Evidence map›Paper›PMID 40640364›Full record

ArticleNPJ precision oncology2025

Development and validation of a pathomics model for accurate grading of pancreatic neuroendocrine tumors.

Ying Li, Chengwei Chen, Yunshuo Zhang, Jieyu Yu, Xu Fang, Fang Liu, Tiegong Wang, Jing Li, Li Wang, Jianping Lu and 3 more

Abstract read
In one paragraph

Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Prognostic value of the dual-tracer PET score (NETPET) in neuroendocrine tumors with [European journal of nuclear medicine and molecular imaging · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Ying Li *Department of Radiology, Changhai Hospital, Shanghai, China.
Chengwei Chen *Department of Radiology, Changhai Hospital, Shanghai, China.
Yunshuo Zhang *Department of Radiology, Changhai Hospital, Shanghai, China.
Jieyu YuDepartment of Radiology, Changhai Hospital, Shanghai, China.
Xu FangDepartment of Radiology, Changhai Hospital, Shanghai, China.
Fang LiuDepartment of Radiology, Changhai Hospital, Shanghai, China.
Tiegong WangDepartment of Radiology, Changhai Hospital, Shanghai, China.
Jing LiDepartment of Radiology, Changhai Hospital, Shanghai, China.
Li WangDepartment of Radiology, Changhai Hospital, Shanghai, China.
Jianping LuDepartment of Radiology, Changhai Hospital, Shanghai, China.
Hui JiangDepartment of Pathology, Changhai Hospital, Shanghai, China. jianghui5131@163.com.
Chengwei ShaoDepartment of Radiology, Changhai Hospital, Shanghai, China. chengweishaoch@163.com.
Yun BianDepartment of Radiology, Changhai Hospital, Shanghai, China. bianyun2012@foxmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PanNETs) is important for patients' prognoses and treatment. Traditional grading methods based on mitotic count and Ki-67 index are subjective and time-consuming. In this work, we developed and validated a pathomics model for accurate grading of PanNETs. Pathomics features were extracted from H&E whole slide images (WSIs) using Lasso regression to create a pathomics score. Its performance was evaluated in three cohorts involving 2 centers and 272 patients. This score was significantly associated with PanNET grade and could differentiate between high- and low-risk groups. In the validation and test cohorts, the pathomics model, which combined the pathomics score with clinical features, achieved AUCs of 0.85 and 0.93, respectively. In conclusion, our model enhances data processing efficiency and provides a quantitative assessment of histological features, holding promise for guiding individualized treatment and risk stratification in PanNET management.This study was registered at ClinicalTrials. Trial number: gov ChiCTR2400090898.

Identifiers

PMID40640364
PMCPMC12246116

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.