scholarly journals Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data

2021 ◽  
Vol 11 ◽  
Author(s):  
Akanksha Farswan ◽  
Anubha Gupta ◽  
Krishnamachari Sriram ◽  
Atul Sharma ◽  
Lalit Kumar ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Risk Stratification ◽  
Risk Prediction ◽  
Superior Performance ◽  
Specific Information ◽  
Prognostic Parameters ◽  
Prognostic Features ◽  
Stratification System ◽  
The Impact ◽  
Risk Stratification System

IntroductionCurrent risk predictors of multiple myeloma do not integrate ethnicity-specific information. However, the impact of ethnicity on disease biology cannot be overlooked. In this study, we have investigated the impact of ethnicity in multiple myeloma risk prediction. In addition, an efficient and robust artificial intelligence (AI)-enabled risk-stratification system is developed for newly diagnosed multiple myeloma (NDMM) patients that utilizes ethnicity-specific cutoffs of key prognostic parameters.MethodsK-adaptive partitioning is used to propose new cutoffs of parameters for two different datasets—the MMIn (MM Indian dataset) dataset and the MMRF (Multiple Myeloma Research Foundation) dataset belonging to two different ethnicities. The Consensus-based Risk-Stratification System (CRSS) is designed using the Gaussian mixture model (GMM) and agglomerative clustering. CRSS is validated via Cox hazard proportional methods, Kaplan–Meier analysis, and log-rank tests on progression-free survival (PFS) and overall survival (OS). SHAP (SHapley Additive exPlanations) is utilized to establish the biological relevance of the risk prediction by CRSS.ResultsThere is a significant variation in the key prognostic parameters of the two datasets belonging to two different ethnicities. CRSS demonstrates superior performance as compared with the R-ISS in terms of C-index and hazard ratios on both the MMIn and MMRF datasets. An online calculator has been built that can predict the risk stage of a multiple myeloma (MM) patient based on the values of parameters and ethnicity.ConclusionOur methodology discovers changes in the cutoffs with ethnicities from the established cutoffs of prognostic features. The best predictor model for both cohorts was obtained with the new ethnicity-specific cutoffs of clinical parameters. Our study also revealed the efficacy of AI in building a deployable risk prediction system for MM. In the future, it is suggested to use the CRSS risk calculator on a large dataset as the cohort size of the present study is 25% of the cohort used in the R-ISS reported in 2015.

scholarly journals Development of a new risk stratification system for patients with newly diagnosed multiple myeloma using R-ISS and 18F-FDG PET/CT

2021 ◽  
Vol 11 (12) ◽  
Author(s):  
Hee Jeong Cho ◽  
Sung-Hoon Jung ◽  
Jae-Cheol Jo ◽  
Yoo Jin Lee ◽  
Sang Eun Yoon ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Risk Stratification ◽  
Fdg Pet ◽  
Survival Rates ◽  
Newly Diagnosed ◽  
Focal Lesions ◽  
Pet Ct ◽  
Stratification System ◽  
Fdg Pet Ct ◽  
Risk Stratification System

AbstractIn 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 18F-fluorodeoxyglucose (18F-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.

scholarly journals Role of postoperative radiotherapy in pT3N0 rectal cancer: A risk‐stratification system based on population analyses

2019 ◽  
Vol 8 (3) ◽  
pp. 1024-1033 ◽  
Author(s):  
Yun‐xia Huang ◽  
Yan‐zong Lin ◽  
Jin‐luan Li ◽  
Xue‐qing Zhang ◽  
Li‐rui Tang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Risk Stratification ◽  
Postoperative Radiotherapy ◽  
Population Analyses ◽  
Stratification System ◽  
Risk Stratification System

666 A NOVEL RISK STRATIFICATION SYSTEM FOR CHEMOTHERAPY FAILURE IN TESTICULAR GERM CELL TUMOR

2012 ◽  
Vol 187 (4S) ◽  
Author(s):  
Jessica Lubahn ◽  
Nicholas Cost ◽  
Mehrad Adibi ◽  
Adam Romman ◽  
Ganesh Raj ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Germ Cell ◽  
Germ Cell Tumor ◽  
Testicular Germ Cell Tumor ◽  
Cell Tumor ◽  
Testicular Germ Cell ◽  
Stratification System ◽  
Risk Stratification System

The Influence of Age on Disease Outcome in 2015 ATA High Risk Differentiated Thyroid Cancer Patients

2021 ◽  
Author(s):  
Evert F.s. van Velsen ◽  
Robin P. Peeters ◽  
Merel T. Stegenga ◽  
F.j. van Kemenade ◽  
Tessa M. van Ginhoven ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Differentiated Thyroid Cancer ◽  
Response To Therapy ◽  
Disease Outcome ◽  
High Risk Patients ◽  
Risk Patients ◽  
Stratification System ◽  
Risk Stratification System

Objective Recent research suggests that the addition of age improves the 2015 American Thyroid Association (ATA) Risk Stratification System for differentiated thyroid cancer (DTC). The aim of our study was to investigate the influence of age on disease outcome in ATA High Risk patients with a focus on differences between patients with papillary (PTC) and follicular thyroid cancer (FTC). Methods We retrospectively studied adult patients with High Risk DTC from a Dutch university hospital. Logistic regression and Cox proportional hazards models were used to estimate the effects of age (at diagnosis) and several age cutoffs (per five years increment between 20 and 80 years) on (i) response to therapy, (ii) developing no evidence of disease (NED), (iii) recurrence, and (iv) disease specific mortality (DSM). Results We included 236 ATA High Risk patients (32% FTC) with a median follow-up of 6 years. Age, either continuously or dichotomously, had a significant influence on having an excellent response after initial therapy, developing NED, recurrence, and DSM for PTC and FTC. For FTC, an age cutoff of 65 or 70 years showed the best statistical model performance, while this was 50 or 60 years for PTC. Conclusions In a population of patients with High Risk DTC, older age has a significant negative influence on disease outcomes. Slightly different optimal age cutoffs were identified for the different outcomes, and these cutoffs differed between PTC and FTC. Therefore, the ATA Risk Stratification System may further improve should age be incorporated as an additional risk factor.

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