Long-Term Survival in Patients with Cancers: A SEER-based analysis

Objectives: Long-term survival is an important endpoint in management of different malignancies which is rarely assessed due to unfeasibility of follow-up for long duration of time. In this study, we explored real-world data on cancer’s long-term survival using historical records from the Surveillance, Epidemiology, and End Results (SEER) Program. Besides reporting the 5-year relative survival, we analyzed the 10- and 20- year survival rates for different types of cancers. Additionally, survival trends as a function of time, age, and tumor type were reviewed and reported. Methods: We used SEER*Stat (version 8.3.6.1) for data acquisition from the SEER 9 Regs (Nov 2019 Submission) database. Data of patients diagnosed with cancer between 1975 and 2014 were retrieved and included in the analysis. Results: For patients diagnosed with any malignant disease (n = 4,412,024), there was a significant increase in median overall survival over time (p<0.001). The 20-, 10-, and 5-year survival rates were higher in solid tumors compared to hematological malignancies (50.8% vs. 38%, 57% vs. 47.4%, and 62.2% vs. 57.4%, respectively). The highest 20-year relative survival rates were observed in thyroid cancer (95.2%), germ cell and trophoblastic neoplasms (90.3%), melanoma (86.8%), Wilms’ tumor (86.2%), and prostate cancer (83.5%). Conclusions: Long-term follow-up data were suggestive of high 20-year relative survival rates for most tumor types. Relative survival showed an improving trend over time especially in solid tumors. Keywords: Survival; Neoplasms; SEER Program; Prognosis; United States

On a novel hybrid Manta ray foraging optimizer and its application on parameters estimation of lithium-ion battery

In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon's test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.Web of Science151art. no. 6