Can Multistate Modeling of Local Recurrence, Distant Metastasis, and Death Improve the Prediction of Outcome in Patients With Soft Tissue Sarcomas?

Abstract

Background: Exploration of the complex relationship between prognostic indicators such as tumor grade and size and clinical outcomes such as local recurrence and distant metastasis in patients with cancer is crucial to guide treatment decisions. However, in patients with soft tissue sarcoma, there are many gaps in our understanding of this relationship. Multistate analysis may help us in gaining a comprehensive understanding of risk factor-outcome relationships in soft tissue sarcoma, because this methodology can integrate multiple risk factors and clinical endpoints into a single statistical model. To our knowledge, no study of this kind has been performed before in patients with soft tissue sarcoma.

Questions/purposes: We implemented a multistate model of localized soft tissue sarcoma to statistically evaluate the relationship among baseline risk factors, recurrence, and death in patients with localized soft tissue sarcoma undergoing curative surgery.

Methods: Between 1998 and 2015, our center treated 539 patients for localized soft tissue sarcoma with surgery as curative intent. Of those, 96 patients (18%) were not included in this single-center retrospective study owing to missing baseline histopathology data (n = 3), not yet observed followup (n = 80), or because a neoadjuvant treatment approach in the presence of synchronous distant metastasis was used (n = 13), leaving 443 patients (82%) for the current analysis, of which 40 were lost to followup during the first year after surgery. All patients had tumors of the stages I to III according to the American Joint Committee on Cancer Stages. The median age of the patients was 62 years (range, 16-96 years), and 217 patients (49%) were female. Three hundred-forty-six patients (78%) had tumors of high grade (Grades 2 and 3), and 310 (70%) tumors were greater than 5 cm in maximum diameter. Patients who had died during the first year of followup were included in this analysis. Median followup for the 443 study patients was 6 years, with 84%, 52%, and 23% of patients being followed for more than 1, 5, and 10 years, respectively. The 15-year cumulative incidences of local recurrence, distant metastasis, and death from any cause, using a competing risk analysis, were 16% (95% CI, 11%-22%), 21% (95% CI, 17%-26%), and 55% (95% CI, 44%-67%), respectively. Wide resection with a margin of 1 mm was the preferred treatment for all patients, except for those with Grade 1 liposarcoma where a marginal resection was considered adequate. Multistate models were implemented with the mstate library in R.

Results: In multistate analysis, patients who experienced a local recurrence were more likely to have distant metastasis develop (hazard ratio [HR] = 8.4; 95% CI, 4.3-16.5; p < 0.001), and to die (HR = 3.4; 95% CI, 2.1-5.6; p < 0.001). The occurrence of distant metastasis was associated with a strong increase in the risk of death (HR = 12.6; 95% CI, 8.7-18.3; p < 0.001). Distant metastasis occurring after a long tumor-free interval was not associated with a more-favorable prognosis with respect to mortality than distant metastasis occurring early after surgery (estimated relative decrease in the adverse effect of distant metastasis on mortality for 1-year delay in the occurrence of distant metastasis = 0.9; 95% CI, 0.7-1.1; p = 0.28). High-grade histology (Grades 2 and 3) was associated with a higher risk of overall recurrence (defined as a composite of local recurrence and distant metastasis, HR = 3.8; 95% CI, 1.8-7.8; p = 0.0003) and a higher risk of death after recurrence developed (HR = 4.4; 95% CI, 1.1-18.2; p = 0.04). Finally, the multistate model predicted distinct outcome patterns depending on baseline covariates and how long a patient has remained free from recurrence after surgery.

Conclusions: In patients with localized soft tissue sarcoma undergoing resection, the occurrence of local recurrence and distant metastasis contributes to a dramatically impaired long-term survival outcome. Local recurrences are a substantial risk factor for distant metastasis. Multistate modeling is a very powerful approach for analysis of sarcoma cohorts, and may be used in the future to obtain highly personalized, dynamic predictions of outcomes in patients with localized soft tissue sarcoma.

Level of evidence: Level III, therapeutic study.

Fig. 1A–B

Prespecification of two types of multistate models for the clinical course of patients with soft tissue sarcoma after curative surgery is presented. The transition hazards for the respective transitions between the states are α_xy(t). (A) Multistate model Type 1 is a three-state, three-transition unidirectional illness death model, with States 1, 2, and 3 representing initial, transient, and absorbing states, respectively. In State 1, patients are alive and free from disease after curative surgery. They can remain in this initial state, transition to intermediate State 2 (Transition 1), or transition to the absorbing State 3 (death) either directly from State 1 (Transition 2) or from State 2. No back-transition is allowed between the states. (B) Multistate model Type 2 is a five-state model. The full clinical course of patients with soft tissue sarcoma after curative surgery is specified. T = transition.

Fig. 2

The cumulative risks of selected clinical endpoints are shown. Whereas the risk of all-cause mortality was calculated with a 1-Kaplan-Meier estimator, all other endpoints were calculated with competing risk estimators. The y-axis scaling only continues until 60%.

Fig. 3

The clinical course of patients with soft tissue sarcoma after surgery is shown. The colored boxes represent state occupation probabilities during a 10-year followup. This graph was derived using a stratified hazards model without covariates based on the model design in Fig. 1B. With time, the state occupation probability of State 1 (alive, no recurrence; green) decreases, because patients transition to State 2 (alive, local recurrence; light green), State 3 (alive, distant metastasis; cream), or State 5 (death; red). The state occupation probabilities of the distant metastasis state (State 3) strongly decreases with time, because patients make Transition 7 to the death state (State 5) or Transition 6 to the local recurrence and distant metastasis state (State 4; orange). LR = local recurrence; DM = distant metastasis.

Fig. 4

The dynamic prediction of clinical outcomes in patients with soft tissue sarcoma during followup is shown. Patient 1 is 45 years old with a Grade 1 tumor at baseline, and given that there is no evidence of disease at the 6-month followup after surgery, this patient has an excellent predicted prognosis with a low probability to occupy the recurrence states or the death state. Patient 2 is 75 years old and had a Grade 2 tumor at surgery. Although this patient also has no evidence of disease at the 6-month followup, his older age and higher tumor grade contribute to higher risks of transitioning to the death and recurrence states, respectively. Finally, 60-year-old Patient 3 has new evidence of pulmonary metastasis at his 6-month visit, leading to a dramatically increased risk of transitioning to the death state. State 1 (alive, no recurrence) = green; State 2 (alive, local recurrence) = light green; State 3 (alive, distant metastasis) = cream; State 4 (local recurrence and distant metastasis) = orange; State 5 (death) = red. LR = local recurrence; DM = distant metastasis.