Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT

Onoue, Keita; Fujima, Noriyuki; Arasa, Vanessa Carlota Andreu; Setty, Bindu N.; Sakai, Osamu

doi:10.1016/j.amjoto.2021.103026

Cited by 11 publications

(17 citation statements)

References 32 publications

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“…Recently, deep learning techniques have been validated for head and neck oncology imaging 26 , 34 – 36 . A prior study applied a deep learning method to diagnose metastatic lymph nodes and identify extracapsular extension in head and neck cancer, with an AUC of 0.91.…”

Section: Discussionmentioning

confidence: 99%

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Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto’s disease from tuberculous lymphadenitis on neck CECT

Kim

Lee

et al. 2022

Sci Rep

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Neck contrast-enhanced CT (CECT) is a routine tool used to evaluate patients with cervical lymphadenopathy. This study aimed to evaluate the ability of convolutional neural networks (CNNs) to classify Kikuchi-Fujimoto’s disease (KD) and cervical tuberculous lymphadenitis (CTL) on neck CECT in patients with benign cervical lymphadenopathy. A retrospective analysis of consecutive patients with biopsy-confirmed KD and CTL in a single center, from January 2012 to June 2020 was performed. This study included 198 patients of whom 125 patients (mean age, 25.1 years ± 8.7, 31 men) had KD and 73 patients (mean age, 41.0 years ± 16.8, 34 men) had CTL. A neuroradiologist manually labelled the enlarged lymph nodes on the CECT images. Using these labels as the reference standard, a CNNs was developed to classify the findings as KD or CTL. The CT images were divided into training (70%), validation (10%), and test (20%) subsets. As a supervised augmentation method, the Cut&Remain method was applied to improve performance. The best area under the receiver operating characteristic curve for classifying KD from CTL for the test set was 0.91. This study shows that the differentiation of KD from CTL on neck CECT using a CNNs is feasible with high diagnostic performance.

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Section: Discussionmentioning

confidence: 99%

“…There have been many studies on cervical lymph node analysis using deep learning, particularly in oncology 34 , 36 , 39 . However, no previous investigations have evaluated the application of a deep-learning model to discriminate benign cervical lymphadenopathy.…”

Section: Discussionmentioning

confidence: 99%

Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto’s disease from tuberculous lymphadenitis on neck CECT

Kim

Lee

et al. 2022

Sci Rep

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“…Recently, in head and neck imaging, deep learning methods have been used to differentiate metastatic lymph nodes in thyroid cancer 17,20,29 , discriminate benign and malignant thyroid nodules, and detect extracapsular extension of metastatic lymph nodes in head and neck cancers 30 . However, no studies have investigated the feasibility of deep learning applications for the classification of benign cervical lymph nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning methods with convolutional neural networks (CNNs) utilize multiple layered neural networks to develop robust predictive models without feature selection by human image evaluation experts 17,18 . In radiology, many studies using CNNs have focused on the detection or classification of lesions and the validation of the deep learning technique performance 17,[19][20][21][22][23][24][25][26][27][28][29] . The performance of CNNs has been improved and found to be comparable to that of radiologists in many studies 21,29,30 .…”

Section: Introductionmentioning

confidence: 99%

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Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto’s disease from tuberculous lymphadenitis on neck CECT

Kim

Lee

et al. 2022

Preprint

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Neck contrast-enhanced CT (CECT) is a routine tool used to evaluate patients with cervical lymphadenopathy. This study aimed to evaluate the ability of convolutional neural networks (CNNs) to classify Kikuchi-Fujimoto’s disease (KD) and cervical tuberculous lymphadenitis (CTL) on neck CECT in patients with benign cervical lymphadenopathy. A retrospective analysis of consecutive patients with biopsy-confirmed KD and CTL in a single center, from January 2012 to June 2020 was performed. This study included 198 patients of whom 125 patients (mean age, 25.1 years ± 8.7, 31 men) had KD and 73 patients (mean age, 41.0 years ± 16.8, 34 men) had CTL. A neuroradiologist manually labelled the enlarged lymph nodes on the CECT images. Using these labels as the reference standard, a CNN was developed to classify the findings as KD or CTL. The CT images were divided into training (70%), validation (10%), and test (20%) subsets. The Cut&Remain method was applied to improve performance. For the test set, the best area under the receiver operating characteristic curve for classifying KD from CTL was 0.91. Our findings show that the differentiation of KD from CTL on neck CECT using a CNN is feasible with high diagnostic performance.

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Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer

Ardakani

Mohammadi

Mirza‐Aghazadeh‐Attari

et al. 2022

J of Ultrasound Medicine

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Objectives Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer. Methods An in‐house deep learning‐based model called “ClymphNet” was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively. Results Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts. Conclusion A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.

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Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT

Cited by 11 publications

References 32 publications

Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto’s disease from tuberculous lymphadenitis on neck CECT

Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto’s disease from tuberculous lymphadenitis on neck CECT

Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto’s disease from tuberculous lymphadenitis on neck CECT

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer

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