Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model.

IF 1.7 Q3 PATHOLOGY Journal of Pathology and Translational Medicine Pub Date : 2024-05-01 Epub Date: 2024-04-30 DOI:10.4132/jptm.2024.03.07
Pedro R F Rende, Joel Machado Pires, Kátia Sakimi Nakadaira, Sara Lopes, João Vale, Fabio Hecht, Fabyan E L Beltrão, Gabriel J R Machado, Edna T Kimura, Catarina Eloy, Helton E Ramos
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Abstract

Background: Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration.

Methods: We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio.

Results: This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value.

Conclusions: The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.

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通过物体检测器模型重新审视将核凹槽识别为独特核变化的实用性。
背景:核沟是甲状腺乳头状癌(PTC)的主要结构之一。考虑到人工智能在甲状腺细胞学中的应用具有常规诊断的潜力,我们的目标是开发一种新的有监督卷积神经网络,该网络能够在甲状腺细针穿刺术获得的 Diff-Quik 染色全切片图像(WSI)中识别核沟:我们选择了 22 张经 Diff-Quik 染色的细胞学切片,细胞学诊断为 PTC,组织学诊断一致。对每张切片进行扫描,形成 WSI。获取包含感兴趣区的图像,然后进行预格式化、核沟标注和数据增强技术。最终数据集按 7:3 的比例分为训练组和验证组:这是首个应用于甲状腺细胞病理学核结构的基于物体检测的人工智能模型。从 22 个 WSI 共获得 7255 张图像,共计 7242 个注释核沟。最佳模型是用训练数据集提交 15 次(第 14 个 epoch)后获得的,真阳性率为 67%,灵敏度为 49.8%,预测阳性值为 43.1%。结论该模型能够制定结构预测规则,表明基于物体检测的人工智能模型在核凹槽鉴定中的应用是可行的。该模型可减少观察者之间的差异和每张切片的时间,这表明核评价是通过计算模型完善诊断的可能性之一。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.00
自引率
4.20%
发文量
45
审稿时长
14 weeks
期刊介绍: The Journal of Pathology and Translational Medicine is an open venue for the rapid publication of major achievements in various fields of pathology, cytopathology, and biomedical and translational research. The Journal aims to share new insights into the molecular and cellular mechanisms of human diseases and to report major advances in both experimental and clinical medicine, with a particular emphasis on translational research. The investigations of human cells and tissues using high-dimensional biology techniques such as genomics and proteomics will be given a high priority. Articles on stem cell biology are also welcome. The categories of manuscript include original articles, review and perspective articles, case studies, brief case reports, and letters to the editor.
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