Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China.
Department of General Surgery, Jinhua Municipal Central Hospital, Jinhua, China.
Clin Transl Gastroenterol. 2019 Oct;10(10):e00098. doi: 10.14309/ctg.0000000000000098.
Preoperative decision-making for differentiating malignant from benign lesions in the gallbladder remains challenging. We aimed to create a diagnostic nomogram to identify gallbladder cancer (GBC), especially for incidental GBC (IGBC), before surgical resection.
A total of 587 consecutive patients with pathologically confirmed gallbladder lesions from a hospital were randomly assigned to a training cohort (70%) and an internal validation cohort (30%), with 287 patients from other centers as an external validation cohort. Radiological features were developed by the least absolute shrinkage and selection operator logistic regression model. Significant radiological features and independent clinical factors, identified by multivariate analyses, were used to construct a nomogram.
A diagnostic nomogram was established by age, CA19.9, and 6 radiological features. The values of area under the curve in the internal and external validation cohorts were up to 0.91 and 0.89, respectively. The calibration curves for probability of GBC showed optimal agreement between nomogram prediction and actual observation. Compared with previous methods, it demonstrated superior sensitivity (91.5%) and accuracy (85.1%) in the diagnosis of GBC. The accuracy using the nomogram was significantly higher in GBC groups compared with that by radiologists in the training cohort (P < 0.001) and similarly in each cohort. Notably, most of the IGBC, which were misdiagnosed as benign lesions, were successfully identified using this nomogram.
A novel nomogram provides a powerful tool for detecting the presence of cancer in gallbladder masses, with an increase in accuracy and sensitivity. It demonstrates an unprecedented potential for IGBC identification.
术前鉴别胆囊良恶性病变仍然具有挑战性。本研究旨在建立一种诊断列线图，以便在手术切除前识别胆囊癌（GBC），尤其是偶发性 GBC（IGBC）。
共纳入 587 例来自医院的经病理证实的胆囊病变患者，随机分为训练队列（70%）和内部验证队列（30%），另将来自其他中心的 287 例患者作为外部验证队列。采用最小绝对收缩和选择算子逻辑回归模型建立影像学特征。通过多变量分析确定有统计学意义的影像学特征和独立临床因素，用于构建列线图。
建立了一个由年龄、CA19.9 和 6 个影像学特征组成的诊断列线图。内部和外部验证队列的曲线下面积分别高达 0.91 和 0.89。GBC 概率的校准曲线显示了列线图预测和实际观察之间的最佳一致性。与以往方法相比，该列线图在 GBC 的诊断中具有更高的敏感性（91.5%）和准确性（85.1%）。与训练队列中的放射科医生相比，该列线图在 GBC 组中的准确性明显更高（P<0.001），在每个队列中也是如此。值得注意的是，大多数被误诊为良性病变的 IGBC 可以使用该列线图成功识别。
一种新的列线图为检测胆囊肿块中的癌症提供了一个强大的工具，提高了准确性和敏感性。它为 IGBC 的识别提供了前所未有的潜力。
