A Novel Approach to Modeling and Forecasting Cancer Incidence and Mortality Rates through Web Queries and Automated Forecasting Algorithms: Evidence from Romania

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models

The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers.

Mining Google Trends data for nowcasting and forecasting colorectal cancer (CRC) prevalence

Background

Colorectal cancer (CRC) is the third most prevalent and second most lethal form of cancer in the world. Consequently, CRC cancer prevalence projections are essential for assessing the future burden of the disease, planning resource allocation, and developing service delivery strategies, as well as for grasping the shifting environment of cancer risk factors. However, unlike cancer incidence and mortality rates, national and international agencies do not routinely issue projections for cancer prevalence. Moreover, the limited or even nonexistent cancer statistics for large portions of the world, along with the high heterogeneity among world nations, further complicate the task of producing timely and accurate CRC prevalence projections. In this situation, population interest, as shown by Internet searches, can be very important for improving cancer statistics and, in the long run, for helping cancer research.

Methods

This study aims to model, nowcast and forecast the CRC prevalence at the global level using a three-step framework that incorporates three well-established univariate statistical and machine-learning models. First, data mining is performed to evaluate the relevancy of Google Trends (GT) data as a surrogate for the number of CRC survivors. The results demonstrate that population web-search interest in the term "colonoscopy" is the most reliable indicator to nowcast CRC disease prevalence. Then, various statistical and machine-learning models, including ARIMA, ETS, and FNNAR, are trained and tested using relevant GT time series. Finally, the updated monthly query series spanning 2004-2022 and the best forecasting model in terms of out-of-sample forecasting ability (i.e., the neural network autoregression) are utilized to generate point forecasts up to 2025.

Results

Results show that the number of people with colorectal cancer will continue to rise over the next 24 months. This in turn emphasizes the urgency for public policies aimed at reducing the population's exposure to the principal modifiable risk factors, such as lifestyle and nutrition. In addition, given the major drop in population interest in CRC during the first wave of the COVID-19 pandemic, the findings suggest that public health authorities should implement measures to increase cancer screening rates during pandemics. This in turn would deliver positive externalities, including the mitigation of the global burden and the enhancement of the quality of official statistics.

Estimated projection of oral squamous cell carcinoma annual incidence from twenty years registry data: a retrospective cross-sectional study in Indonesia

Background

The incidence of oral squamous cell carcinoma (OSCC) has not been well documented in Indonesia. Thus, we aimed to analyze trends and clinicopathological profiles of OSCC cases in Indonesia, focusing on differences between age and sex groups.

Methods

A cross-sectional study was conducted in Indonesia's main referral hospital, analyzing 1,093 registered OSCC cases from 2001 to 2020. Trend analysis was performed using Joinpoint regression analysis to determine the annual percentage change (APC) for overall cases and each case group based on age, sex, and anatomical subsites. APC significance was assessed using a Monte Carlo permutation test. The projection of case numbers for the following 5 years (2021-2025) was estimated using linear/non-linear regression analysis and presented as a mathematical function. The significance of the trend slope was measured using an ANOVA test. Demographic and clinicopathological characteristics of OSCC were analyzed according to age and sex, and their comparative analysis was assessed using Chi-square and its alternatives.

Results

The incidence of OSCC in female patients and in the tongue and buccal mucosa showed a positive trend (APC 2.06%; 3.48%; 8.62%, respectively). Moreover, the incidence of OSCC overall, and in women with OSCC, is projected to increase significantly in the next 5 years following the quadratic model. The mean age of patients was 51.09 ± 14.36 years, with male patients being younger than female patients. The male-to-female ratio was 1.15, and 36.5% of these patients were categorized as young (≤45 years old). The tongue was the predominantly affected site. Prominent pathologic characteristics included well-differentiation, keratinization, and grade I of Bryne's (1992) cellular differentiation stage. Most patients presented with advanced staging, lymphovascular invasion, and uninvaded margins. Tumor sites and staging varied according to age, while age and tumor sites differed between sexes.

Conclusion

The rising incidence trends of OSCC among Indonesian patients, both in the past and projected future, are concerning and warrant attention. Further research into risk factors should be conducted as preventive measures.

New Regional Dynamic Cancer Model across the European Union

BACKGROUND

Can increasing levels of economic wealth significantly influence changes in cancer incidence and mortality rates?

METHODS

We investigated this issue by means of regression analyses based on the study of incidence and mortality indicators for lip, oral cavity, and pharyngeal; colon; pancreatic; lung; leukaemia; brain and central nervous system cancers in correlation with the levels of economic welfare and financial allocations to health at the level of the European Union member states, with the exception of Luxembourg and Cyprus for which there are no official statistical data reported.

RESULTS

The results of the study showed that there were significant disparities both regionally and by gender, requiring corrective public policy measures that were formulated in this study.

CONCLUSIONS

The conclusions highlight the main findings of the study in terms of the evolution of the disease, present the significant aspects that characterise the evolution of each type of cancer during the period analysed (1993-2021), and highlight the novelty and limitations of the study and future directions of research. As a result, increasing economic welfare is a potential factor in halting the effects of cancer incidence and mortality at the population level, while the financial allocations to health of EU member countries' budgets are a drawback due to large regional disparities.

Biogenic Synthesis of Cu-Mn Bimetallic Nanoparticles Using Pumpkin Seeds Extract and Their Characterization and Anticancer Efficacy

BACKGROUND

Cancer is a chronic, heterogeneous illness that progresses through a spectrum of devastating clinical manifestations and remains the 2nd leading contributor to global mortality. Current cancer therapeutics display various drawbacks that result in inefficient management. The present study is intended to evaluate the anticancer potential of Cu-Mn bimetallic NPs (CMBNPs) synthesized from pumpkin seed extract against colon adenocarcinoma cancer cell line (HT-29).

METHODS

The CMBNPs were biosynthesized by continuously stirring an aqueous solution of pumpkin seed extract with CuSO4 and manganese (II) acetate tetrahydrate until a dark green solution was obtained. The characteristic features of biogenic CMBNPs were assessed by UV-visible spectrophotometry (UV-vis), X-ray powder diffraction (XRD), energy-dispersive X-ray (EDX), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). A battery of biological assays, viz. neutral red uptake (NRU) assay, in vitro scratch assay, and comet assay, were performed for anticancer efficacy evaluation.

RESULTS

The formation of spherical monodispersed bimetallic nanoparticles with an average size of 50 nm was recorded using TEM. We observed dose-dependent cytotoxicity of CMBNPs in the HT-29 cell line with an IC₅₀ dose of 115.2 µg/mL. On the other hand, CMBNPs did not show significant cytotoxicity against normal cell lines (Vero cells). Furthermore, the treatment of CMBNPs inhibited the migration of cancer cells and caused DNA damage with a significant increase in comet tail length.

CONCLUSIONS

The results showed substantial anticancer efficacy of CMBNPs against the studied cancer cell line. However, it is advocated that the current work be expanded to different in vitro cancer models so that an in vivo validation could be carried out in the most appropriate cancer model.