention) and reformation (secondary prevention) of intrauterine adhesions (IUAs) and associated pregnancy- and birth-related adverse outcomes. Event rates and costs were extrapolated from data available in the existing literature. Sensitivity analyses were conducted to corroborate the base case results.ResultsIn this model, using intrauterine spacers for adhesion prevention led to net cost savings for US payers of $2,905 per patient over a 3.5-year time horizon. These savings were driven by the direct benefit of preventing procedures associated with IUA formation ($2,162 net savings) and the indirect benefit of preventing pregnancy-related complications often associated with IUA formation ($3,002). These factors offset the incremental cost of intrauterine spacer use of $1,539 based on an assumed price of $1,800 and the related increase in normal deliveries of $931. Model outcomes were sensitive to the probability of preterm and normal deliveries. Budget impact analyses show overall cost savings of $19.96 per initial member within a US healthcare plan, translating to $20 million over a 5-year time horizon for a one-million-member plan.LimitationsThere are no available data on the effects of intrauterine spacers or IUAs on patients' quality of life. Resultingly, the model could not evaluate patients' utility related to treatment with or without intrauterine spacers and instead focused on costs and events avoided.ConclusionThis analysis robustly demonstrated that intrauterine spacers would be cost-saving to healthcare payers, including both per-patient and per-plan member, through a reduction in IUAs and improvements to patients' pregnancy-related outcomes.

A portion of individuals diagnosed with primary central nervous system lymphomas (PCNSL) may experience early relapse or refractory (R/R) disease following treatment. This research explored the potential of MRI-based radiomics in forecasting R/R cases in PCNSL. Forty-six patients with pathologically confirmed PCNSL diagnosed between January 2008 and December 2020 were included in this study. Only patients who underwent pretreatment brain MRIs and complete postoperative follow-up MRIs were included. Pretreatment contrast-enhanced T1WI, T2WI, and T2 FLAIR imaging were analyzed. A total of 107 radiomic features, including 14 shape-based, 18 first-order statistical, and 75 texture features, were extracted from each sequence. Predictive models were then built using five different machine learning algorithms to predict R/R in PCNSL. Of the included 46 PCNSL patients, 20 (20/46, 43.5%) patients were found to have R/R. In the R/R group, the median scores in predictive models such as support vector machine, k-nearest neighbors, linear discriminant analysis, naïve Bayes, and decision trees were significantly higher, while the apparent diffusion coefficient values were notably lower compared to those without R/R (p < 0.05). The support vector machine model exhibited the highest performance, achieving an overall prediction accuracy of 83%, a precision rate of 80%, and an AUC of 0.78. Additionally, when analyzing tumor progression, patients with elevated support vector machine and naïve Bayes scores demonstrated a significantly reduced progression-free survival (p < 0.05). These findings suggest that preoperative MRI-based radiomics may provide critical insights for treatment strategies in PCNSL.

BACKGROUND: Invasive lobular carcinoma (ILC) is the second most common subtype of breast cancer. Although mammography is known to have low sensitivity for ILC, there are no data to guide the optimal surveillance after treatment. We explored surveillance strategies after breast-conserving surgery (BCS) for ILC and determined the proportion of imaging-detected recurrences versus interval cancers. METHODS: From an institutional database of 813 women, we retrospectively identified patients who underwent BCS for stage I-III ILC and subsequently had a recurrence. We categorized patients by surveillance strategy and determined the modality of recurrence detection. Interval cancer rates for local recurrences were compared across surveillance strategies using the Chi-square test. We evaluated overall survival with the log-rank test and a Cox proportional hazards model. RESULTS: We included 58 patients with ILC who had a recurrence after BCS. Of these, 22 (37.9%) had local recurrence, 27 (46.6%) had distant recurrence, and 9 (15.5%) had both local and distant recurrence. Most patients underwent routine mammographic surveillance (65.2%), with 19.6% having supplemental breast magnetic resonance imaging (MRI) and 15.2% having no surveillance. The interval cancer rate was significantly higher in the mammographic surveillance group compared with the MRI surveillance group (61.9% vs. 16.7%; p