A Radiomic Nomogram for the Ultrasound-Based Evaluation of Extrathyroidal Extension in Papillary Thyroid Carcinoma

PurposeTo construct a sequence diagram based on radiological and clinical factors for the evaluation of extrathyroidal extension (ETE) in patients with papillary thyroid carcinoma (PTC).Materials and MethodsBetween January 2016 and January 2020, 161 patients with PTC who underwent preoperative ultrasound examination in the Affiliated People’s Hospital of Jiangsu University were enrolled in this retrospective study. According to the pathology results, the enrolled patients were divided into a non-ETE group and an ETE group. All patients were randomly divided into a training cohort (n = 97) and a validation cohort (n = 64). A total of 479 image features of lesion areas in ultrasonic images were extracted. The radiomic signature was developed using least absolute shrinkage and selection operator algorithms after feature selection using the minimum redundancy maximum relevance method. The radiomic nomogram model was established by multivariable logistic regression analysis based on the radiomic signature and clinical risk factors. The discrimination, calibration, and clinical usefulness of the nomogram model were evaluated in the training and validation cohorts.ResultsThe radiomic signature consisted of six radiomic features determined in ultrasound images. The radiomic nomogram included the parameters tumor location, radiological ETE diagnosis, and the radiomic signature. Area under the curve (AUC) values confirmed good discrimination of this nomogram in the training cohort [AUC, 0.837; 95% confidence interval (CI), 0.756–0.919] and the validation cohort (AUC, 0.824; 95% CI, 0.723–0.925). The decision curve analysis showed that the radiomic nomogram has good clinical application value.ConclusionThe newly developed radiomic nomogram model is a noninvasive and reliable tool with high accuracy to predict ETE in patients with PTC.

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A pilot study of radiomics signature based on biparametric MRI for preoperative prediction of extrathyroidal extension in papillary thyroid carcinoma

Journal of X-Ray Science and Technology ◽

10.3233/xst-200760 ◽

2020 ◽

pp. 1-13

Author(s):

Junlin He ◽

Heng Zhang ◽

Xian Wang ◽

Zongqiong Sun ◽

Yuxi Ge ◽

...

Keyword(s):

Feature Selection ◽

Papillary Thyroid Carcinoma ◽

Random Forest ◽

Thyroid Carcinoma ◽

Model Performance ◽

Image Features ◽

Extrathyroidal Extension ◽

Papillary Thyroid ◽

Forest Model ◽

Radiomics Signature

OBJECTIVE: To investigate efficiency of radiomics signature to preoperatively predict histological features of aggressive extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) with biparametric magnetic resonance imaging findings. MATERIALS AND METHODS: Sixty PTC patients with preoperative MR including T2WI and T2WI-fat-suppression (T2WI-FS) were retrospectively analyzed. Among them, 35 had ETE and 25 did not. Pre-contrast T2WI and T2WI-FS images depicting the largest section of tumor were selected. Tumor regions were manually segmented using ITK-SNAP software and 107 radiomics features were computed from the segmented regions using the open Pyradiomics package. Then, a random forest model was built to do classification in which the datasets were partitioned randomly 10 times to do training and testing with ratio of 1:1. Furthermore, forward greedy feature selection based on feature importance was adopted to reduce model overfitting. Classification accuracy was estimated on the test set using area under ROC curve (AUC). RESULTS: The model using T2WI-FS image features yields much higher performance than the model using T2WI features (AUC = 0.906 vs. 0.760 using 107 features). Among the top 10 important features of T2WI and T2WI-FS, there are 5 common features. After feature selection, the models trained using top 2 features of T2WI and the top 6 features of T2WI-FS achieve AUC 0.845 and 0.928, respectively. Combining features computed from T2WI and T2WI-FS, model performance decreases slightly (AUC = 0.882 based on all features and AUC = 0.913 based on top features after feature selection). Adjusting hyper parameters of the random forest model have negligible influence on the model performance with mean AUC = 0.907 for T2WI-FS images. CONCLUSIONS: Radiomics features based on pre-contrast T2WI and T2WI-FS is helpful to predict aggressive ETE in PTC. Particularly, the model trained using the optimally selected T2WI-FS image features yields the best classification performance. The most important features relate to lesion size and the texture heterogeneity of the tumor region.

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Classification of papillary thyroid carcinoma histological images based on deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210100 ◽

2021 ◽

pp. 1-11

Author(s):

Yaning Liu ◽

Lin Han ◽

Hexiang Wang ◽

Bo Yin

Keyword(s):

Neural Network ◽

Differential Diagnosis ◽

Deep Learning ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Image Features ◽

Papillary Thyroid ◽

Histological Image ◽

Histological Images

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.

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Enemy Beyond the Wall: Clinicopathological Features of Intrathyroidal Papillary Thyroid Carcinoma (T1-T3a) That Metastasize to Lymph Nodes Without Extrathyroidal Extension

Cancer Investigation ◽

10.1080/07357907.2021.1897833 ◽

2021 ◽

pp. 1-8

Author(s):

Abbas Ali Tam ◽

Nurcan Ince ◽

Husniye Baser ◽

Ayşegul Aksoy Altinboga ◽

Mehmet Kilic ◽

...

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Lymph Nodes ◽

Extrathyroidal Extension ◽

Clinicopathological Features ◽

Papillary Thyroid

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Risk of recurrence in patients with papillary thyroid carcinoma and minimal extrathyroidal extension not treated with radioiodine

Journal of Endocrinological Investigation ◽

10.1007/s40618-018-0969-y ◽

2018 ◽

Vol 42 (6) ◽

pp. 687-692 ◽

Cited By ~ 5

Author(s):

P. W. Rosario ◽

G. Mourão ◽

M. R. Calsolari

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Extrathyroidal Extension ◽

Papillary Thyroid ◽

Risk Of Recurrence ◽

Minimal Extrathyroidal Extension

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Papillary Thyroid Carcinoma Arising in Children and Adolescent Hashimoto’s Thyroiditis: Ultrasonographic and Pathologic Findings

International Journal of Endocrinology ◽

10.1155/2016/2397690 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Sun Hye Jeong ◽

Hyun Sook Hong ◽

Eun Hye Lee ◽

Jeong Ja Kwak

Keyword(s):

Lymph Node ◽

Papillary Thyroid Carcinoma ◽

Thyroid Gland ◽

Thyroid Carcinoma ◽

Lymph Node Metastases ◽

Hashimoto’S Thyroiditis ◽

Extrathyroidal Extension ◽

Hashimoto's Thyroiditis ◽

Papillary Thyroid ◽

Node Metastases

Objectives. We compared the ultrasonography and pathology features of papillary thyroid carcinoma (PTC) in pediatric and adolescents with Hashimoto’s thyroiditis (HT) with those of non-HT patients.Materials and Methods. Eleven patients who were surgically confirmed to have pediatric or adolescent PTC from 2006 to 2014 were included in this study. We retrospectively analyzed the preoperative ultrasonography and pathology features of PTC arising in HT and non-HT patients.Results. On ultrasonography, thyroid gland was lobulated and enlarged, with many scattered microcalcifications in four of five HT patients. Four of six non-HT patients had suspicious masses with calcifications. The diffuse sclerosing variant of PTC (DSVPTC) was found in three of five HT patients, but none in non-HT patients. Macroscopic or microscopic extrathyroidal extension was evident in all of the HT patients and four of the non-HT patients. Neck lymph node metastases were in all HT patients and five of non-HT patients.Conclusions. Three of five PTCs in pediatric and adolescent HT patients were DSVPTC, whereas all PTCs of the non-HT patients were classic type. On ultrasonography, thyroid gland was diffusely enlarged with scattered microcalcifications in four of five HT patients. All five HT cases had aggressive disease, including extrathyroidal extension and cervical lymph node metastases.

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Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics

Nature Communications ◽

10.1038/s41467-020-18497-3 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Jinhua Yu ◽

Yinhui Deng ◽

Tongtong Liu ◽

Jin Zhou ◽

Xiaohong Jia ◽

...

Keyword(s):

Lymph Node ◽

Papillary Thyroid Carcinoma ◽

Lymph Node Metastasis ◽

Thyroid Carcinoma ◽

Transfer Learning ◽

Area Under The Curve ◽

The Other ◽

Papillary Thyroid ◽

Node Metastasis ◽

Diagnostic Time

Abstract Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatment option selection. The purpose of this paper is to develop a transfer learning radiomics (TLR) model for preoperative prediction of LNM in PTC patients in a multicenter, cross-machine, multi-operator scenario. Here we report the TLR model produces a stable LNM prediction. In the experiments of cross-validation and independent testing of the main cohort according to diagnostic time, machine, and operator, the TLR achieves an average area under the curve (AUC) of 0.90. In the other two independent cohorts, TLR also achieves 0.93 AUC, and this performance is statistically better than the other three methods according to Delong test. Decision curve analysis also proves that the TLR model brings more benefit to PTC patients than other methods.

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