Cost-effectiveness of chimeric antigen receptor T-cell therapy in adults with relapsed or refractory follicular lymphoma.

Follicular lymphoma (FL) is traditionally considered treatable but incurable. In March 2021, the US Food and Drug Administration approved the use of chimeric antigen receptor (CAR) T-cell therapy in patients with relapsed or refractory (R/R) FL after ≥2 lines of therapy. Priced at $373 000, CAR T-cell therapy is potentially curative, and its cost-effectiveness compared with other modern R/R FL treatment strategies is unknown. We developed a Markov model to assess the cost-effectiveness of third-line CAR T-cell vs standard of care (SOC) therapies in adults with R/R FL. We estimated progression rates for patients receiving CAR T-cell and SOC therapies from the ZUMA-5 trial and the LEO CReWE study, respectively. We calculated costs, discounted life years, quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER) of CAR T-cell vs SOC therapies with a willingness-to-pay threshold of $150 000 per QALY. Our analysis was conducted from a US payer's perspective over a lifetime horizon. In our base-case model, the cost of the CAR T-cell strategy was $731 682 compared with $458 490 for SOC therapies. However, CAR T-cell therapy was associated with incremental clinical benefit of 1.50 QALYs, resulting in an ICER of $182 127 per QALY. Our model was most sensitive to the utilities associated with CAR T-cell therapy remission and third-line SOC therapies and to the total upfront CAR T-cell therapy cost. Under current pricing, CAR T-cell therapy is unlikely to be cost-effective in unselected patients with FL in the third-line setting. Both randomized clinical trials and longer term clinical follow-up can help clarify the benefits of CAR T-cell therapy and optimal sequencing in patients with FL.

Artificial Intelligence in Breast Cancer Screening: Evaluation of FDA Device Regulation and Future Recommendations.

Importance: Contemporary approaches to artificial intelligence (AI) based on deep learning have generated interest in the application of AI to breast cancer screening (BCS). The US Food and Drug Administration (FDA) has approved several next-generation AI products indicated for BCS in recent years; however, questions regarding their accuracy, appropriate use, and clinical utility remain. Objectives: To describe the current FDA regulatory process for AI products, summarize the evidence used to support FDA clearance and approval of AI products indicated for BCS, consider the advantages and limitations of current regulatory approaches, and suggest ways to improve the current system. Evidence Review: Premarket notifications and other publicly available documents used for FDA clearance and approval of AI products indicated for BCS from January 1, 2017, to December 31, 2021. Findings: Nine AI products indicated for BCS for identification of suggestive lesions and mammogram triage were included. Most of the products had been cleared through the 510(k) pathway, and all clearances were based on previously collected retrospective data; 6 products used multicenter designs; 7 products used enriched data; and 4 lacked details on whether products were externally validated. Test performance measures, including sensitivity, specificity, and area under the curve, were the main outcomes reported. Most of the devices used tissue biopsy as the criterion standard for BCS accuracy evaluation. Other clinical outcome measures, including cancer stage at diagnosis and interval cancer detection, were not reported for any of the devices. Conclusions and Relevance: The findings of this review suggest important gaps in reporting of data sources, data set type, validation approach, and clinical utility assessment. As AI-assisted reading becomes more widespread in BCS and other radiologic examinations, strengthened FDA evidentiary regulatory standards, development of postmarketing surveillance, a focus on clinically meaningful outcomes, and stakeholder engagement will be critical for ensuring the safety and efficacy of these products.

Diabetic bladder dysfunction is associated with bladder inflammation triggered through hyperglycemia, not polyuria.

PURPOSE: Diabetes is a grave and progressive condition characterized by debilitating complications. Diabetic bladder dysfunction (DBD) is a very common complication with no specific treatments currently available. Unlike other tissues affected by this disease, the bladder is subjected to two independent insults; 1) polyuria, created by the osmotic effects of glucose in the urine, and 2) hyperglycemia itself. Based on our understanding of inflammation as a major contributor to the underlying organ damage in several other diabetic complications, its presence in the bladder during DBD and the contribution of polyuria and hyperglycemia to its development were assessed. METHODS: Awake, restrained cystometry was performed on wild type C57BL/6 mice and diabetic (Akita) mice on a C57BL/6 background at 15 weeks of age. A subgroup of the Akita mice were treated with phlorizin, an inhibitor of sodium-glucose linked transporter types 1 and 2 that prevents glucose reabsorption in the kidney. All groups were assessed for serum glucose, 4-hour voiding totals, and inflammation in the bladder (Evans blue assay). RESULTS: Akita mice develop cystometrically-defined DBD by 15 weeks of age, as evidenced by an increase in urinary frequency, a decrease in voiding volume, and an increase in post-voiding residual volume. Phlorizin effectively normalized serum glucose in these animals while increasing the urine output. Inflammation in the bladder was present in the diabetic animals at this time point, but not detectable in animals receiving phlorizin. CONCLUSION: Inflammation in the bladder of diabetic mice correlates with the development of DBD and is triggered by hyperglycemia, not polyuria.