Interpretable multi-scale deep learning to detect malignancy in cell blocks and cytological smears of pleural effusion and identify aggressive endometrial cancer

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ching-Wei Wang , Hikam Muzakky , Yu-Pang Chung , Po-Jen Lai , Tai-Kuang Chao
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引用次数: 0

Abstract

The pleura is a serous membrane that surrounds the surface of the lungs. The visceral surface secretes fluid into the serous cavity, while the parietal surface ensures that the fluid is properly absorbed. However, when this balance is disrupted, it leads to the formation of pleural Effusion. The most common malignant pleural effusion (MPE) caused by lung cancer or breast cancer, and benign pleural effusions (BPE) caused by Mycobacterium tuberculosis infection, heart failure, or infections related to pneumonia. Today, with the rapid advancement of treatment protocols, accurately diagnosing MPE has become increasingly important. Although cytology smears and cell blocks examinations of pleural effusion are the clinical gold standards for diagnosing MPE, the diagnostic accuracy of these tools can be affected by certain limitations, such as low sensitivity, diagnostic variability across different regions and significant inter-observer variability, leading to a certain proportion of misdiagnoses. This study presents a deep learning (DL) framework, namely Interpretable Multi-scale Attention DL with Self-Supervised Learning Feature Encoder (IMA-SSL), to identifyMPE or BPE using 194 Cytological smears whole-slide images (WSIs) and 188 cell blocks WSIs. The use of DL on WSIs of pleural effusion allows for preliminary results to be obtained in a short time, giving patients the opportunity for earlier diagnosis and treatment. The experimental results show that the proposed IMA-SSL consistently obtained superior performance and outperformed five state-of-the-art (SOTA) methods in malignancy prediction on both cell block and cytological smear datasets and also in identification of aggressive endometrial cancer (EC) using a public TCGA dataset. Fisher’s exact test confirmed a highly significant correlation between the outputs of the proposed model and the slide status in the EC and pleural effusion datasets (p < 0.001), substantiating the model’s predictive reliability. The proposed method has the potential for practical clinical application in the foreseeable future. It can directly detect the presence of malignant tumor cells from cost-effective cell blocks and pleural effusion cytology smears and facilitate personalized cancer treatment decisions.
可解释的多尺度深度学习在胸腔积液细胞块和细胞学涂片中检测恶性肿瘤并识别侵袭性子宫内膜癌。
胸膜是包裹在肺表面的浆膜。内脏表面分泌液体进入浆液腔,而壁表面确保液体被适当吸收。然而,当这种平衡被破坏时,就会导致胸腔积液的形成。最常见的恶性胸腔积液(MPE)由肺癌或乳腺癌引起,良性胸腔积液(BPE)由结核分枝杆菌感染、心力衰竭或肺炎相关感染引起。今天,随着治疗方案的快速发展,准确诊断MPE变得越来越重要。尽管胸膜积液细胞学涂片和细胞块检查是诊断MPE的临床金标准,但这些工具的诊断准确性可能受到某些局限性的影响,如低灵敏度、不同地区的诊断变异性和显著的观察者间变异性,导致一定比例的误诊。本研究提出了一个深度学习(DL)框架,即带有自监督学习特征编码器(IMA-SSL)的可解释多尺度注意力深度学习(Interpretable Multi-scale Attention DL),使用194个细胞学片整片图像(wsi)和188个细胞块wsi来识别ympe或BPE。在胸腔积液的wsi中使用DL可以在短时间内获得初步结果,使患者有机会进行早期诊断和治疗。实验结果表明,所提出的IMA-SSL在细胞阻滞和细胞学涂片数据集上的恶性肿瘤预测以及使用公共TCGA数据集识别侵袭性子宫内膜癌(EC)方面始终获得优异的性能,优于五种最先进的(SOTA)方法。Fisher的精确检验证实了所提出模型的输出与EC和胸腔积液数据集中的载玻片状态之间的高度显著相关性(p < 0.001),证实了该模型的预测可靠性。该方法在可预见的未来具有实际临床应用的潜力。它可以直接检测恶性肿瘤细胞的存在,从成本效益的细胞块和胸膜积液细胞学涂片,促进个性化的癌症治疗决策。
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
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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