ArticleHistopathology2022
A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma.
Article in Histopathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 30 citations in OpenAlex.
- A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.NPJ digital medicine · 2026Article
- ILD-Slider: A Parameter-Efficient Model for Identifying Progressive Fibrosing Interstitial Lung Disease from Chest CT Slices.Journal of imaging · 2025Article
- Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Review
- Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis.NPJ precision oncology · 2025Article
- The routine use of a digital tool for the tumor cell fraction quantification in molecular pathology: an international validation of QuANTUM.Pathologica · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Digital counting of tissue cells for molecular analysis: the QuANTUM pipeline.Virchows Archiv : an international journal of pathology · 2025Article
- Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.The Journal of pathology · 2025Article
- Artificial intelligence algorithm for neoplastic cell percentage estimation and its application to copy number variation in urinary tract cancer.Journal of pathology and translational medicine · 2024Article
- Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists' Visual Estimates.Diagnostics (Basel, Switzerland) · 2024Article
- The Current Achievements of Multi-Gene Panel Tests in Clinical Settings for Patients with Non-Small-Cell Lung Cancer.Cancers · 2024Article
- Review
- Review
- Application of Artificial Intelligence in Lung Cancer.Cancers · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
aimsThe reporting of tumour cellularity in cancer samples has become a mandatory task for pathologists. However, the estimation of tumour cellularity is often inaccurate. Therefore, we propose a collaborative workflow between pathologists and artificial intelligence (AI) models to evaluate tumour cellularity in lung cancer samples and propose a protocol to apply it to routine practice. METHODS AND
resultsWe developed a quantitative model of lung adenocarcinoma that was validated and tested on 50 cases, and a collaborative workflow where pathologists could access the AI results and adjust their original tumour cellularity scores (adjusted-score) that we tested on 151 cases. The adjusted-score was validated by comparing them with a ground truth established by manual annotation of haematoxylin and eosin slides with reference to immunostains with thyroid transcription factor-1 and napsin A. For training, validation, testing the AI and testing the collaborative workflow, we used 40, 10, 50 and 151 whole slide images of lung adenocarcinoma, respectively. The sensitivity and specificity of tumour segmentation were 97 and 87%, respectively, and the accuracy of nuclei recognition was 99%. One pathologist's visually estimated scores were compared to the adjusted-score, and the pathologist's scores were altered in 87% of cases. Comparison with the ground truth revealed that the adjusted-score was more precise than the pathologists' scores (P < 0.05).
conclusionWe proposed a collaborative workflow between AI and pathologists as a model to improve daily practice and enhance the prediction of tumour cellularity for genetic tests.
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.