Background and objectives

People living with HIV/AIDS are at an increased risk of developing cancer. The goals of this study were to obtain data on the prevalence of HIV in the cancer population and vice versa at a major tertiary cancer and HIV center in North India.

Methods

This cross-sectional study was conducted over a 3-year period from July 2013 to June 2016, wherein successive HIV positive patients from an anti-retroviral therapy (ART) center were screened for malignancy. Simultaneously, successive cancer patients at the cancer center were screened for HIV. Baseline demographic details, risk factors, and laboratory investigations were obtained for all the patients.

Results

Among the 999 HIV-positive patients at the ART center, the prevalence of malignancy was 2% (n=20; 95% confidence interval (CI) 1.13, 2.87). Among the 998 patients with a malignancy, the prevalence of HIV infection was 0.9% (n=9; 95% CI 0.31, 1.49). Weight loss, loss of appetite, and fever were the most common symptoms in patients with HIV and cancer. Among 29 patients with HIV and cancer, AIDS-defining cancer was found in 19 patients; non-Hodgkin's lymphoma was the most common malignancy reported (n=13).

Interpretation and conclusion

There is a low prevalence of HIV in cancer patients as well as a low prevalence of cancer in HIV patients. AIDS-defining cancers remain much more common than non-AIDS-defining cancers. With the increased coverage of ART, it is expected that non-AIDSdefining cancers will increase, as is evident from data from more developed countries.

Amongst the scientific frameworks powered by the Monte Carlo (MC) toolkit Geant4 (Agostinelli et al., 2003), the TOPAS (Tool for Particle Simulation) (Perl et al., 2012) is one. TOPAS focuses on providing ease of use, and has significant implementation in the radiation oncology space at present. TOPAS functionality extends across the full capacity of Geant4, is freely available to non-profit users, and is being extended into radiobiology via TOPAS-nBIO (Ramos-Mendez et al., 2018). A current "grand problem" in cancer therapy is to convert the dose of treatment from physical dose to biological dose, optimized ultimately to the individual context of administration of treatment. Biology MC calculations are some of the most complex and require significant computational resources. In order to enhance TOPAS's ability to become a critical tool to explore the definition and application of biological dose in radiation therapy, we chose to explore the use of Field Programmable Gate Array (FPGA) chips to speedup the Geant4 calculations at the heart of TOPAS, because this approach called "Reconfigurable Computing" (RC), has proven able to produce significant (around 90x) (Sajish et al., 2012) speed increases in scientific computing. Here, we describe initial steps to port Geant4 and TOPAS to be used on FPGA. We provide performance analysis of the current TOPAS/Geant4 code from an RC implementation perspective. Baseline benchmarks are presented. Achievable performance figures of the subsections of the code on optimal hardware are presented; Aspects of practical implementation of "Monte Carlo on a chip" are also discussed.