BMJ：新型诊断技术可帮助医生选择治疗卵巢癌的最佳疗法

  刊登在国际杂志British Medical Journal上的一篇研究论文中，来自帝国理工学院和鲁汶大学的科学家们开发了一种新型的测试技术可以帮助医生进行卵巢癌诊断并且针对患者选择最合适的癌症疗法。成功的疗法往往部分依赖于对肿瘤类型的鉴别，但是通常情况下这个过程非常困难，许多患癌女性经常得不到最专业外科的诊治从而导致病情延误。
  这项研究中，研究人员开发了一种名为ANDEX的新型检测技术，其可以帮助区分恶性瘤和良性瘤，并且可以高准确率地鉴别出不同类型的恶性肿瘤。ANDEX检测技术是基于病人的临床信息而开发的，其检测具有较高的准确率。Tom Bourne教授说道，获得正确的术前诊断非常关键，如果术前诊断的结果并不准确，那么病人很有可能会进行更多的手术操作而延误病情，比如有时候医生会移除一个本不必要移除的患者卵巢组织。
  如果肿瘤是良性的，那么女性患者并不需要进行过多的治疗；然而如果肿瘤是恶性的，那么就需要精确区分患者肿瘤的具体类型，以便于选择最佳的疗法治疗疾病。本文中开发的可以对卵巢癌精确分类的技术可以帮助医生进行正确的判断，这将可以有效改善癌症患者的治疗及预后结果，也将会降低患者进行其它不必要疗法的可能性。
  研究者表示，开发这种新型技术是基于在1999年至2007年对10个欧洲国家的3506名病人的研究开发而成的，研究者首先分析了这些患者术前的信息来用于预测诊断结果，随后在2009年至2012年间对2403名患者进行试验。研究揭示，如果患病女性在专门的妇科癌症机构进行诊断治疗的话，那么她们会有一个较好的生存率，但是目前这仅限于欧洲和美国的一少部分女性。
  当前的预测模型可以区分出良性和恶性瘤，但是缺少准确性而且并不能对恶性肿瘤进行再分类；ANDEX技术则可以对良性瘤、临界瘤、I期阶段、II-IV阶段及二次转移性肿瘤进行很好地区分；本文研究由国家卫生研究所(NIHR)帝国生物医学研究中心等机构提供资助。（转化医学网360zhyx.com）
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Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study
BMJ doi: http://dx.doi.org/10.1136/bmj.g5920
Ben Van Calster, professor1, Kirsten Van Hoorde, doctoral researcher23, Lil Valentin, professor4, Antonia C Testa, professor5, Daniela Fischerova, consultant gynaecologist6, Caroline Van Holsbeke, consultant gynaecologist7, Luca Savelli, consultant gynaecologist8, Dorella Franchi, consultant gynaecologist9, Elisabeth Epstein, professor10, Jeroen Kaijser, research fellow111, Vanya Van Belle, postdoctoral researcher23, Artur Czekierdowski, professor12, Stefano Guerriero, professor13, Robert Fruscio, consultant gynaecologist14, Chiara Lanzani, consultant gynaecologist15, Felice Scala, consultant gynaecologist16, Tom Bourne, professor11117, Dirk Timmerman, professor111 International Ovarian Tumour Analysis (IOTA) group
Objectives To develop a risk prediction model to preoperatively discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian tumours.
Design Observational diagnostic study using prospectively collected clinical and ultrasound data.
Setting 24 ultrasound centres in 10 countries.
Participants Women with an ovarian (including para-ovarian and tubal) mass and who underwent a standardised ultrasound examination before surgery. The model was developed on 3506 patients recruited between 1999 and 2007, temporally validated on 2403 patients recruited between 2009 and 2012, and then updated on all 5909 patients.
Main outcome measures Histological classification and surgical staging of the mass.
Results The Assessment of Different NEoplasias in the adneXa (ADNEX) model contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of centre (oncology centres v other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites. The area under the receiver operating characteristic curve (AUC) for the classic discrimination between benign and malignant tumours was 0.94 (0.93 to 0.95) on temporal validation. The AUC was 0.85 for benign versus borderline, 0.92 for benign versus stage I cancer, 0.99 for benign versus stage II-IV cancer, and 0.95 for benign versus secondary metastatic. AUCs between malignant subtypes varied between 0.71 and 0.95, with an AUC of 0.75 for borderline versus stage I cancer and 0.82 for stage II-IV versus secondary metastatic. Calibration curves showed that the estimated risks were accurate.
Conclusions The ADNEX model discriminates well between benign and malignant tumours and offers fair to excellent discrimination between four types of ovarian malignancy. The use of ADNEX has the potential to improve triage and management decisions and so reduce morbidity and mortality associated with adnexal pathology.