Radiomics meets sarcopenia: Machine learning-based multimodal modeling for esophageal cancer outcomes

Table 1 Six studies published between 2022 and 2024 combined sarcopenia and radiomics assessments to predict clinical outcomes in esophageal cancer

Ref.	Tumor type	Prognostic targets	Imaging time/IV contrast	Muscle/fat image level	Radiomics image (ROI)
Zhou et al[16]	Esophageal squamous cell carcinoma	PFS and OS	Pretreatment/no	L3 CT	Tumor ROI on PET and CT
Hinzpeter et al[17]	Esophageal or gastroesophageal cancer	Metastatic disease and OS	Pretreatment/no	L3 CT	Tumor ROI on PET and CT
Vogele et al[18]	Esophageal or gastric cancer	Sarcopenia and PD	Pretreatment and follow-up/yes	L3 CT	CT muscle ROIs on psoas major, quadratus lumborum, erector spinae
Iwashita et al[19]	Esophageal cancer	OS	Pretreatment/no	L3 CT	CT muscle ROIs on psoas, erector spinae, quadratus, lumborum, and abdominal wall muscles
Hinzpeter et al[20]	Metastatic esophageal and gastroesophageal cancer	PFS and OS	Pretreatment/no	L3 CT	Tumor ROI on PET and CT
Anconina et al[21]	Esophagogastric adenocarcinoma	RFS and OS	Pretreatment/no	L3 CT	Tumor ROI on PET and CT

CT: Computed tomography; IV: Intravenous; L3: The third lumbar vertebral level; OS: Overall survival; PD: Progressive disease; PET: Positron emission tomography; PFS: Progression-free survival; RFS: Relapse-free survival; ROI: Region of interest.