OBJECTIVES 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. METHODS 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. RESULTS 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. DISCUSSION 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.

中文翻译：

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开发和验证用于早期检测恶性胆囊病变的线型图。

目的在胆囊癌中区分恶性和良性病变的术前决策仍然具有挑战性。我们旨在创建诊断性诺模图，以在手术切除之前识别胆囊癌（GBC），尤其是偶发性GBC（IGBC）。方法总共587例由医院经病理证实的胆囊病变的连续患者被随机分配到一个训练队列（70％）和一个内部验证队列（30％），其他中心的287例患者作为外部验证队列。放射学特征是通过最小绝对收缩和选择算子逻辑回归模型开发的。通过多变量分析确定的重要放射学特征和独立的临床因素被用于构建诺模图。结果按年龄建立诊断列线图，CA19.9和6个放射学特征。内部和外部验证队列中曲线下的面积值分别高达0.91和0.89。GBC概率的校准曲线显示了列线图预测与实际观测之间的最佳一致性。与以前的方法相比，它在诊断GBC方面具有出众的灵敏度（91.5％）和准确性（85.1％）。在训练队列中，GBC组使用诺模图的准确性显着高于放射线医师（P <0.001），并且在每个队列中相似。值得注意的是，使用该列线图可以成功地识别出大多数被误诊为良性病变的IGBC。讨论新颖的列线图为检测胆囊肿块中癌症的存在提供了强大的工具，从而提高了准确性和灵敏度。