ArticleJournal of pathology informatics2021
Digital Image Analysis for Estimating Stromal CD8+ Tumor-Infiltrating Lymphocytes in Lung Adenocarcinoma.
Article in Journal of pathology informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 11 citations in OpenAlex.
- AI driven pre-regulatory validation of PD-L1 analysis in lung cancer.Scientific reports · 2025Article
- Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms.Journal of clinical pathology · 2025Article
- Article
- Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images.Frontiers in oncology · 2023Article
- Validation of automated positive cell and region detection of immunohistochemically stained laryngeal tumor tissue using digital image analysis.Journal of pathology informatics · 2023Article
- Tumour immune microenvironment in resected thymic carcinomas as a predictor of clinical outcome.British journal of cancer · 2022Article
- Simultaneous and Spatially-Resolved Analysis of T-Lymphocytes, Macrophages and PD-L1 Immune Checkpoint in Rare Cancers.Cancers · 2022Article
- Identification and Quantification of Radiotherapy-related Protein Expression in Cancer Tissues Using the Qupath Software and Prediction of Treatment Response.In vivo (Athens, Greece)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
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStromal CD8+ tumor-infiltrating lymphocytes (TILs) are an important prognostic and predictive indicator in non-small cell lung cancer (NSCLC). In this study, we aimed to develop and test the feasibility of a digital image analysis (DIA) workflow for estimating stromal CD8+ TIL density.
methodsA DIA workflow developed in a software platform (QuPath) was applied to a specified region of interest (ROI) within the stromal compartment of dual PD-L1/CD8 immunostained slides from 50 lung adenocarcinoma patients. A random tree classifier was trained from 25 training cases and applied to 25 test cases. The DIA-estimated CD8+ TIL densities were compared to manual estimates of three pathologists, who independently quantitated the percentage of CD8+ TILs from predefined ROIs in QuPath.
resultsThe average estimated total stromal cell count per case was 520 (range: 282-816) by QuPath and 551 (range: 265-744) by pathologists. The DIA-estimated CD8+ TIL density (mean = 16.9%) was comparable to pathologists' manual estimates (mean = 15.9%). A paired
conclusionsThese findings demonstrate the feasibility of applying a DIA workflow for estimating stromal CD8+ TIL density in NSCLC. DIA has the potential to provide an efficient and standardized approach for estimating stromal CD8+ TIL density.
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.