ArticlePloS one2018
Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled 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.
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
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Pooled it
- Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer.Frontiers in oncology · 2026Review
- Ki67 in cytological specimens of pancreatic neuroendocrine tumors: A literature review and validation of automated quantification.Journal of pathology informatics · 2025Article
- Interpretable deep learning model and nomogram for predicting pathological grading of PNETs based on endoscopic ultrasound.BMC medical informatics and decision making · 2025Article
- Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.Scientific reports · 2025Article
- A Challenging Decision: Identifying and Managing Hepatic Hydrothorax in the Setting of a Pancreatic Neuroendocrine Tumor.Case reports in gastrointestinal medicine · 2025Article
- Artificial intelligence in healthcare (Review).Biomedical reports · 2025Review
- Ki67 proliferation index in medullary thyroid carcinoma: a comparative study of multiple counting methods and validation of image analysis and deep learning platforms.Histopathology · 2023Article
- Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using deep-learning-based semantic segmentation in histology.Biology open · 2023Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.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
- Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.PloS one · 2023Article
- Deep Learning Approaches in Histopathology.Cancers · 2022Review
- Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms-A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence in pancreatic cancer.Theranostics · 2022Review
- Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models.Computers in biology and medicine · 2021Article
- State of machine and deep learning in histopathological applications in digestive diseases.World journal of gastroenterology · 2021Review
- Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images.Annals of translational medicine · 2021Article
- Pancreatic cancer grading in pathological images using deep learning convolutional neural networks.F1000Research · 2021Article
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 at 3 institutions in 1 country.
Funding
Abstract
The World Health Organization (WHO) has clear guidelines regarding the use of Ki67 index in defining the proliferative rate and assigning grade for pancreatic neuroendocrine tumor (NET). WHO mandates the quantification of Ki67 index by counting at least 500 positive tumor cells in a hotspot. Unfortunately, Ki67 antibody may stain both tumor and non-tumor cells as positive depending on the phase of the cell cycle. Likewise, the counter stain labels both tumor and non-tumor as negative. This non-specific nature of Ki67 stain and counter stain therefore hinders the exact quantification of Ki67 index. To address this problem, we present a deep learning method to automatically differentiate between NET and non-tumor regions based on images of Ki67 stained biopsies. Transfer learning was employed to recognize and apply relevant knowledge from previous learning experiences to differentiate between tumor and non-tumor regions. Transfer learning exploits a rich set of features previously used to successfully categorize non-pathology data into 1,000 classes. The method was trained and validated on a set of whole-slide images including 33 NETs subject to Ki67 immunohistochemical staining using a leave-one-out cross-validation. When applied to 30 high power fields (HPF) and assessed against a gold standard (evaluation by two expert pathologists), the method resulted in a high sensitivity of 97.8% and specificity of 88.8%. The deep learning method developed has the potential to reduce pathologists' workload by directly identifying tumor boundaries on images of Ki67 stained slides. Moreover, it has the potential to replace sophisticated and expensive imaging methods which are recently developed for identification of tumor boundaries in images of Ki67-stained NETs.
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.