BMC medical imaging · 2026
Differentiating sub-centimeter lung metastases in colorectal cancer by deep learning: a multicenter retrospective study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Gao Xinyi, Ma Xiao, Zhang Zhiyuan, Yuan Jie, Li Qianling, Zheng Pengwen, Lv Min, Ma Dening, Sun Jingjing
Аннотация
BACKGROUND: Early diagnosis of sub-centimeter lung metastases is critical for timely decision-making and improved prognosis in patients with colorectal cancer. The diagnostic evaluation of indeterminate sub-centimeter lung nodules in colorectal cancer patients remains a crucial challenge. We aim to develop and validate a deep learning model for differentiating sub-centimeter lung metastases noninvasively.
METHODS: Our retrospective study included 1335 colorectal cancer patients with pretreated sub-centimeter lung metastases from 3 centers and 1335 benign lung nodules from one center. The primary cohort comprised 1194 patients, who were randomly assigned (8:2) to training and internal validation cohorts. Two external validation cohorts (EVC) consisted of 101 (EVC1) and 40 (EVC2) patients. The deep learning framework employed a fully automated VNet-based segmentation model for non-contrast computed tomography (CT) scans, integrated with a ResNet18 classifier for discriminating whether sub-centimeter lung nodules are benign or metastatic. Moreover, stepwise validation of subgroups was performed according to the maximum diameter of the nodules (10, 9, 8, 7, 6, 5, ≤ 4 mm).
RESULTS: The automatic segmentation model achieved a dice coefficient of 0.825. In primary cohort, the accuracy by radiologists was 0.705. The AUC in deep learning model showed 0.953 (95%CI: 0.937-0.967), 0.906 (95%CI: 0.874-0.926), and 0.951 (95%CI: 0.938-0.965) in internal and two external validation cohorts. Furthermore, the stepwise validation demonstrated that the diagnostic performance remain stable as the nodule maximum diameter shrinks when size≥5 mm.
CONCLUSION: The deep learning model can accurately and non-invasively distinguish between benign and metastatic sub-centimeter lung nodules in patients with colorectal cancer.
METHODS: Our retrospective study included 1335 colorectal cancer patients with pretreated sub-centimeter lung metastases from 3 centers and 1335 benign lung nodules from one center. The primary cohort comprised 1194 patients, who were randomly assigned (8:2) to training and internal validation cohorts. Two external validation cohorts (EVC) consisted of 101 (EVC1) and 40 (EVC2) patients. The deep learning framework employed a fully automated VNet-based segmentation model for non-contrast computed tomography (CT) scans, integrated with a ResNet18 classifier for discriminating whether sub-centimeter lung nodules are benign or metastatic. Moreover, stepwise validation of subgroups was performed according to the maximum diameter of the nodules (10, 9, 8, 7, 6, 5, ≤ 4 mm).
RESULTS: The automatic segmentation model achieved a dice coefficient of 0.825. In primary cohort, the accuracy by radiologists was 0.705. The AUC in deep learning model showed 0.953 (95%CI: 0.937-0.967), 0.906 (95%CI: 0.874-0.926), and 0.951 (95%CI: 0.938-0.965) in internal and two external validation cohorts. Furthermore, the stepwise validation demonstrated that the diagnostic performance remain stable as the nodule maximum diameter shrinks when size≥5 mm.
CONCLUSION: The deep learning model can accurately and non-invasively distinguish between benign and metastatic sub-centimeter lung nodules in patients with colorectal cancer.