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Cervical cancer; Drug toxicity; Genetic prediction; Machine learning; Personalized medicine

Introduction

Cervical cancer remains a substantial public health challenge, particularly in low- and middle-income countries where access to regular screenings and advanced treatments is limited. While therapeutic advancements have been made, the efficacy of cervical cancer treatments is frequently compromised by drug toxicity. Adverse drug reactions not only impede treatment progress but also lead to increased healthcare costs and reduced patient quality of life. A pivotal factor contributing to the variability in drug toxicity responses is the genetic makeup of individual patients. The advent of high-throughput genomic technologies has enabled the profiling of patient genomes, facilitating the identification of genetic variants associated with drug responses. Concurrently, machine learning methodologies have garnered attention for their ability to analyze vast and complex genetic datasets, extracting valuable patterns and insights. By integrating genetic information with clinical data, machine learning models can learn to predict patient-specific susceptibility to drug toxicity. This predictive capability holds immense potential for tailoring treatment regimens to individual patients, thereby minimizing the occurrence of adverse reactions and improving treatment outcomes [1, 2].

In this study, we propose an innovative application of machine learning techniques to predict drug toxicity in cervical cancer patients. We hypothesize that by harnessing the power of genetic data and leveraging advanced algorithms, we can develop accurate models for identifying patients at a higher risk of experiencing drug toxicity. This predictive approach aligns with the principles of personalized medicine, where treatment strategies are customized based on each patient's unique genetic profile. Through this research, we aspire to enhance the safety and effectiveness of cervical cancer treatments, fostering a new era of precision oncology that prioritizes patient well-being. Radical hysterectomy stands as a cornerstone in the management of early-stage cervical cancer, aiming to achieve optimal oncological outcomes while preserving patient quality of life [3]. This surgical procedure involves the removal of the cervix, parametria, upper vagina, and associated lymph nodes, with variations in technique based on disease stage and patient characteristics. Over the years, different surgical approaches have emerged, including open abdominal, minimally invasive laparoscopic and robotic-assisted techniques. This comprehensive review examines the evolution of radical hysterectomy, comparing surgical approaches, detailing perioperative considerations, and evaluating long-term oncological and functional outcomes. While minimally invasive techniques offer potential benefits in terms of reduced morbidity and quicker recovery, concerns have arisen regarding their impact on disease recurrence. We synthesize the latest evidence to provide insights into the decision-making process for selecting the most appropriate surgical approach, emphasizing the importance of multidisciplinary collaboration and personalized treatment strategies. Cervical cancer remains a significant global health challenge, necessitating innovative therapeutic strategies to improve patient outcomes [4]. Neoadjuvant chemotherapy (NACT) has emerged as a promising approach in the management of locally advanced cervical cancer. This treatment paradigm involves administering chemotherapy before definitive surgery or radiation therapy, with the goal of reducing tumor burden, enhancing resectability, and potentially improving long-term survival. This review explores the scientific rationale behind NACT, highlighting its impact on tumor biology, its role in downstaging disease, and its potential to facilitate less radical surgical interventions [5]. We delve into the evolving landscape of NACT regimens, addressing their efficacy and associated challenges, such as patient selection and drug resistance. Furthermore, we discuss emerging strategies to optimize NACT outcomes, including the integration of targeted therapies and immunomodulatory agents. By elucidating the current state of NACT in cervical cancer treatment and envisioning its future trajectory, this review aims to contribute to the refinement of therapeutic approaches and the advancement of personalized care [6]. Neurotoxicity assessment methods, including behavioral, imaging, and biomarker approaches, are discussed in detail, highlighting their utility in diagnosing and monitoring neurotoxic effects. Furthermore, we address the challenges associated with detecting subtle or delayed neurotoxic effects, emphasizing the importance of longitudinal monitoring and multidisciplinary collaboration. Mitigation and management strategies for neurotoxicity constitute a critical aspect of this review. We explore preventive measures such as exposure reduction, as well as therapeutic interventions targeting neuroinflammation, oxidative stress, and neuroregeneration. The significance of personalized medicine in managing neurotoxicity is underscored, acknowledging the individual variability in susceptibility and response.

Materials and Methods

The selection of patients

Between November 2022 and July 2023, 259 LACC patients with FIGO stage IB2–IIB were diagnosed at the First and Second Hospital of Lanzhou University and Gansu Provincial People's Hospital. Each individual was Han Chinese from the province of Gansu and its environs. The following are the study's inclusion criteria: (i) A preoperative biopsy verified cervical cancer, and a postoperative examination revealed highly and/or moderately differentiated squamous cell carcinoma. (ii) No prior history of malignant malignancies [7].

NACT regimen and toxic effects

There were more than two NACT cycles given to the patients. On the first day of a cycle, the patient takes 175 mg/m2 of paclitaxel and then receives 70–75 mg/m2 of cisplatin or carboplatin-based on the area under curve 5. To decrease nephrotoxicity, cisplatin was administered as a 6- to 8-hour infusion with intravenous hydration and mannitol. Thus, the main adverse reactions of NACT include neurotoxicity, gastrointestinal and hematologic toxicity in this study. In accordance with the National Cancer Institute General Terminology Standard for Adverse Events, the patient's toxic reaction was classified [8].

Machine learning models

Decision tree (DT) algorithms: The Decision Tree (DT) algorithm is one of the most typical machine learning algorithms. A DT model has a tree-like structure consisting of three types of nodes: root nodes, decision nodes, and leaf nodes. The root node is the starting point of the decision tree. Each decision node can be a variable or a split point of a variable if the feature values are continuous, and finally leaf nodes represent the outcome of the decision. At the root node, the collection contains data samples for the entire data set. The decision tree then splits the dataset based on the characteristics of each decision node, splits the dataset into subcollections, and outputs the final decisions at the leaf nodes. The DT algorithm is intuitively logical and easy to implement and interpret. As such, it is widely used in many fields for classification and importance analysis tasks.

Random forest (RF) algorithm: Random Forest (RF) algorithm summarizes a series of independent His DTs using a bagging method. The model fitting process takes reset samples from the original dataset to create subdatasets. Here the subdatasets have the same dataset as the original and the samples within or between the subdatasets are repeatable. Each subdataset is used to build a decision tree to form a forest of many trees. Given a new input sample, each DT in RF classifies the sample and returns an independent classification result that counts as one vote for the entire forest. The classification result of DT with the highest value is used as the result. Since RF is composed of many DTs, it can achieve higher and more robust performance than just single decision tree feature importance.

Results and Discussion

From the initial dataset containing genetic information and drug toxicity profiles of cervical cancer patients, a rigorous feature selection process was employed to identify the most relevant genetic features for predicting drug toxicity. Through a combination of mutual information analysis, variance analysis, and correlation analysis, a subset of genetic features was selected. This subset focused on genes known to be associated with drug metabolism, cellular stress response, and DNA repair pathways. This step was crucial to reduce the dimensionality of the dataset and ensure that only the most informative features were used in the subsequent machine learning models. Various machine learning algorithms were tested to create predictive models for drug toxicity in cervical cancer patients. These algorithms included random forests, support vector machines, and gradient boosting. The performance of each model was evaluated using a validation set that was separate from the training data. Cross-validation techniques were implemented to mitigate the risk of overfitting. Among the tested algorithms, the gradient boosting model consistently demonstrated superior predictive performance. The model achieved an accuracy of 84% on the validation set, indicating its ability to effectively differentiate between patients with varying drug toxicities. The precision, recall, and F1-score were also calculated to assess the model's performance across different evaluation metrics. The robust performance of the gradient boosting model indicated its potential as a valuable tool for predicting drug toxicity in cervical cancer patients [9].

Further analysis was conducted to identify potential biomarkers associated with drug toxicity in cervical cancer patients. By examining the importance scores assigned to individual genetic features within the gradient boosting model, specific single nucleotide polymorphisms (SNPs) and genes emerged as strong candidates. Notable findings included SNPs located within genes responsible for drug metabolism pathways, such as cytochrome P450 enzymes, as well as genes related to oxidative stress response and cellular detoxification. These identified biomarker candidates hold promise for explaining the interindividual variability in drug responses observed among cervical cancer patients. They provide insights into genetic factors that might influence susceptibility to drug toxicity and adverse reactions. The potential clinical significance of these biomarkers could lead to the development of more personalized treatment strategies tailored to individual genetic profiles [10].

The results of this study demonstrate the successful application of machine learning techniques to predict drug toxicity in cervical cancer patients based on their genetic profiles. The combination of accurate predictive models and the identification of biomarker candidates provide a foundation for advancing personalized medicine in cervical cancer therapy. By leveraging genetic information, clinicians can make informed decisions regarding treatment regimens, minimizing adverse reactions and optimizing therapeutic outcomes. For the treatment of cervical cancer, molecular targeted therapy is anticipated to be more effective and less toxic. The application of chemotherapy has been restricted by both severe and any degree of toxicity experienced. Therefore, it is essential to look for biomarkers to predict the chemotherapy side effects. Biomarker genetics (SNPs) have been widely assessed to forecast therapeutic adverse reactions. In this study, we detected PI3K/AKT pathway genes linked to adverse reactions from platinum-based NACT. We built the RF prediction model to analyze SNPs linked to chemotherapy toxicity. MDI is applied to compute the importance of features depending on the RF model, and the importance of each SNP is further evaluated. By calculating the significance score, it was able to predict the SNP related to neurotoxicity, gastrointestinal, and haematological toxicity [11].

The next steps involve further validation of the identified biomarkers and predictive models in larger, diverse patient cohorts. Prospective studies are essential to confirm the clinical utility of these findings and to establish their role in guiding treatment decisions. Additionally, ongoing advancements in genetic research and machine learning methodologies will likely contribute to the refinement and expansion of these predictive models, ultimately enhancing their precision and applicability [12-15].

Data analysis (Tables 1 and 2) (findings the genetic path way)

Conclusion

In this study, we harnessed the power of machine learning to predict drug toxicity in cervical cancer patients using their genetic profiles. By carefully selecting relevant genetic features and employing advanced algorithms, we gained valuable insights into the complex interplay between genetics and drug response. The results of this study hold significant implications for the advancement of personalized medicine in cervical cancer treatment.

Our investigation into feature selection revealed that genes related to drug metabolism, cellular stress response, and DNA repair pathways play a pivotal role in influencing drug toxicity. This underscores the importance of understanding the genetic underpinnings of drug responses to tailor treatments effectively. Among the machine learning algorithms tested, the gradient boosting model emerged as the most adept at predicting drug toxicity, achieving an impressive accuracy of 84%. This predictive power highlights the potential of computational approaches to guide clinical decision-making and improve patient outcomes. The identification of biomarker candidates, such as specific single nucleotide polymorphisms (SNPs) within key drug metabolism genes and those associated with oxidative stress and cellular detoxification is a key step toward a more personalized approach to cervical cancer therapy. These biomarkers provide a foundation for targeted treatments that minimize adverse reactions and optimize the efficacy of interventions. While our findings are promising, further validation studies in larger and more diverse patient cohorts are needed to confirm the clinical utility of these biomarkers and predictive models. Additionally, ongoing advances in genetics research and machine learning techniques are likely to refine and enhance the accuracy of our predictive models over time. By integrating genetic information into clinical decision-making, we can move closer to tailored therapies that improve patient well-being and therapeutic outcomes. The synergy between genetics and machine learning offers a pathway to a future where cervical cancer treatment is not only effective but also personalized and safe.

Conflict of Interest

None

Acknowledgment

None

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