Application and Development of Artificial Intelligence in A Pathological Study of Urinary System Tumors
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摘要:
2020年全球癌症统计数据显示，泌尿系肿瘤发病人数约占癌症总人数的13%。目前泌尿系肿瘤的诊断方法以影像学检查、内窥镜检查和病理检查为主。作为肿瘤诊断的“金标准”，病理检查存在病理医生缺乏、操作时间长等问题。人工智能具有强大的病理图像识别和特征分析能力，可作为辅助诊断，已经在多种泌尿系肿瘤中实现了肿瘤的自动诊断、分型、分期、分级和预后预测。但人工智能仍存在诸多不足，限制了其在临床的应用。本文就人工智能及其在泌尿系肿瘤病理研究中的应用进展作一综述。
Abstract:Global Cancer Statistics for 2020 show that urinary system tumors account for approximately 13% of the total number of cancers. At present, the diagnostic methods of urinary system tumors are imaging, endoscopy, and pathological examination. As the gold standard of tumor diagnosis, pathological examination has problems such as lack of pathologists and long operation time. Artificial intelligence (AI), with a strong ability for pathology image recognition and feature analysis, can be used as an auxiliary diagnosis. It has realized automatic diagnosis, typing, staging, grading, and prognosis prediction in several urinary system tumors. However, AI still has many shortcomings, which limit its clinical application. This article will review the progress of AI and its application in the pathological study of urinary system tumors.
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Key words:
- Artificial intelligence /
- Pathology /
- Prostate cancer /
- Bladder cancer /
- Kidney cancer
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0 引言
鼻咽癌是最常见的头颈部肿瘤之一。我国为鼻咽癌高发地区，每年的发病率约为20/10万[1]，由于鼻咽解剖结构及生物学行为的特殊性，很难行手术治疗，目前鼻咽癌公认和有效的治疗手段为放射治疗或以放疗为主的综合治疗。虽然放疗技术不断进步与放疗设备的不断更新，鼻咽癌的生存率有了较大的提高，但5年生存率仍为60%~80%[2]，部分患者仍未能获得长期生存。TNM分期系统是鼻咽癌预后判断和指导治疗的重要依据，但临床发现同一分期患者即使接受相同的治疗方案，预后却不同[3-4]，这提示鼻咽癌生物学差异的存在，仅基于解剖学信息的TNM临床分期系统还不能准确地预测鼻咽癌患者的预后。虽然EB病毒滴度、表皮生长因子受体、microRNA也可提示鼻咽癌的预后[5-7]，但检测成本高，需要多中心合作，临床上可行性差。所以，亟需检测方便、价格低廉可预测鼻咽癌预后的标志物。
流行病学研究证实，约25%的肿瘤由炎性反应发展而来，其与肿瘤的发生发展密切相关并且影响肿瘤患者的预后[8]。炎性反应指标，如白细胞计数[9]、血小板计数[10-11]、中性粒淋巴细胞比（neutrophil-lymphocyte ratio, NLR）[12-13]、血小板淋巴细胞比（platelet-lymphocyte ratio, PLR）[14-15]被发现可作为肿瘤的独立预后因素。这些血液指标检测方便，价格低廉，可广泛应用于临床，评估患者预后。本研究通过对91例鼻咽癌患者临床资料进行回顾性分析，评价治疗前PLR和NLR与鼻咽癌患者预后的相关性，为评估预后提供客观依据。
1 资料与方法
1.1 临床资料
回顾性收集2009年1月至2013年9月期间于西安交通大学第一附属医院和陕西省人民医院初治的91例鼻咽癌患者，所有病例均经病理证实。临床资料完整。排除标准：（1）合并有免疫性疾病以及其他恶性肿瘤的患者；（2）治疗前合并有急性或慢性感染；（3）合并有血液系统疾病、血栓或出血性疾病；（4）合并有严重的肝、肾疾病；（5）治疗前曾接受过放疗或化疗；（6）无远处转移。记录患者治疗前的中性粒细胞计数、淋巴细胞计数及血小板计数结果。
1.2 治疗及随访方法
入选患者采用3D-CRT或IMRT根治性放疗（有或无化疗），Ⅰ期患者仅接受单纯放射治疗，Ⅱ、Ⅲ、Ⅳ期患者接受以顺铂和5-氟尿嘧啶为主的辅助或同步放化疗。鼻咽原发灶和颈部转移淋巴结剂量为（70~76）Gy/（7~8）w/（35~38）f，颈部预防区域剂量为（50~60）Gy/（5~6）w/（25~30）f。根据患者的临床分期及不良反应给予2~6周期的全身化疗，化疗方案为：顺铂25 mg/m2，第1~3天静脉滴注；5-氟尿嘧啶500 mg/m2，第1~5天静脉滴注，每21天重复1周期。患者治疗结束后均定期随访，治疗后前2年，每3月检查一次，2年后半年复查一次，5年后1年复查1次。随访截止时间为2016年9月。
1.3 统计学方法
采用SPSS19.0软件对数据进行统计学分析。绘制ROC曲线确定PLR和NLR与总生存期（overall survival, OS）及无进展生存期（progression-free survival, PFS）的相关性，选取截断值。应用Kaplan-Meier法进行生存分析并采用Log rank检验来检验。采用Cox比例风险回归模型分析多种因素对生存时间的影响。以P < 0.05为差异有统计学意义。
2 结果
2.1 鼻咽癌患者临床病理资料
91例患者的基本特征资料见表 1，中位年龄53岁（12~72）岁，女30例，男61例，男女比例2:1，Ⅰ、Ⅱ、Ⅲ、Ⅳ期患者分别为2、27、42、20例。单纯放疗患者9例，82例患者接受辅助或同步放化疗，所有患者均按期完成放化疗。中位随访时间为44月（6~87）月，其中44例出现复发或转移，39例患者死亡。患者的1、3、5年总生存率分别为92.3%、72.1%、56.8%，1、3、5年无进展生存率分别为82.4%、60.9%、53.3%。
表 1 91例鼻咽癌患者临床基本特征资料(n(%))Table 1 Basic clinical features of 91 nasopharyngeal carcinoma patients (n(%))
2.2 ROC曲线选取PLR和NLR预后相关截断值
以OS作为终点，PLR、NLR为检测变量，绘制ROC曲线选取截断值分别为143.3、2.6，两者的曲线下面积分别为0.640、0.739，见图 1。
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图 1 治疗前PLR、NLR与OS关系的ROC曲线图Figure 1 ROC curves of relationship between OS and PLR, NLR before treatmentPLR: platelet-lymphocyte ratio; NLR: neutrophil-lymphocyte ratio以PFS作为终点，PLR、NLR为检测变量，绘制ROC曲线选取截断值分别为143.3、2.6，两者的曲线下面积分别为0.657、0.694，见图 2。说明治疗前PLR、NLR与患者的预后存在相关性，利用ROC曲线选取的截断值进行进一步生存分析。
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图 2 治疗前PLR、NLR与PFS关系的ROC曲线图Figure 2 ROC curves of relationship between PFS and PLR, NLR before treatment2.3 Kaplan-Meier生存分析、Cox单因素和多因素分析
PLR≥143.3组和PLR < 143.3组患者生存曲线比较，差异有统计学意义（P=0.022），见图 3~4。NLR≥2.6组和NLR < 2.6组患者生存曲线比较，差异有统计学意义（P=0.044），见图 5~6。
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图 3 治疗前PLR＜143.3和PLR≥143.3组OS曲线的比较Figure 3 Comparison of OS between PLR < 143.3 and PLR≥143.3 groups before treatmentOS: overall survival![]()
图 4 治疗前PLR＜143.3组和PLR≥143.3组PFS曲线的比较Figure 4 Comparison of PFS between PLR < 143.3 and PLR≥143.3 groups before treatmentPFS: progression-free survival![]()
图 5 治疗前NLR＜2.6组和NLR≥2.6组OS曲线的比较Figure 5 Comparison of OS between NLR < 2.6 and NLR≥2.6 groups before treatment![]()
图 6 治疗前NLR＜2.6组和NLR≥2.6组PFS曲线的比较Figure 6 Comparison of PFS between NLR < 2.6 and NLR≥2.6 groups before treatmentCox单因素分析显示除性别、年龄以外，TNM分期、治疗前PLR≥143.3、NLR≥2.6均是影响鼻咽癌患者OS和PFS的不良预后因素（P < 0.05），见表 2。Cox多因素分析显示治疗前PLR≥143.3（RR=2.491, 95%CI=1.139~5.451, P=0.022）、NLR≥2.6（RR=2.186, 95%CI=1.021~4.682，P=0.044）是鼻咽癌患者OS的独立危险因素，而治疗前PLR≥143.3（RR=2.461，95%CI=1.242~4.874, P=0.010）是鼻咽癌患者PFS的独立危险因素，见表 3。
表 2 影响鼻咽癌患者生存预后的Cox单因素分析Table 2 Cox univariate analysis of prognostic factors for nasopharyngeal carcinoma patients
表 3 影响鼻咽癌患者生存预后的Cox多因素分析Table 3 Cox multivariate analysis of prognostic factors for nasopharyngeal carcinoma patients
3 讨论
鼻咽癌对放射线高度敏感，因此放疗成为主要治疗手段。随着三维适形放疗和调强放射治疗的临床应用，鼻咽癌的生存率较前明显提高，但5年生存率仍仅为60%~80%。多项研究表明鼻咽癌患者预后与众多因素有关，包括患者年龄、临床分期、EB病毒感染及贫血等。此外，肿瘤的预后还与机体本身的炎性反应有关。炎性反应包含中性粒细胞、淋巴细胞、血小板、C反应蛋白等多种指标，其中PLR、NLR已受到越来越多专家的关注。本研究发现治疗前PLR和NLR可能成为鼻咽癌的独立预后因素。
恶性肿瘤患者常伴随血小板的升高，实验研究表明血小板参与肿瘤细胞生长、转移及血管生成[16]。临床研究表明血小板数目升高与肿瘤患者较差预后相关[11, 17]。此外研究表明中性粒细胞可促使机体产生多种促肿瘤生长因子和蛋白酶，促进肿瘤的发生、发展[18]。而淋巴细胞参与机体的免疫反应是抗肿瘤免疫的重要组成部分，淋巴细胞减少说明机体免疫机制异常，抗肿瘤免疫力下降，为肿瘤生长、浸润和转移提供条件。随着肿瘤进展，机体内炎性反应与肿瘤失去平衡，体内淋巴细胞降低，而血小板、中性粒细胞升高，相应的PLR和NLR比值也增高，机体内促进肿瘤炎性反应与抗肿瘤炎性反应的平衡状态被打破。因此PLR和NLR是反应机体免疫情况的重要指标，两者的升高能促进肿瘤进展，导致肿瘤患者预后不良。既往研究结果显示高PLR和NLR可影响宫颈癌、乳腺癌、结直肠癌等恶性肿瘤的预后[19-21]。而目前关于PLR、NLR与鼻咽癌患者预后相关性的研究较少，Sun等[21]分析了251例鼻咽癌患者治疗前PLR和NLR，结果证明治疗前两者水平是影响鼻咽癌患者生存独立预后因素。本研究结果显示治疗前PLR、NLR与鼻咽癌患者的总生存期和无进展生存期具有相关性。Cox多因素分析提示PLR≥143.3、NLR≥2.6和TNM分期是影响鼻咽癌患者治疗后的独立危险因素。PLR≥143.3组患者有较短OS和PFS，而NLR≥2.6组患者有较差的OS，和本研究结果相一致。因此，高PLR、NLR的鼻咽癌患者总生存率要低于低PLR、NLR的患者，且高PLR的患者复发或转移风险明显增加。据此，临床上或许可以通过提高鼻咽癌患者免疫功能及降低机体炎性反应，改善患者的预后。
但由于本研究是一个相对小样本的回顾性研究，不能代表大部分的鼻咽癌患者，且随访时间较短，存在一定的局限性，因此需要进行多中心、大样本的前瞻性研究来进一步证实。
本研究结果表明，治疗前PLR和NLR水平与鼻咽癌患者预后具有相关性，可能是影响鼻咽癌患者预后的独立危险因素，NLR和PLR的获取具有简便、经济的优点，可以作为鼻咽癌患者病情评估的一个有益补充，值得推广。目前鼻咽癌相关有效预后指标较多，笔者将在今后的临床研究工作中继续探索，将本研究指标与已有的有效预后指标进行比较，从而提高治疗前PLR和NLR水平这一预后指标应用于临床的合理性及可靠性。
Competing interests: The authors declare that they have no competing interests.作者贡献：倪鑫淼：提纲设计，文献收集整理，论文撰写杨瑞、陈志远：论文修改刘修恒：总体策划，论文审校 -
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