Can MR textural analysis improve the prediction of extracapsular nodal spread in patients with oral cavity cancer?

Frood, Russell; Palkhi, Ebrahim; Barnfield, Mark C.; Prestwich, R.; Vaidyanathan, Sriram; Scarsbrook, Andrew

doi:10.1007/s00330-018-5524-x

Cited by 17 publications

(9 citation statements)

References 27 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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Background: Radiomics represents an emerging field of precision-medicine. Its application in head and neck is still at the beginning. Methods: Retrospective study about magnetic resonance imaging (MRI) based radiomics in oral tongue squamous cell carcinoma (OTSCC) surgically treated (2010-2019; 79 patients). All preoperative MRIs include different sequences (T1, T2, DWI, ADC). Tumor volume was manually segmented and exported to radiomic-software, to perform feature extraction. Statistically significant

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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…More recently, newer imaging modalities such as texture analysis and machine learning methods are currently being investigated in attempts to improve ENE detection and reduce the current subjective nature of its assessment among radiologists 33,34 . Recent findings by Kann et al 34 in SCC‐related HNC suggest their CT machine learning algorithm showed superior diagnostic advantage at detecting ENE with an achieved AUC of 0.90 (88.6% accuracy), outperforming their radiologists AUCs of 0.60 and 0.82 ( p < 0.0001 and p = 0.16).…”

Section: Discussionmentioning

confidence: 99%

Correlation between radiologic and pathologic extranodal extension in HPV‐associated oropharyngeal cancer: Systematic review

et al. 2022

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Pretreatment determination of extranodal extension (ENE) has significant clinical implications in human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC). Unfortunately there is no gold‐standard imaging modality for radiological assessment of ENE in HPV+ OPSCC, leading to subjective assessments and complex decision making concerning ENE. A systematic review of diagnostic test accuracy was therefore undertaken, with five databases systemically searched to evaluate the diagnostic performance of an imaging modality for detection of ENE in HPV+ OPSCC. A meta‐analysis was conducted on four CT studies using a random‐effects model. While a narrative synthesis was provided for the studies using PET/CT and “CT and MRI.” Out of 1772 hits, six studies were included in the review. Meta‐analysis on four CT studies showed CT had an overall sensitivity of 77% and specificity of 60%. PET/CT had a sensitivity of 37.5% and specificity of 97%. “CT and MRI” had a sensitivity of 62% and specificity of 78%. Further diagnostic studies involving CT, PET/CT and MRI are ultimately required.

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“…Previous studies also analyzed the role of texture analysis applied to CT and MR imaging in head and neck cancer patients. Recently, first-order MR texture analysis applied to both primary tumor and lymph-nodes was found useful to predict extracapsular nodal spread in patients with OC SCC (47). Furthermore, TA has been previously applied on CT images to assess its reliability in predicting HPV status (15,16).…”

Section: Discussionmentioning

confidence: 99%

Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach

et al. 2019

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Background/Aim: To investigate whether a radiomic machine learning (ML) approach employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able to predict tumor grade (TG) and nodal status (NS) in patients with oropharyngeal (OP) and oral cavity (OC) squamous-cell carcinoma (SCC). Patients and Methods: Contrast-enhanced CT images of 40 patients with OP and OC SCC were post-processed to extract TA features from PTLs. A feature selection method and different ML algorithms were applied to find the most accurate subset of features to predict TG and NS. Results: For the prediction of TG, the best accuracy (92.9%) was achieved by Naïve Bayes (NB), bagging of NB and K Nearest Neighbor (KNN). For the prediction of NS, J48, NB, bagging of NB and boosting of J48 overcame the accuracy of 90%. Conclusion: A radiomic ML approach applied to PTLs is able to predict TG and NS in patients with OC and OP SCC.

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Can MR textural analysis improve the prediction of extracapsular nodal spread in patients with oral cavity cancer?

Abstract: • Nodal MR textural analysis can aid in predicting extracapsular spread (ECS). • Medium filter contrast-enhanced T1 nodal entropy was strongly significant in predicting ECS. • Combining nodal entropy with irregular nodal contour improves predictive accuracy.

Cited by 17 publications

References 27 publications

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

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

Correlation between radiologic and pathologic extranodal extension in HPV‐associated oropharyngeal cancer: Systematic review

Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach

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