ArticleCancers2021
The Potential of Artificial Intelligence to Detect Lymphovascular Invasion in Testicular Cancer.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 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
- Artificial Intelligence and Digital Pathology for Preoperative Lymphovascular Invasion and Metastatic Risk Prediction in Breast Cancer: Bridging the Biopsy-Resection Gap.Journal of clinical medicine · 2026Review
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Adaptive compression framework for giga-pixel whole slide images.Nature communications · 2025Article
- Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms.Haematologica · 2025Article
- Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology.British journal of haematology · 2025Article
- Deep Learning Predicts Lymphovascular Invasion Status in Muscle Invasive Bladder Cancer Histopathology.Annals of surgical oncology · 2025Article
- The Use of Artificial Intelligence in Urologic Oncology: Current Insights and Challenges.Research and reports in urology · 2025Review
- LVI-PathNet: Segmentation-classification pipeline for detection of lymphovascular invasion in whole slide images of lung adenocarcinoma.Journal of pathology informatics · 2024Article
- Article
- A robust model training strategy using hard negative mining in a weakly labeled dataset for lymphatic invasion in gastric cancer.The journal of pathology. Clinical research · 2024Article
- Review
- The Present and Future of Artificial Intelligence in Urological Cancer.Journal of clinical medicine · 2023Review
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Digital Pathology Transformation in a Supraregional Germ Cell Tumour Network.Diagnostics (Basel, Switzerland) · 2021Article
- Deep Learning for Subtypes Identification of Pure Seminoma of the Testis.Clinical pathology (Thousand Oaks, Ventura County, Calif.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
Testicular cancer is the most common cancer in men aged from 15 to 34 years. Lymphovascular invasion refers to the presence of tumours within endothelial-lined lymphatic or vascular channels, and has been shown to have prognostic significance in testicular germ cell tumours. In non-seminomatous tumours, lymphovascular invasion is the most powerful prognostic factor for stage 1 disease. For the pathologist, searching multiple slides for lymphovascular invasion can be highly time-consuming. The aim of this retrospective study was to develop and assess an artificial intelligence algorithm that can identify areas suspicious for lymphovascular invasion in histological digital whole slide images. Areas of possible lymphovascular invasion were annotated in a total of 184 whole slide images of haematoxylin and eosin (H&E) stained tissue from 19 patients with testicular germ cell tumours, including a mixture of seminoma and non-seminomatous cases. Following consensus review by specialist uropathologists, we trained a deep learning classifier for automatic segmentation of areas suspicious for lymphovascular invasion. The classifier identified 34 areas within a validation set of 118 whole slide images from 10 patients, each of which was reviewed by three expert pathologists to form a majority consensus. The precision was 0.68 for areas which were considered to be appropriate to flag, and 0.56 for areas considered to be definite lymphovascular invasion. An artificial intelligence tool which highlights areas of possible lymphovascular invasion to reporting pathologists, who then make a final judgement on its presence or absence, has been demonstrated as feasible in this proof-of-concept study. Further development is required before clinical deployment.
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.