scholarly journals Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ran Wei ◽  
Hao Wang ◽  
Lanyun Wang ◽  
Wenjuan Hu ◽  
Xilin Sun ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Papillary Thyroid Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multiparametric Mri ◽  
Extrathyroidal Extension ◽  
Papillary Thyroid ◽  
Predictive Capability ◽  
Contrast Enhanced ◽  
Selection Operator

Abstract Background To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically. Methods The present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model’s performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic curve. Results Totally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively. Conclusions Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.

scholarly journals Radiomics Based on Multiparametric MRI for Extrathyroidal Extension Feature Prediction in Papillary Thyroid Cancer

2020 ◽  
Author(s):  
Ran Wei ◽  
Hao Wang ◽  
Lanyun Wang ◽  
Wenjuan Hu ◽  
Xilin Sun ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Papillary Thyroid Cancer ◽  
Operating Characteristic ◽  
Thyroid Nodules ◽  
Multiparametric Mri ◽  
Extrathyroidal Extension ◽  
Papillary Thyroid ◽  
Predictive Capability ◽  
Contrast Enhanced ◽  
Selection Operator

Abstract Purpose: To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically.Methods: The present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator (LASSO) were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model’s performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic (ROC) curve. Results: Totally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively.Conclusions: Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.

scholarly journals Extrathyroidal extension predicts negative clinical outcomes in papillary thyroid cancer

Surgery ◽  
2021 ◽  
Vol 169 (1) ◽  
pp. 2-6
Author(s):  
Michael D. Bortz ◽  
Kristine Kuchta ◽  
David J. Winchester ◽  
Richard A. Prinz ◽  
Tricia A. Moo-Young
Keyword(s):  
Thyroid Cancer ◽  
Clinical Outcomes ◽  
Papillary Thyroid Cancer ◽  
Extrathyroidal Extension ◽  
Papillary Thyroid

scholarly journals Radiomic Analysis of Contrast-Enhanced Mammography With Different Image Types: Classification of Breast Lesions

2021 ◽  
Vol 11 ◽  
Author(s):  
Simin Wang ◽  
Ning Mao ◽  
Shaofeng Duan ◽  
Qin Li ◽  
Ruimin Li ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
High Energy ◽  
Breast Lesions ◽  
Wilcoxon Rank Sum Test ◽  
Contrast Enhanced ◽  
Malignant Breast ◽  
The Mean ◽  
Selection Operator

Objective: A limited number of studies have focused on the radiomic analysis of contrast-enhanced mammography (CEM). We aimed to construct several radiomics-based models of CEM for classifying benign and malignant breast lesions.Materials and Methods: The retrospective, double-center study included women who underwent CEM between November 2013 and February 2020. Radiomic analysis was performed using high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images from CEM. Datasets were randomly divided into the training and testing sets at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) method and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the radiomic features and construct the best classification models. The performances of the models were assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). Leave-group-out cross-validation (LGOCV) for 100 rounds was performed to obtain the mean AUCs, which were compared by the Wilcoxon rank-sum test and the Kruskal–Wallis rank-sum test.Results: A total of 192 women with 226 breast lesions (101 benign; 125 malignant) were enrolled. The median age was 48 years (range, 22–70 years). For the classification of breast lesions, the AUCs of the best models were 0.931 (95% CI: 0.873–0.989) for HE, 0.897 (95% CI: 0.807–0.981) for LE, 0.882 (95% CI: 0.825–0.987) for DES images and 0.960 (95% CI: 0.910–0.998) for all of the CEM images in the testing set. According to LGOCV, the models constructed with the HE images and all of the CEM images showed the highest mean AUCs for the training (0.931 and 0.938, respectively; P < 0.05 for both) and testing sets (0.892 and 0.889, respectively; P = 0.55 for both), which were significantly higher than those of the two models constructed with the LE and DES images in the training (0.912 and 0.899, respectively; all P < 0.05) and testing sets (0.866 and 0.862, respectively; all P < 0.05).Conclusions: Radiomic analysis of CEM images was valuable for classifying benign and malignant breast lesions. The use of HE images or all three types of CEM images can achieve the best performance.

Intraoperative Frozen Section for the Evaluation of Extrathyroidal Extension in Papillary Thyroid Cancer

2014 ◽  
Vol 39 (1) ◽  
pp. 187-193 ◽  
Author(s):  
Young Min Park ◽  
Soo-Geun Wang ◽  
Jin Young Goh ◽  
Dong Hoon Shin ◽  
In-Ju Kim ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Papillary Thyroid Cancer ◽  
Frozen Section ◽  
Extrathyroidal Extension ◽  
Papillary Thyroid ◽  
Intraoperative Frozen Section

scholarly journals Ultrasonic Intelligent Diagnosis of Papillary Thyroid Carcinoma Based on Machine Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Heng Zhou ◽  
Bin Liu ◽  
Yang Liu ◽  
Qunan Huang ◽  
Wei Yan
Keyword(s):  
Machine Learning ◽  
Thyroid Cancer ◽  
Papillary Thyroid Cancer ◽  
Clinical Experience ◽  
Thyroid Diseases ◽  
Paper Machine ◽  
Contrast Enhanced Ultrasound ◽  
Papillary Thyroid ◽  
Intelligent Diagnosis ◽  
Contrast Enhanced

Thyroid diseases are divided into papillary carcinoma and nodular diseases, which are very harmful to the human body. Ultrasound is a common diagnostic method for thyroid diseases. In the process of diagnosis, doctors need to observe the characteristics of ultrasound images, combined with professional knowledge and clinical experience, to give the disease situation of patients. However, different doctors have different clinical experience and professional backgrounds, and the diagnosis results lack objectivity and consistency, so an intelligent diagnosis technology for thyroid diseases based on the ultrasound image is needed in clinic, which can give objective and reliable diagnosis opinions on thyroid diseases by extracting the texture, shape, and other information of the image and assist doctors in clinical diagnosis. This paper mainly studies the intelligent ultrasonic diagnosis of papillary thyroid cancer based on machine learning, compares the ultrasonic characteristics of PTMC diagnosed by using the new ultrasound technology (CEUS and UE), and summarizes the differential diagnosis effect and clinical application value of the two technology methods for PTMC. In this paper, machine learning, diffuse thyroid image features, and RBM learning methods are used to study the ultrasonic intelligent diagnosis of papillary thyroid cancer based on machine learning. At the same time, the new contrast-enhanced ultrasound (CEUS) technology and ultrasound elastography (UE) technology are used to obtain the experimental phenomena in the experiment of ultrasonic intelligent diagnosis of papillary thyroid cancer. The results showed that 90% of the cases were diagnosed by contrast-enhanced ultrasound and confirmed by postoperative pathology. CEUS and UE have reliable practical value in the diagnosis of PTMC, and the combined application of CEUS and UE can improve the sensitivity and accuracy of PTMC diagnosis.

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