Borys Katarzyna, Lodde Georg, Livingstone Elisabeth, Weishaupt Carsten, Römer Christian, Künnemann Marc-David, Helfen Anne, Zimmer Lisa, Galetzka Wolfgang, Haubold Johannes, Friedrich Christoph M, Umutlu Lale, Heindel Walter, Schadendorf Dirk, Hosch René, Nensa Felix
Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, 245131, Essen, Germany.
Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
J Transl Med. 2025 May 12;23(1):532. doi: 10.1186/s12967-025-06507-1.
Accurate assessment of expected survival in melanoma patients is crucial for treatment decisions. This study explores deep learning-based body composition analysis to predict overall survival (OS) using baseline Computed Tomography (CT) scans and identify fully volumetric, prognostic body composition features.
A deep learning network segmented baseline abdomen and thorax CTs from a cohort of 495 patients. The Sarcopenia Index (SI), Myosteatosis Fat Index (MFI), and Visceral Fat Index (VFI) were derived and statistically assessed for prognosticating OS. External validation was performed with 428 patients.
SI was significantly associated with OS on both CT regions: abdomen (P ≤ 0.0001, HR: 0.36) and thorax (P ≤ 0.0001, HR: 0.27), with lower SI associated with prolonged survival. MFI was also associated with OS on abdomen (P ≤ 0.0001, HR: 1.16) and thorax CTs (P ≤ 0.0001, HR: 1.08), where higher MFI was linked to worse outcomes. Lastly, VFI was associated with OS on abdomen CTs (P ≤ 0.001, HR: 1.90), with higher VFI linked to poor outcomes. External validation replicated these results.
SI, MFI, and VFI showed substantial potential as prognostic factors for OS in malignant melanoma patients. This approach leveraged existing CT scans without additional procedural or financial burdens, highlighting the seamless integration of DL-based body composition analysis into standard oncologic staging routines.
准确评估黑色素瘤患者的预期生存期对于治疗决策至关重要。本研究探索基于深度学习的身体成分分析，以利用基线计算机断层扫描（CT）预测总生存期（OS），并识别完全容积性的、具有预后价值的身体成分特征。
一个深度学习网络对495例患者队列的基线腹部和胸部CT进行分割。得出肌少症指数（SI）、肌脂变性脂肪指数（MFI）和内脏脂肪指数（VFI），并对其进行统计学评估以预测OS。对428例患者进行外部验证。
SI在腹部（P≤0.0001，HR：0.36）和胸部（P≤0.0001，HR：0.27）这两个CT区域均与OS显著相关，较低的SI与生存期延长相关。MFI在腹部（P≤0.0001，HR：1.16）和胸部CT（P≤0.0001，HR：1.08）上也与OS相关，较高的MFI与较差的预后相关。最后，VFI在腹部CT上与OS相关（P≤0.001，HR：1.90），较高的VFI与不良预后相关。外部验证重复了这些结果。
SI、MFI和VFI显示出作为恶性黑色素瘤患者OS预后因素的巨大潜力。这种方法利用现有的CT扫描，无需额外的程序或经济负担，突出了基于深度学习的身体成分分析无缝融入标准肿瘤分期程序。
