癌症相关血栓风险评估与个体化抗凝治疗策略

2.1 Risk Assessment for Cancer-Associated Thrombosis 2.1.1 Clinical Risk Scoring Systems The risk of cancer-associated thrombosis (CAT) in cancer patients is significantly higher than in non-cancer patients. Accurate assessment of thrombotic risk is crucial for guiding anticoagulant therapy. Several risk scoring systems have been proposed for CAT risk assessment, with the Khorana score and Vienna score being the most representative. The Khorana score was first introduced in 2008, based on clinical indicators such as tumor type, platelet count, hemoglobin level, white blood cell count, and body mass index (BMI) to stratify risk. It is currently the most widely used tool for CAT risk assessment and is applicable to various solid tumors, particularly showing good predictive value in high-risk patients (score ≥ 2). However, the Khorana score has limited ability to distinguish low-risk patients and is less applicable to certain malignancies, such as hematological cancers [35][36]. The Vienna score builds upon the Khorana score by adding biomarkers such as plasma D-dimer and soluble P-selectin levels, enhancing the sensitivity and specificity of risk prediction, especially in clinical settings requiring more precise risk stratification. This score emphasizes the importance of molecular biological markers and is suitable for dynamic monitoring of CAT risk [37]. Additionally, multifactorial combined scoring models that integrate clinical variables, biomarkers, and imaging indicators have improved the accuracy and individualization of risk assessment. For example, models combining the Caprini score, Michigan risk score, and biomarkers have shown excellent performance in predicting PICC-related thrombosis risk in specific cancer types such as breast and lung cancer [38][39]. Machine learning methods have also been applied to risk model construction, effectively improving predictive performance, particularly in dynamic risk assessment [40]. Despite the availability of multiple risk scoring systems, most have limitations, such as differences in applicability across different cancer types and treatment stages, and a lack of comprehensive assessment of bleeding risk. Future research should focus on multicenter validation of these scoring systems, integrating emerging biomarkers and dynamic monitoring technologies to develop more adaptable and precise multifactorial combined risk assessment models to guide individualized anticoagulant therapy [41][2]. 2.1.2 Application of Biomarkers in Risk Assessment Biomarkers play an increasingly important role in assessing the risk of cancer-associated thrombosis. Traditional blood biomarkers such as D-dimer, platelet count, and tissue factor (TF) are closely related to the occurrence of CAT. D-dimer, a fibrin degradation product, reflects the activation of coagulation in the body and is commonly used to predict the risk of venous thromboembolism [35]. An increased platelet count is associated with a procoagulant state in cancer, while tissue factor directly participates in the coagulation process induced by tumor cells, making it a key molecule in the pathogenesis of CAT [2]. Molecular-level biomarkers, such as genetic polymorphisms and inflammatory cytokines, are also gaining attention. Certain genetic polymorphisms affect the expression or function of coagulation factors, increasing the risk of thrombosis. Inflammatory cytokines such as C-reactive protein (CRP) and tumor necrosis factor-α (TNF-α) are associated with immune activation and reflect procoagulant and inflammatory activities within the tumor microenvironment, further enhancing the accuracy of risk prediction [42][42]. Multimarker combination testing techniques, which integrate information from multiple biomarkers, have significantly improved risk prediction outcomes. For example, composite risk models combining D-dimer, platelet count, inflammatory cytokines, and genetic polymorphisms have shown superior predictive capabilities compared to single indicators in preclinical studies [43]. Additionally, machine learning algorithms have demonstrated significant advantages in integrating multimarker data and dynamic risk assessment, advancing the development of individualized risk prediction [40]. Although biomarkers hold great potential in CAT risk assessment, they currently face challenges such as a lack of standardized detection methods, insufficient clinical validation, and cost issues. Future research should conduct large-scale, multicenter prospective studies to validate the clinical applicability and cost-effectiveness of biomarker combinations, promoting their use in individualized anticoagulant therapy [44][45]. 2.1.3 Imaging and Dynamic Monitoring Technologies Imaging techniques play a crucial role in the early detection and dynamic monitoring of cancer-associated thrombosis. Ultrasound, particularly Doppler ultrasound, is a non-invasive and convenient vascular imaging method widely used for screening and monitoring deep vein thrombosis (DVT) in the lower limbs. It has shown high sensitivity and specificity in the early diagnosis of PICC-related thrombosis and postoperative DVT [46][25]. CT angiography, due to its high resolution and ability to visualize the entire vascular system, is suitable for diagnosing pulmonary embolism and abdominal thrombosis. Combining CT with biomarker testing can improve the early diagnosis rate and accuracy of CAT risk assessment [47]. Dynamic monitoring of hemorheological parameters, such as blood viscosity, platelet activity, and coagulation factor levels, can reflect changes in blood status and is essential for dynamically assessing the risk of thrombosis formation. Combined with continuous biomarker monitoring, it helps identify high-risk periods for thrombosis formation and guides adjustments in individualized anticoagulant therapy [37]. The introduction of artificial intelligence (AI)-assisted diagnostic technology has significantly enhanced the efficiency and accuracy of analyzing imaging and biomarker data. Deep learning-based image processing and risk prediction models can automatically recognize thrombus imaging features, aiding clinical decision-making and achieving precise and real-time risk assessment. For example, using machine learning for multiparametric analysis of ultrasound and CT images, combined with clinical data, has been shown to outperform traditional scoring systems [40][48]. In summary, imaging and dynamic monitoring technologies, especially when assisted by AI, will become important tools for future CAT risk assessment. Through the integration of multimodal data, they enable early warning and real-time dynamic monitoring of thrombosis, providing robust support for individualized anticoagulant therapy [49][50]. 2.2 Individualized Anticoagulant Treatment Strategies 2.2.1 Selection and Optimization of Anticoagulant Drugs The selection and optimization of anticoagulant drugs are core components of individualized treatment for cancer-associated thrombosis (CAT). Currently, the most commonly used anticoagulant drugs in clinical practice include low molecular weight heparin (LMWH), direct oral anticoagulants (DOACs), and vitamin K antagonists (VKAs). LMWH, due to its effective anticoagulant properties and minimal drug interactions, remains the preferred choice for anticoagulant therapy in cancer patients, especially those with highly active tumors and relatively preserved renal function [51][52]. DOACs have gained widespread application in the treatment and prevention of cancer-related thrombosis due to their oral administration convenience and lack of routine monitoring requirements, with apixaban showing an advantage in reducing bleeding risk [53][54]. VKAs, however, are now primarily used as alternative treatments in special cases, such as patients with severe liver or kidney dysfunction or specific cancer types [55][56]. For different cancer types, the selection of anticoagulant drugs should consider the tumor type and related complications. For example, patients with gastrointestinal cancers have a higher bleeding risk when using certain DOACs, and LMWH is recommended [51][57]. Renal function is a critical factor affecting the metabolism and clearance of anticoagulant drugs. Patients with renal insufficiency should use DOACs with higher renal clearance cautiously, adjust the dose of LMWH, and consider VKAs as alternatives [58][59]. For patients with a high bleeding risk, especially those with a history of bleeding or using other drugs with bleeding risks, drug selection must be more cautious, balancing the benefits of antithrombotic therapy with the risk of bleeding [60][61]. Drug interactions and resistance mechanisms are also important considerations for optimizing treatment regimens. DOACs, which rely on P-gp and CYP3A4 metabolism, are prone to interactions with multiple drugs, affecting drug concentrations and, consequently, efficacy and safety [62]. LMWH has fewer drug interactions but can increase bleeding risk when used with certain plant extracts and anticoagulants [63]. Regarding resistance mechanisms, long-term anticoagulant therapy may lead to decreased efficacy as tumor cells express multiple procoagulant factors or induce the expression of anticoagulant drug-metabolizing enzymes, necessitating dynamic monitoring and timely adjustments in drug type and dosage [64][65]. In summary, the selection and optimization of anticoagulant drugs should consider the patient's cancer type, renal and hepatic function, bleeding risk, and potential drug interactions, integrating the latest clinical evidence and individualized assessments to achieve precise anticoagulant therapy. 2.2.2 Individualized Adjustment of Treatment Regimens Individualized adjustment of treatment regimens is key to ensuring efficacy and safety in anticoagulant therapy for cancer-associated thrombosis. First, the anticoagulant dose and duration should be tailored based on the patient's thrombotic and bleeding risks, as well as the type of cancer. Studies show that the risk of recurrent thrombosis is significantly higher in cancer patients, especially those with advanced tumors, necessitating extended anticoagulant therapy, and in some cases, long-term anticoagulation [66][51]. Bleeding risk assessment is equally important, and the treatment dose should balance the reduction of thrombotic risk with the minimization of bleeding complications [60][61]. For example, patients with a higher risk of cancer-related bleeding should use lower doses or adjusted dosing intervals, with strict monitoring of coagulation parameters. Second, the treatment regimen should be optimized considering the patient's quality of life and adherence. The fixed dosing and oral administration of DOACs significantly enhance patient adherence, making them particularly suitable for long-term therapy [67][68]. While LMWH, though effective, may impact quality of life due to injection requirements, patient acceptance should be assessed. Regular monitoring of renal function, bleeding symptoms, and drug concentrations helps adjust doses promptly, ensuring treatment safety [68][69]. Third, treatment adjustments for special populations are particularly important. Elderly patients, due to reduced renal function and comorbidities, have decreased drug metabolism and clearance, leading to drug accumulation and increased bleeding risk, requiring careful drug selection and dosing [70][61]. Patients with liver or kidney dysfunction should adjust doses based on functional assessments to avoid excessive anticoagulation and bleeding [59][58]. Additionally, patients taking multiple medications should monitor potential drug interactions to prevent abnormal fluctuations in drug concentrations [62]. In summary, individualized adjustments to anticoagulant treatment regimens should be based on comprehensive risk assessments, considering the patient's specific condition and lifestyle, and flexibly adjusting drug doses, duration, and monitoring frequency, with a particular focus on medication safety and treatment effectiveness in special populations. 2.2.3 Combined Treatment and Multidisciplinary Collaboration The combination of anticoagulant therapy with cancer treatment presents new challenges and opportunities for managing cancer-associated thrombosis. Chemotherapy, radiotherapy, and targeted therapy not only influence tumor progression but may also increase the risk of thrombosis by damaging vascular endothelium, inducing inflammation, and altering coagulation mechanisms [51][71]. Additionally, some anticancer drugs interact with anticoagulants at pharmacokinetic and pharmacodynamic levels, potentially increasing bleeding risk or reducing anticoagulant efficacy [72][73]. For instance, CDK4/6 inhibitors used in breast cancer treatment have shown a certain thrombotic risk, requiring appropriate adjustments in anticoagulant strategies [74]. Therefore, collaboration among multidisciplinary teams, including hematology, oncology, and thrombosis specialists, is crucial. These teams can comprehensively assess the patient's thrombotic and bleeding risks, develop individualized anticoagulant treatment plans, and coordinate cancer treatment strategies to ensure safety and effectiveness [52][75]. Furthermore, team collaboration aids in monitoring potential complications during treatment and adjusting strategies promptly to improve patient quality of life. Optimizing clinical pathways and management models is also key to improving treatment outcomes and patient safety. Standardized anticoagulant treatment protocols, combined with intelligent clinical decision support systems (CDSS) to assist dose adjustments and monitoring, can reduce human errors and enhance the precision of anticoagulant therapy [73][76]. Additionally, digital health tools and remote monitoring technologies can improve patient adherence and facilitate the timely identification of adverse reactions [67]. In summary, combined treatment strategies must fully consider the interactions between cancer treatment and anticoagulant therapy, relying on multidisciplinary team collaboration and optimized clinical management processes to achieve individualized and precise treatment for cancer-associated thrombosis. 2.3 Latest Advances and Future Directions in Precision Medication 2.3.1 Application of Pharmacogenomics in Anticoagulant Therapy The importance of pharmacogenomics in anticoagulant therapy is becoming increasingly evident, as genetic polymorphisms significantly impact the metabolism and efficacy of anticoagulant drugs. For example, warfarin response varies among patients, primarily due to polymorphisms in key genes such as CYP2C9 and VKORC1, which directly influence drug metabolism rates and therapeutic effects, leading to differences in dose requirements [77][78][79]. Other genes, such as CYP4F2, have also been found to affect warfarin dose, indicating a complex genetic regulatory network [80]. While the pharmacokinetics of novel oral anticoagulants (NOACs) like rivaroxaban are influenced by multiple factors, current studies suggest that CYP3A5 and ABCB1 gene polymorphisms have limited effects on their clearance rates [81]. The application of genetic testing in selecting and adjusting anticoagulant drugs is gradually maturing, with research showing that genetically guided warfarin dose adjustments can shorten the time to reach stable INR, enhancing treatment safety and effectiveness [82][83][82]. The feasibility of integrating individual genetic information into clinical decision support systems is also being explored, with new biomarker technologies assessing drug-metabolizing enzymes and transporters laying the foundation for precision drug therapy [83]. However, the clinical translation of genomic data still faces ethical, privacy, and data integration challenges [84]. In the future, combining polygenic risk scores and big data analysis will further optimize individualized anticoagulant treatment regimens. 2.3.2 Novel Anticoagulant Drugs and Targeted Therapies The development and clinical application of novel anticoagulant drugs are revolutionizing the treatment of cancer-associated thrombosis (CAT). Direct oral anticoagulants (DOACs) such as dabigatran, rivaroxaban, apixaban, and edoxaban are already widely used for the prevention and treatment of non-valvular atrial fibrillation and venous thromboembolism [85][86]. Extensive clinical trial data indicate that DOACs effectively prevent recurrent thrombosis with relatively lower bleeding risks, although certain cancer patients, particularly those with gastrointestinal cancers, require careful assessment of bleeding risks [87]. Additionally, innovative drugs targeting key factors in thrombosis, such as factor XI/XIa inhibitors, are becoming a research hotspot. These drugs specifically inhibit FXI in the intrinsic coagulation pathway, aiming to reduce thrombosis while minimizing bleeding risks. They have shown promising safety and efficacy in multiple phase II clinical trials [88][89][90][91][92][93][94]. Nanotechnology and advanced drug delivery systems offer new possibilities for precise anticoagulation, with 3D-printed hydrogel carriers enabling targeted and controlled release of anticoagulant drugs, enhancing bioavailability and safety [95][96]. Moreover, DNA aptamer drugs and their controllable antidotes provide rapid reversal and precise control in anticoagulant therapy [97]. Overall, the integration of novel anticoagulant drugs with targeted therapies and nanotechnology is moving toward a safer and more efficient era of CAT treatment. 2.3.3 Big Data and Artificial Intelligence for Precision Treatment The integration of big data and artificial intelligence (AI) technologies provides strong support for precision treatment of cancer-associated thrombosis (CAT). Risk prediction models based on big data integrate multi-dimensional data such as patient clinical information, genomics, and laboratory data to achieve precise assessment of thrombosis risk and design individualized treatment plans [83][95]. AI algorithms demonstrate significant potential in patient data analysis, treatment response prediction, and dynamic adjustment. AI can handle high-dimensional and heterogeneous medical data, assisting clinicians in formulating anticoagulant regimens, predicting recurrence and bleeding risks, optimizing drug dosages, and enhancing treatment outcomes [32][48][98]. The development of digital health technologies, such as wearable devices and remote monitoring, ensures real-time data collection and feedback during patient treatment, promoting dynamic management [95][32]. In the future, AI-driven clinical decision support systems are expected to achieve more intelligent CAT management by integrating multi-omics data and machine learning models, advancing precision medicine [98][99][98][100]. However, transparency of models, data privacy protection, and clinical validation remain key challenges for broader application [32][101]. Looking ahead, the integration of big data and AI in digital health technologies will profoundly transform CAT precision medicine, shifting from static diagnostics to dynamic, personalized management.