Magnetic Resonance Imaging‐Based Radiomics Features Associated with Depth of Invasion Predicted Lymph Node Metastasis and Prognosis in Tongue Cancer

Abstract

Background Adequate safe margin in tongue cancer radical surgery is one of the most important prognostic factors. However, the role of peritumoral tissues in predicting lymph node metastasis (LNM) and prognosis using radiomics analysis remains unclear. Purpose To investigate whether magnetic resonance imaging (MRI)‐based radiomics analysis with peritumoral extensions contributes toward the prediction of LNM and prognosis in tongue cancer. Study type Retrospective. Population Two hundred and thirty‐six patients (38.56% female) with tongue cancer (training set, N = 157; testing set, N = 79; 37.58% and 40.51% female for each). Field Strength/Sequence 1.5 T; T2‐weighted turbo spin‐echo images. Assessment Radiomics models (Rprim, Rprim+3, Rprim+5, Rprim+10, Rprim+15) were developed with features extracted from the primary tumor without or with peritumoral extensions (3, 5, 10, and 15 mm, respectively). Clinicopathological characteristics selected from univariate analysis, including MRI‐reported LN status, radiological extrinsic lingual muscle invasion, and pathological depth of invasion (DOI) were further incorporated into radiomics models to develop combined radiomics models (CRprim, CRprim+3, CRprim+5, CRprim+10, CRprim+15). Finally, the model performance was validated in the testing set. DOI was measured from the adjacent normal mucosa to the deepest point of tumor invasion. Statistical Tests Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, decision analysis, spearman correlation analysis. The Delong test was used to compare area under the ROC (AUC). P < 0.05 was considered statistically significant. Results Of all the models, the CRprim+10 reached the highest AUC of 0.995 in the training set and 0.872 in the testing set. Radiomics features were significantly correlated with pathological DOI (correlation coefficients, −0.157 to −0.336). The CRprim+10 was an independent indicator for poor disease‐free survival (hazard ratio, 5.250) and overall survival (hazard ratio, 17.464) in the testing set. Data Conclusion Radiomics analysis with a 10‐mm peritumoral extension had excellent power to predict LNM and prognosis in tongue cancer.

The role of radiomics in tongue cancer: A new tool for prognosis prediction

“…At the current time only four studies focused on MRIbased radiomics in oral cavity cancer and only one focused on survival outcomes. 26 Frood et al 27 (2018) conducted a retrospective study on 115 cases of oral cavity SCC to detect MRI radiomic textures indicative of lymphadenopathy with ENE. Nodal entropy derived from CE-T1 was significant in predicting ENE and nodal entropy combined with irregular boundary was the best predictor of ENE.…”

“…To the best of our knowledge, this is the first study of radiomics for OTSCC that includes multiple MRI sequences and survival outcomes as clinical endpoints. At the current time only four studies focused on MRI‐based radiomics in oral cavity cancer and only one focused on survival outcomes 26 …”

“…Also Yu et al 29 (2021) conduced an analysis on 127 OTSCC determining the degree of differentiation through radiomics on MRI fat‐suppressed T2W images. Wang et al 26 (2021) conducted a retrospective study on 236 patients with tongue cancer. MRI based radiomics of primary tumor and peritumoral tissue resulted an independent indicator for poor disease‐free survival and overall survival.…”

Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging

“…Radiomics is an advanced method for quantitative analysis that can reveal information from microscopic features that are not easily observable by the naked eye in medical imaging (17,36). In recent years, various studies have attempted to predict LNM based on MRI radiomic analysis of primary lesions (22,(37)(38)(39)(40)(41)(42)(43). To the best of our knowledge, only one study has analyzed the predictive efficacy of radiomics based on MRI for LNM of PDAC (43), but only the arterial phase of the T1WI enhanced sequence was used.…”

A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma

“…Kudoh et al ( 39 ) also found that the model based on positron emission tomography radiomics features performed well in predicting cervical lymph node metastasis in TSCC. Wang et al ( 40 ) showed that models based on MR radiomics signature from the primary tumor with 10 mm peritumoral extensions and clinicopathological characteristics had the highest AUC of 0.995 in the training cohort and 0.872 in the testing cohort. These satisfactory results have revealed the promising prospect of ML.…”