Perioperative chemotherapy for upper tract urothelial carcinoma: A microsimulation Markov model.

Abstract

508 Background: Upper tract urothelial carcinoma (UTUC) accounts for less than 5% of all urothelial cancers. As a result, this disease is clinically understudied and there are no definitive recommendations regarding use and timing of peri-operative chemotherapy. The objective of this study was to create a decision model comparing three treatment pathways in UTUC: nephroureterectomy (NU) alone, neoadjuvant chemotherapy (NAC), and adjuvant chemotherapy (AC). Methods: A Markov microsimulation model was constructed using TreeAge Pro to compare treatment strategies for patients with newly diagnosed UTUC. Our primary outcome was quality adjusted life expectancy (QALE). Secondary outcomes included rates of adverse chemotherapy events, bladder cancer diagnoses, and crude survival. Markov cycle length was 3 months to mimic the follow up interval used in clinical practice for patients with UTUC. A systematic literature review was used to generate probabilities to populate the model. The base case was a 70-year-old patient with a radiographically localized upper tract tumor. Patients could have evidence of nodal disease, but no distant metastasis. Results: A total of 100,000 microsimulations were generated. NAC was preferred with an estimated QALE of 7.52 years versus 6.80 years with NU alone and 7.20 years with AC. Overall, 39.6% of patients in the AC group with invasive pathology received and were able to complete chemotherapy. A total of 37.5% of patients in the NAC group experienced an adverse chemotherapy event compared to 15.1% of patients in the AC group. Bladder cancer recurrence rates were 64.9%, 66.0%, and 67.1% over the patient’s lifetime in the NU, NAC, and AC groups, respectively. Conclusions: This study provides evidence to support the increased use of NAC in UTUC until robust randomized trials can be completed. While the use of NAC in this population appears favourable, the ultimate choice rests with the clinician and should be based on patient and tumor factors.