ArticleJournal of pathology informatics2025
Benford's Law in histology.
Article in Journal of pathology informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- First-Digit Distributions of Human Long-Bone Morphometric Parameters: An Exploratory X-Ray-Based Comparison with Benford's Law.International journal of general medicine · 2026Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital pathology is an emerging field that is gaining popularity due to its numerous advantages over traditional pathology methods. Digital pathology allows for the remote examination of tissue samples, increasing efficiency and reducing costs. The field of digital pathology is experiencing a boom of data, creating space for new tools to be implemented that have not been used in pathology prior. Benford's Law is a statistical tool commonly used to analyze large datasets by other top organizations. Benford's Law is a law of frequency of first and second digits and whether they would appear normally in nature. With research in multiple fields of medicine moving into a digital era, tools that had once been used elsewhere to analyze digital images could translate well into pathology. Quantitative histomorphometry is a tool in digital pathology that analyzes digital images and collects morphological and histological data of whole-slide images, with more techniques being developed in digital pathology, such as deep learning, creating a more accurate 3D analysis of the cell. Easy and quick tools are needed to analyze the large datasets that are being extracted quickly. We believe that Benford's Law is a statistical outlook that can be easily implemented for similar use in whole-slide image analysis. When a system is disrupted by disease, it will distort the normal, natural growth of cells throughout the organ. Open access tools such as QuPath have created a way to obtain categories of data to analyze, such as the size of a cell or the amount of staining it absorbs. Slides of normal liver cells were collected and compared to slides of a liver with cancer. The liver was selected because of its well-demarcated cytoplastic borders and nucleus. A total of 25 liver tissue slides were collected. The graph of naturalness is compared to analyze ways to detect variability between normal liver cells and cancer liver cells. 206,700 cells from 15 slides of 7 cancer patients' liver tissue samples (15 slides total) and 116,339 cells from 5 slides of normal liver tissue were collected, totaling 323,039 cells from 20 slides. Of the seven cancer patients, five were previously diagnosed with cholangiocarcinoma, and two were diagnosed with adenomas/adenocarcinoma. The study found that of the 13 data categories provided by QuPath, such as cell size, nucleus size, and color absorbance, two met the Chi-square goodness of fit (χ
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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.