ArticleAPMIS : acta pathologica, microbiologica, et immunologica Scandinavica2022
Automated assessment of Ki-67 proliferation index in neuroendocrine tumors by deep learning.
Article in APMIS : acta pathologica, microbiologica, et immunologica Scandinavica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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.
Who cites it
15 citing papers in PubMed.
- Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks: Foundations and Emerging Opportunities in Spatial Pathobiology.The American journal of pathology · 2026Review
- From Tissue Architecture To Genetic Signature: Artificial intelligence-based Analysis of Reticulin Framework and Clinical Variables Predicts Molecular Cluster in Paragangliomas.Endocrine pathology · 2026Article
- Validation of fibroblast activation protein and α-smooth muscle actin as prognostic biomarkers in prostate cancer through AI-assisted image analysis of dual-marker IHC.The journal of pathology. Clinical research · 2026Article
- Multi scale deep learning quantifies Ki67 index in breast cancer histopathology images.Scientific reports · 2025Article
- Development of convolutional neural networks for automated brain-wide histopathological analysis in mouse models of synucleinopathies.NPJ Parkinson's disease · 2025Article
- A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole Slide Images.Cancers · 2025Article
- Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.Scientific reports · 2025Article
- The Evolving Landscape of GEP-NENs in the Era of Precision Oncology: Molecular Insights into Tumor Heterogeneity.Cancers · 2025Review
- Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma.PloS one · 2025Article
- Digitally derived Ki-67 proliferation index for GastroEnteroPancreatic neuroendocrine neoplasms.Pathology oncology research : POR · 2025Article
- Effects ofHeliyon · 2024Article
- Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis.Medical & biological engineering & computing · 2024Article
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors - Analytical Validation.International journal of general medicine · 2023Article
- Automated assessment of Ki-67 proliferation index in neuroendocrine tumors by deep learning.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Ki-67 proliferation index (PI) is a prognostic factor in neuroendocrine tumors (NETs) and defines tumor grade. Analysis of Ki-67 PI requires calculation of Ki-67-positive and Ki-67-negative tumor cells, which is highly subjective. To overcome this, we developed a deep learning-based Ki-67 PI algorithm (KAI) that objectively calculates Ki-67 PI. Our study material consisted of NETs divided into training (n = 39), testing (n = 124), and validation (n = 60) series. All slides were digitized and processed in the Aiforia
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.