講演情報
[AS1-4]Clinical impact of muscle quality and quantity in CRC patients
Jeonghyun Kang (Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea)
Sarcopenia is a condition characterized not only by a general reduction in muscle mass but also by a decline in the quality of muscle tissue. Various diagnostic techniques have been proposed to identify sarcopenia, and multiple guidelines have been developed to outline the most accurate and applicable methods for its diagnosis.
Research has shown that skeletal muscle measured at the L3 vertebral level in cross-sectional CT images correlates strongly with total body muscle mass. Consequently, CT-based methods for estimating muscle mass have been introduced. Since most cancer patients undergo CT scans as part of their treatment planning―whether for surgery or chemotherapy―leveraging these scans to diagnose sarcopenia could be particularly advantageous. Moreover, the discovery that myosteatosis, or fatty infiltration within muscle, can also be evaluated in this region and is predictive of various clinical outcomes has spurred an increase in related studies.
In colorectal cancer patients, numerous studies focus on using CT scans to assess both sarcopenia and myosteatosis as predictors of clinical outcomes. There is substantial evidence indicating that both conditions can serve as reliable indicators of patient prognosis. However, challenges persist, including potential variations across different ethnic populations and difficulties in defining precise cutoff values for using CT as a prognostic tool. Therefore, further research is necessary to address these challenges.
Additionally, it is crucial to determine whether models that predict postoperative complications based on body composition assessments can be effectively applied, particularly in the context of colorectal cancer surgery. While sarcopenia has been shown to be a useful predictor of complications in gastrointestinal surgeries, including colorectal cancer, the inconsistency in diagnostic criteria remains a significant limitation.
Given the high utility of CT-based prediction models that evaluate skeletal muscle area and radiodensity, ongoing efforts are needed to refine these models and improve their accuracy, while also addressing the current limitations.
Research has shown that skeletal muscle measured at the L3 vertebral level in cross-sectional CT images correlates strongly with total body muscle mass. Consequently, CT-based methods for estimating muscle mass have been introduced. Since most cancer patients undergo CT scans as part of their treatment planning―whether for surgery or chemotherapy―leveraging these scans to diagnose sarcopenia could be particularly advantageous. Moreover, the discovery that myosteatosis, or fatty infiltration within muscle, can also be evaluated in this region and is predictive of various clinical outcomes has spurred an increase in related studies.
In colorectal cancer patients, numerous studies focus on using CT scans to assess both sarcopenia and myosteatosis as predictors of clinical outcomes. There is substantial evidence indicating that both conditions can serve as reliable indicators of patient prognosis. However, challenges persist, including potential variations across different ethnic populations and difficulties in defining precise cutoff values for using CT as a prognostic tool. Therefore, further research is necessary to address these challenges.
Additionally, it is crucial to determine whether models that predict postoperative complications based on body composition assessments can be effectively applied, particularly in the context of colorectal cancer surgery. While sarcopenia has been shown to be a useful predictor of complications in gastrointestinal surgeries, including colorectal cancer, the inconsistency in diagnostic criteria remains a significant limitation.
Given the high utility of CT-based prediction models that evaluate skeletal muscle area and radiodensity, ongoing efforts are needed to refine these models and improve their accuracy, while also addressing the current limitations.