Preoperative differentiation of adrenal pheochromocytoma and lipid-poor adenoma using CT-based radiomics: a systematic review and meta-analysis.

Authors

Nima Broomand Lomer, Amir Mahmoud Ahmadzadeh, Anahita Azinfar

Published in

Acta radiologica (Stockholm, Sweden : 1987). Pages 2841851261462504. Jul 07, 2026. Epub Jul 07, 2026.

Abstract

Differentiating pheochromocytoma from lipid-poor adenoma (LPA) on conventional CT remains challenging because of overlapping imaging features. Radiomics captures relevant quantitative imaging information that maybe useful for this differentiation. We aimed to evaluate the diagnostic performance of CT-based radiomics for the preoperative differentiation of pheochromocytoma from LPA. PubMed, Scopus, Embase, and Web of Science were comprehensively searched from inception through October 13, 2025. Eligible studies applied CT-based radiomics to differentiate pheochromocytoma from LPA. Pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the curve (AUC) were estimated. Statistical heterogeneity was assessed using I2 statistic and subgroup analyses were conducted to find their potential sources. Publication bias was assessed using Deek's funnel plot asymmetry test. After deduplication and thorough screening process, seven studies encompassing 1,272 patients met the inclusion criteria. Six studies were included in the meta-analysis, yielding a pooled sensitivity of 0.92 (95% CI, 0.83-0.97), specificity of 0.89 (95% CI, 0.79-0.94), and AUC of 0.85 (95% CI, 0.81-0.94). Subgroup analyses showed higher sensitivity in studies reporting image preprocessing (0.97 vs 0.83, P < 0.01). No considerable publication bias was detected (P = 0.12). Nomogram models integrating radiomics with clinicoradiological features achieved significantly higher AUCs than radiomics-only models (0.95 vs 0.87, P = 0.004). In conclusion, CT-based radiomics demonstrated high accuracy for distinguishing pheochromocytoma from LPA, with unenhanced CT providing the strongest discrimination. Integration of radiomics with clinicoradiological variables further enhances performance. However, multicenter validation with standardized pipelines is needed for clinical translation.

PMID:
42412007
Bibliographic data and abstract were imported from PubMed on 07 Jul 2026.

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