UNASSIGNED: 134 Background: Although @entity2240 is effective first-line therapy (1LT) for metastatic @entity1358 (mRCC), ~20% of @entity1 experience rapid @entity74 ( @entity74 ). Traditional RECIST monitoring often does not detect @entity74 until 90 days after 1LT initiation. Investigational angiogenesis-specific imaging (AI) may identify @entity74 as early as 14 days post-1LT initiation, thus allowing a switch to a potentially more effective second-line therapy and avoiding unnecessary risk of AEs. This study's goal was to quantify the potential reduction in futile 1LT length, AEs, and costs by using AI for early detection of @entity74 . METHODS: Decision modeling with a 90-day horizon evaluated a comparator arm using RECIST monitoring at 90 days and an intervention arm using AI at 14 days. @entity2240 costs were  21,250 for the comparator arm and  13,282 for the intervention arm. RECIST costs were  619 and AI costs were tested as a breakeven analysis. A literature review quantified AE rates associated with 1LT @entity2240 and claims data were used to determine costs. Early @entity74 detection was estimated based on a 20% rapid @entity74 rate. RESULTS: For AI sensitivity of 50% to predict rapid @entity74 , a 38-day reduction in futile 1LT could be achieved per @entity74 @entity1 by using AI vs. RECIST (AI sensitivity of 75%/100% yielded 57/76 fewer days). The potential number of AEs avoided through early @entity74 identification is shown below. Costs saved per 1,000 mRCC @entity1 were  684,566 for AEs and  3,187,400 for futile 1LT. Based on these results,  3,872 per mRCC @entity1 would be freed up for AI. CONCLUSIONS: Continuing 1LT after @entity74 brings unnecessary risk of AEs and delays potentially effective 2nd-line therapy. Results of this study indicate that early @entity74 identification using AI may improve quality of care by minimizing duration of futile 1LT and avoiding unnecessary AEs. [Table: see text].

Avoidance of futile treatment and adverse events by using angiogenesis-specific imaging for early detection of disease progression in @entity1 with first-line metastatic XXXX .

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity1358 :: ('MESH:D002292', 'Disease') :: ['renal cell carcinoma'] @entity74 :: ('MESH:D030342', 'Disease') :: ['PD', 'progressive disease'] @entity2240 :: ('MESH:C473478', 'Chemical') :: ['Sunitinib', 'sunitinib']
@entity1358 :: (MESH:D002292,Disease) :: ['renal cell carcinoma']
