p16与人乳头瘤病毒不一致口咽癌的预后及使用自然语言处理分析自由文本病理报告的探索

IF 2.8 Q2 ONCOLOGY JCO Clinical Cancer Informatics Pub Date : 2025-02-01 Epub Date: 2025-02-18 DOI:10.1200/CCI-24-00177
Ethan Shin, Justin Choi, Tony K W Hung, Chester Poon, Nadeem Riaz, Yao Yu, Jung Julie Kang
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引用次数: 0

摘要

目的:人乳头瘤病毒阳性(HPV+)相关口咽癌(OPC)的去强化治疗一直是世界各国专家的催化剂。原位杂交是鉴定HPV+ OPC的最佳方法,但其替代物p16INK4a (p16)的免疫组织化学是考虑到其可用性和敏感性的标准治疗方法。临床管理不需要HPV检测,因此通常仅根据p16状态进行治疗。然而,p16/HPV不一致肿瘤的预后是不确定的。材料和方法:该队列研究纳入了727例连续的OPC患者,这些患者都有数字化的非结构化病理报告,在学术癌症中心接受治疗性放射治疗。使用自然语言处理(NLP)方法对生物标记物状态进行分类,并与人工分类进行比较。如果没有进行p16或HPV检测或有疑问,则排除患者。主要终点为无进展生存期(PFS)、癌症特异性生存期(CSS)和总生存期。结果:NLP从大多数(91%)的报告中分类了p16和HPV状态。nlp衍生的p16/HPV的准确性、阳性预测值、敏感性和f评分分别为84%/82%、91%/87%、90%/89%和90%/88%。分为四组:p16阳性(p16+)/HPV+ (75%), p16+/HPV阴性(HPV-;13%), p16-negative (p16) / HPV -(10%),和p16 / HPV +(2%)。p16+/HPV-和p16-/HPV-患者的结局无统计学差异(5年PFS 76.1% vs 68.9%;P = .12;5年CSS 81.5% v 84.9%;P = .22)。与预期的p16+/HPV+患者相比,每10个p16+/HPV-患者中需要计算的伤害数估计有一个额外的癌症相关死亡。结论:NLP对头颈癌病理报告的分类与金标准分类一致性高,但仍有相当一部分报告无法解释。值得注意的是,p16/HPV不一致的OPC在患者中占少数。p16+/HPV-的不良预后表明,p16单独预测是不够的,特别是考虑到治疗降级。
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Prognosis of p16 and Human Papillomavirus Discordant Oropharyngeal Cancers and the Exploration of Using Natural Language Processing to Analyze Free-Text Pathology Reports.

Purpose: Treatment deintensification for human papillomavirus-positive (HPV+)-associated oropharyngeal cancer (OPC) has been the catalyst of experts worldwide. In situ hybridization is optimal to identify HPV+ OPC, but immunohistochemistry for its surrogate p16INK4a (p16) is standard-of-care given its availability and sensitivity. HPV testing is not required for clinical management, so treatments are often administered on the basis of p16 status alone. However, the prognosis of p16/HPV discordant tumors is uncertain.

Materials and methods: This cohort study included 727 consecutive patients with OPC with digitized unstructured pathology reports receiving curative radiation therapy at an academic cancer center. Natural language processing (NLP) methods were used to classify biomarker status and compared against manually derived classification. Patients were excluded if either p16 or HPV testing was not performed or equivocal. Primary end points were progression-free survival (PFS), cancer-specific survival (CSS), and overall survival.

Results: NLP classified p16 and HPV status from a majority (91%) of reports. Accuracy, positive predictive value, sensitivity, and F-score for NLP-derived p16/HPV were 84%/82%, 91%/87%, 90%/89%, and 90%/88%, respectively. Four groups were identified: p16-positive (p16+)/HPV+ (75%), p16+/HPV-negative (HPV-; 13%), p16-negative (p16-)/HPV- (10%), and p16-/HPV+ (2%). There was no statistically significant difference in outcomes between p16+/HPV- and p16-/HPV- patients (5-year PFS 76.1% v 68.9%; P = .12; 5-year CSS 81.5% v 84.9%; P = .22). Number needed to harm calculations estimated one excess cancer-related death for every 10 p16+/HPV- patients, compared with that expected with p16+/HPV+ patients.

Conclusion: NLP classified head and neck cancer pathology reports with high concordance with gold-standard categorization, but a conspicuous portion of reports could not be interpreted. p16/HPV discordant OPC constitutes a noteworthy minority of patients. The inferior prognosis of p16+/HPV- suggests that p16 alone for prognostication is insufficient-especially when considering treatment de-escalation.

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