ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2015
Detection of malignant mesothelioma using nuclear structure of mesothelial cells in effusion cytology specimens.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled 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.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools.Cancer cytopathology · 2026Pooled it
- Global bibliometric analysis of pleural mesothelioma biomarkers (2002-2024): trends and emerging frontiers.Journal of thoracic disease · 2025Article
- Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025Article
- Deep-Learning-Based Screening and Ancillary Testing for Thyroid Cytopathology.The American journal of pathology · 2023Article
- Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid.Cells · 2023Article
- Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review.Cancers · 2022Review
- Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- PARTITIONING SIGNAL CLASSES USING TRANSPORT TRANSFORMS FOR DATA ANALYSIS AND MACHINE LEARNING.Sampling theory, signal processing, and data analysis · 2021Article
- Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images.BioMed research international · 2018Article
- Molecular markers and new diagnostic methods to differentiate malignant from benign mesothelial pleural proliferations: a literature review.Journal of thoracic disease · 2018Review
- A TransportationJournal of mathematical imaging and vision · 2017Article
- Spatial Statistics for Segmenting Histological Structures in H&E Stained Tissue Images.IEEE transactions on medical imaging · 2017Article
- Malignant pleural mesothelioma and mesothelial hyperplasia: A new molecular tool for the differential diagnosis.Oncotarget · 2017Article
- Predictive Nuclear Chromatin Characteristics of Melanoma and Dysplastic Nevi.Journal of pathology informatics · 2017Article
- A continuous linear optimal transport approach for pattern analysis in image datasets.Pattern recognition · 2016Article
- The Radon Cumulative Distribution Transform and Its Application to Image Classification.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Mesothelioma is a form of cancer generally caused from previous exposure to asbestos. Although it was considered a rare neoplasm in the past, its incidence is increasing worldwide due to extensive use of asbestos. In the current practice of medicine, the gold standard for diagnosing mesothelioma is through a pleural biopsy with subsequent histologic examination of the tissue. The diagnostic tissue should demonstrate the invasion by the tumor and is obtained through thoracoscopy or open thoracotomy, both being highly invasive surgical operations. On the other hand, thoracocentesis, which is removal of effusion fluid from the pleural space, is a far less invasive procedure that can provide material for cytological examination. In this study, we aim at detecting and classifying malignant mesothelioma based on the nuclear chromatin distribution from digital images of mesothelial cells in effusion cytology specimens. Accordingly, a computerized method is developed to determine whether a set of nuclei belonging to a patient is benign or malignant. The quantification of chromatin distribution is performed by using the optimal transport-based linear embedding for segmented nuclei in combination with the modified Fisher discriminant analysis. Classification is then performed through a k-nearest neighborhood approach and a basic voting strategy. Our experiments on 34 different human cases result in 100% accurate predictions computed with blind cross validation. Experimental comparisons also show that the new method can significantly outperform standard numerical feature-type methods in terms of agreement with the clinical diagnosis gold standard. According to our results, we conclude that nuclear structure of mesothelial cells alone may contain enough information to separate malignant mesothelioma from benign mesothelial proliferations.
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.