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.

scholarly journals Blood-Rich Enhancement in Ultrasonography Predicts Worse Prognosis in Patients With Papillary Thyroid Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Luying Gao ◽  
Xuehua Xi ◽  
Qiong Gao ◽  
Jiajia Tang ◽  
Xiao Yang ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Papillary Thyroid Cancer ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Papillary Thyroid ◽  
Predictive Values ◽  
Incomplete Response ◽  
Potential Association ◽  
Contrast Enhanced ◽  
Patient Prognosis

Contrast-enhanced ultrasound (CEUS) can be used to evaluate microcirculation in cancers, which in turn is associated with the biologic features and ultimately patient prognosis. We conducted a retrospective analysis to examine potential association between CEUS parameters and prognosis in patients with papillary thyroid cancer (PTC). The analysis included 306 patients who underwent CEUS prior to thyroidectomy at our center during a period from 2012 to 2019. Subjects with excellent response (ER) were compared to the non-ER group (including indeterminate response, biochemical incomplete response and structural incomplete response). During the median follow-up of 34 months, ER was observed in 195 (63.7%) subjects. The remaining 111 (36.3%) patients developed non-ER events, with distant metastasis in five (1.6%) cases. In a multivariate COX regression, non-ER event was associated with the male sex (OR = 1.83, 95%CI: 1.21–2.76) and blood-rich enhancement in CEUS (OR = 1.69, 95%CI: 1.04–2.75). Based on this finding, we developed a predictive model: high risk for developing non-ER events was defined as having both risk factors; low risk was defined as having none or only one risk. In receiver operating characteristic (ROC) analysis, the area under the curve was 0.59 (95%CI: 0.52–0.66). The sensitivity and specificity were 17.1 and 95.4%, respectively. The positive and negative predictive values were 67.9 and 66.9%, respectively. In conclusion, blood-rich enhancement in CEUS is associated with non-ER events after thyroidectomy in patients with PTC.

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 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.

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