Abstract：
OBJECTIVE: To develop and validate imaging-based models for predicting the malignancy risk of intraductal papillary mucinous neoplasm (IPMN).
METHODS: We retrospectively analyzed data from 241 IPMN patients who underwent preoperative CT and MRI for model development. Cyst size, presence and size of the enhancing mural nodule (EMN), main pancreatic duct (MPD) diameter, thickened/enhancing cyst wall, abrupt MPD caliber change with distal atrophy, and lymphadenopathy were assessed. Multiple logistic regression models predicting malignancy risk were created using either continuous (Model C) or dichotomized variables (Model D) using these imaging features. Validation included internal (n = 55) and external (n = 43) datasets. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and compared with that of the international guideline-based model (Model F).
RESULTS: Model C identified age, EMN size, MPD diameter, and lymphadenopathy as independent predictors on CT, and age and presence and size of EMN on MRI. Model D identified age ≥ 68, cyst size ≥ 31 mm, EMN ≥ 6 mm, MPD ≥ 7 mm, and lymphadenopathy as independent predictors on CT, and age ≥ 68, EMN ≥ 4.5 mm, and lymphadenopathy on MRI. Model C (AUC, 0.763-0.899) performed slightly better than Model D (AUC, 0.753-0.912) without statistical significance. No significant difference was observed between Models C and F (AUC, 0.729-0.952). Combining Model C with obstructive jaundice improved performance (AUC, 0.802-0.941) without statistical significance.
CONCLUSIONS: Our imaging-based models effectively predicted the malignancy risk of IPMNs, comparable to international consensus guidelines.
CONCLUSIONS: Imaging features are important for predicting the malignant potential of IPMNs. Our imaging-based model may help determine surgical candidacy for patients with IPMNs.
CONCLUSIONS: Non-invasively determining the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) allows for appropriate treatment decision-making We identified multiple imaging features that are associated with malignant transformation and developed models for this prediction. Our model performs comparably with international consensus guidelines in predicting the malignant potential of IPMNs.
METHODS: We retrospectively analyzed data from 241 IPMN patients who underwent preoperative CT and MRI for model development. Cyst size, presence and size of the enhancing mural nodule (EMN), main pancreatic duct (MPD) diameter, thickened/enhancing cyst wall, abrupt MPD caliber change with distal atrophy, and lymphadenopathy were assessed. Multiple logistic regression models predicting malignancy risk were created using either continuous (Model C) or dichotomized variables (Model D) using these imaging features. Validation included internal (n = 55) and external (n = 43) datasets. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and compared with that of the international guideline-based model (Model F).
RESULTS: Model C identified age, EMN size, MPD diameter, and lymphadenopathy as independent predictors on CT, and age and presence and size of EMN on MRI. Model D identified age ≥ 68, cyst size ≥ 31 mm, EMN ≥ 6 mm, MPD ≥ 7 mm, and lymphadenopathy as independent predictors on CT, and age ≥ 68, EMN ≥ 4.5 mm, and lymphadenopathy on MRI. Model C (AUC, 0.763-0.899) performed slightly better than Model D (AUC, 0.753-0.912) without statistical significance. No significant difference was observed between Models C and F (AUC, 0.729-0.952). Combining Model C with obstructive jaundice improved performance (AUC, 0.802-0.941) without statistical significance.
CONCLUSIONS: Our imaging-based models effectively predicted the malignancy risk of IPMNs, comparable to international consensus guidelines.
CONCLUSIONS: Imaging features are important for predicting the malignant potential of IPMNs. Our imaging-based model may help determine surgical candidacy for patients with IPMNs.
CONCLUSIONS: Non-invasively determining the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) allows for appropriate treatment decision-making We identified multiple imaging features that are associated with malignant transformation and developed models for this prediction. Our model performs comparably with international consensus guidelines in predicting the malignant potential of IPMNs.
摘要：
目的：开发并验证基于影像学的模型，以预测导管内乳头状黏液性肿瘤（IPMN）的恶性风险。
方法：我们回顾性分析了241例IPMN患者的数据，这些患者在术前接受了CT和MRI进行模型开发。囊肿大小,增强壁结节（EMN）的存在和大小，主胰管(MPD)直径,增厚/增强囊肿壁，MPD口径突变伴远端萎缩，和淋巴结病进行评估。使用连续（模型C）或二分变量（模型D）使用这些成像特征创建预测恶性肿瘤风险的多元逻辑回归模型。验证包括内部(n=55)和外部(n=43)数据集。使用受试者工作特征曲线下面积（AUC）评估模型性能，并与基于国际指南的模型（模型F）进行比较。
结果：模型C确定了年龄，EMN尺寸,MPD直径,淋巴结病是CT的独立预测因子，MRI上EMN的年龄、存在和大小。D型鉴定年龄≥68,囊肿大小≥31mm,EMN≥6mm,MPD≥7mm,淋巴结病是CT的独立预测因子，年龄≥68岁，EMN≥4.5mm，和MRI上的淋巴结肿大。型号C(AUC，0.763-0.899)的性能略优于D型(AUC，0.753-0.912)无统计学意义。模型C和F之间没有观察到显著差异(AUC，0.729-0.952).将模型C与阻塞性黄疸结合使用可改善性能（AUC，0.802-0.941)无统计学意义。
结论：我们基于成像的模型有效地预测了IPMNs的恶性风险，与国际共识准则相当。
结论：影像学特征对于预测IPMNs的恶性潜能很重要。我们基于成像的模型可能有助于确定IPMN患者的手术候选资格。
结论：非侵入性地确定导管内乳头状黏液性肿瘤（IPMNs）的恶性潜能可以做出适当的治疗决策。我们确定了与恶性转化相关的多种影像学特征，并开发了该预测模型。我们的模型在预测IPMNs的恶性潜力方面与国际共识指南具有可比性。
方法：我们回顾性分析了241例IPMN患者的数据，这些患者在术前接受了CT和MRI进行模型开发。囊肿大小,增强壁结节（EMN）的存在和大小，主胰管(MPD)直径,增厚/增强囊肿壁，MPD口径突变伴远端萎缩，和淋巴结病进行评估。使用连续（模型C）或二分变量（模型D）使用这些成像特征创建预测恶性肿瘤风险的多元逻辑回归模型。验证包括内部(n=55)和外部(n=43)数据集。使用受试者工作特征曲线下面积（AUC）评估模型性能，并与基于国际指南的模型（模型F）进行比较。
结果：模型C确定了年龄，EMN尺寸,MPD直径,淋巴结病是CT的独立预测因子，MRI上EMN的年龄、存在和大小。D型鉴定年龄≥68,囊肿大小≥31mm,EMN≥6mm,MPD≥7mm,淋巴结病是CT的独立预测因子，年龄≥68岁，EMN≥4.5mm，和MRI上的淋巴结肿大。型号C(AUC，0.763-0.899)的性能略优于D型(AUC，0.753-0.912)无统计学意义。模型C和F之间没有观察到显著差异(AUC，0.729-0.952).将模型C与阻塞性黄疸结合使用可改善性能（AUC，0.802-0.941)无统计学意义。
结论：我们基于成像的模型有效地预测了IPMNs的恶性风险，与国际共识准则相当。
结论：影像学特征对于预测IPMNs的恶性潜能很重要。我们基于成像的模型可能有助于确定IPMN患者的手术候选资格。
结论：非侵入性地确定导管内乳头状黏液性肿瘤（IPMNs）的恶性潜能可以做出适当的治疗决策。我们确定了与恶性转化相关的多种影像学特征，并开发了该预测模型。我们的模型在预测IPMNs的恶性潜力方面与国际共识指南具有可比性。
