There has been a considerable emphasis on applying recurrent neural networks (RNNs) in predictive modeling within the medical field, particularly for cancer treatment. The cancer lifespan along with its diverse health indicators and response to treatment has temporal dynamics that strongly aligns with the capabilities of RNNs in sequential data processing. The models can offer critical value insights across early diagnosis, treatment tailoring and even prognosis forecasting by identifying complex patterns at various time snapshots. This chapter analyzes the application of RNN architectures, specifically long short-term memory (LSTM) and gated recurrent units (GRUs), for cancer monitoring over time series data. The chapter discusses the clinical workflows involving model implementation, which include data cleansing, feature extraction, validation and even automation. In addition, we present a systematic review of the methods used within the literature to document, alongside the datasets and benchmarks to discover some emerging trends, gaps and best practices that are available. Also, we use concrete actions emanating from case studies which are scrutinized for real-life application. However, the review also notes the sparse explainable multimodal genomic, imaging and clinical record data as some of the primary unresolved core issues. A conversation also exists concerning the ethical ramifications brought about by the use of such models, particularly patient confidentiality and various data security measures in place. Finally, we provide recommendations regarding the factors that affect the scale, accuracy and uses of RNN-based predictive models in clinical practice with special attention to cancer care, stressing the need for teamwork and ongoing modification intervention models in cancer care. In contemporary times, cancer remains one of the most significant factors contributing to the global mortality rate, claiming millions of lives from diverse groups every year. As medical science evolves, there is always a greater demand for accurate, prompt and customized intervention plans. In oncology, the framework of dynamic foresight requires appropriate and timely prediction of illness progression and response to treatment along with the outcomes, which means advanced decision-making on care strategies is always essential due to the multiple pathways that each illness could take (Khoury et al. 2015). In regard to predicting outcomes for cancer, researchers have made some initial achievements through using older statistical datasets such as autoregressive integrated moving average (ARIMA) time-series models, linear regression or even Cox proportional hazards regression. However, when dealing with intricate high-dimensional inputs involving sampling intervals, noise or omissions in data and sparse scattered inputs, these models that rely on capturing patterns systematically begin to break down. The ongoing challenge of sequential data is worked on within the discipline of deep learning models such as recurrent neural networks (RNNs), which are tailor-made for such tasks. Compared to RNNs, reformulated feedforward neural networks (RFNNs) are specialized for the spatial rather than sequential data (Ahmed et al. 2023). With time, RNNs can learn time-related contextual information, which improves the performance with longitudinal patient records, monopole supersonic biomarker shifts, clinical events and the measurement of various clinical phenomena. The increased availability of longitudinal datasets, such as EHRs, devices and genomic information, has created new possibilities for predictive modeling in cancer care. With the help of RNNs, predictive models can determine the expected time of patient decline, possibility of treatment resistance and recommend active therapies aimed especially at assisting osteoclasts in decision-making – improving the already enhanced treatment provided to the patients by clinicians (Damaševičius et al. 2024). In this chapter, we investigate the use of RNNs in the predictive model of cancer-related time series data. Our focus will be on the intersection of advancement in machine learning and its application in oncology. In oncology, the time series data includes dimensional patient data such as clinical notes and bone CT scans, levels of blood markers, medications taken, genomic data and even the size of the tumor. This data gives the holistic view of a patient’s health over time, helping to capture complex patterns or trends suggesting disease progression, recurrence or response to treatment over time. The time relations connected with the data aid in the building of multiple models, which may assist in the forecasting of critical clinical events such as cancer relapse, life expectancy and follow-up complications (Suresh et al. 2022). For example, frequent imaging can reveal incremental morphologic changes of neoplasms, and stepwise measuring of a biomarker can indicate clinical decline. Alongside that, other traditional clinical records provide in situ real-time data of high cadence with remote health monitoring devices such as heart rate and oxygen saturation. The integration of various time series modalities into a single model increases the probability of uncovering hidden temporal links, as well as novel patterns pertaining to the patient. However, it presents issues regarding the alignment of synchronization, resolution, sequential gaps and variable-length timelines. In particular, these issues are critical to fully leverage time series data, especially in the scope of treatment of cancer. In the field of oncology, the following statistical prediction methods are available: Cox proportional hazards model, ARIMA and support vector machines (SVMs) (Ozer et al. 2020). The Cox model specializes in survival analysis and has been widely adopted due to its interpretable hazard ratios, which aid clinical decision-making (Cox 1972). Forecasting of incidents of cancer and its mortality rate has been done with the help of ARIMA models, which in the beginning were introduced in the domain for economy. The application of SVMs, which perform optimally in high-dimensional spaces, has been applied to classify subtypes of cancer and predict outcomes from often obsolete clinical or genomic information (Guyon et al. 2002). The varying degrees of flexibility and diversity in longitudinal cancer data remain problematic, even when bypassing the usefulness that these models provide. Multiomics data and other features with high dimensionality, particularly those gleaned from genomics, often cross the limits of traditional statistical approaches. Moreover, the absence of periodic data point timeframes, gaps in the data and non-uniform lengths of sequences add to the problem. These challenges reveal important dependencies and irregularities that are crucial to oncology time series and show a gap in their intricate management. Such sophisticated model design gaps highlight the onset of deep learning, where those complex interrelations and dependencies are carved. The application of deep learning to high-dimensional data has led to groundbreaking advancements in diagnostic imaging, genomic medicine and clinical decision support, greatly transforming healthcare. The transformation in healthcare does not end here, as deep learning brings shifts to many domains, such as natural language processing and computer vision. Tumor detection in radiology and pathology slides has reached new levels of expertise, thanks to the extensive use of convolutional neural networks (CNNs), which are considered the gold standard for medical image analysis (Esteva et al. 2017). In comparison to other neural networks, RNNs are specialized for sequential and time-dependent data. In more recent research, they have been linked to oncology. Unlike earlier models, RNNs have a hidden state that is capable of remembering the information from certain previous time steps, allowing them to model temporal dependencies in longitudinal health data. Studies point out that RNNs outperform classical statistical models in survival analysis by restructuring estimation of risks based on changing patient data (Choi et al. 2017). RNNs have also been used in predicting treatment response where underlying temporal relationships in laboratory results or clinical observations dictate therapeutic responses. With respect to these goals, more advanced RNNs such as long short-term memory (LSTM) and gated recurrent units (GRUs) have fared better at modeling disease trajectories by taking both short-term and long-term dependencies of patient histories into consideration. These models help clinicians predict some future clinical events such as metastasis or relapse and thus allow timely and proactive interventions to be administered (Miotto et al. 2018). Therefore, techniques of deep learning, especially those using the RNN architecture, have begun to be applied in oncology in order to develop more adaptable and sophisticated patient management systems. A traditional RNN has a recurrent layer with a loop that uses the output of the current time step as input for the next step. This simple RNN structure can maintain major data across time steps due to this loop. As a result, the architecture can capture dependencies over time. It can therefore process sequential data such as time series data (Kag and Saligrama 2021). However, even with the benefits of intuitive understanding, plain RNNs were the most limited in scope for long sequences. One of the most lacking issues is over-getting the vanishing and exploding gradient problem while backpropagating through time (BPTT), as this cumulatively decreases effective learning due to gradients getting too small or large. This limits the ability to achieve long-term dependency, which is crucial in modeling cancer progression; something that requires long time intervals. These limitations greatly impede the usefulness of traditional RNNs in obtaining the longitudinal recurrence data in oncology or monitoring tumor growth or treatment response over time. For this reason, more advanced frameworks have been made to tackle such problems, including LSTM networks and GRUs (Sachin et al. 2020). In any case, traditional RNNs retain the form of foundational frameworks and design for sophisticated modern recurrent structures and serve as a convenient entry point to explore the evolution of these systems. The cell structure of LSTM networks is designed with memory cells and input, forget and output-gating features that improve upon the limitations of RNNs. Unlike RNNs, LSTMs structure their internal memory and update it to avoid the vanishing gradient problem. LSTMs are able to store and recall information over long periods of time, which is critical for accurately assessing a complex multifactored disease progression model for cancer patients (Mienye et al. 2024). In healthcare, LSTMs have been successfully applied in longitudinal modeling tasks such as anticipating time to event, treatment response and tracking patient decline. An example is the time-series gene expression and clinical data-based LSTM models for breast cancer relapse prediction. In addition, LSTMs are proven to more accurately predict outcomes when dealing with heterogeneous time-dependent data such as periodic imaging and continuous physiological signal integration. Outside of modeling single patients, hybrid LSTMs with attention layers or convolutional layers have been developed to analyze longitudinal population-level data and detect temporal patterns to proactively inform clinical decision support systems and risk stratification guidelines (Xie et al. 2022). The discussed approaches focus on enhancing feature extraction as well as model interpretability, thus enabling clinicians to understand the temporal reasoning underlying model predictions. According to the literature, GRUs are regarded as less advanced when placed alongside LSTM. While LSTMs are capable of modeling temporal dependencies, they incorporate a greater level of complexity when compared to GRUs. GRUs do not possess the same number of gates as LSTMs do; in place of the LSTM input, forget and output gates, GRUs have a reset and an update gate. This streamlined design enables faster training times with less computational resources, making GRUs more appealing in scenarios constrained by data or hardware (Cahuantzi et al. 2023). With respect to cancer time series modeling, GRUs have demonstrated effectiveness on par with LSTMs in estimating outcomes, measuring treatment effects and predicting adverse events. Their relative efficiency also means that sophisticated real-time predictive modeling, such as mobile health monitoring, can be performed under heightened latency constraints. GRUs have recently been implemented for merging complex time series data, which includes laboratory tests, imaging metrics and medication history for accurately predicting disease progression. GRUs are addable to ensemble and hybrid deep learning architectures, which is useful in a clinical setting because of the interpretability, while complexity and performance are preserved (Patharkar et al. 2024).
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Recurrent Neural Networks for Predictive Modeling in Cancer Time Series Data
13.1. Introduction
13.2. Background and related work
13.2.1. Time series data in oncology
13.2.2. Traditional predictive models
13.2.3. The development of deep learning
13.3. RNN architectures
13.3.1. Vanilla RNN
13.3.2. LSTM
13.3.3. GRUs
13.3.4. Bidirectional RNNs
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