Comparative Analysis of Predictive Models for Individual Cancer Risk: Approaches and Applications

Simple Summary

Predicting who might develop cancer is an important part of preventing and detecting the disease early. This research explores and compares hundreds of tools that estimate a person’s individual risk for different types of cancer. The goal is to understand how these tools are built, which cancers they focus on, and how reliable and easy they are to use. The study finds that while some cancers have many prediction tools, others—especially rare types—have very few or none at all. It also shows that most models still need better testing to confirm they work well across different populations. By highlighting what’s working and what’s missing, this research aims to improve how cancer risk is assessed and support the development of better tools for doctors, researchers, and individuals seeking to understand and reduce their cancer risk.

Abstract

Introduction: This article provides a comprehensive analysis of predictive models for individual cancer risk, examining their development, application, and evaluation. The study covers various cancer types, highlighting the diversity and sophistication of models over time. Methods: Utilizing data from PubMed, Web of Science, and Scopus, the research includes models developed for 22 cancer types, with significant emphasis on breast and colorectal cancers due to their prevalence and early detection benefits. Results: The analysis reveals an uneven distribution of models, often concentrated in the United States and the United Kingdom, with a notable gap in models for rarer cancers. Key methodologies such as logistic regression and Cox proportional hazards models dominate the field, reflecting a preference for established statistical techniques. The study underscores the importance of incorporating multiple risk factors, including genetic, environmental, lifestyle, and clinical data, to enhance predictive accuracy. Despite advancements, the article identifies a critical need for external validation and standardization in reporting practices to improve model reliability and generalizability. Conclusions The findings emphasize the potential of these models in personalized cancer prevention and early detection, while also calling for continued research and methodological harmonization to address existing gaps and challenges.

1. Introduction

The genesis of cancer risk prediction models dates back several decades, with pioneering efforts aimed at identifying individuals at higher risk of developing chronic diseases. Among the first of these was the Framingham Coronary Risk Prediction Model introduced in 1976 [1], which utilized a combination of clinical and biological factors to estimate the risk of heart disease. This model set the precedent for future endeavors in risk prediction, demonstrating the utility of incorporating multiple risk factors into a cohesive model to inform clinical decision-making. Its success paved the way for the development of models focused on cancer risk, beginning in earnest in the late 1980s and early 1990s. These early models primarily targeted breast cancer, integrating known risk factors such as age, reproductive history, and family history to calculate an individual’s absolute risk of developing the disease over a specified timeframe.

The interest in and reliance on cancer risk prediction models have only intensified since. Today, the proliferation of digital platforms, from informational websites to comprehensive handbooks and professional society resources, underscores the growing public and professional interest in these tools. This is further evidenced by the emergence of companies offering genetic risk profiling services and the prioritization of risk prediction research by leading cancer institutions like the National Cancer Institute (NCI). The NCI, recognizing the significance of risk prediction in cancer research, has highlighted it as an area of “extraordinary opportunity” [2]. Cancer risk prediction models play a growing role in clinical decision-making by identifying individuals at elevated risk, enabling targeted screening, early intervention, and tailored preventive strategies. Their use is increasingly supported by healthcare systems seeking to move toward personalized medicine and population-level risk stratification.

However, as the number of cancer types studied and the sophistication of predictive models have expanded, so too has the variability in their development, application, and evaluation. The proliferation of models has led to a landscape marked by significant disparities in the number and type of models available for different cancer types. This uneven distribution raises important questions about the factors driving these disparities and the implications for cancer risk prediction across the spectrum of disease. It underscores the need for a comprehensive examination of the current state of cancer risk prediction modeling, with a focus on understanding the diversity of approaches and the challenges and opportunities they present.

The objective of this scoping analysis is to provide a comprehensive descriptive synthesis of cancer risk prediction models, analyzing their development, methodological characteristics, and scope across 22 cancer types. We aim to map the diversity of modeling approaches, highlight disparities in model availability, and explore methodological trends, limitations, and areas for future development.

2. Materials and Methods

2.1. Study Selection

We evaluated cancer risk prediction models by searching PubMed, Web of Science, and Scopus up to December 2023. The PubMed search string was as follows:

(“cancer risk model”[Title/Abstract] OR “risk prediction model”[Title/Abstract] OR “risk assessment model”[Title/Abstract] OR “cancer prediction model”[Title/Abstract]) AND (“neoplasms”[MeSH Terms] OR cancer[Title/Abstract] OR tumor[Title/Abstract] OR tumour[Title/Abstract] OR neoplasm*[Title/Abstract]) AND (“humans”[MeSH Terms]).

The inclusion criteria mandated studies to be peer-reviewed, detailed risk models of cancer. We included studies that reported the development or external validation of a quantitative cancer risk prediction model, defined as a model that outputs an individual’s risk of developing cancer based on one or more predictors. Studies were required to report a sample size of at least 500 individuals in the development or validation cohort to ensure statistical reliability. Both non-genetic and genetics-based models were included. Models solely based on biomarkers or imaging were excluded unless integrated into a broader risk model. Diagnostic models were included, but diagnostic testing studies were excluded, as were feasibility studies and cost–benefit studies. Models for the development of a second cancer were included, but prognostic models for the risk of cancer relapse, metastasis, or cancer-specific survival were excluded.

2.2. Data Extraction and Synthesis

For each study, we extracted comprehensive data including the model name, year, type, targeted population, geographical area, follow-up duration, number of subjects, derivation set size, validation metrics, discrimination power, factors incorporated, TRIPOD level, data sources, data collection years, participant age, prediction rule risk thresholds, study design, methods, applicability, strengths, limitations, risk measures, calibration, accuracy (sensitivity/specificity), independent testing, inclusion/exclusion criteria, prognostic/diagnostic focus, validation efforts, and reproducibility.

3. Results

3.1. Type of Cancer

Our comprehensive analysis encompassed a wide array of cancer types, each represented by distinct models focusing on risk prediction. The models spanned across 22 cancer types (

Table 1. Division of models across cancers and articles.

Cancer Type	Number of Articles	Number of Models
Bladder	17	29
Breast	103	143
Colorectal	94	144
Esophageal	23	47
Blood	6	9
Kidney	25	39
Head and neck	16	23
Liver	33	46
Lung	40	65
Ovary	14	14
Pancreas	35	48
Prostate	68	97
Melanoma	35	62
Stomach	17	19
Testis	2	2
Thyroid	4	6
Cervix	4	4
Endometrium	16	17
General	3	4
Non-melanoma skin cancer	7	10
Eye	1	1
Gallbladder	1	3

). This diverse collection illustrated the breadth of research efforts aimed at developing predictive models that incorporate a range of risk factors.

We did not find any models for cancer of the brain or nervous system, Kaposi sarcoma, mesothelioma, penis cancer, anal cancer, vaginal cancer, bone sarcoma, soft tissue sarcoma, small intestine cancer, and or sinonasal cancer. There are several possible reasons for this. First, some cancers, such as Kaposi sarcoma and sinonasal cancer, are relatively rare, making it challenging to gather sufficient data for model development. Second, several of these cancers might have complex pathophysiologies that may complicate risk prediction modeling. And third, there may be less research focus or funding for certain cancers compared to more prevalent types like breast or lung cancer. In any case, the research focus is clearly skewed towards the most frequent cancers [3], and particularly towards cancers for whom early diagnosis might be the most feasible and beneficial. Efforts are now underway to have such models inform screening [4].

We have distinguished between melanoma and non-melanoma skin cancers (NMSCs) in our dataset. Melanoma, characterized by its aggressive behavior and distinct etiologic pathways, is biologically and clinically separate from more common NMSCs such as basal cell carcinoma or squamous cell carcinoma. Where applicable, these categories have been treated separately in the analysis to ensure accurate representation of the models and their respective risk profiles.

The term “blood cancers” as used in this review encompasses a diverse group of hematologic malignancies, including leukemia, lymphoma, and multiple myeloma. While grouped together for reporting purposes, these cancers differ significantly in their pathophysiology, diagnostic criteria, and risk factors. In particular, several included models focused on myeloma, such as those using routine blood test patterns to predict early disease. We acknowledge this heterogeneity, but since the group is small we do not believe it is useful to disaggregate the category for more precise analysis.

The integration of subtype-specific data into cancer risk prediction models offers a nuanced approach that may significantly enhance the accuracy and clinical utility of these models. The underlying logic for the development of specific models within these cancers is somewhat different for each. Figure 1 presents a breakdown of the subtypes within selected cancer categories: colorectal, esophageal, head and neck, and prostate cancers. These specific groupings were chosen based on clinically and epidemiologically meaningful differences that directly influence risk modeling.

In colorectal cancer, models often distinguish between colon and rectal cancer, as these subtypes differ in anatomical location, risk factors, progression, and treatment strategies. Esophageal cancer is typically modeled as either adenocarcinoma or squamous cell carcinoma, reflecting distinct etiologies—primarily gastroesophageal reflux and obesity for the former, and tobacco and alcohol use for the latter. In head and neck cancer, subsite-specific models are prevalent due to the high anatomical complexity and variation in prognostic factors, such as HPV status. Prostate cancer models frequently separate indolent from clinically significant disease, which has important implications for surveillance and treatment decisions.

This overview is not intended as a strict classification system but as a descriptive summary to reflect real-world modeling practices and to clarify where and why subtype-specific stratification is commonly used.

3.2. Year of Publication

The earliest model we identified was published in 1988, and models have continued to be published up until the present (Figure 2a). Upon examining the frequency of publications over these years, we see steadily rising interest in the field, although we also observe a non-uniform distribution. The late 1980s and early 1990s show sporadic activity, with a few publications, signaling the nascent stages of cancer risk modeling. This period likely represents the foundational research efforts, characterized by pioneering studies exploring the feasibility and methodologies for cancer risk prediction. A good example of this is the Gail model for breast cancer [5] which was published in 1989 and has not only been adapted to specific populations [6] but has also led to the development of other models that have tried to imitate its unique methodology [7,8,9]. Starting in 2000, we see a noticeable uptick in the number of publications, which can be attributed to advances in computational methods, the increased availability of epidemiological data, and a growing recognition of the potential for predictive models in personalizing cancer screening and prevention strategies. This continued interest is likely driven by the integration of new technologies (e.g., machine learning, big data analytics) into risk modeling, the identification of new risk factors through genomic studies, and a push towards more personalized and precise oncology.

3.3. Applicability

The examination of target populations for the applicability of newly developed cancer risk prediction models reveals a broad spectrum of demographics and clinical conditions, reflecting the diverse nature of cancer risk factors and tailored preventive strategies (Figure 2b). For this analysis, we did not take ethnic background or nationality into account, since this is inherent in the development population. We did not represent age criteria to allow for the easier representation of the data. Without considering such age criteria, roughly half of the prediction models were applicable to the general public. The pronounced emphasis on gender-specific cancer risk prediction models can be largely attributed to the prevalence of breast and prostate cancers, which are the most common cancers among women and men, respectively. This focus is not only reflective of the high incidence rates but also underscores the significant impact these cancers have on public health. However, the influence of sex and gender extends well beyond these cases. Biological sex can impact hormonal milieu, immune response, and metabolism, all of which may modulate cancer risk across a wide range of cancer types. For instance, studies have identified sex-based differences in colorectal, lung, liver, and thyroid cancers, with potential implications for risk stratification.

In addition to biological sex, gender-related behaviors—such as tobacco and alcohol use, occupational exposure, and health-seeking behavior—may also affect cancer risk and screening participation. However, these variables are often underreported or inconsistently captured in model development studies.

Notably, our review found that many models include sex as a predictor even in non-sex-specific cancers, though its role is not always explored in depth. Enhancing attention to sex and gender as cross-cutting risk modifiers, and validating models across sex-diverse subpopulations, may improve the generalizability and equity of future prediction tools.

Models tailored to chronic hepatitis (3.8%), other medical conditions (1.7%), and symptomatic patients (1.9%) highlight the integration of clinical indicators in risk prediction and a move towards more personalized medicine. Targeted models for high-risk groups, including those with a family history of breast (1.6%) or ovarian cancer (1.0%), point towards the use of genetic information and family medical history as critical components in predicting cancer risks. These models are crucial for early intervention strategies in populations known to carry higher genetic risks.

Many pancreatic cancer risk prediction models have been developed for populations with recent-onset or pre-existing diabetes, particularly type 2 diabetes. This reflects growing evidence that new-onset diabetes may be an early marker of pancreatic neoplasia. Some models also incorporate metabolic indicators [10] or patterns of glucose dysregulation [11] to enhance early detection in at-risk individuals, underscoring the link between endocrine disruption and tumorigenesis in this context.

3.4. Population Used for Development

The development of robust cancer risk prediction models is critically dependent on the demographic and statistical characteristics of the subjects included in the development cohorts. An analysis of the cohort sizes used across various studies provides insights into the statistical power and potential generalizability of the resulting models (Figure 2c).

A significant number of studies rely on relatively small cohorts. While these studies can offer highly detailed data on specific populations, they may lack the statistical power necessary for broader applicability and may be more prone to overfitting. Larger cohorts can provide the robust data needed to account for the variety of genetic, environmental, and lifestyle factors that influence cancer risk. They also typically provide a more reliable basis for developing predictive models due to their greater diversity and statistical power. However, the feasibility of assembling such large cohorts often limits their availability. Therefore, strategies that combine data from multiple smaller studies (meta-analysis) or the use of synthetic data augmentation techniques may be necessary to enhance the predictive accuracy and generalizability of risk models.

In our dataset, several cancer risk prediction models were derived from similar or even identical study populations, particularly in areas with large, well-known cohorts (e.g., SEER, PLCO, NHS). While each study reported a distinct modeling approach, the underlying data sources were occasionally shared or partially overlapping. This introduced a degree of dependency among certain models, which may have inflated the apparent diversity of the literature.

For the purposes of our analysis, we included all models that met the inclusion criteria and reported distinct development methods, regardless of potential dataset overlap. However, we acknowledge that this approach may overestimate model independence and advise caution when interpreting aggregate model counts as a proxy for model originality or population diversity. Future systematic reviews may benefit from explicitly tracking the data sources behind each model to better assess redundancy, representativeness, and the coverage of distinct patient populations.

Our analysis of the geographical areas utilized for the development of such models reveals a concentrated effort across a select number of countries, with the United States (USA) and the United Kingdom (UK) leading in terms of the volume of contributions (Figure 2d). This distribution highlights the significant engagement of these countries in cancer research and particularly their pivotal role in the development of large databases that are critical in the development of risk prediction models.

The geographical distribution of cancer risk prediction model development efforts also reflects a targeted approach, often dictated by the incidence rates of specific cancers within regions. This targeted focus is not arbitrary but is a strategic alignment with the pressing needs of each region, informed by the prevalent cancer types. For example, liver cancer, which has a markedly higher incidence in Asia compared to Western countries, sees a proportionately larger number of predictive models developed within Asian countries. This regional concentration in model development is driven by the imperative to address the most significant cancer threats affecting the population, leveraging local research capacities and clinical insights to devise accurate predictive tools. The emphasis on developing region-specific models based on prevalent cancer types does not necessarily detract from the global utility of these models. Instead, it highlights the complexity of cancer as a global health challenge and underscores the importance of a multifaceted approach in prediction model development. However, what might pose a potential limitation in the global applicability of these models is the under-representation of many other countries and regions. This skew towards data from predominantly Western and Asian populations might limit the effectiveness of the prediction models when applied to populations with different genetic backgrounds, lifestyles, and environmental exposures. The integration of data from multinational studies into these models serves to bridge the gap between regional specificity and global applicability. This approach ensures that the models are not only reflective of the unique cancer profiles of different regions but are also versatile enough to be adapted across various global contexts.

3.5. Reproducibility

In the context of cancer risk prediction models, user-friendliness and accessibility are essential for ensuring that these tools can be widely adopted and effectively utilized across various clinical and research settings, particularly because automated tools remain relatively rare [12]. We used a scoring system, with each entry categorized according to its implied ease of use based on several indicators:

• Easy: Models that allowed for straightforward usage by including elements such as scoring tables, nomograms, or simple formulas. Models that were supported by a website or mobile application were also included here.
• Medium: Models that required a working knowledge of statistics or dedicated software to reproduce were included here to reflect a moderate level of user accessibility.
• Hard: Models involving advanced methods like machine learning or missing significant information.

This analysis (see Supplementary Materials) reveals that a significant proportion of cancer risk prediction models are user-friendly, potentially facilitating their broader adoption and application in diverse settings (Figure 3a). We consider this to be of critical importance, particularly because we have attempted to reproduce a large number of these models by way of a mobile application [13], thereby facilitating access to them. However, the considerable number of models with a moderate or challenging ease of use highlights the ongoing need for improved design and documentation practices to make these tools more accessible.

3.6. Inclusion/Exclusion Criteria

The inclusion and exclusion criteria of the population used to develop a model are critical, as they directly influence the model’s applicability, accuracy, and generalizability. We scored the inclusion and exclusion criteria according to our own internal framework for their specificity and comprehensiveness. Based on the content and specificity of the descriptions, the criteria were classified into three categories (Figure 3b).

Strong criteria were often detailed and tailored to the study’s specific cancer type or risk factor. For example, criteria such as “Patients aged ≥18 years referred for haematuria investigations” and “Previous history of bladder cancer” reflect a focused approach to participant selection, aiming to isolate effects of specific variables on cancer risk.

Moderate criteria, while still significant, offered less granularity. These included conditions like general cancer histories or broader demographic specifications, e.g., “No prior history of cancer (except nonmelanoma skin cancer)” or “African-American ethnicity aged 35–64 years.” Such criteria help refine the study population but do not delve into as much detail as strong criteria.

Weak criteria were noted to be the least specific, sometimes due to incomplete data or overly broad definitions, such as participants described simply by the lack of certain diagnostic data or minimal demographic details without further health specifications.

The strength of the inclusion and exclusion criteria is pivotal in determining the precision and relevance of cancer risk prediction models. Strong criteria enhance a model’s predictive power by ensuring that the cohort closely matches the intended population. However, overly restrictive criteria can limit the generalizability of the results. Therefore, it is important to clearly define criteria without diluting the predictive accuracy due to a less targeted participant pool.

3.7. Discrimination Power

Discriminatory power, measured by the Area Under the Receiver Operating Characteristic Curve (AUROC), is crucial for the clinical utility of cancer risk models. Our dataset comprises AUROC values derived from various studies or models focused on cancer risk prediction. A total of 716 AUROC values were extracted and analyzed after appropriate data cleaning, including the conversion of percentage values and removal of entries before validation (Figure 3c). The concentration of scores around the upper end of the spectrum (0.85–0.89) suggests that most cancer risk prediction models perform well in distinguishing between high-risk and low-risk individuals [14]. This high level of performance is essential for models used in clinical settings where the cost of false negatives (failing to identify at-risk individuals) can be significant.

A small number of models exhibit AUROC values below 0.7, which, while still considered acceptable, indicate lower predictive accuracy. These models may require further refinement or might be specific to cancers that are inherently more challenging to predict due to overlapping symptoms with other conditions or less distinct biomarker profiles.

The histogram’s wide spread also raises important considerations regarding the variability in model construction, such as differences in underlying algorithms, training datasets, and the specific cancer types being predicted. For instance, models trained on large, well-annotated datasets or those utilizing more advanced machine learning techniques may demonstrate higher AUROC values.

3.8. TRIPOD Level

The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, which came out in 2015 [15], encompasses a checklist of 22 essential items, designed to standardize the reporting of studies that develop, validate, or update multivariable prediction models, irrespective of the diagnostic or prognostic aim.

The primary thrust of the TRIPOD guidelines is to foster transparency in reporting prediction model studies. This is achieved by mandating detailed disclosures regarding model development, statistical analysis, validation processes, and performance metrics. Specifically, the guidelines advocate for the explicit reporting of external validation efforts, which are indispensable for gauging a model’s generalizability and performance in real-world scenarios. The initiative categorizes predictive models based on their developmental and validation stages into distinct levels: 1a, 1b, 2a, 2b, 3, and 4.

In analyzing the distribution of TRIPOD levels within our dataset (see Supplementary Materials), it is evident that the practices surrounding the development and validation of predictive models vary significantly across studies (Figure 3d). We can say that roughly one quarter of the published models rely solely on apparent performance, one quarter exclusively use resampling techniques, one quarter randomly split the data into development and validation sets, and one quarter tries to externally validate the model. In other words, for a clear majority of the models, attempts were made to validate them, although only a minority externally validated them. A larger focus on external validation would be welcome, since this is crucial for determining the generalizability and applicability of predictive models across different populations and settings. Furthermore, for almost a quarter of the models, no validation efforts were made. This is unfortunate, since techniques such as bootstrapping or cross-validation are possible even when data are limited, while still mitigating overfitting and providing a more robust estimate of model performance. The observed distribution reflects a growing recognition within the scientific community of the need for rigorous evaluation methods to ensure the reliability and generalizability of prediction models.

It should be noted that no additional searches were made for independent validations of the models, which explains the low number of level 4 publications, and that many of the studies that externally validated their data also used resampling techniques beforehand.

3.9. Model Methodology

The analysis shows a clear preference for “Logistic regression” and “Cox proportional hazards model” designs, making up almost two-thirds of the models. Logistic regression, used in 44.2% of the cases, is favored for its straightforward interpretation of binary outcomes like cancer presence, aiding clinical decision-making (Figure 4a). The Cox proportional hazards model, at 20.4%, excels in survival analysis, crucial for assessing variables affecting time and events such as recurrence or mortality, thanks to its ability to handle censored data and time-dependent variables. Additionally, “Other” models, which account for 24.2%, indicate researchers’ openness to diverse and innovative methods for complex cancer-related questions. The aggregation category “Other”, which constitutes approximately 24.2% of the studies, was used to group modeling strategies that occurred five times or less. Its significant size demonstrates the willingness among researchers to innovate and tailor approaches to complex cancer-related questions. Of note, 2.3% of models relied on the Gail method, which was developed specifically for cancer risk prediction [5].

3.10. Calibration

Calibration ensures that cancer risk prediction models’ predicted probabilities match observed outcomes, enhancing model reliability and clinical decision-making [16]. Calibration was primarily evaluated using calibration plot analysis, the Hosmer–Lemeshow test and calculating the observed-to-expected ratio (Figure 4b).

The high number of “NR” (Not Reported) entries indicate that calibration is underreported, particularly when compared to discrimination. The common use of the Hosmer–Lemeshow test and calibration plot analysis indicates their popularity in assessing model calibration. The “Other/combination” category reflects mixed reporting practices, highlighting the need for standardized reporting.

3.11. Factors Incorporated

As depicted in the accompanying bar graph, there is significant variance in the number of risk factors utilized across different models (Figure 4c). The majority of models incorporate between 4 and 10 risk factors, suggesting a preference for models that balance predictive power and model simplicity. Notably, the models employing exactly five risk factors represent the peak in our distribution, indicating a common model configuration that may offer an optimal balance between complexity and ease of interpretation. This could be reflective of the fact that, beyond a certain point, adding more risk factors can lead to diminishing returns in terms of predictive accuracy and model usability.

A notable observation from the analysis of cancer risk prediction models is the relatively large number of models that utilize only one risk factor. This phenomenon may initially seem counterintuitive given the complex nature of cancer, but two key factors contribute to its prevalence. First, the single risk factor in question is often a score of some sort that relies on several elements. These are usually high-resolution imaging [17] and sophisticated genetic sequencing techniques [18,19], allowing for comprehensive insights. Second, the models in question were usually developed for highly selected target groups, which is clear from the intended inclusion and exclusion criteria for their development cohort [20].

The presence of models with 20 or more risk factors highlights an approach where extensive data collection and analysis are prioritized. These models, although less common, were usually models employing machine learning [21,22,23] or ones where highly individualized clinical information was available. The risk factors in these models might include genetic markers, lifestyle factors, and detailed medical histories, which can significantly enhance predictive accuracy at the cost of increased data requirements and computational complexity. Still, this accounts for a relatively modest number of models, suggesting a threshold beyond which the inclusion of additional risk factors may not be practical or beneficial in everyday clinical practice.

3.12. Data for Development

The dataset shows a significant reliance on prospective cohort studies (36.4%), valued for their ability to establish temporal sequences between risk factors and cancer outcomes (Figure 4d). Retrospective cohort studies (21.8%) offer the cost-effective exploration of large populations and historical data, crucial for hypothesis generation. Case–Control studies (21.4%) are efficient for studying rare cancers by comparing individuals with and without cancer to identify risk factors. Interestingly, the dataset also includes Pooled Cohort and Pooled Case–Control studies, signifying a collaborative effort to enhance statistical power and generalize findings across different populations. These pooled analyses, though less common, demonstrate the research community’s commitment to overcoming individual study limitations and variability in risk factor exposure across populations.

The analysis of the most frequently incorporated risk factors across various models offers a revealing glimpse into the current priorities and methodologies in cancer risk assessment (Figure 5).

The most prominent risk factors are as follows:

• Age and Gender: Unsurprisingly, age remains the most commonly cited risk factor, reflecting its fundamental influence on cancer susceptibility across multiple types. Similarly, gender is frequently considered, underlining specific cancer risks that are prevalent in either males or females, such as prostate and breast cancers, respectively.
• Genetic Markers: The inclusion of genetic markers, notably Polygenic Risk Scores and SNPs (Single Nucleotide Polymorphisms), highlights a significant shift towards genetic profiling in cancer prediction. These factors are crucial for assessing hereditary risks and are increasingly used to personalize screening and prevention strategies.
• Family History: This risk factor, often broken down into specific cancers such as lung cancer, underscores the importance of genetic predispositions in cancer risk assessments. The recurrence of family history across various models indicates a general consensus about its predictive value for hereditary cancer types.
• Lifestyle Factors (Smoking and Alcohol): Lifestyle choices such as smoking and alcohol consumption are well-represented in cancer risk models. These modifiable risk factors are critical for public health strategies and are actionable in preventative measures.
• Ethnicity: The inclusion of ethnicity and race as consolidated factors reflects the recognition of the different cancer risks and outcomes among ethnic groups, possibly due to genetic, socioeconomic, or environmental variations.
• Medical History and Symptoms: Conditions like diabetes have been linked to an increased risk of certain cancers, illustrating the interconnected nature of chronic diseases and cancer risk.

The diversity of these risk factors across models points to a multifaceted approach to cancer risk prediction, where both genetic and environmental factors are considered. This broad spectrum of risk factors aids clinicians in developing more accurate risk assessments and tailored prevention strategies. Moreover, it emphasizes the need for interdisciplinary research to further refine the impact of each risk factor on cancer development.

4. Discussion

We have mapped the landscape of cancer risk prediction models, illustrating a diversity of approaches that span traditional epidemiological factors and emerging methodologies. The variation in model development, validation, and performance metrics across different cancer types highlights the multifaceted nature of cancer risk prediction and the ongoing evolution of research methodologies in this field. The descriptive framework employed in this analysis aims to synthesize a highly heterogeneous landscape without oversimplifying it. This mapping of diversity—including the heterogeneity in endpoints (e.g., 5-year vs. lifetime risk) and the existence of overlapping datasets (e.g., NHS- or SEER-derived models)—highlights the fragmentation of these risk prediction models. It also helps to identify areas where modeling efforts have been most concentrated, as well as gaps in the literature where further research may be warranted. Overall, this structured overview supports a better understanding of current trends in cancer risk prediction and encourages greater consistency and transparency in future model development.

Cancer risk prediction models employ a variety of statistical and machine learning methods, each with distinct strengths and limitations. Traditional approaches, such as logistic regression and the Cox proportional hazards model, dominate the field due to their interpretability, simplicity, and established statistical properties. Logistic regression is commonly used to predict binary outcomes, such as the presence or absence of cancer, while Cox models are ideal for time-to-event analyses, incorporating censoring and survival times. Although our approach did not exclude any models based on their methodological approach, the final dataset includes a relatively small number of models built using machine learning (ML) techniques such as random forests, gradient boosting, or neural networks. This low representation was not due to a deliberate exclusion of ML methods, but rather reflects two key factors:

• Search Terminology and Reporting Bias: Many ML-based models are not explicitly labeled as “risk prediction models” or do not use standardized terms in their titles or abstracts, making them more difficult to capture in traditional systematic searches. Moreover, ML models are sometimes published in technical or engineering journals not indexed in the primary biomedical databases we used (PubMed, Scopus, Web of Science).
• Model Validation Requirements: Our inclusion criteria emphasized externally validated or robustly developed models, often with clear discrimination and calibration metrics. A considerable number of ML-based models did not meet these criteria due to incomplete reporting or the absence of validation, a known issue in the translational literature for ML in medicine.

The growing availability of large-scale biomedical datasets—including genomics, electronic health records, imaging, and wearable data—has opened the door for more advanced predictive modeling approaches. ML and deep learning (DL) are increasingly being applied in cancer risk prediction due to their ability to handle complex, nonlinear relationships and high-dimensional data. ML models, such as random forests, gradient boosting machines, and support vector machines, have been used to integrate clinical, genetic, and behavioral variables to improve predictive performance. Deep learning models, including neural networks and convolutional neural networks (CNNs), are particularly valuable when working with unstructured data like radiological images [24] or free-text clinical notes [25]. Despite promising results, several challenges limit widespread adoption. These include the lack of interpretability (“black-box” models), the need for large and representative datasets, data harmonization across centers, and the risk of overfitting in smaller or biased samples [26]. Furthermore, validation practices vary, and the lack of standardization in reporting hinders reproducibility and clinical translation. Nevertheless, AI-based models are likely to play a growing role in personalized risk assessment, particularly when combined with traditional statistical frameworks and validated in prospective cohorts. Future directions may include federated learning for privacy-preserving model development across institutions, and the integration of multimodal data to develop richer, more context-aware risk estimations. A key trade-off in model selection is between interpretability and predictive accuracy. While simpler models are easier to implement and explain, they may underperform in complex clinical contexts. Conversely, more complex models can be prone to overfitting and often require large datasets and robust validation to be clinically useful. Furthermore, calibration and external validation remain critical components for model reliability. Many high-performing models still fall short in these areas, highlighting a persistent gap between development and real-world deployment [27].

The categorization of cancer risk models by subtype, anatomical location, and clinical relevance reflects the diverse ways in which predictive models are developed and implemented. These groupings are based on established distinctions in risk factors, disease behavior, and management strategies that necessitate tailored modeling approaches. Risk prediction models vary not only by cancer type but also by the specific outcomes they aim to predict. Some models focus on estimating lifetime risk, while others are designed to assess short-term risk, such as over five or ten years. In addition, the models differ in their clinical objectives—ranging from predicting initial diagnosis to identifying individuals at risk of progression to advanced stages or symptomatic disease. To help navigate this complexity, detailed methodological attributes such as prediction timeframes, validation approaches, and risk factor composition are included in the Supplementary Materials. This paper underscores a prevalent challenge in the external validation of risk prediction models. Many models have not undergone rigorous testing in diverse populations, which raises questions about their generalizability and utility in broader clinical and public health contexts. Addressing this challenge requires a concerted effort to standardize validation practices and ensure models are tested across varied demographic groups, enhancing their applicability and impact [28]. If a model is trained predominantly on data from high-income populations and not externally validated, its predictions may be less accurate—or even misleading—when applied to underserved groups, potentially leading to inappropriate screening or missed diagnoses [29], unintentionally perpetuating or exacerbating health disparities. Additionally, some models are closely tied to specific healthcare settings or data types, making them less adaptable in systems with different infrastructure or diagnostic pathways. For instance, models that rely on routinely collected laboratory or imaging data may not be applicable in settings where such data are unavailable or inconsistently recorded. To address these issues, future models should prioritize external validation across diverse demographic and clinical settings. Standardized reporting using guidelines like TRIPOD [15] should be accompanied by the transparent documentation of dataset characteristics, inclusion/exclusion criteria, and performance metrics.

A key observation from our analysis is the nuanced manner in which risk factors are integrated into predictive models. While genetic markers, including Polygenic Risk Scores, play a role in certain models, it is evident that the most robust models incorporate a blend of genetic, environmental, lifestyle, and clinical factors [21,22,23]. This comprehensive approach mirrors the complex etiology of cancer, suggesting that an interplay of diverse risk factors contributes to the disease’s development.

Another notable finding from our analysis is the lack of cancer risk prediction models for many rare cancers, including sinonasal, penile, vaginal, Kaposi sarcoma, and small intestine cancers. The absence of models in these areas reflects a broader challenge in oncological research: data scarcity for low-incidence diseases. Rare cancers often suffer from limited sample sizes, fragmented registries, and underfunding [28]. We have attempted to develop our own model for these rare cancers [13], but there are several other strategies that could enable the development of more robust risk models:

• International Collaborations and Research Consortia: Pooling data across countries and institutions can dramatically increase the number of eligible cases. Initiatives like the International Rare Cancers Initiative (IRCI) or rare cancer-focused branches of the Cancer Genome Atlas are examples of how such partnerships can facilitate model development.
• Federated Learning and Privacy-Preserving Analytics: These emerging approaches allow researchers to train models across multiple datasets without transferring sensitive data. This method respects privacy regulations while leveraging broader and more diverse datasets.
• Synthetic Data and Data Augmentation: Advanced generative techniques, including generative adversarial networks (GANs), can be used to simulate realistic but artificial datasets, providing additional training data for algorithm development without compromising patient privacy.
• Adaptive Model Architectures: Using techniques such as transfer learning or Bayesian frameworks can allow models developed for common cancers to be adapted or extended to rarer types, especially when certain pathophysiological traits overlap.
• Mandated Reporting and Registry Improvements: Strengthening cancer registries and enforcing comprehensive data reporting standards can improve the quality and completeness of rare cancer data, laying the groundwork for future modeling efforts.

The practical adoption of cancer risk prediction models depends not only on their clinical accuracy and methodological rigor, but also on their cost-effectiveness [30]. The development of such models can require substantial investment in data acquisition, preprocessing, feature engineering, model training, validation, and software implementation. These costs are further amplified when models are tailored for specific populations or incorporate high-dimensional data such as genomics or imaging. Models that require specialized inputs (e.g., genetic testing, advanced biomarkers) may not be feasible in resource-limited environments, even if they demonstrate superior performance in research settings. To optimize cost-efficiency without sacrificing predictive value, several strategies are emerging:

• The use of routinely collected clinical data can minimize development costs and maximize applicability.
• Simplified models that use a smaller number of readily available variables can be nearly as accurate as more complex models, especially when supported by robust statistical validation.
• Hybrid models combining traditional statistical approaches with AI methods can reduce computational burden while improving adaptability.
• Open-source software tools and shared model repositories can reduce the duplication of effort and promote collaborative refinement.
• Health economic evaluations, such as cost–utility or budget impact analyses, are increasingly being integrated into risk model research, providing evidence for policymakers and insurers.

The potential impact of these predictive models on cancer prevention and early detection is substantial. Tailored risk assessment can guide personalized screening strategies, potentially leading to earlier detection and more effective interventions for high-risk individuals. However, the translation of these models into clinical practice necessitates not only methodological rigor but also careful consideration of the ethical implications associated with risk prediction, particularly regarding data privacy, algorithmic bias, and health equity. These models often rely on large-scale datasets that include sensitive health, genetic, behavioral, and socioeconomic information. Ensuring informed consent, data security, and transparency in data use is critical to maintaining public trust and complying with legal frameworks like the General Data Protection Regulation (GDPR) or Health Insurance Portability and Accountability Act (HIPAA). A central challenge lies in balancing the utility of detailed personal health data with the imperative to protect individual privacy. Strategies such as de-identification, federated learning, and secure data enclaves offer partial solutions, but the risk of re-identification persists, particularly with genomic and longitudinal data [31].

The large methodological diversity observed among the included models calls for a move towards harmonization. Establishing a consensus on methodological best practices, including the selection and weighting of risk factors, could improve the reliability and reproducibility of predictive models. Future research should also prioritize the exploration of under-represented cancer types and risk factors, broadening the scope of predictive modeling to encompass a wider array of cancers.

Ultimately, the responsible use of cancer risk prediction models demands a multidisciplinary approach, integrating technical excellence with ethical oversight, stakeholder engagement, and continuous monitoring for unintended consequences.

5. Conclusions

This analysis underscored the progress and challenges within the field of cancer risk prediction. While significant strides have been made, gaps remain, particularly in model validation and in covering a broader spectrum of cancer types. Our findings suggest an ongoing need for rigorous external validation to ascertain models’ applicability and for continued research to fill the existing gaps, thereby enhancing the predictive accuracy and utility of cancer risk models in personalized medicine and public health.