Introduction: The international metabolic prognostic index (IMPI) is a recently proposed model that shows improved outcome prediction for patients with diffuse large B-cell lymphoma (DLBCL) compared to the International Prognostic Index (IPI) (Mikhaeel et al.). IMPI combines total metabolic tumor volume (TMTV) as continuous variables with clinical factors, allowing individualized prognosis estimation. However, the applicability of IMPI to routine clinical practice remains uncertain since the model construction was based on data from clinical trials. Furthermore, the comparison of the model performance between IMPI and the National Comprehensive Cancer Network IPI (NCCN-IPI) is not reported so far.
Methods: We performed retrospective analysis of consecutive patients with newly diagnosed DLBCL who were initially treated with R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) at Kameda Medical Center between 2006 and 2020. Patients who had no fluorodeoxyglucose-avid lesions were excluded. TMTV was defined as the volume of lymphoma-associated lesions with a standardized uptake value of ≥4 as the absolute threshold (Metavol, Hokkaido University, Japan). According to the original study (Mikhaeel et al.), IMPI was calculated using the following formula: 0.003077330 × (MTV lower than the median of 307.9ml) - 0.002761985 × (MTV higher than the median of 307.9ml) + 0.008092449 × Age -0.114645415 × Stage 2 + 0.281141117 × Stage 3 + 0.322247142 × Stage 4. The receiver operating curve (ROC) for predicting 5-year survival was compared among IMPI-, IPI-, and NCCN-IPI-based classifications. The predictive performance of these three systems was evaluated using the Akaike information criterion (AIC) and Harrell's concordance index (c-index).
Results: A total of 314 patients were included in the study. The median age was 72 years old (interquartile range [IQR] 64-79), which was younger than the original data (median 62, IQR 51-70). Regarding the distributions of IPI risk groups, this cohort had a lower number of low-risk patients (27.0% vs. 32.4%) and a higher number of high-risk patients (23.9% vs. 19.5%).
The median TMTV was 168 (IQR 47-530), which was approximately two times lower than that in the original study (median 307, IQR 77-838). After calculating and ranking the individual IMPI scores, nearly half of the patients (n=147, 46.8%) were reclassified into different risk groups from the original IPI categories (Figure 1); 76 (51.7%) were upstaged, and 71 (48.3%) were downstaged. Of note, the reclassification resulted in a significantly better OS prediction only for the IPI high-risk group (5-year OS rate, IMPI high-risk vs. IMPI not high-risk, 41.9% vs. 59%, P =0.042), while it did not for the IPI low-, low-intermediate-, and high-intermediate-risk groups. The area under the curve (AUC) from the ROC curve predicting 5-year survival was not significantly different between IMPI- (AUC 0.65) and either IPI- (AUC 0.67, P =0.42) or NCCN-IPI-categories (AUC 0.68, P=0.25).
Finally, the predictive performance and model fitness were compared among these three indices. IMPI had the highest AIC (1117.68) and the lowest c-index (0.643) for OS prediction, whereas NCCNIPI yielded the best performance (AIC 1097.66 and c-index 0.677) (Table 1).
Conclusions: We demonstrated that the IMPI model did not outperform the conventional IPI and NCCN-IPI systems in a real-world cohort of DLBCL patients. The difference in age and tumor burden, which are the major components of IMPI, may lead to poorer prognostic performance. Further large-scale studies are warranted to validate the prognostic value of IMPI outside of clinical trials.
Disclosures
Matsue:AstraZeneca: Research Funding.
