Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography

Tomita, Hayato; Yamashiro, Tsuneo; Heianna, Joichi; Nakasone, Toshiyuki; Kimura, Yusuke; Mimura, Hidefumi; Murayama, Sadayuki

doi:10.1007/s00330-021-07758-4

Cited by 26 publications

(24 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the results of previous radiomics studies related to cancer ( Su et al, 2020 , Tomita et al, 2021 ), the radiomics features not only reflect the presence of active bleeding and time course of bleeding, but may also reflect microglia/macrophage activation and neuroinflammation in the hemorrhage and PHE areas. A comparison of selected radiomic features showed no significant differences in AUC values, specificity, and sensitivity among the Rad-score model of the hematoma area, the PHE area alone, and the hematoma-edema Rad-score model.…”

Section: Discussionmentioning

confidence: 94%

Radiomics for prediction of intracerebral hemorrhage outcomes: A retrospective multicenter study

Huang

Wang

Zhang

et al. 2022

NeuroImage: Clinical

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 94%

Radiomics for prediction of intracerebral hemorrhage outcomes: A retrospective multicenter study

Huang

Wang

Zhang

et al. 2022

NeuroImage: Clinical

View full text Add to dashboard Cite

“…In 2021, in a retrospective, monocentric study performed in Japan, Tomita and co-workers investigated whether CT-based texture analysis with machine learning can accurately distinguish pathologic from benign cervical LNs in patients suffering from HNSCC of the oral cavity [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

“…Intraoperatively, the LNs were labeled by the surgeon and thereafter correlated with histopathology. Segmentation was performed manually as three-dimensional, peripheral border delineation across all slices, performed in axial CT scans using LIFEx Software by two experience radiologists [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

“…Two experienced radiologists and one experienced maxillofacial surgeon, blinded to each other and the histopathologic findings, reviewed all LNs attributed to the test data set based on the established criteria. An a priori LN sample size calculation was performed using a type I error of 5% and power of 80% resulting in a minimum sample size of 27 with a 3:1 ratio of non-pathologic to pathologic cervical LNs [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

Santer

Kloppenburg

Gottfried

et al. 2022

Cancers

View full text Add to dashboard Cite

Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10–258) and of LNs was 340 (SD ± 268; range 21–791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43–99%) and for testing sets 86% (SD ± 5%; range 76–92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI’s role in LN-classification in locally-advanced HNSCC.

show abstract

“…Recently, Radiomics features extracted from primary tumors exhibited great potential in prediction of cervical lymph node metastasis (13,14). However, only a few studies have explored the role of radiomics features in lymph node evaluation of head and neck cancers (15,16). To our knowledge, there is no radiomics study for prediction of cervical lymph node metastasis in HPSCC.…”

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma

Ling²,

Chen³

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

ObjectiveTo investigate the role of pre-treatment magnetic resonance imaging (MRI) radiomics for the preoperative prediction of lymph node (LN) metastasis in patients with hypopharyngeal squamous cell carcinoma (HPSCC).MethodsA total of 155 patients with HPSCC were eligibly enrolled from single institution. Radiomics features were extracted from contrast-enhanced axial T-1 weighted (CE-T1WI) sequence. The most relevant features of LN metastasis were selected by the least absolute shrinkage and selection operator (LASSO) method. Univariate and multivariate logistic regression analysis was adopted to determine the independent clinical risk factors. Three models were constructed to predict the LN metastasis status: one using radiomics only, one using clinical factors only, and the other one combined radiomics and clinical factors. Receiver operating characteristic (ROC) curves and calibration curve were used to evaluate the discrimination and the accuracy of the models, respectively. The performances were tested by an internal validation cohort (n=47). The clinical utility of the models was assessed by decision curve analysis.ResultsThe nomogram consisted of radiomics scores and the MRI-reported LN status showed satisfactory discrimination in the training and validation cohorts with AUCs of 0.906 (95% CI, 0.840 to 0.972) and 0.853 (95% CI, 0.739 to 0.966), respectively. The nomogram, i.e., the combined model, outperformed the radiomics and MRI-reported LN status in both discrimination and clinical usefulness.ConclusionsThe MRI-based radiomics nomogram holds promise for individual and non-invasive prediction of LN metastasis in patients with HPSCC.

show abstract

Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography

Cited by 26 publications

References 28 publications

Radiomics for prediction of intracerebral hemorrhage outcomes: A retrospective multicenter study

Radiomics for prediction of intracerebral hemorrhage outcomes: A retrospective multicenter study

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma

Contact Info

Product

Resources

About