Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Cancer Surveillance Unit, International Agency for Research on Cancer, Lyon, France.
Acta Oncol. 2023 Apr;62(4):335-341. doi: 10.1080/0284186X.2023.2205548. Epub 2023 Apr 27.
BACKGROUND/PURPOSE: Stage at diagnosis is an important metric in treatment and prognosis of cancer, and also in planning and evaluation of cancer control. For the latter purposes, the data source is the population-based cancer registry (PBCR), but, although stage is usually among the variables collected by cancer registries, it is often missing, especially in low-income settings. Essential TNM has been introduced to facilitate abstraction of stage data by cancer registry personnel, but the accuracy with which they can do so is unknown.
51 cancer registrars from 20 countries of sub-Saharan Africa (13 anglophone, 7 francophone) were tasked with abstracting stage at diagnosis, using Essential TNM, from scanned extracts of case. The panel comprised 28 records of each of 8 common cancer types, and the participants chose how many to attempt (between 48 and 128). Stage group (I-IV), derived from the eTNM elements that they assigned to each cancer, was compared with a gold standard, as decided by two expert clinicians.
The registrars assigned the correct stage (I-IV) in between 60 and 80% of cases, with the lowest values for ovary, and the highest for oesophagus. The weighted kappa statistic suggested a moderate level of agreement between participant and expert (0.41-0.60) for 5 cancers, and substantial agreement (0.61-0.80) for three, with the best for cervix, large bowel, oesophagus and ovary, and the worst (weighted kappa 0.46) for non-Hodgkin lymphoma (NHL). For all except NHL, early stage (I/II) and late stage (III/IV) was correctly identified in 80% or more of the cases.
A single training in staging using Essential TNM resulted in an accuracy that was not much inferior to what has been observed in clinical situations in high income settings. Nevertheless, some lessons were learned on how to improve both the guidelines for staging, and the training course.
背景/目的：诊断时的分期是癌症治疗和预后的重要指标，也是癌症控制规划和评估的重要指标。为此目的，数据源是基于人群的癌症登记处（PBCR），但是，尽管分期通常是癌症登记处收集的变量之一，但它经常缺失，尤其是在低收入环境中。基本 TNM 已被引入，以方便癌症登记处人员提取分期数据，但他们的准确性尚不清楚。
来自撒哈拉以南非洲 20 个国家（13 个英语国家，7 个法语国家）的 51 名癌症登记员被要求使用基本 TNM 从病例的扫描摘录中提取诊断时的分期。该小组由每种 8 种常见癌症类型的 28 个记录组成，参与者选择尝试多少个（48 到 128 个之间）。根据他们分配给每种癌症的 eTNM 元素，将分期组（I-IV）与由两位临床专家决定的金标准进行比较。
登记员在 60%至 80%的病例中正确分配了分期（I-IV），卵巢的最低值和食道的最高值。参与者与专家之间的加权 kappa 统计表明，对于 5 种癌症，存在中度一致性（0.41-0.60），对于 3 种癌症，存在高度一致性（0.61-0.80），其中宫颈癌、大肠、食道和卵巢的一致性最好，非霍奇金淋巴瘤（NHL）的一致性最差（加权 kappa 为 0.46）。除 NHL 外，80%或更多的病例中正确识别了早期（I/II）和晚期（III/IV）分期。
使用基本 TNM 进行一次分期培训，其准确性与高收入环境下观察到的临床情况相差不大。然而，我们从中学到了一些如何改进分期指南和培训课程的经验教训。
