Artificial intelligence–based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT

Cai, Wei; Yu, Pengyi; Zhang, Haicheng; Han, Xiao; Song, Zheying; Zheng, Guibin; Wang, Guangkuo; Zheng, Haitao; Mao, Ning; Song, Xicheng

doi:10.1007/s00330-023-09700-2

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…Wang et al, in turn, developed an AI model to recognize cervical lymph node metastases [88] . In their retrospective multicenter study, preoperative CT images were used to train the AI neural network.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review

Palomba,

Fernicola,

Corte

et al. 2024

AIMSPH

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<abstract> <p>Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.</p> </abstract>

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review

Palomba,

Fernicola,

Corte

et al. 2024

AIMSPH

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show abstract

“…Wu et al proposed an end-to-end deep multimodal learning network, called the multimodal classification network, which used both clinical records and gray-scale US and CDFI images as input and corresponding metastatic status as output ( 105 ). In addition to DL models based on US images, those based on CT have been shown to be capable of predicting LNM preoperatively in patients with PTC ( 103 , 104 ). Patients with gross ETE are more likely to have LNM and distant metastasis, higher rates of tumor recurrence, and worse overall survival ( 124 ).…”

Section: Clinical Applications Of DL In Thyroid Imagingmentioning

confidence: 99%

A narrative review of deep learning in thyroid imaging: current progress and future prospects

Yang,

Ma,

Chen

2024

Quant Imaging Med Surg

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Background and Objective Deep learning (DL) has contributed substantially to the evolution of image analysis by unlocking increased data and computational power. These DL algorithms have further facilitated the growing trend of implementing precision medicine, particularly in areas of diagnosis and therapy. Thyroid imaging, as a routine means to screening for thyroid diseases on large-scale populations, is a massive data source for the development of DL models. Thyroid disease is a global health problem and involves structural and functional changes. The objective of this study was to evaluate the general rules and future directions of DL networks in thyroid medical image analysis through a review of original articles published between 2018 and 2023. Methods We searched for English-language articles published between April 2018 and September 2023 in the databases of PubMed, Web of Science, and Google Scholar. The keywords used in the search included artificial intelligence or DL, thyroid diseases, and thyroid nodule or thyroid carcinoma. Key Content and Findings The computer vision tasks of DL in thyroid imaging included classification, segmentation, and detection. The current applications of DL in clinical workflow were found to mainly include management of thyroid nodules/carcinoma, risk evaluation of thyroid cancer metastasis, and discrimination of functional thyroid diseases. Conclusions DL is expected to enhance the quality of thyroid images and provide greater precision in the assessment of thyroid images. Specifically, DL can increase the diagnostic accuracy of thyroid diseases and better inform clinical decision-making.

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Seizing the fate of lymph nodes in immunotherapy: To preserve or not?

Xu,

Li,

Cao

et al. 2024

Cancer Letters

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Artificial intelligence–based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT

Cited by 9 publications

References 34 publications

Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review

Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review

A narrative review of deep learning in thyroid imaging: current progress and future prospects

Seizing the fate of lymph nodes in immunotherapy: To preserve or not?

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