دورية أكاديمية
أعرض في EDS

Identifying Lymph Nodes and Their Statuses from Pretreatment Computer Tomography Images of Patients with Head and Neck Cancer Using a Clinical-Data-Driven Deep Learning Algorithm.

التفاصيل البيبلوغرافية
العنوان: Identifying Lymph Nodes and Their Statuses from Pretreatment Computer Tomography Images of Patients with Head and Neck Cancer Using a Clinical-Data-Driven Deep Learning Algorithm.
المؤلفون: Huang SY; Institute of Medical Science, Tzu Chi University, Hualien 970374, Taiwan.; Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan., Hsu WL; Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan.; Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan.; School of Medicine, Tzu Chi University, Hualien 970374, Taiwan., Liu DW; Institute of Medical Science, Tzu Chi University, Hualien 970374, Taiwan.; Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan.; Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan.; School of Medicine, Tzu Chi University, Hualien 970374, Taiwan., Wu EL; DeepQ Technology Corp, New Taipei City 242062, Taiwan., Peng YS; DeepQ Technology Corp, New Taipei City 242062, Taiwan., Liao ZT; DeepQ Technology Corp, New Taipei City 242062, Taiwan., Hsu RJ; Institute of Medical Science, Tzu Chi University, Hualien 970374, Taiwan.; Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan.; School of Medicine, Tzu Chi University, Hualien 970374, Taiwan.
المصدر: Cancers [Cancers (Basel)] 2023 Dec 18; Vol. 15 (24). Date of Electronic Publication: 2023 Dec 18.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101526829 Publication Model: Electronic Cited Medium: Print ISSN: 2072-6694 (Print) Linking ISSN: 20726694 NLM ISO Abbreviation: Cancers (Basel) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI
مستخلص: Background: Head and neck cancer is highly prevalent in Taiwan. Its treatment mainly relies on clinical staging, usually diagnosed from images. A major part of the diagnosis is whether lymph nodes are involved in the tumor. We present an algorithm for analyzing clinical images that integrates a deep learning model with image processing and attempt to analyze the features it uses to classify lymph nodes.
Methods: We retrospectively collected pretreatment computed tomography images and surgery pathological reports for 271 patients diagnosed with, and subsequently treated for, naïve oral cavity, oropharynx, hypopharynx, and larynx cancer between 2008 and 2018. We chose a 3D UNet model trained for semantic segmentation, which was evaluated for inference in a test dataset of 29 patients.
Results: We annotated 2527 lymph nodes. The detection rate of all lymph nodes was 80%, and Dice score was 0.71. The model has a better detection rate at larger lymph nodes. For those identified lymph nodes, we found a trend where the shorter the short axis, the more negative the lymph nodes. This is consistent with clinical observations.
Conclusions: The model showed a convincible lymph node detection on clinical images. We will evaluate and further improve the model in collaboration with clinical physicians.
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معلومات مُعتمدة: TCRD-110-15, IMAR-110-01-08, TCRD112-032, TCRD112-047 Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation; TCMF-IMC 112-02 Buddhist Tzu Chi Medical Foundation
فهرسة مساهمة: Keywords: computed tomography; deep learning; head and neck cancer; image processing; semantic segmentation
تواريخ الأحداث: Date Created: 20231223 Latest Revision: 20231225
رمز التحديث: 20231225
مُعرف محوري في PubMed: PMC10741600
DOI: 10.3390/cancers15245890
PMID: 38136434
قاعدة البيانات: MEDLINE
الوصف
تدمد:2072-6694
DOI:10.3390/cancers15245890