Management of Stage III Oral Cavity Squamous Cell Carcinoma in Light of the New Staging System: a Critical Review

Matos, Leandro Luongo de; Guimarães, Yasmin Laryssa Moura; Leite, Ana Kober Nogueira; Cernea, Cláudio Roberto

doi:10.1007/s11912-022-01353-6

Cited by 6 publications

(4 citation statements)

References 47 publications

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“…Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for approximately 90% of oral cancer cases and with a poor prognosis ( 1 , 2 ). Assessing the lymph node (LN) status determines the staging, management, and ultimately the survival outcomes of patients with OSCC ( 3 – 5 ). Cervical LN metastasis is associated with poor prognosis and is one of the most important independent prognostic factors in OSCC ( 6 , 7 ).…”

Section: Introductionmentioning

confidence: 99%

Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis

Deng,

Hu,

Tang

et al. 2024

Front. Oncol.

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BackgroundThe performance of artificial intelligence (AI) in the prediction of lymph node (LN) metastasis in patients with oral squamous cell carcinoma (OSCC) has not been quantitatively evaluated. The purpose of this study was to conduct a systematic review and meta-analysis of published data on the diagnostic performance of CT and MRI based on AI algorithms for predicting LN metastases in patients with OSCC.MethodsWe searched the Embase, PubMed (Medline), Web of Science, and Cochrane databases for studies on the use of AI in predicting LN metastasis in OSCC. Binary diagnostic accuracy data were extracted to obtain the outcomes of interest, namely, the area under the curve (AUC), sensitivity, and specificity, and compared the diagnostic performance of AI with that of radiologists. Subgroup analyses were performed with regard to different types of AI algorithms and imaging modalities.ResultsFourteen eligible studies were included in the meta-analysis. The AUC, sensitivity, and specificity of the AI models for the diagnosis of LN metastases were 0.92 (95% CI 0.89–0.94), 0.79 (95% CI 0.72–0.85), and 0.90 (95% CI 0.86–0.93), respectively. Promising diagnostic performance was observed in the subgroup analyses based on algorithm types [machine learning (ML) or deep learning (DL)] and imaging modalities (CT vs. MRI). The pooled diagnostic performance of AI was significantly better than that of experienced radiologists.DiscussionIn conclusion, AI based on CT and MRI imaging has good diagnostic accuracy in predicting LN metastasis in patients with OSCC and thus has the potential for clinical application.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/#recordDetails, PROSPERO (No. CRD42024506159).

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Section: Introductionmentioning

confidence: 99%

Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis

Deng,

Hu,

Tang

et al. 2024

Front. Oncol.

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“…The major prognostic factors are depth of invasion (DOI) > 5 mm, extranodal extension, positive or close surgical margins, pT3 or pT4 tumor (i.e., larger than 4 cm or infiltrating bony structures such as the mandible), pN2 or pN3 nodal disease, perineural invasion, vascular invasion, and lymphatic invasion. In particular, the presence of lymph node metastases (LNM) alone is known to reduce survival by approximately 50% [4].…”

Section: Introductionmentioning

confidence: 99%

Radiomics-Based Analysis in the Prediction of Occult Lymph Node Metastases in Patients with Oral Cancer: A Systematic Review

Jiang

Locatello

Maggiore

et al. 2023

JCM

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Background: Tumor extension and metastatic cervical lymph nodes’ (LNs) number and dimensions are major prognostic factors in patients with oral squamous cell carcinoma (OSCC). Radiomics-based models are being integrated into clinical practice in the prediction of LN status prior to surgery in order to optimize the treatment, yet their value is still debated. Methods: A systematic review of the literature was conducted according to the PRISMA guideline. Baseline study characteristics, and methodological items were extracted and summarized. Results: A total of 10 retrospective studies were included into the present study, each of them exploiting a single imaging modality. Data from a cohort of 1489 patients were analyzed: the highest AUC value was 99.5%, ACC ranges from 68% to 97.5%, and sensibility and specificity were over 0.65 and 0.70, respectively. Conclusion: Radiomics may be a noninvasive tool to predict occult LN metastases (LNM) in OSCC patients prior to treatment; further prospective studies are warranted to create a reproducible and reliable method for the detection of LNM in OSCC.

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“…Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for approximately 90% of oral cancer cases and with a poor prognosis (1,2). Assessing the lymph node (LN) status determines the staging, management, and ultimately the survival outcomes of patients with OSCC (3)(4)(5). Cervical LN metastasis is associated with poor prognosis and is one of the most important independent prognostic factors in OSCC (6,7).…”

Section: Introductionmentioning

confidence: 99%

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Management of Stage III Oral Cavity Squamous Cell Carcinoma in Light of the New Staging System: a Critical Review

Cited by 6 publications

References 47 publications

Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis

Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis

Radiomics-Based Analysis in the Prediction of Occult Lymph Node Metastases in Patients with Oral Cancer: A Systematic Review

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