Seza Katsushi, Tawada Katsunobu, Kobayashi Akitoshi, Nakamura Kazuyoshi
Gastroenterology, Chiba Medical Center, Chiba, JPN.
Gastroenterology, Chiba Kaihin Municipal Hospital, Chiba, JPN.
Cureus. 2025 Jun 8;17(6):e85547. doi: 10.7759/cureus.85547. eCollection 2025 Jun.
Introduction Serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) often exhibit similar imaging features when evaluated with a single imaging modality. Differentiating between SCN and MCN typically necessitates the utilization of multiple imaging techniques, including computed tomography (CT), magnetic resonance imaging (MRI), and endoscopic ultrasonography (EUS). Recent research indicates that artificial intelligence (AI) can effectively distinguish between SCN and MCN using single-modal imaging. Despite these advancements, the diagnostic performance of AI has not yet reached an optimal level. This study compares the efficacy of AI in classifying SCN and MCN using multimodal imaging versus single-modal imaging. The objective was to assess the effectiveness of AI utilizing multimodal imaging with EUS, CT, and MRI to classify these two types of pancreatic cysts. Methods We retrospectively gathered data from 25 patients with surgically confirmed SCN and 24 patients with surgically confirmed MCN as part of a multicenter study. Imaging was conducted using four modalities: EUS, early-phase contrast-enhanced abdominal CT, T2-weighted MRI, and magnetic resonance pancreatography. Four images per modality were obtained for each tumor. Data augmentation techniques were utilized, resulting in a final dataset of 39,200 images per modality. An AI model with ResNet was employed to categorize the cysts as SCN or MCN, incorporating clinical features and combinations of imaging modalities (single, double, triple, and all four modalities). The classification outcomes were compared with those of five experienced gastroenterologists with over 10 years of experience. The comparison is based on three performance metrics: sensitivity, specificity, and accuracy. Results For AI utilizing a single imaging modality, the sensitivity, specificity, and accuracy were 87.0%, 92.7%, and 90.8%, respectively. Combining two imaging modalities improved the sensitivity, specificity, and accuracy to 95.3%, 95.1%, and 94.9%. With three modalities, AI achieved a sensitivity of 96.0%, a specificity of 99.0%, and an accuracy of 97.0%. Ultimately, employing all four imaging modalities resulted in AI achieving 98.0% sensitivity, 100% specificity, and 99.0% accuracy. In contrast, experts utilizing all four modalities attained a sensitivity of 78.0%, specificity of 82.0%, and accuracy of 81.0%. The AI models consistently outperformed the experts across all metrics. A continuous enhancement in performance was observed with each additional imaging modality, with AI utilizing three and four modalities significantly surpassing single-modal imaging AI. Conclusion AI utilizing multimodal imaging offers better performance compared to both single-modal imaging AI and experienced human experts in classifying SCN and MCN.
浆液性囊性肿瘤（SCN）和黏液性囊性肿瘤（MCN）在使用单一成像方式评估时，通常表现出相似的影像学特征。区分SCN和MCN通常需要使用多种成像技术，包括计算机断层扫描（CT）、磁共振成像（MRI）和内镜超声检查（EUS）。最近的研究表明，人工智能（AI）可以使用单模态成像有效地鉴别SCN和MCN。尽管有这些进展，但AI的诊断性能尚未达到最佳水平。本研究比较了AI在使用多模态成像与单模态成像对SCN和MCN进行分类时的效果。目的是评估AI利用EUS、CT和MRI多模态成像对这两种类型的胰腺囊肿进行分类的有效性。方法：作为一项多中心研究的一部分，我们回顾性收集了25例经手术确诊为SCN的患者和24例经手术确诊为MCN的患者的数据。使用四种模态进行成像：EUS、腹部早期增强CT、T2加权MRI和磁共振胰胆管造影。为每个肿瘤在每种模态下获取四张图像。利用数据增强技术，最终每种模态的数据集为39200张图像。采用具有残差网络（ResNet）的AI模型将囊肿分类为SCN或MCN，并纳入临床特征和成像模态的组合（单模态、双模态、三模态和四模态）。将分类结果与五位经验超过10年的资深胃肠病学家的结果进行比较。比较基于三个性能指标：敏感性、特异性和准确性。结果：对于使用单模态成像的AI，敏感性、特异性和准确性分别为87.0%、92.7%和90.8%。结合两种成像模态可将敏感性、特异性和准确性提高到95.3%、95.1%和94.9%。对于三模态，AI的敏感性为96.0%，特异性为99.0%，准确性为97.0%。最终，使用所有四种成像模态使AI的敏感性达到98.0%，特异性达到100%，准确性达到99.0%。相比之下，使用所有四种模态的专家的敏感性为78.0%，特异性为82.0%，准确性为81.0%。在所有指标上，AI模型始终优于专家。随着每种额外的成像模态的增加，性能持续提高，使用三模态和四模态的AI显著优于单模态成像AI。结论：在对SCN和MCN进行分类时，与单模态成像AI和经验丰富的人类专家相比，利用多模态成像的AI表现更好。
