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Updated: Jun 17, 2026
Cell Block Preparation from Cytology Specimen with Predominance of Individually Scattered Cells
Published on: July 21, 2009
A Two-Dimensional Plot of Cell Aggregates and Their Largest Nuclei Distinguishes Benign From Malignant Pleural
Rio Kaneko1, Sayaka Kobayashi1, Hayato Ikota2
1Laboratory of Histopathology and Cytopathology, Department of Laboratory Sciences, Graduate School of Health Sciences, Gunma University, Maebashi, Gunma, Japan.
Objective:
This study aimed to analyze the morphological characteristics of cells observed in pleural effusion cytology specimens using computer-assisted image analysis (CAIA).
Methods:
We examined 166 pleural effusion cytology specimens obtained for suspected lung cancer, including 80 negative, 22 suspicious and 64 positive cases. Whole-slide images (WSIs) were generated from Papanicolaou-stained specimens, and image analysis was performed using virtual slide cytology image analysis software. A scatter plot was then created, with the x-axis representing the area of each cell or cell cluster and the y-axis representing the maximum nuclear area within that cluster. The resulting plot type and the positive rate (PR) were subsequently evaluated.
Results:
Four distinct plot types were defined from the scatter plots: small cluster type (S-type), horizontal type (H-type), vertical type (V-type) and diagonal type (D-type). Overall, S-type and H-type patterns were commonly observed in non-cancer or cytologically negative cases, whereas V-type and D-type patterns predominated in cytologically positive cases. In suspicious cases, S-type and V-type plots each accounted for roughly half of the samples. The PR was significantly higher in cytologically positive cases relative to non-cancer and cytologically negative cases (p < 0.0001).
Conclusions:
In pleural cytology specimens from patients with lung cancer, the degree of cell aggregation and nuclear overlap is an important factor for distinguishing benign from malignant lesions. Moreover, although AI-based image analysis has advanced rapidly in recent years, this study emphasizes the continued value of CAIA approaches that employ algorithms readily interpretable by humans.
