Evaluation of RECIST v1.1 for predicting overall survival in sarcoma patients with pulmonary metastasis

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Authors: Lukas Gold, Konstantin Klambauer, Christian A. Dascalescu, Luca Klinge, Michael Winkelmann, Nabeel Mansour, Dirk Mehrens, Jens Ricke, Michael von Bergwelt-Baildon, Dorit Di Gioia, Lars Hartwin Lindner, Wolfgang G. Kunz
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References: 18

Abstract

PURPOSE: Response assessment in the treatment of metastatic sarcoma primarily depends on imaging, as no established clinical or serological biomarkers reliably predict survival outcomes. This study evaluates the utility of Response Evaluation Criteria in Solid Tumors (RECIST v1.1) in predicting overall survival (OS) in sarcoma patients with pulmonary metastases. METHODS: We selected consecutive study subjects from a prospective registry based on the following criteria: (1) available CT imaging at first diagnosis of pulmonary metastases from sarcoma, (2) available follow-up CT imaging within 16 weeks of systemic therapy initiation, (3) documentation of OS. Volumetric segmentation of up to 5 lung metastases was performed over time. Progressive disease (PD) was defined as increase of the unidimensional sum of lesions ≥ 20% or appearance of new metastases according to RECIST v1.1. Kaplan-Meier survival analyses were performed. P values < 0.05 were considered statistically significant. RESULTS: Ninety-two patients were included (median age: 58 years; 50% female). Average time of follow-up CT was 67 days after baseline imaging. Patients with PD on first follow-up imaging (n = 24; 26%) showed significantly shorter OS (13.9 months vs. 29.3 months; p = 0.014). The unidimensional growth threshold of 20% proposed by RECIST did not stratify OS (14.6 months vs. 26.8 months, p = 0.221). The appearance of new metastases (n = 16; 17%) indicated significantly shorter OS (7.8 months vs. 27.0 months; p < 0.001) and was frequently observed even in patients with decreasing size of existing metastases (n = 7; 8%). CONCLUSION: Imaging progression patterns of pulmonary metastatic sarcoma demonstrate distinct associations with OS, highlighting the need for sarcoma-specific adaptations to established response criteria.

Fields of Study & Subjects

Sarcoma Diagnosis and Treatment Medical Imaging Techniques and Applications Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
Cancer Imaging
Volume
26
Issue
1
Publication date
26 February 2026
Language
EN