Int J Stomatol ›› 2025, Vol. 52 ›› Issue (4): 507-513.doi: 10.7518/gjkq.2025076

• Reviews • Previous Articles     Next Articles

Application of radiomics in cervical lymph node metastasis of oral squamous cell carcinoma

Qian Wang(),Hui Peng,Liyu Zhang,Zongcheng Yang,Yuqi Wang,Yu Pan,Yu Zhou()   

  1. Dept. of Oral and Maxillofacial Surgery, the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital), Hefei 230001, China
  • Received:2024-08-06 Revised:2024-09-06 Online:2025-07-01 Published:2025-06-20
  • Contact: Yu Zhou E-mail:3582464090@qq.com;zyugj@sina.com
  • Supported by:
    National Natural Science Foundation of China(82203277)

Abstract:

Oral squamous cell carcinoma (OSCC) is a common malignant tumor in the oral and maxillofacial region, and accurate staging of cervical lymph nodes is crucial for treatment planning of this disease. Precise clinical staging can prevent unnecessary neck dissections and postoperative complications. However, traditional imaging techniques mainly rely on the size and morphology of lymph nodes to assess their nature, leading to subjective biases. To provide more objective and accurate data, radiomics converts images into quantitative variables that can be processed by software. In this study, the application of radiomics in the advancement of cervical lymph node metastasis in OSCC is reviewed. By employing radiomic techniques, healthcare professionals can utilize quantitative data to evaluate the nature of lymph nodes and tailor more personalized treatment plans based on these results.

Key words: oral squamous cell carcinoma, lymph node metastasis, radiomics, magnetic resonance imaging, computed tomography

CLC Number: 

  • R739.8

TrendMD: 
1 Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249.
2 Leemans CR, Braakhuis BJM, Brakenhoff RH. The molecular biology of head and neck cancer[J]. Nat Rev Cancer, 2011, 11(1): 9-22.
3 Chinn SB, Myers JN. Oral cavity carcinoma: current management, controversies, and future directions[J]. J Clin Oncol, 2015, 33(29): 3269-3276.
4 Pantel K, Brakenhoff RH. Dissecting the metastatic cascade[J]. Nat Rev Cancer, 2004, 4(6): 448-456.
5 Feng ZE, Li JN, Li CZ, et al. Elective neck dissection versus observation in the management of early tongue carcinoma with clinically node-negative neck: a retrospective study of 229 cases[J]. J Craniomaxillofac Surg, 2014, 42(6): 806-810.
6 Arain AA, Rajput MSA, Ansari SA, et al. Occult nodal metastasis in oral cavity cancers[J]. Cureus, 2020, 12(11): e11640.
7 Vassiliou LV, Acero J, Gulati A, et al. Management of the clinically N0 neck in early-stage oral squamous cell carcinoma (OSCC). An EACMFS position paper[J]. J Craniomaxillofac Surg, 2020, 48(8): 711-718.
8 Rathod R, Bakshi J, Panda NK, et al. Can sentinel lymph node biopsy predict various levels of echelon nodes in oral cancers[J]. Int Arch Otorhinolaryngol, 2020, 24(2): e125-e131.
9 Van den Brekel MW, Leemans CR, Snow GB. Assessment and management of lymph node metastases in the neck in head and neck cancer patients[J]. Crit Rev Oncol Hematol, 1996, 22(3): 175-182.
10 Oh LJ, Phan K, Kim SW, et al. Elective neck dissection versus observation for early-stage oral squamous cell carcinoma: systematic review and meta-analysis[J]. Oral Oncol, 2020, 105: 104661.
11 Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446.
12 van den Brekel MW, Castelijns JA, Stel HV, et al. Modern imaging techniques and ultrasound-guided aspiration cytology for the assessment of neck node metastases: a prospective comparative study[J]. Eur Arch Otorhinolaryngol, 1993, 250(1): 11-17.
13 Sun R, Tang XY, Yang Y, et al. 18FDG-PET/CT for the detection of regional nodal metastasis in patients with head and neck cancer: a meta-analysis[J]. Oral Oncol, 2015, 51(4): 314-320.
14 de Bree R, Takes RP, Castelijns JA, et al. Advances in diagnostic modalities to detect occult lymph node metastases in head and neck squamous cell carcinoma[J]. Head Neck, 2015, 37(12): 1829-1839.
15 Kinner S, Maderwald S, Albert J, et al. Discrimination of benign and malignant lymph nodes at 7.0T compared to 1.5T magnetic resonance imaging u-sing ultrasmall particles of iron oxide: a feasibility preclinical study[J]. Acad Radiol, 2013, 20(12): 1604-1609.
16 Fong ZV, Tan WP, Lavu H, et al. Preoperative ima-ging for resectable periampullary cancer: clinicopa-thologic implications of reported radiographic fin-dings[J]. J Gastrointest Surg, 2013, 17(6): 1098-1106.
17 司呈云, 刘梦秋, 翁海燕, 等. MRI测量和评估口腔黏膜鳞状细胞癌临床分期指标的准确性分析[J]. 中国口腔颌面外科杂志, 2023, 21(4): 390-396.
Si CY, Liu MQ, Weng HY, et al. Accuracy of MRI to measure and evaluate clinical staging of oral squamous cell carcinoma[J]. Chin J Oral Maxillofac Surg, 2023, 21(4): 390-396.
18 Gillies RJ, Kinahan PE, Hricak H. Radiomics: ima-ges are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577.
19 van Timmeren JE, Cester D, Tanadini-Lang S, et al. Radiomics in medical imaging- “how-to” guide and critical reflection[J]. Insights Imaging, 2020, 11(1): 91.
20 Park H, Lim Y, Ko ES, et al. Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer[J]. Clin Cancer Res, 2018, 24(19): 4705-4714.
21 Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology[J]. Nat Rev Cancer, 2018, 18(8): 500-510.
22 Li X, Yang LF, Jiao X. Comparison of traditional radiomics, deep learning radiomics and fusion me-thods for axillary lymph node metastasis prediction in breast cancer[J]. Acad Radiol, 2023, 30(7): 1281-1287.
23 Currie G, Rohren E. The deep radiomic analytics pipeline[J]. Vet Radiol Ultrasound, 2022, 63(): 889-896.
24 McCague C, Ramlee S, Reinius M, et al. Introduction to radiomics for a clinical audience[J]. Clin Radiol, 2023, 78(2): 83-98.
25 Xia TY, Zhao B, Li BR, et al. MRI-based radiomics and deep learning in biological characteristics and prognosis of hepatocellular carcinoma: opportunities and challenges[J]. J Magn Reson Imaging, 2024, 59(3): 767-783.
26 Han XY, Wang ML, Zheng YT, et al. Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer[J]. Eur Radiol, 2024, 34(4): 2716-2726.
27 Malhaire C. Radiomics in 18F-FDG PET/CT predicts HER2 status in breast cancer with equivocal immunohistochemistry[J]. Eur J Radiol, 2024, 170: 111238.
28 Li X, Wu M, Wu M, et al. A radiomics and geno-mics-derived model for predicting metastasis and prognosis in colorectal cancer[J]. Carcinogenesis, 2024, 45(3): 170-180.
29 Geng XT, Zhang YP, Li Y, et al. Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma[J]. Br J Radiol, 2024, 97(1155): 652-659.
30 Elmahdy M, Sebro R. Radiomics analysis in medical imaging research[J]. J Med Radiat Sci, 2023, 70(1): 3-7.
31 Jubair F, Al-Karadsheh O, Malamos D, et al. A no-vel lightweight deep convolutional neural network for early detection of oral cancer[J]. Oral Dis, 2022, 28(4): 1123-1130.
32 Ling X, Alexander GS, Molitoris J, et al. Identification of CT-based non-invasive radiographic biomarkers for overall survival stratification in oral cavity squamous cell carcinoma[J]. Res Sq, 2023: rs.3.rs-rs.3263887.
33 Yu Q, Ning YQ, Wang AR, et al. Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study[J]. Eur Radiol, 2023, 33(9): 6054-6065.
34 Zheng ML, Chen Q, Ge YQ, et al. Development and validation of CT-based radiomics nomogram for the classification of benign parotid gland tumors[J]. Med Phys, 2023, 50(2): 947-957.
35 Committeri U, Fusco R, Di Bernardo E, et al. Radiomics metrics combined with clinical data in the surgical management of early-stage (cT1-T2 N0) tongue squamous cell carcinomas: a preliminary study[J]. Biology, 2022, 11(3): 468.
36 Kubo K, Kawahara D, Murakami Y, et al. Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer[J]. Oral Surg Oral Med Oral Pathol Oral Radiol, 2022, 134(1): 93-101.
37 Tomita H, Yamashiro T, Heianna J, et al. 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[J]. Eur Ra-diol, 2021, 31(10): 7440-7449.
38 Chen Z, Yu Y, Liu S, et al. A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma[J]. Clin Oral Investig, 2023, 28(1): 39.
39 Angelelli G, Moschetta M, Limongelli L, et al. Endocavitary sonography of early oral cavity malignant tumors[J]. Head Neck, 2017, 39(7): 1349-1356.
40 Baba A, Okuyama Y, Ikeda K, et al. Undetectability of oral tongue cancer on magnetic resonance ima-ging; clinical significance as a predictor to avoid unnecessary elective neck dissection in node negative patients[J]. Dentomaxillofac Radiol, 2019, 48(3): 20180272.
41 Liu JJ, Song LN, Zhou JR, et al. Prediction of prognosis of tongue squamous cell carcinoma based on clinical MR imaging data modeling[J]. Technol Cancer Res Treat, 2023, 22: 15330338231207006.
42 Park YM, Lim JY, Koh YW, et al. Prediction of treatment outcome using MRI radiomics and machine learning in oropharyngeal cancer patients after surgical treatment[J]. Oral Oncol, 2021, 122: 105559.
43 Ren JL, Qi M, Yuan Y, et al. Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the oral tongue and floor of mouth: a preliminary study[J]. Acta Radiol, 2021, 62(4): 453-461.
44 Romeo V, Cuocolo R, Ricciardi C, et al. Prediction of tumor grade and nodal status in oropharyngeal and oral cavity squamous-cell carcinoma using a radiomic approach[J]. Anticancer Res, 2020, 40(1): 271-280.
45 Ren JL, Yuan Y, Tao XF. Histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma[J]. Eur Radiol, 2022, 32(4): 2739-2747.
46 Yuan Y, Ren JL, Tao XF. Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma[J]. Eur Radiol, 2021, 31(9): 6429-6437.
47 Liu S, Zhang AH, Xiong JJ, et al. The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients[J]. Head Neck, 2024, 46(3): 513-527.
48 Reel PS, Reel S, Pearson E, et al. Using machine learning approaches for multi-omics data analysis: a review[J]. Biotechnol Adv, 2021, 49: 107739.
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