Development of a new risk stratification system for patients with newly diagnosed multiple myeloma using R-ISS and 18F-FDG PET/CT
Hee Jeong Cho, Sung-Hoon Jung, Jae‐Cheol Jo, Yoo Jin Lee, Sang Eun Yoon, Sung‐Soo Park, Do Young Kim, Ho‐Jin Shin, Yeung‐Chul Mun, Jun Ho Yi, Hyo Jung Kim, Da Jung Kim, Ho Sup Lee, Sung Hwa Bae, Chae Moon Hong, Shin Young Jeong, Jung‐Joon Min, Sang Kyun Sohn, Chang‐Ki Min, Kihyun Kım, Je‐Jung Lee, Joon Ho Moon, The Korean Multiple Myeloma Working Party, Hee Jeong Cho, Sung-Hoon Jung, Jae‐Cheol Jo, Yoo Jin Lee, Sang Eun Yoon, Sung‐Soo Park, Do Young Kim, Ho‐Jin Shin, Yeung‐Chul Mun, Jun Ho Yi, Hyo Jung Kim, Da Jung Kim, Ho Sup Lee, Sung Hwa Bae, Chae Moon Hong, Shin Young Jeong, Jung-Joon Min, Sang Kyun Sohn, Chang‐Ki Min, Kihyun Kım, Je‐Jung Lee, Joon Ho Moon
Abstract
Abstract In multiple myeloma (MM), a high number of focal lesions (FL) detected using positron emission tomography/computed tomography (PET/CT) was found to be associated with adverse prognosis. To design a new risk stratification system that combines the Revised International Staging System (R-ISS) with FL, we analyzed the data of 380 patients with newly diagnosed MM (NDMM) who underwent 18 F-fluorodeoxyglucose ( 18 F-FDG) PET/CT upon diagnosis. The K-adaptive partitioning algorithm was adopted to define subgroups with homogeneous survival. The combined R-ISS with PET/CT classified NDMM patients into four groups: R-ISS/PET stage I ( n = 31; R-ISS I with FL ≤ 3), stage II ( n = 156; R-ISS I with FL > 3 and R-ISS II with FL ≤ 3), stage III ( n = 162; R-ISS II with FL > 3 and R-ISS III with FL ≤ 3), and stage IV ( n = 31; R-ISS III with FL > 3). The 2-year overall survival rates for stages I, II, III, and IV were 96.7%, 89.8%, 74.7%, and 50.3%. The 2-year progression-free survival rates were 84.1%, 64.7%, 40.8%, and 17.1%, respectively. The new R-ISS/PET was successfully validated in an external cohort. This new system had a remarkable prognostic power for estimating the survival outcomes of patients with NDMM. This system helps discriminate patients with a good prognosis from those with a poor prognosis more precisely.