Wang Xiang-Ning, Qian Jiang, Yuan Yi-Fei, Zhang Rui, Zhang Yan-Qing
Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Department of Ophthalmology, Eye and ENT Hospital of Fudan University, Shanghai, China.
Can J Ophthalmol. 2017 Feb;52(1):30-33. doi: 10.1016/j.jcjo.2016.08.009. Epub 2016 Nov 16.
To investigate the application of Rose and Wright's algorithm in diagnosing lacrimal gland masses.
Retrospective observational cases series.
A total of 93 consecutive patients with primary masses within the orbital lobe of the lacrimal gland were reviewed.
Before treatment, patients' detailed history was collected verbally and all patients underwent image examination (computed tomography and magnetic resonance imaging). The clinical and radiological features of every patient were evaluated by experienced orbital surgeons. Based on Rose and Wright's criteria, patients were scored and then treated using surgery with radiotherapy and/or chemotherapy. The final diagnoses were based on the histopathologic results. Based on the histopathologic diagnosis, the data from Rose and Wright's algorithm were evaluated.
The accuracy of Rose and Wright's algorithm for benign and malignant tumour diagnoses was 75% and 50%, respectively. The diagnostic sensitivity, specificity, and accuracy of Rose and Wright's algorithm were 64%, 93%, and 86%, respectively. The algorithm demonstrated significant accuracy in the clinicoradiological criterion in differentiating between benign tumours and malignant tumours (p < 0.05).
Rose and Wright's algorithm has great advantages in distinguishing benign from malignant tumours within the orbital lobe of the lacrimal gland. However, the algorithm should be used with great caution because of its low diagnostic sensitivity for malignant tumours.
探讨罗斯和赖特算法在泪腺肿物诊断中的应用。
回顾性观察病例系列。
共纳入93例连续的泪腺眶叶原发性肿物患者并进行回顾性分析。
治疗前，通过口头询问收集患者详细病史，所有患者均接受影像学检查（计算机断层扫描和磁共振成像）。由经验丰富的眼眶外科医生评估每位患者的临床和放射学特征。根据罗斯和赖特标准对患者进行评分，然后采用手术联合放疗和/或化疗进行治疗。最终诊断基于组织病理学结果。根据组织病理学诊断，对罗斯和赖特算法的数据进行评估。
罗斯和赖特算法对良性和恶性肿瘤诊断的准确率分别为75%和50%。该算法的诊断敏感性、特异性和准确率分别为64%、93%和86%。该算法在区分良性肿瘤和恶性肿瘤的临床放射学标准方面显示出显著的准确性（p<0.05）。
罗斯和赖特算法在区分泪腺眶叶良性和恶性肿瘤方面具有很大优势。然而，由于其对恶性肿瘤的诊断敏感性较低，应谨慎使用该算法。
