Prognostic Value of Cervical Nodal Necrosis Observed in Preoperative CT and MRI of Patients With Tongue Squamous Cell Carcinoma and Cervical Node Metastases: A Retrospective Study

Baik, Sang‐Ho; Seo, Ji Won; Kim, Jun Hwee; Lee, Seung Koo; Choi, Eun Chang; Kim, Jinna

doi:10.2214/ajr.18.20405

Cited by 16 publications

(17 citation statements)

References 25 publications

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“…Baik et al and Lan et al had highlighted the prognostic value of cervical node necrosis in CT and MRI studies [16,21]. In our analysis, the survival outcomes in the CNN group were critically poorer than those in the non-CNN group and similar to those of patients with higher N stage disease without CNN.…”

Section: Discussionsupporting

confidence: 45%

Significance of cervical node necrosis in preoperative MRI as a prognostic indicator: retrospective study of patients with SCC of tongue

Dhara

Hoda

Rajini

et al. 2021

J Oral Med Oral Surg

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Aim: To ascertain the prognostic value of cervical nodal necrosis (CNN) observed in patients of tongue squamous cell carcinoma with magnetic resonance imaging. Materials and methods: In this retrospective observational study, records of 144 patients diagnosed with newly diagnosed SCC of tongue were considered. Preoperative MRI study, demographic and clinical data were reviewed. Based on MRI reports, patients were categorised into: with or without the presence of cervical node necrosis (CNN or non CNN). Subsequent treatments, histopathological reports and follow up data were studied to determine key prognostic elements, overall survival and disease free survival by statistical analysis. Results: The incidence of CNN was 55.55% in the study sample. CNN category, depth of invasion, N stage and extra nodal extension were significant negative prognostic factors for overall and disease free survival. Conclusion: Based on our results, pre operative MRI based presence of cervical node necrosis in tongue squamous cell carcinoma is an independent prognostic indicator for poor overall and disease free survival. Long term prospective studies with larger cohorts could be undertaken to establish its role as an important biomarker for precision treatments.

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Section: Discussionsupporting

confidence: 45%

Significance of cervical node necrosis in preoperative MRI as a prognostic indicator: retrospective study of patients with SCC of tongue

Dhara

Hoda

Rajini

et al. 2021

J Oral Med Oral Surg

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“…This finding was similar to several previous studies, which have shown that the large cervical SCCs tended to generate central necrosis in either the primary tumors or metastatic lymph nodes. [ 18 , 19 ]…”

Section: Discussionmentioning

confidence: 99%

Morphologic CT and MRI features of primary parotid squamous cell carcinoma and its predictive factors for differential diagnosis with mucoepidermoid carcinoma

Ban

et al. 2022

Insights Imaging

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Background Primary parotid squamous cell carcinoma (SCC) is a rare entity with a poor prognosis. Pathologically, the diagnosis of it requires the exclusion of parotid mucoepidermoid carcinoma (MEC). Currently, the imaging features of primary parotid SCC and the predictive indicators for differential diagnosis of the two entities have not been well reported. Our purpose was to identify the imaging characteristics of primary parotid SCC and to determine the predictive factors for its’ differential diagnosis. Results Thirty-one participants with primary parotid SCC and 59 with primary parotid MEC were enrolled. Clinical, CT and MRI features were reviewed and compared by univariate analysis. Then, multinomial logistic regression was used to determine the predictors to distinguish parotid SCC from MEC. Most primary parotid SCCs exhibited irregular shape, ill-defined margin, incomplete or no capsule, heterogeneous and marked or moderate enhancement, necrosis, local tumor invasiveness (LTI). Age, maximal dimension, shape, degree of enhancement, gradual enhancement, necrosis, and LTI were different between the primary parotid SCCs and MECs in univariate analysis (p < 0.05). While in multinomial logistic regression analysis, only age and necrosis were the independent predictors for distinguishing parotid SCC from MEC, and this model exhibited an area under curve of 0.914 in ROC curve analysis. Conclusions Primary parotid SCC has some distinct imaging features including the large tumor size, irregular shape, ill-defined margin, and particularly the marked central necrosis. Patients with age ≥ 51.5 years and necrosis on the image of the primary tumor in the parotid gland could be more likely to be SCCs than MECs.

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“…e RNN is one of the common deep learning algorithms. It is a type of algorithm that takes sequence data as input and recursively in the direction of sequence evolution, its nodes are connected in a chain, and it is featured with memory, parameter sharing, and Turing completeness [17,18]. It has great advantages when learning the nonlinear features of the sequence.…”

Section: Discussionmentioning

confidence: 99%

Ultrasound Elastography under Deep Learning Algorithm to Analyze the Therapeutic Effect of Clustered Regularly Interspaced Short Palindromic Repeats Short Hairpin Ribonucleic Acid Nanoparticles on Cervical Cancer

Zhao

2021

Journal of Healthcare Engineering

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This study aimed to analyze the effect of the deep learning algorithm on ultrasound elastography on the treatment of cervical cancer with clustered regularly interspaced short palindromic repeats (CRISPR) short hairpin ribonucleic acid (shRNA) nanoparticles, aiming to provide a reference for the clinical application of deep learning to analyze the therapeutic effect of the disease. In this study, CRISPR and shRNA plasmid nanoparticle drugs were used to treat 55 patients with cervical cancer in the experimental group, and normal saline was injected to another 53 patients in the control group, so compare the effect of nanoparticles in the treatment of cervical cancer. Professional doctors and the recurrent neural network (RNN) intelligent algorithm were used to score cervical cancer based on the ultrasound elastograph images by taking blue, green, and red (BGR) as diagnosis criteria. As a result, the experimental group had a total of 217 points before drug administration and a total of 224 points after drug administration. Each patient had an average increase of 0.13 points. The control group had a total of 200 points before drug administration and a total of 223 points after drug administration, and each patient had an average increase of 0.43 points. The experimental group was obviously different from the control group ( P < 0.05 ). Each tissue image output by the RNN was clearer than the original image, and the score given by intelligent calculation was faster than that of professional doctors. The monitoring effect of the deep learning RNN intelligent algorithm on the therapeutic effect of nanomedicine was analyzed. It was found that the average accuracy of the experimental group and the control group was 98.95% and 90.34%, respectively; and the experimental group was greatly different from the control group ( P < 0.05 ). In short, nano-CRISPR and shRNA drugs had remarkable effects on the treatment of cervical cancer, and the scores given by the deep learning intelligent algorithm were faster and more accurate, which provided theoretical guidance for the clinical application of deep learning algorithms to analyze the treatment effects of diseases.

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Prognostic Value of Cervical Nodal Necrosis Observed in Preoperative CT and MRI of Patients With Tongue Squamous Cell Carcinoma and Cervical Node Metastases: A Retrospective Study

Cited by 16 publications

References 25 publications

Significance of cervical node necrosis in preoperative MRI as a prognostic indicator: retrospective study of patients with SCC of tongue

Significance of cervical node necrosis in preoperative MRI as a prognostic indicator: retrospective study of patients with SCC of tongue

Morphologic CT and MRI features of primary parotid squamous cell carcinoma and its predictive factors for differential diagnosis with mucoepidermoid carcinoma

Ultrasound Elastography under Deep Learning Algorithm to Analyze the Therapeutic Effect of Clustered Regularly Interspaced Short Palindromic Repeats Short Hairpin Ribonucleic Acid Nanoparticles on Cervical Cancer

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