Sun Jing, Wu Pu-Yeh, Shen Fangmin, Chen Xingfa, She Jieqiong, Luo Mingcong, Feng Feifei, Zheng Dechun
Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, Fujian, 350014, China.
GE Healthcare, Beijing, China.
BMC Med Imaging. 2025 May 19;25(1):173. doi: 10.1186/s12880-025-01692-3.
To establish and validate deep learning (DL) models based on pre-treatment multiparametric magnetic resonance imaging (MRI) images of primary rectal cancer and basic clinical data for the prediction of synchronous liver metastases (SLM) in patients with Rectal cancer (RC).
In this retrospective study, 176 and 31 patients with RC who underwent multiparametric MRI from two centers were enrolled in the primary and external validation cohorts, respectively. Clinical factors, including sex, primary tumor site, CEA level, and CA199 level were assessed. A clinical feature (CF) model was first developed by multivariate logistic regression, then two residual network DL models were constructed based on multiparametric MRI of primary cancer with or without CF incorporation. Finally, the SLM prediction models were validated by 5-fold cross-validation and external validation. The performance of the models was evaluated by decision curve analysis (DCA) and receiver operating characteristic (ROC) analysis.
Among three SLM prediction models, the Combined DL model integrating primary tumor MRI and basic clinical data achieved the best performance (AUC = 0.887 in primary study cohort; AUC = 0.876 in the external validation cohort). In the primary study cohort, the CF model, MRI DL model, and Combined DL model achieved AUCs of 0.816 (95% CI: 0.750, 0.881), 0.788 (95% CI: 0.720, 0.857), and 0.887 (95% CI: 0.834, 0.940) respectively. In the external validation cohort, the CF model, DL model without CF, and DL model with CF achieved AUCs of 0.824 (95% CI: 0.664, 0.984), 0.662 (95% CI: 0.461, 0.863), and 0.876 (95% CI: 0.728, 1.000), respectively.
The combined DL model demonstrates promising potential to predict SLM in patients with RC, thereby making individualized imaging test strategies.
Accurate synchronous liver metastasis (SLM) risk stratification is important for treatment planning and prognosis improvement. The proposed DL signature may be employed to better understand an individual patient's SLM risk, aiding in treatment planning and selection of further imaging examinations to personalize clinical decisions.
Not applicable.
基于原发性直肠癌的治疗前多参数磁共振成像（MRI）图像和基本临床数据，建立并验证深度学习（DL）模型，以预测直肠癌（RC）患者的同步肝转移（SLM）。
在这项回顾性研究中，分别从两个中心纳入了176例和31例接受多参数MRI检查的RC患者作为主要队列和外部验证队列。评估了包括性别、原发肿瘤部位、癌胚抗原（CEA）水平和糖类抗原199（CA199）水平在内的临床因素。首先通过多变量逻辑回归建立临床特征（CF）模型，然后基于原发性癌症的多参数MRI构建两个残差网络DL模型，分别为纳入CF和未纳入CF的模型。最后，通过五折交叉验证和外部验证对SLM预测模型进行验证。通过决策曲线分析（DCA）和受试者工作特征（ROC）分析评估模型的性能。
在三个SLM预测模型中，整合原发性肿瘤MRI和基本临床数据的联合DL模型表现最佳（主要研究队列中的AUC = 0.887；外部验证队列中的AUC = 0.876）。在主要研究队列中，CF模型、MRI DL模型和联合DL模型的AUC分别为0.816（95%CI：0.750，0.881）、0.788（95%CI：0.720，0.857）和0.887（95%CI：0.834，0.940）。在外部验证队列中，CF模型、未纳入CF的DL模型和纳入CF的DL模型的AUC分别为0.824（95%CI：0.664，0.984）、0.662（95%CI：0.461，0.863）和0.876（95%CI：0.728，1.000）。
联合DL模型在预测RC患者的SLM方面显示出有前景的潜力，从而制定个体化的影像检查策略。
准确的同步肝转移（SLM）风险分层对于治疗计划和改善预后很重要。所提出的DL特征可用于更好地了解个体患者的SLM风险，有助于治疗计划制定和选择进一步的影像检查以实现临床决策个体化。
不适用。
