Evidence map›Paper›PMID 41417992›Full record

ArticleInternational journal of surgery (London, England)2025

Integrated local and global Ki67 profiling for prognostic stratification and therapeutic guidance in high-grade neuroendocrine neoplasms: a multicenter validation study.

Jin Chu, Haiyu Gong, Heli Gao, Zhi Zhu, Juanhong Shi, Yihui Ma, Yuan Ji, Qiongyan Zhang, Lijuan Cao, Feng Tang and 6 more

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Article in International journal of surgery (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

16 authors.

Jin ChuDepartment of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
Haiyu GongSchool of Data Science, Fudan University, Shanghai, China.
Heli GaoDepartment of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
Zhi ZhuDepartment of Pathology, Changzheng Hospital, Second Military Medical University, Shanghai, China.
Juanhong ShiDepartment of Pathology, Changzheng Hospital, Second Military Medical University, Shanghai, China.
Yihui MaDepartment of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yuan JiDepartment of Pathology, Zhongshan Hospital Fudan University, Shanghai, China.
Qiongyan ZhangDepartment of Pathology, Zhongshan Hospital Fudan University, Shanghai, China.
Lijuan CaoDepartment of Pathology, Huashan Hospital, Fudan University, Shanghai, China.
Feng TangDepartment of Pathology, Huashan Hospital, Fudan University, Shanghai, China.
Yun LiangDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Jie ChenDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Weiqi ShengDepartment of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
Qingchao ChenNational Institute of Health Data Science, Peking University, Beijing, China.
Xiahai ZhuangSchool of Data Science, Fudan University, Shanghai, China.
Dan HuangDepartment of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe distinction between high-grade neuroendocrine G3 tumors (G3 NETs) and carcinomas (NECs) is a critical diagnostic dilemma with profound therapeutic implications. Conventional Ki67 hotspot analysis often fails to provide robust risk stratification. We aimed to develop and validate a robust, interpretable digital pathology biomarker that overcomes these limitations to improve risk stratification and guide therapeutic decisions.

methodsWe developed the Integrated Local-Global Ki67 (IL-GKi67) score, which synergizes localized hotspot quantification with global intratumoral heterogeneity assessment from Ki67-stained whole-slide images. The score's prognostic and predictive performance was rigorously evaluated in a multicenter cohort (N = 349) with internal and external validation sets.

resultsThe IL-GKi67 score was a powerful and independent prognostic factor, where high scores correlated with drastically worse overall survival (multicenter validation: HR = 4.169, P = 0.009), remaining significant where the traditional hotspot method failed (P = 0.128). Most importantly, IL-GKi67 functioned as a definitive predictive biomarker. Patients with low IL-GKi67 scores derived a profound survival benefit from adjuvant chemotherapy (mOS: 45.0 months, at 50 months, the restricted mean survival time of chemo subset was 48.9 months (95% CI, 45.36 to 52.02); HR = 0.069, P = 0.002), an effect particularly pronounced in the aggressive NEC subgroup (HR = 0.061, P < 0.001). The score served as a powerful morphological surrogate for TP53 mutation status (P < 0.001) and refined the conventional G3 NET/NEC classification by re-stratifying a substantial portion of patients into more clinically relevant risk groups.

conclusionsIL-GKi67 is an interpretable and validated digital biomarker that overcomes the limitations of manual Ki67 assessment. By integrating local and global proliferation patterns, it improves risk stratification and provides a practical tool to guide personalized adjuvant chemotherapy decisions for patients with HGNENs.

Indexed as

computational pathologyhigh-grade neuroendocrine neoplasmintratumoral heterogeneityki67predictive biomarker

Identifiers

PMID41417992
PMCPMC13105724

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