Ultrasound features value in the diagnosis and prognosis of medullary thyroid carcinoma

Automatic detection and diagnosis of thyroid ultrasound images based on attention mechanism

Incidents of thyroid cancer have dramatically increased in recent years; however, early ultrasound diagnosis can reduce morbidity and mortality. The work in clinical situations relies heavily on the subjective experience of the sonographer. Numerous computer-aided diagnostic techniques exist, but most consider how good the results are, ignoring the pre-image collecting and its usefulness in post-clinical practise. To address these issues, this study proposes a computer-aided diagnosis method based on an attentional mechanism. Due to its lightweight properties, the model can rapidly identify nodules and distinguish between benign and malignant ones without using much hardware. The model uses a bounding box to locate the thyroid nodule and determines whether it is benign or cancerous, and outputs the diagnostic result of the thyroid nodule ultrasound images. The latest attention mechanisms are used to get better results at a fraction of the cost. Additionally, ultrasound images with different features of benign and malignant thyroid nodules were collected following the Thyroid Imaging Reporting and Data System standards. The experimental results showed that the approach identifies and classifies thyroid nodules rapidly and effectively; the mAP value of the results reached 0.89, and the mAP value of malignant nodules reached 0.94, with detection rate of single image reached 7 ms. Young physicians and small hospitals with limited resources can benefit from using this method to assist with thyroid ultrasound examination diagnosis.

Neck Surgery for Non-Well Differentiated Thyroid Malignancies: Variations in Strategy According to Histopathology

Lymph node metastases in non-well differentiated thyroid cancer (non-WDTC) are common, both in the central compartment (levels VI and VII) and in the lateral neck (Levels II to V). Nodal metastases negatively affect prognosis and should be treated to maximize locoregional control while minimizing morbidity. In non-WDTC, the rate of nodal involvement is variable and depends on the histology of the tumor. For medullary thyroid carcinomas, poorly differentiated thyroid carcinomas, and anaplastic thyroid carcinomas, the high frequency of lymph node metastases makes central compartment dissection generally necessary. In mucoepidermoid carcinomas, malignant peripheral nerve sheath tumors, sarcomas, and malignant thyroid teratomas or thyroblastomas, central compartment dissection is less often necessary, as clinical lymphnode involvement is less common. We aim to summarize the medical literature and the opinions of several experts from different parts of the world on the current philosophy for managing the neck in less common types of thyroid cancer.

Association between diagnostic efficacy of acoustic radiation force impulse for benign and malignant thyroid nodules and the presence or absence of non-papillary thyroid cancer: A meta-analysis

Purpose

The aim of this study was to investigate the diagnostic efficacy of Acoustic Radiation Force Impulse (ARFI) for benign and malignant thyroid nodules in the presence and absence of non-papillary thyroid cancer (NPTC) and to determine the cut-off values of Shear Wave Velocity (SWV) for the highest diagnostic efficacy of Virtual Touch Quantification (VTQ) and Virtual Touch Tissue Imaging and Quantification (VTIQ).

Methods

The diagnostic accuracy of ARFI for benign and malignant thyroid nodules was assessed by pooling sensitivity, specificity and area under the curve (AUC) in each group in the presence and absence of both non-papillary thyroid glands, using histology and cytology as the gold standard. All included studies were divided into two groups according to VTQ and VTIQ, and each group was ranked according to the magnitude of the SWV cutoff value to determine the SWV cutoff interval with the highest diagnostic efficacy for VTQ and VTIQ.

Results

A total of 57 studies were collected on the evaluation of ARFI for the diagnosis of benign and malignant thyroid nodules. The results showed that the presence of non-papillary thyroid carcinoma led to differences in the specificity of VTIQ for the identification of benign and malignant thyroid nodules, and the differences were statistically significant. In addition, the diagnostic efficacy of VTQ was best when the cutoff value of SWV was in the interval of 2.48-2.55 m/s, and the diagnostic efficacy of VTIQ was best when the cutoff value of SWV was in the interval of 3.01-3.15 m/s.

Conclusion

VTQ and VTIQ have a high diagnostic value for benign and malignant thyroid nodules; however, when the malignant nodules in the study contain non-papillary thyroid carcinoma occupying the thyroid gland, the findings should be viewed in a comprehensive manner.

Deep learning based on ultrasound to differentiate pathologically proven atypical and typical medullary thyroid carcinoma from follicular thyroid adenoma

OBJECTIVES

To investigate the feasibility and value of deep learning based on grayscale ultrasonography in the differentiation of pathologically proven atypical and typical medullary thyroid carcinoma (MTC) from follicular thyroid adenoma (FTA).

METHODS

The preoperative 770 ultrasound images consisted of 354 MTCs (66% were typical MTCs with a high suspicion sonographic pattern, 34% were atypical MTCs with a suspicion pattern of intermediate or less) and 416 FTAs. All images were delineated manually by a senior sonographer to achieve the regions of interest. Two deep neural networks of ResNet-34 and ResNet-18 were performed on the training set (n = 690). The test data set (n = 80) was subsequently evaluated by the two models and two sonographers, their diagnostic performances and misdiagnosis lesions were compared and analyzed.

RESULTS

The ResNet-34 model shows higher diagnostic ability than the junior sonographer with an area under the receiver operating curve of 0.992 (95% CI: 0.840-0.970)versus 0.754 (95% CI:0.645-0.843). Moreover, 12 of 16 atypical MTCs were successfully identified by the ResNet-34, which is significantly better than the senior and junior sonographer, suggesting that these patients could benefit from timely serological examination and surgical strategy at an earlier stage.

CONCLUSION

Deep learning to differentiate MTC from FTA on grayscale ultrasound may be a useful diagnostic support tool, especially in atypical MTC and FTA. Moreover, the computing time of deep learning is short, which will help to incorporate it into real-time ultrasound diagnosis.

Diagnostic Challenges of Medullary Thyroid Carcinoma

BACKGROUND

Medullary thyroid carcinoma (MTC) comprises 1-2% of all thyroid cancers, yet 15% of all thyroid cancer-related deaths. While up to 20% of cases may be predicted due to autosomal dominant germline mutations, 80% of cases are sporadic. However, due to non-specific presenting symptoms and diagnostic imaging, prompt diagnosis and treatment has remained elusive. This article will further investigate the limitations of MTC diagnosis and look into future areas for diagnostic improvement.

METHODS

Relevant articles were identified using a systematic PubMed and Google Scholar search.

RESULTS

Prophylactic total thyroidectomy for the 20% of MTC cases that are present in autosomal dominant disorder provides definitive treatment. Serum calcitonin (Ctn) screening has several technical limitations due to population variability and laboratory assay interference, but advances in laboratory technology and combined use with fine needle aspiration increase its sensitivity. Other serum assays such as carcinoembryonic antigen and procalcitonin have limited applicability. Thyroid ultrasound remains the gold standard for the initial diagnostic planning, with limited application for CT, MRI, and PET imaging.

CONCLUSION

With complete surgical resection the only definitive treatment, early MTC diagnosis has presented an elusive challenge, mainly due to its relative rarity and difficulty in finding an economic screening strategy. Careful family history combined with fine needle aspiration with serum Ctn analysis can improve diagnostic sensitivity and specificity to greater than 95%.

The Prediction of Metastases of Lateral Cervical Lymph Node in Medullary Thyroid Carcinoma

PURPOSE

Development and validation of a nomogram for the prediction of lateral lymph node metastasis (LLNM) in medullary thyroid carcinoma (MTC).

METHODS

We retrospectively reviewed the clinical features of patients with MTC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017 and in our Department of Surgical Oncology, Hangzhou First People's Hospital between 2009 and 2019. The log-rank test was used to compare the difference in the Kaplan-Meier (K-M) curves in recurrence and survival. The nomogram was developed to predict the risk of LLNM in MTC patients. The prediction efficiency of the predictive model was assessed by area under the curve (AUC) and concordance index (C-index) and calibration curves. Decision curve analysis (DCA) was performed to determine the clinic value of the predictive model.

RESULT

A total of 714 patients in the SEER database and 35 patients in our department were enrolled in our study. Patients with LLNM had worse recurrence rate and cancer-specific survival (CSS) compared with patients without LLNM. Five clinical characteristics including sex, tumor size, multifocality, extrathyroidal extension, and distant metastasis were identified to be associated with LLNM in MTC patients, which were used to develop a nomogram. Our prediction model had satisfied discrimination with a C-index of 0.825, supported by both training set and internal testing set with a C-index of 0.825, and 0.816, respectively. DCA was further made to evaluate the clinical utility of this nomogram for predicting LLNM.

CONCLUSIONS

Male sex, tumor size >38mm, multifocality, extrathyroidal extension, and distant metastasis in MTC patients were significant risk factors for predicting LLNM.