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Objective: About 50%–60% of all cancer cases will require radiotherapy during their treatment. Nonetheless, radiotherapy facilities are limited in low- and middle-income countries and despite high cancer burden in these regions of the world, only 5% have access to radiation therapy. This study identified the location of radiotherapy centers, the types of radiotherapy machines available and the personnel available in each radiotherapy center in Nigeria. Methods: A cross-sectional questionnaire-based study conducted across the six geopolitical zones of Nigeria from May 2020 to April 2021. A questionnaire having sections on facility profile, status of facility, and human resources, was used to elucidate information for the study. Descriptive statistics (frequency and proportion) were used to describe facilities’ characteristics, status, and human resources available. Results: Out of nine radiotherapy centers evaluated, the majority 33.3% ( n = 3) were found in the southwest geopolitical zone of Nigeria. Out of 10 equipment and accessories evaluated for availability, Ahmadu Bello University Teaching Hospital and University of Benin Teaching Hospital had the highest number of available equipment and accessories 90% ( n = 9) each respectively. Out of the nine centers evaluated, only four centers had at least one functional equipment. The highest number 64.3% ( n = 9) out of the 14 required number of staff in each center was found at University College Hospital. Out of 31 medical physicists identified, the majority 22.6% ( n = 7) was found at University of Nigeria Teaching Hospital. Conclusion: A high percentage of radiotherapy centers in Nigeria lacks the equipment and manpower to function optimally and is located within the southwest geopolitical zone of Nigeria. Therefore, proper investment in the radiotherapy service through private–public partnership, staff training, and equipment upgrade and maintenance could substantially improve the state of cancer care.

Background: Survival analysis is a statistical method for modeling the probability that a subset of a given population will survive past a certain time. In the context of cancer, this probability would represent a recurrence of tumor, or remission (i.e. being disease-free). This study seeks to compare the traditional frequentist approach and the Bayesian approach to survival analysis in estimating, and the predictors of prostate cancer (CaP) survivorship. Prostate cancer starts when healthy cells in the prostate gland change and grow out of place, forming a mass called a tumor. Method: A retrospective analytical study design was employed, through the extraction of case files of patients diagnosed and treated for CaP from January 2010 to December 2017 at UCH, Ibadan. The extracted data were further divided into two cohorts (2010 - 2014) and (2015 - 2017). A proforma was used for extraction which includes the following sections; socio-demographic, clinical/pathological characteristics, date of diagnosis, date last seen, and treatment received. Descriptive statistics were used to describe these characteristics. The survival probability was determined by the KM survival method. Cox proportional hazard (CPH), Weibull AFT, and Bayesian Weibull AFT (using normal prior distribution) models were used to determine predictors of survivorship. Results: The average age of the patients was 72 years, with a peak incidence of CaP among those aged 70 79 years. Most patients 87.3% were diagnosed at stage IV, with many having metastasis to the spine. Among the patients, 33.6% received chemotherapy and surgery. Patients from Northcentral had the highest median survival (MS) time in the (2015 - 2017) cohort. The overall MS time for the (2010 - 2014) cohort was 2.9 months, and 3.3 months for the (2015 - 2017) cohort while the overall MS time for the study was 3.2 months. Patients treated with chemotherapy and surgery in both cohorts experienced delayed remission. The Weibull AFT model found that patients with a moderately differentiated Gleason experienced a 50% increase in time (TR = 0.5; 95%CI: 0.3 0.9) to remission. Patients, with a poorly differentiated Gleason, experienced a 70% decrease in time (aTR = 1.7; 95%CI: 1.0 - 2.7) to remission. The Bayesian AFT model also found delay in time to remission for patients with moderately differentiated Gleason (TR = 0.6; 95%CrI: 0.3 0.9), and those treated with Chemotherapy and Surgery (aTR = 3.3; 95%CrI: 2.6 4.4). The Bayesian model showed that age, south-south, north-central, no family history of CaP, moderately and poorly differentiated Gleason and treatment with Chemotherapy, Radiotherapy, and Surgery, Chemotherapy, and Surgery to significantly delay time to remission. Conclusion: This study found that in considering predictors of survivorship a host of factors should be considered, particularly age, location, marital status, occupation, stage, method of diagnosis, Gleason group, site, and treatment received. In terms of approaches to survival analysis, greater emphasis should be given to the Bayesian approach, as observed in this study, the Bayesian approach extracted more significant predictors of survivorship than the CPH and Weibull AFT models and besides, it is more suitable for studies with fewer observations.

Background: Prostate cancer (CaP) develops when healthy cells in the prostate gland change and grow out of place, forming a tumour. In Nigeria, this disease is on an upward trajectory, despite the availability of screening services. This study seeks to estimate the time-to-remission and to determine the prognostic factors for prostate cancer remission.Method: A retrospective analytical study design was employed, through the extraction of case files of patients diagnosed and treated for CaP from January 2010 to December 2017 at the University College Hospital, Ibadan. The extracted data were further divided into two cohorts 2010 to 2014 and 2015 to 2017 to account for non-treatment months. Kaplan Meier method was used to estimate the time-to-remission. Bayesian parametric (Weibull) Accelerated Failure Time (AFT) model was used to determine the factors associated with time-to-remission.Results: The average age(SD) of the patients was 72(3.65) years, with peak incidence among those aged 70 – 79 years. Most CaP patients (87.3%) were diagnosed at stage IV, with many having metastasis to the spine. Among the patients, (33.6%) received chemotherapy and surgery. Patients from Northcentral part of Nigeria had the highest Median Time to Remission (MTR) of 3.7 months in the (2015 - 2017) cohort. The MTR for the 2010 - 2014 cohort was 2.9 months, 3.3 months for the 2015 - 2017 cohort while the overall MTR for the study was 3.2 months. Ages 60 – 69 years and 79 years and above in the 2010 – 2014 cohort decelerated time-to-remission by 30% (adjusted Time Ratio (aTR) = 1.3; 95% CrI (Credible Interval): 1.1 – 1.5) and 40% (aTR = 1.4; 95% CrI: 1.1 – 1.8) respectively. Time-to-remission was significantly delayed by 40% (aTR = 1.4; 95% CrI: 1.1 – 1.7), and 230% (aTR = 3.3; 95% CrI: 2.1 – 4.9) for patients from the south-south and north-central respectively for the 2015 – 2017 cohort compared to patients from the south-west. Conclusion: We found that patients age, location, marital status, occupation, stage, method of diagnosis, Gleason group, site and treatment received significantly influence the time to CaP remission. Stakeholders should therefore sensitize men well advanced in age to take up regular prostate examination as well as emphasize early presentation.