Evidence map›Paper›PMID 40736208›Full record

ArticleAmerican journal of clinical pathology2025

Deep learning neural network of adenocarcinoma detection in effusion cytology.

Katsuhide Ikeda, Nanako Sakabe, Kenta Fukuda, Shouichi Sato, Toshiaki Hara, Harumi Kobayashi, Masato Nakaguro, Kennosuke Karube, Kohzo Nagata

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PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of clinical pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Katsuhide IkedaPathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.ORCID 0000-0002-9138-928X
Nanako SakabePathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Kenta FukudaPathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Shouichi SatoClinical Engineering, Faculty of Medical Sciences, Juntendo University, Urayasu, Chiba, Japan.
Toshiaki HaraDepartment of Medical Technique, Division of Pathology, Nagoya University Hospital, Nagoya, Aichi, Japan.
Harumi KobayashiDepartment of Medical Technique, Division of Pathology, Nagoya University Hospital, Nagoya, Aichi, Japan.
Masato NakaguroDepartment of Medical Technique, Division of Pathology, Nagoya University Hospital, Nagoya, Aichi, Japan.
Kennosuke KarubeDepartment of Medical Technique, Division of Pathology, Nagoya University Hospital, Nagoya, Aichi, Japan.
Kohzo NagataPathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.

Funding

Japan Society for the Promotion of Science JP21K18077
6 · The paper itself

Abstract

objectiveCytologic examination, which confirms the presence or absence of malignant cells, detects malignant cells from various organs, with adenocarcinoma as the most common histologic type. We developed a deep learning model to detect malignant cells in images obtained following effusion cytology.

methodsThe deep learning model was created using the YOLOv8 object detection algorithm (Roboflow, Inc) and 275 cases of adenocarcinoma comprising 12 182 images and 29 245 labels as well as 188 cases negative for malignancy comprising 1980 images.

resultsThe adenocarcinoma test dataset exhibited Precision, Recall, F1, and mean average Precision scores of 0.909, 0.911, 0.910, and 0.955, respectively. The number of adenocarcinoma test images in which 1 or more malignant cells were detected was 2710 of 2731. The sensitivity in the nonadenocarcinoma dataset was 97.1%, and the false-positive rate in the negative-for-malignancy dataset was 7.3%. The accuracy, sensitivity, and specificity of the model using all the test datasets were 96.3%, 98.5%, and 92.7%, respectively.

conclusionsAlthough some issues regarding cell annotation when creating an object detection model remain, the accuracy is sufficient to assist cancer screening in effusion cytology. It is vital to reliably detect malignant cells in effusion cytology, and the further development of automated systems to reduce false-negative results is expected.

Indexed as

AdenocarcinomaCytodiagnosisDeep LearningNeural Networks, ComputerPleural Effusion, MalignantAlgorithmsHumansSensitivity and Specificityartificial intelligencedeep learningeffusion cytologyobject detectionYOLO

Identifiers

PMID40736208
PMCPMC12628318

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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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 Socratic graph.