Body composition as a predictor of chemotherapy-related toxicity in ovarian cancer patients: A systematic review

Association of CVAI, LAP and SMI with risk of haematological toxicity after immunochemotherapy in patients with DLBCL: a retrospective study

Background

The occurrence of adverse events after immunochemotherapy in patients with diffuse large B-cell lymphoma (DLBCL) frequently affects the course of chemotherapy, leading to a further decline in quality of life and survival.

Objectives

The primary objective of this study was to investigate the association between Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP) index and skeletal muscle mass index (SMI) at initial diagnosis and the risk of haematological toxicity following immunochemotherapy in patients with DLBCL.

Design

Retrospective, single-centre study.

Methods

CVAI, LAP and SMI were calculated by combining clinical data of the patients. This study included 213 patients, of whom 117 (55%) patients experienced grades 3-4 haematological toxicity after immunochemotherapy. Participants were divided into four groups (Q1, Q2, Q3, Q4) based on the quartiles of CVAI, LAP and SMI.

Results

In the fully adjusted model, the risk of grades 3-4 haematological toxicity in group with the highest CVAI and LAP was reduced by 75.1% (OR: 0.249, 95% CI: 0.102-0.606, p = 0.002) and by 77.3% (OR: 0.227, 95% CI: 0.095-0.542, p = 0.001) compared to the group with the lowest CVAI and LAP. For SMI, the risk of grades 3-4 haematological toxicities in the group with the highest SMI was reduced by 62.9% compared with the lowest SMI group in the unadjusted model. The multivariable-adjusted restricted cubic spline curves and subgroup interaction analyses further confirmed the robustness of these findings.

Conclusion

The results indicate that DLBCL patients with relatively high CVAI, LAP and SMI at initial diagnosis have a lower risk of severe haematological toxicity following chemotherapy. Therefore, CVAI, LAP and SMI at initial diagnosis are reliable and effective biomarkers for predicting severe haematological toxicity after immunochemotherapy in DLBCL patients.

Trial registration

This is a retrospective study, and no registration on ClinicalTrials.gov.

Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine

PURPOSE

Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This study aimed to evaluate and compare the effectiveness of using three-dimensional (3D) CT body composition measures from the entire lumbar spine levels (L1-L5) versus a single vertebral level (L3), the current gold standard, in predicting chemotherapy DLT in colon cancer patients.

METHODS

Retrospective analysis of 184 non-metastatic colon cancer patients receiving adjuvant chemotherapy was performed. DLT was defined as any occurrence of dose reduction or discontinuation due to chemotherapy toxicity. Using artificial intelligence (AI) auto-segmentation, 3D body composition measurements were obtained from patients' L1-L5 levels on CT imaging. The effectiveness of patients' 3D L3 body composition measurement and incorporating data from the entire L1-L5 (including L3) region in predicting DLT was examined.

RESULTS

Of the 184 patients, 112 (60.9%) experienced DLT. Neuropathy was the most common toxicity (49/112, 43.8%) followed by diarrhea (35.7%) and nausea/vomiting (33%). Patients with DLT had lower muscle volume at all lumbar levels compared to those without. The machine learning model incorporating L1-L5 data and patient clinical data achieved high predictive performance (AUC = 0.75, accuracy = 0.75), outperforming the prediction using single L3 level (AUC = 0.65, accuracy = 0.65).

CONCLUSION

Evaluating a patient's body composition allowed prediction of chemotherapy toxicities for colon cancer. Incorporating fully automated body composition analysis of CT slices from the entire lumbar region offers promising performance in early identification of high-risk individuals, with the ultimate aim of improving patient's quality of life.

Using maximum plasma concentration (Cmax) to personalize taxane treatment and reduce toxicity

Taxanes are a widely used class of anticancer agents that play a vital role in the treatment of a variety of cancers. However, toxicity remains a major concern of using taxane drugs as some toxicities are highly prevalent, they can not only adversely affect patient prognosis but also compromise the overall treatment plan. Among all kinds of factors that associated with taxane toxicity, taxane exposure has been extensively studied, with different pharmacokinetic (PK) parameters being used as toxicity predictors. Compared to other widely used predictors such as the area under the drug plasma concentration curve versus time (AUC) and time above threshold plasma drug concentration, maximum plasma concentration (Cmax) is easier to collect and shows promise for use in clinical practice. In this article, we review the previous research on using Cmax to predict taxane treatment outcomes. While Cmax and toxicity have been extensively studied, research on the relationship between Cmax and efficacy is lacking. Most of the articles find a positive relationship between Cmax and toxicity but several articles have contradictory findings. Future clinical trials are needed to validate the relationship between Cmax and treatment outcome and determine whether Cmax can serve as a useful surrogate endpoint of taxane treatment efficacy.

Mechanisms of Ovarian Cancer-Associated Cachexia

Cancer-associated cachexia occurs in 50% to 80% of cancer patients and is responsible for 20% to 30% of cancer-related deaths. Cachexia limits survival and treatment outcomes, and is a major contributor to morbidity and mortality during cancer. Ovarian cancer is one of the leading causes of cancer-related deaths in women, and recent studies have begun to highlight the prevalence and clinical impact of cachexia in this population. Here, we review the existing understanding of cachexia pathophysiology and summarize relevant studies assessing ovarian cancer-associated cachexia in clinical and preclinical studies. In clinical studies, there is increased evidence that reduced skeletal muscle mass and quality associate with worse outcomes in subjects with ovarian cancer. Mouse models of ovarian cancer display cachexia, often characterized by muscle and fat wasting alongside inflammation, although they remain underexplored relative to other cachexia-associated cancer types. Certain soluble factors have been identified and successfully targeted in these models, providing novel therapeutic targets for mitigating cachexia during ovarian cancer. However, given the relatively low number of studies, the translational relevance of these findings is yet to be determined and requires more research. Overall, our current understanding of ovarian cancer-associated cachexia is insufficient and this review highlights the need for future research specifically aimed at exploring mechanisms of ovarian cancer-associated cachexia by using unbiased approaches and animal models representative of the clinical landscape of ovarian cancer.

Whole-Body Composition Features by Computed Tomography in Ovarian Cancer: Pilot Data on Survival Correlations

BACKGROUND

The primary objective of this study was to assess the associations of computed tomography (CT)-based whole-body composition values with overall survival (OS) and progression-free survival (PFS) in epithelial ovarian cancer (EOC) patients. The secondary objective was the association of body composition with chemotherapy-related toxicity.

METHODS

Thirty-four patients (median age 64.9 years; interquartile range 55.4-75.4) with EOC and thorax and abdomen CT scans were included. Clinical data recorded: age; weight; height; stage; chemotherapy-related toxicity; and date of last contact, progression and death. Automatic extraction of body composition values was performed by dedicated software. Sarcopenia was defined according to predefined cutoffs. Statistical analysis included univariate tests to investigate associations of sarcopenia and body composition with chemotoxicity. Association of body composition parameters and OS/PFS was evaluated by log-rank test and Cox proportional hazard model. Multivariate models were adjusted for FIGO stage and/or age at diagnosis.

RESULTS

We found significant associations of skeletal muscle volume with OS (p = 0.04) and PFS (p = 0.04); intramuscular fat volume with PFS (p = 0.03); and visceral adipose tissue, epicardial and paracardial fat with PFS (p = 0.04, 0.01 and 0.02, respectively). We found no significant associations between body composition parameters and chemotherapy-related toxicity.

CONCLUSIONS

In this exploratory study, we found significant associations of whole-body composition parameters with OS and PFS. These results open a window to the possibility to perform body composition profiling without approximate estimations.