Kim Hyemin, Choi Jin Ho, Lim Yoojoo, Yoon So Jeong, Jang Kee-Taek, Ock Chan-Young, Choi Young Hoon, Joe Cheolyong, Song Sanghoon, Moon Jimin, Song Heon, Pereira Sergio, Lee Seungeun, Park Sujin, Kim Kyunga, Lee Se-Hoon, Kim Hongbeom, Shin Sang Hyun, Heo Jin Seok, Lee Kwang Hyuck, Lee Kyu Taek, Lee Jong Kyun, Han In Woong, Park Joo Kyung
Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
JAMA Surg. 2025 Jun 25. doi: 10.1001/jamasurg.2025.1999.
Although tumor-infiltrating lymphocytes (TILs) have been implicated as prognostic biomarkers across various malignancies, the clinical application remains challenging. This study evaluated the applicability of artificial intelligence (AI)-powered spatial mapping of TIL density for prognostic assessment in resected pancreatic ductal adenocarcinoma (PDAC).
To evaluate the prognostic significance of AI-powered spatial TIL analysis in resected PDAC and its clinical applicability.
DESIGN, SETTING, AND PARTICIPANTS: This cohort study included patients with PDAC who underwent up-front R0 resection at a tertiary referral center between January 2017 and December 2020. Whole-slide images of retrospectively enrolled patients with PDAC and up-front R0 resection were analyzed. An AI-powered whole-slide image analyzer was used for spatial TIL quantification, segmentation of tumor and stroma, and immune phenotype classification as immune-inflamed phenotype, immune-excluded phenotype, or immune-desert phenotype. Study data were analyzed from January 2017 to August 2023.
Use of AI-powered spatial analysis of the tumor microenvironment in resected PDACs.
Tumor microenvironment-related risk factors and their associations with overall survival (OS) and recurrence-free survival (RFS) outcomes were identified.
Among 304 patients, the mean (SD) age was 66.8 (9.4) years with 171 male patients (56.3%), and preoperative clinical stages I and II were represented by 54.3% patients (165 of 304) and 45.7% patients (139 of 304), respectively. The TILs in the tumor microenvironment were predominantly concentrated in the stroma, and the median intratumoral TIL and stromal TIL densities were 100.64/mm2 (IQR, 53.25-121.39/mm2) and 734.88/mm2 (IQR, 443.10-911.16/mm2), respectively. Overall, 9.9% of tumors (30 of 304) were immune inflamed, 85.2% (259 of 304) were immune excluded, and 4.9% (15 of 304) were immune desert. The immune-inflamed phenotype was associated with the most prolonged OS (median not reached; P < .001) and RFS (median not reached; P = .001), followed by immune-excluded phenotype and immune-desert phenotype. High intratumoral TIL density was associated with longer OS (median, 52.47 months; 95% CI, 41.98-62.96; P = .004) and RFS (median, 21.67 months; 95% CI, 14.43-28.91; P = .02). A combined analysis of the pathologic stage with immune phenotype predicted better survival of stage II PDAC stratified as immune-inflamed phenotype than stage I PDAC stratified as non-immune-inflamed phenotype.
Results of this cohort study suggest that the use of AI has markedly condensed the labor-intensive process of TIL assessment, potentially rendering the process more feasible and practical in clinical application. Importantly, the IP may be one of the most important prognostic biomarkers in resected PDACs.
尽管肿瘤浸润淋巴细胞(TILs)已被认为是多种恶性肿瘤的预后生物标志物,但其临床应用仍具有挑战性。本研究评估了人工智能(AI)驱动的TIL密度空间映射在切除的胰腺导管腺癌(PDAC)预后评估中的适用性。
评估AI驱动的空间TIL分析在切除的PDAC中的预后意义及其临床适用性。
设计、设置和参与者:这项队列研究纳入了2017年1月至2020年12月在三级转诊中心接受 upfront R0切除的PDAC患者。对回顾性纳入的接受upfront R0切除的PDAC患者的全切片图像进行分析。使用AI驱动的全切片图像分析仪进行空间TIL定量、肿瘤和基质分割以及免疫表型分类,分为免疫炎症表型、免疫排除表型或免疫荒漠表型。研究数据于2017年1月至2023年8月进行分析。
在切除的PDAC中使用AI驱动的肿瘤微环境空间分析。
确定肿瘤微环境相关危险因素及其与总生存期(OS)和无复发生存期(RFS)结局的关联。
在304例患者中,平均(标准差)年龄为66.8(9.4)岁,男性患者171例(56.3%),术前临床I期和II期患者分别占54.3%(304例中的165例)和45.7%(304例中的139例)。肿瘤微环境中的TILs主要集中在基质中,肿瘤内TIL和基质TIL密度的中位数分别为100.64/mm²(四分位间距,53.25 - 121.39/mm²)和734.88/mm²(四分位间距,443.10 - 911.16/mm²)。总体而言,9.9%的肿瘤(304例中的30例)为免疫炎症型,85.2%(304例中的259例)为免疫排除型,4.9%(304例中的15例)为免疫荒漠型。免疫炎症表型与最长的OS(中位数未达到;P <.001)和RFS(中位数未达到;P =.001)相关,其次是免疫排除表型和免疫荒漠表型。高肿瘤内TIL密度与更长的OS(中位数,52.47个月;95%置信区间,41.98 - 62.96;P =.004)和RFS(中位数,21.67个月;95%置信区间,14.43 - 28.91;P =.02)相关。病理分期与免疫表型的联合分析预测,II期PDAC分层为免疫炎症表型的患者比I期PDAC分层为非免疫炎症表型的患者生存期更好。
这项队列研究的结果表明,AI的使用显著简化了TIL评估中劳动密集型的过程,可能使该过程在临床应用中更可行、更实用。重要的是,免疫表型可能是切除的PDAC中最重要的预后生物标志物之一。