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Treatment Resistant Cancers02:56

Treatment Resistant Cancers

Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...

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Deep Learning-Based Body Composition Analysis for Outcome Prediction in Relapsed/Refractory Diffuse Large B-Cell

Russ A Kuker1, Juan P Alderuccio2, Sunwoo Han3

  • 1Division of Nuclear Medicine, Department of Radiology, Sylvester Comprehensive Cancer Center, University of Miami School of Medicine, Miami, FL.

JCO Clinical Cancer Informatics
|July 16, 2025
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Summary

Body composition, specifically the skeletal muscle to visceral fat ratio, can predict treatment response in relapsed or refractory diffuse large B-cell lymphoma (DLBCL) patients. A deep learning approach offers a cost-effective alternative for this analysis.

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) presents a significant clinical challenge.
  • Loncastuximab tesirine is an emerging treatment option for rel/ref DLBCL.
  • Predictive biomarkers are crucial for optimizing patient selection and treatment strategies.

Purpose of the Study:

  • To evaluate body composition as an independent image-derived biomarker for predicting clinical outcomes in rel/ref DLBCL patients treated with loncastuximab tesirine.
  • To assess the utility of skeletal muscle (SM), subcutaneous fat (SF), and visceral fat (VF) indices derived from CT scans.
  • To compare manual and deep learning-based body composition analysis methods.

Main Methods:

  • Analysis of baseline CT scans from 140 rel/ref DLBCL patients in the LOTIS-2 trial.
  • Segmentation of SM, SF, and VF at the L3 level using manual and deep learning techniques.
  • Derivation of body composition ratio indices (SM*/VF*, SF*/VF*, SM*/(VF*+SF*)).
  • Logistic and Cox regression analyses to assess associations with treatment response and survival outcomes (PFS, OS).

Main Results:

  • Dichotomized manual and automated SM*/VF* indices significantly predicted failure to achieve complete metabolic response.
  • The dichotomized manual SM*/VF* index was significantly associated with progression-free survival (PFS) but not overall survival (OS).
  • High agreement was observed between manual and automated segmentation methods.

Conclusions:

  • The pretreatment SM*/VF* index shows potential as a predictive biomarker for rel/ref DLBCL patients receiving loncastuximab tesirine.
  • Deep learning-based body composition analysis is a viable, cost-effective alternative to manual segmentation.
  • Image-derived body composition analysis can enhance clinical outcome prediction in DLBCL.