ArticleJournal of pathology informatics2022
Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 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
6 citing papers in PubMed, 15 citations in OpenAlex.
- Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.European respiratory review : an official journal of the European Respiratory Society · 2026Article
- MetFinder: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models.Pigment cell & melanoma research · 2025Article
- Next-generation probiotics and engineered BEVs for precision therapeutics in osteoporosis.Frontiers in nutrition · 2025Review
- Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning.Scientific reports · 2024Article
- ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.Frontiers in bioengineering and biotechnology · 2024Article
- Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer.Journal of pathology informatics · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundMouse models are highly effective for studying the pathophysiology of lung adenocarcinoma and evaluating new treatment strategies. Treatment efficacy is primarily determined by the total tumor burden measured on excised tumor specimens. The measurement process is time-consuming and prone to human errors. To address this issue, we developed a novel deep learning model to segment lung tumor foci on digitally scanned hematoxylin and eosin (H&E) histology slides.
methodsDigital slides of 239 mice from 9 experimental cohorts were split into training (
resultsThe best model on patch-based validation was DeepLabV3+ using a Resnet-50 backbone, which achieved Dice 0.890 and 0.873 on validation and testing cohort, respectively. This result corresponded to 91.3 Sensitivity and 51.0 PPV in the validation cohort and 93.7 Sensitivity and 51.4 PPV in the testing cohort. False positives could be reduced 10-fold with thresholding artificial intelligence (AI) predicted output by area, without negative impact on Dice Coefficient. Evaluation at various stain normalization strategies did not demonstrate improvement from the baseline model.
conclusionsA robust AI-based algorithm for detecting and segmenting lung tumor foci in the pre-clinical mouse models was developed. The output of this algorithm is compatible with open-source software that researchers commonly use.
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.