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4423 Association of Body Composition Factors and Toxicity in Patients with Advanced Stage Hodgkin Lymphoma Receiving BV-AVD

Program: Oral and Poster Abstracts
Session: 624. Hodgkin Lymphomas: Clinical and Epidemiological: Poster III
Hematology Disease Topics & Pathways:
Research, Hodgkin lymphoma, Artificial intelligence (AI), Lymphomas, Clinical Research, Diseases, Real-world evidence, Lymphoid Malignancies, Adverse Events, Technology and Procedures, Imaging, Machine learning, Omics technologies
Monday, December 9, 2024, 6:00 PM-8:00 PM

Michele D. Stanchina, DO, MS1, Russ Kuker, MD2*, Sunwoo Han, PhD3*, Fei Yang, PhD2*, Mark K Polar, MD2*, Jillian Lorayne Lykon, PharmD4*, Tracy Crane, PhD, RDN5*, Melissa Lopez-Pentecost, PhD, RDN5*, Craig H. Moskowitz, MD1 and Juan Pablo Alderuccio, MD1

1Sylvester Comprehensive Cancer Center, Division of Hematology, University of Miami, Miller School of Medicine, Miami, FL
2Department of Radiology, Division of Nuclear Medicine, University of Miami, Miller School of Medicine, Miami, FL
3Biostatistics and Bioinformatics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL
4Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL
5Sylvester Comprehensive Cancer Center, Medical Oncology and Public Health, University of Miami, Miami, FL

Introduction:

Brentuximab vedotin (BV) in combination with doxorubicin, vinblastine, and dacarbazine (BV-AVD) has improved progression-free survival (PFS) and overall survival (OS) in advanced stage Hodgkin Lymphoma (ASHL). However, peripheral neuropathy (PN) remains a significant toxicity leading to BV discontinuation and long-term sequelae (Connors, 2018; Ansell, 2022). BV dosing is calculated using actual body weight (up to 100 kg) but does not distinguish between body composition measures such as fat, bone or muscle mass. Previous studies in solid tumors have shown that low skeletal muscle (SM) is associated with increased morbidity and mortality including chemotoxicity. Breast cancer patients showed an inverse relationship between lean body mass (LBM) and chemotoxicity (Wopat, 2023) and patients with gastrointestinal cancers with lower LBM had less distribution of oxaliplatin and higher grade 3-5 chemotoxicity (Williams, 2021). Here we examine body composition parameters and risk of toxicity with BV-AVD in ASHL.

Methods:

We retrospectively reviewed baseline, interim and end of treatment (EOT) PET/CT images of patients with newly diagnosed ASHL treated with BV-AVD. PET biomarkers consisting of metabolic tumor volume (MTV), total lesion glycolysis (TLG), and SUVmax were obtained using Hermes Affinity Viewer (Hermes Medical Solutions, Stockholm, Sweden). The thresholds utilized for MTV calculation were SUV 4.0 and 1 mL volume. Body composition analysis was performed using manual and artificial intelligence (AI) segmentation of three main tissue compartments, including SM, subcutaneous fat (SF) and visceral fat (VF) obtained at the L3 level. Manual segmentation was performed by a single experienced nuclear medicine radiologist using MIM Maestro (MIM Software, Cleveland, OH). The AI method was constructed using the attention-guided U-Net architecture. Body composition compartments were expressed as indices, SM/VF+SF, SM/VF, and SF/VF. We evaluated the density in Hounsfield units of the L3 vertebral body (L3VB). Additionally, we recorded patient weight at baseline, EOT and at last follow-up. Toxicity was graded by the presence of PN or any treatment-related toxicity. Logistic regression was used to assess the association between body composition indices, PET biomarkers and presence of toxicity. Binary variables defined by cutpoints were obtained from maximizing Youden’s index.

Results

39 patients were available for review. The median age was 36.5 years, 17 (43.6%) were male, 22 (56.4%) were Hispanic, 17 (43.6%) and 22 (56.4%) had Stage 3 and 4 diseases, respectively. The majority of patients presented with B symptoms (64.1%), ESR >50 (70.3%) and elevated IPS >1 (95%). Patient’s weight varied throughout the treatment course. At baseline, median weight was 69.9 kg (interquartile range (IQR) 56.7-83.7), which deceased to 63.7 kg (IQR 57.2-82.6) at the EOT. However, with long-term follow-up, patients rebounded gaining additional weight, with a median 74.6 kg (IQR 63.5-86.2).

Of the 39 patients, 41% (n=16) experienced PN (Grade 1=7, Grade 2=7, Grade 3=2). Six patients required dose reductions due to PN, and 3 had to discontinue BV treatment early. Univariable logistic analyses showed that baseline L3VB, SM/VF, SF/VF and SM/SF+VF (by manual and AI) as dichotomized were significant predictors of the presence of any toxicity by grade (P<0.05). Furthermore, higher patient weight at baseline, EOT, and last follow-up was significantly associated with higher overall toxicity and PN (P<0.05). SUVmax, TLG and stage as dichotomized did not predict overall toxicity but did predict PN specifically (p=0.028, p=0.013, and p=0.003 respectively). Age and gender were not a significant predictor of toxicity.

Conclusions

BV-AVD is a commonly used regimen in ASHL but is limited by toxicity. Body composition measures including L3VB, SM/VF, SF/VF and SM/SF+VF, and patient weight throughout treatment were all significant predictors of toxicity including PN. Since BV induced PN is dose-related, we posit this could explain the relationship between higher patient weight and PN. Future investigation of interim and EOT PET-CT will analyze body composition changes and toxicity over time. Furthermore, the Lifestyle Intervention of Food and Exercise for Lymphoma Survivors (LIFE-L) (NCT NCT05839210) study will examine if toxicity can be mitigated via a structured diet and exercise program.

Disclosures: Kuker: ADCT: Research Funding. Moskowitz: Pfizer: Membership on an entity's Board of Directors or advisory committees; ADCT: Research Funding; Merk: Research Funding; SGEN: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees. Alderuccio: ADC Therapeutics: Consultancy, Research Funding; BeiGene: Research Funding; Genmab: Research Funding; AbbVie: Consultancy; Regeneron: Consultancy; Genentech: Consultancy.

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