# Unmet Needs and Challenges in Cancer-Associated Venous Thromboembolism

## TL;DR

This review discusses challenges in predicting and managing blood clots in cancer patients, highlighting the need for better tools and personalized approaches.

## Contribution

The paper identifies unmet needs in CA-VTE management and highlights novel risk models and emerging strategies for precision medicine.

## Key findings

- Current risk models like Khorana have limited sensitivity, prompting development of more refined models.
- DOACs show promise for VTE prophylaxis, but challenges remain in complex patient populations.
- Future directions include integrating genomics and machine learning for precision risk modeling.

## Abstract

Cancer-associated venous thromboembolism (CA-VTE) is a significant complication in oncology, contributing to morbidity, mortality, and increased healthcare utilization. Due to multiple patient- and disease-related factors, patients with cancer are at a markedly elevated risk for VTE, particularly within the first 6 months of diagnosis. The aim of this review is to provide an overview of current challenges and unmet needs in CA-VTE prediction, prevention and management. While the Khorana score remains the most widely used risk stratification tool, its limited sensitivity has prompted the development of more refined models such as PROTECHT, CONKO, ONKOTEV, Vienna-CATS, and COMPASS-CAT. These models incorporate additional clinical variables including cancer subtype, systemic therapies, comorbidities, and emerging biomarkers. However important gaps remain, particularly in addressing bleeding risk, underrepresented racial/ethnic groups, and adapting to novel cancer therapeutics. Recent clinical trials (AVERT, CASSINI) have supported the use of direct oral anticoagulants (DOACs) for primary and secondary prophylaxis in select high-risk populations. However, anticoagulation strategies in complex populations, including those with thrombocytopenia, brain tumors, or concurrent antiplatelet therapy, remain areas of active investigation. Future directions include the integration of genomics, proteomics, and machine learning into risk modeling to enable precision medicine approaches. Ongoing clinical trials are testing the promise of safer prophylactic and therapeutic strategies. Personalized risk assessment and treatment of CA-VTE remain essential to improving patient outcomes in oncology. By consolidating existing evidence and identifying key unmet needs, this review seeks to guide more personalized and effective management of CA-VTE.