Annals of oncology : official journal of the European Society for Medical Oncology · 2026
A multimodal deep learning model for preoperative prediction of post-operative complications in gastric cancer.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Ding P A, Yang S, Meng L J, Chen S, Li S P, Guo H H, Yang J X, Ma S, Yang Y H, Gao K X, Gu R J, Zhang L L, Chen Y L, Shi Y L, Long H, Xia Z J, Meng N, Li X L, Guo Z J, Han X, Zhao Q
Аннотация
BACKGROUND: Clavien-Dindo (CD) grade ≥II complications occur in approximately one in five patients after curative gastrectomy for gastric cancer and independently shorten overall survival (OS) by compromising adjuvant therapy delivery. Current nutritional, inflammatory, and global surgical-risk scores identify only a minority of affected patients. We developed and validated a multimodal deep learning framework for simultaneous preoperative prediction of CD grade ≥II complications and long-term survival.
PATIENTS AND METHODS: This multicenter study analyzed 5237 patients with gastric adenocarcinoma from 11 Chinese centers and additionally validated the model using prospectively collected data from six registered clinical trials covering three neoadjuvant treatment settings (chemotherapy, chemoradiation, and immunochemotherapy). DeepComp integrates clinical variables with foundation-model image features from the target lesion region, 5-mm peritumoral region, and L3 body composition compartments through a tabular dual-task architecture to jointly predict CD grade ≥II complications and OS.
RESULTS: DeepComp achieved an area under the receiver operating characteristic curve of 0.888 [95% confidence interval (CI) 0.854-0.921] in the merged internal validation set and 0.824-0.869 across nine external cohorts, outperforming the best clinical baseline by 15.3 percentage points (P < 0.001) and all nine established clinical scores (all P < 0.001). With DeepComp assistance, the mean sensitivity of 10 surgeons increased from 47.1% to 87.9% (P < 0.001). Through target-trial emulation, DeepComp-guided prophylactic intensive care unit monitoring, preoperative nutritional support with delayed surgery, and minimally invasive triage yielded absolute CD grade ≥II risk reductions of 5.9%, 20.6%, and 11.7%, respectively (all P < 0.01; E-values 1.90-5.73). DeepComp independently predicted OS (adjusted hazard ratio 3.08 per standard deviation, 95% CI 2.91-3.25; pooled C-index 0.766), with 5-year survival ranging from 97.5% in quintile 1 to 2.4% in quintile 5.
CONCLUSIONS: DeepComp showed consistent performance across validation cohorts for preoperative risk stratification of post-operative complications and long-term survival and may support individualized perioperative management.
PATIENTS AND METHODS: This multicenter study analyzed 5237 patients with gastric adenocarcinoma from 11 Chinese centers and additionally validated the model using prospectively collected data from six registered clinical trials covering three neoadjuvant treatment settings (chemotherapy, chemoradiation, and immunochemotherapy). DeepComp integrates clinical variables with foundation-model image features from the target lesion region, 5-mm peritumoral region, and L3 body composition compartments through a tabular dual-task architecture to jointly predict CD grade ≥II complications and OS.
RESULTS: DeepComp achieved an area under the receiver operating characteristic curve of 0.888 [95% confidence interval (CI) 0.854-0.921] in the merged internal validation set and 0.824-0.869 across nine external cohorts, outperforming the best clinical baseline by 15.3 percentage points (P < 0.001) and all nine established clinical scores (all P < 0.001). With DeepComp assistance, the mean sensitivity of 10 surgeons increased from 47.1% to 87.9% (P < 0.001). Through target-trial emulation, DeepComp-guided prophylactic intensive care unit monitoring, preoperative nutritional support with delayed surgery, and minimally invasive triage yielded absolute CD grade ≥II risk reductions of 5.9%, 20.6%, and 11.7%, respectively (all P < 0.01; E-values 1.90-5.73). DeepComp independently predicted OS (adjusted hazard ratio 3.08 per standard deviation, 95% CI 2.91-3.25; pooled C-index 0.766), with 5-year survival ranging from 97.5% in quintile 1 to 2.4% in quintile 5.
CONCLUSIONS: DeepComp showed consistent performance across validation cohorts for preoperative risk stratification of post-operative complications and long-term survival and may support individualized perioperative management.