Journal Article Ahead of print
PubMed: 42509079 DOI: 10.1016/j.landig.2026.101021
Identifying subsets of patients with retroperitoneal sarcoma who benefit from radiotherapy: an Interpretable AI reanalysis of the STRASS randomised trial.
Lancet Digit Health
Authors: Dimitris Bertsimas, Georgios Antonios Margonis, Angelos Koulouras, Martin Kreis, Felix Balzer, Murray F Brennan, Sylvie Bonvalot, Samuel Singer
Affiliations
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of General and Visceral Surgery, Charité-Universitätsmedisin Berlin, Campus Benjamin Franklin, Berlin, Germany; Institute of Medical Informatics, Charité-Universitätsmedisin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Department of General and Visceral Surgery, Charité-Universitätsmedisin Berlin, Campus Benjamin Franklin, Berlin, Germany.
- Institute of Medical Informatics, Charité-Universitätsmedisin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Surgical Oncology, Institut Curie, Université Paris Sciences et Lettres, Paris, France.
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Abstract
BACKGROUND
The European Organisation for Research and Treatment of Cancer's STRASS trial, the only completed randomised study of preoperative radiotherapy in retroperitoneal sarcoma, showed no overall benefit. Its subgroup analysis has been interpreted as supporting radiotherapy for all patients with liposarcoma, whereas the STREXIT extension has been interpreted as supporting radiotherapy for well differentiated liposarcoma and low-grade or intermediate-grade dedifferentiated liposarcoma. We aimed to identify subsets of patients who might benefit from preoperative radiotherapy and to quantify this benefit in terms of abdominal recurrence.
METHODS
In this artificial intelligence (AI)-based reanalysis of the STRASS dataset, we trained a random survival forest model on all 266 randomly assigned patients (radiotherapy plus surgery vs surgery alone) to predict 5-year abdominal recurrence-free survival under both treatment options, based on pretreatment variables. These predictions were used to fit an optimal policy tree (OPT) that partitions patients into nodes by predicted abdominal recurrence-free survival benefit from radiotherapy. We then compared outcomes in OPT-defined subgroups, STRASS subgroups, and STREXIT subgroups through Kaplan-Meier curves, and Fine-Gray competing-risks models within STRASS.
FINDINGS
The OPT partitioned the cohort into seven subgroups; three subgroups (152 of 266 patients) were predicted to benefit from radiotherapy, and for two of these subgroups the benefit was statistically significant: patients with well differentiated liposarcoma aged 60 years or younger and patients with dedifferentiated liposarcoma who underwent curative-intent surgery. In these two subgroups combined, 3-year abdominal recurrence-free survival was 79% (95% CI 70-89) with radiotherapy versus 58% (48-72) without (hazard ratio [HR] 0·40 [95% CI 0·22-0·71], p=0·0016), and the cumulative incidence of abdominal recurrence was significantly lower with radiotherapy (17% [95% CI 9-27] vs 33% [22-45] without radiotherapy; HR 0·40, p=0·0090). Inverse probability of censoring weight-adjusted 5-year abdominal recurrence-free survival estimates showed similar absolute gains (25·6 percentage points). A Cox model found a significant radiotherapy-age interaction (p=0·012). By contrast, STRASS-defined and STREXIT-defined radiotherapy subgroups did not show significant abdominal recurrence-free survival improvement when re-evaluated within STRASS.
INTERPRETATION
The AI-guided partition of STRASS identified younger patients with well differentiated liposarcoma and patients with dedifferentiated liposarcoma and curative-intent surgery as subgroups in which radiotherapy appears to meaningfully improve abdominal recurrence-free survival, whereas radiotherapy strategies for all well differentiated liposarcoma and low-grade or intermediate-grade dedifferentiated liposarcoma were not supported by the randomised controlled trial data. These findings argue for a more selective use of preoperative radiotherapy in retroperitoneal sarcoma and provide a concrete basis for focused future trials, which, if successful, could substantiate these findings before these strategies become standards of care.
FUNDING
National Cancer Institute and Memorial Sloan Kettering Cancer Center.
MeSH
Radiotherapy, Medical Informatics, Liposarcoma, Sarcoma, Therapeutics, Artificial Intelligence