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

Table 3 Summary of selected features, final or best-performing models, and predictive performance in the six reviewed studies

Ref.	Sample size	Selected features	Best/final model	Performance
Zhou et al[16]	91	Clinical: Stage, LVI, BMI. Body composition: SMI, VATI. Rad-score: PFS: Conventional_SUVbwmin_CT, shape_sphericity, GLCM_correlation_CT, GLRLM_LGRE_CT, GLCM_homogeneity_PET, GLRLM_LRE_PET, and GLZLM_SZHGE_PET; OS: Shape_volume(mL)_CT, shape_compacity_CT, GLCM_contrast_CT, GLZLM_SZECT, conventional_SUVbwmean_PET, shape_compacity_PET, GLRLM_SRHGE_PET, GLZLM_SZHGE_PET, and GLZLM_GLNU_PET	Cox regression and nomogram (clinical + body composition + Rad-score)	PFS: C-index/AUC = 0.810 (95%CI: 0.737-0.884). OS: C-index/AUC = 0.806 (95%CI: 0.720-0.891)
Hinzpeter et al[17]	243	Metastatic disease: Radiomics features, clinical data (age, sex, race, BMI, ECOG, histopathology, grade), SMI; OS: Clinical (ECOG ≥ 2, age ≥ 70, M1 disease), radiomics (CT: Shape volume, PET: GLZLM ZLNU)	Metastatic disease: LGBM. OS: Cox regression	Metastatic disease: Accuracy 80%, AUC 88%, recall 84%, precision 80%, F1-score 82%. OS: Kaplan-Meier survival analysis and hazard ratios showed statistical significance in the selected features; specific AUC/C-index not reported
Vogele et al[18]	83	Radiomics features (area, HMXG, HP10, HRMAD, IIQR, IMN, Intensity P10th, GCS)	RF	Sarcopenia: Accuracy 0.90 ± 0.03; AUC 0.96 ± 0.02. PD: Accuracy 0.88 ± 0.04; AUC 0.93 ± 0.04. Sarcopenia + PD: Accuracy 0.93 ± 0.04; AUC 0.97 ± 0.04
Iwashita et al[19]	98	Clinical (chemotherapy, prophylactic lymph node area irradiation, albumin, PNI, PLR, CRP/Alb ratio). Radiomics (L3wavelet.LHLfirstorderSkewness, L3wavelet.HLLgldmSmallDependenceLowGrayLevelEmphasis, L3wavelet.HLHfirstorderMean, L3wavelet.HLHglcmMCC, Lumbowavelet.HLLngtdmBusyness, Lumbowavelet.HLLngtdmContrast, AbsoriginalfirstorderUniformity, Abswavelet.HHHglcmImc2)	Cox regression and nomogram (clinical + radiomic)	OS: Validation dataset: Accuracy 75%, specificity 92%, sensitivity 75%, AUC = 0.86 (0.70-1.00), C-index = 0.88 (0.85-0.91)
Hinzpeter et al[20]	128	PFS: Clinical (ECOG, bone metastases), SMI, CT radiomics (NGLDM contrast), PET radiomics (shape volume, 70_Kurtosis). OS: Clinical (ECOG, bone metastases), SMI, CT radiomics (NGLDM Coarseness), PET radiomics (GLZLM_SZLGE)	Cox regression. OS: 6-36 months, clinical + SMI + CT + PET; at 3 month, clinical + SMI + CT; PFS: 3-21 months, clinical + SMI + CT + PET; 24-36 months, clinical + SMI	PFS: At 30 months, model (clinical + SMI) AUC = 0.86. OS: At 24 months, model (clinical + SMI + CT + PET) AUC = 0.88
Anconina et al[21]	145	RFS: Clinical (ECOG, tumor stage, nodes stage), sarcopenia, CT mean HU, PET kurtosis. OS: Clinical (ECOG, tumor stage, nodes stage), sarcopenia, CT mean HU	Cox regression and nomogram (clinical + sarcopenia + CT + PET)	RFS: At 24 months and 30 months, model (clinical + SMI + CT + PET) AUC = 0.81. OS: At 18 months and 24 months, model (clinical + SMI + CT) AUC = 0.80

AUC: Area under the curve; BMI: Body mass index; C-index: Concordance index; CI: Confidence interval; CRP/Alb: C-reactive protein/albumin ratio; CT: Computed tomography; ECOG: Eastern Cooperative Oncology Groups; GCS: Clustershade; GLCM: Gray-level co-occurrence matrix; GLNU: Gray level non-uniformity; GLRLM: Gray-level run-length matrix; GLZLM: Gray-level zone length matrix; HMXG: Maximum histogram gradient; HP10: Histogram P10^th; HRMAD: Histogram robust mean absolute deviation; HU: Hounsfield unit; IIQR: Intensity interquartile range; IMN: Intensity mean; LGBM: Light gradient boosting machine; LVI: Lymphovascular invasion; NGLDM: Neighborhood gray-level difference matrix; OS: Overall survival; PD: Progressive disease; PET: Positron emission tomography; PFS: Progression-free survival; PNI: Prognostic nutritional index; PLR: Platelet-lymphocyte ratio; RF: Random forest; RFS: Relapse-free survival; SMI: Skeletal muscle index; SUV: Standardized uptake value; SZHGE: Short-zone high gray-level emphasis; SZLGE: Short-zone low gray-level emphasis; VATI: Visceral adipose tissue index; ZLNU: Zone length non-uniformity.