Ultrasound Guided Thyroid Biopsy

The use of telecytology for the evaluation of thyroid nodules fine-needle aspiration biopsy specimens: a systematic review

PURPOSE

Fine needle aspiration biopsy (FNAB) is currently the gold standard for diagnosis and treatment of thyroid nodules, but the growing need for anatomic pathology services in small communities is becoming a challenge. Telecytology (TC) is defined as the electronic transmission of cytological digital images, and allows for the collection of samples, primary diagnosis, and other applications without the physical presence of a pathologist. Our aim is to systematically report, summarize, and critically analyze the most up to date applications of TC to thyroid nodules FNAB evaluation.

METHODS

We performed a systematic literature review by searching PubMed, Embase, and Cochrane Library databases. Only studies published in peer-reviewed scientific journals were included. Data were extracted using the PICO framework and critically analyzed. PRISMA guidelines were applied, and the risk of bias in the included studies was assessed using the ROBINS-I tools. The methodological quality was assessed following GRADE criteria.

RESULTS

We included 13 observational studies, resulting in a total of 3856 evaluated FNAB specimens. The majority of studies (63.6%) showed an excellent concordance rate of diagnosis via TC and conventional cytology. TC can be used to perform preliminary assessment of samples with a concordance rate ranging from 74 and 100%, showing a significant reduction of the non-diagnostic rate. Image quality was referred to as perfect or nearly perfect in most cases, regardless of telecytology technique.

CONCLUSION

Telecytology could be a valuable implementation for thyroid FNAB evaluation both for primary diagnosis and preliminary assessment of samples.

The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets

BACKGROUND AND AIMS

It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid nodule imaging diagnosis in both internal and external test sets.

METHODS

Until the end of December 2022, PubMed, IEEE, Embase, Web of Science, and the Cochrane Library were searched. We included primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. This systematic review was registered on PROSPERO (CRD42022362892).

RESULTS

We evaluated evidence from 17 primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. Fourteen studies were deemed eligible for meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) of these DL algorithms were 0.89 (95% confidence interval 0.87-0.90), 0.84 (0.82-0.86), and 0.93 (0.91-0.95), respectively. For the internal validation set, the pooled sensitivity, specificity, and AUC were 0.91 (0.89-0.93), 0.88 (0.85-0.91), and 0.96 (0.93-0.97), respectively. In the external validation set, the pooled sensitivity, specificity, and AUC were 0.87 (0.85-0.89), 0.81 (0.77-0.83), and 0.91 (0.88-0.93), respectively. Notably, in subgroup analyses, DL algorithms still demonstrated exceptional diagnostic validity.

CONCLUSIONS

Current evidence suggests DL-based imaging shows diagnostic performances comparable to clinicians for differentiating thyroid nodules in both the internal and external test sets.

Automatic segmentation of thyroid with the assistance of the devised boundary improvement based on multicomponent small dataset

Deep learning has been widely considered in medical image segmentation. However, the difficulty of acquiring medical images and labels can affect the accuracy of the segmentation results for deep learning methods. In this paper, an automatic segmentation method is proposed by devising a multicomponent neighborhood extreme learning machine to improve the boundary attention region of the preliminary segmentation results. The neighborhood features are acquired by training U-Nets with the multicomponent small dataset, which consists of original thyroid ultrasound images, Sobel edge images and superpixel images. Afterward, the neighborhood features are selected by min-redundancy and max-relevance filter in the designed extreme learning machine, and the selected features are used to train the extreme learning machine to obtain supplementary segmentation results. Finally, the accuracy of the segmentation results is improved by adjusting the boundary attention region of the preliminary segmentation results with the supplementary segmentation results. This method combines the advantages of deep learning and traditional machine learning, boosting the accuracy of thyroid segmentation accuracy with a small dataset in a multigroup test.

Comparison of fine-needle aspiration and core-needle biopsy in the pathological evaluation on thyroid lesions: A single-center experience on 407 Chinese patients

BACKGROUND

The object of this study was to compare the diagnosis performance of US-guided fine-needle aspiration (FNA) and core-needle biopsy (CNB) for patients with thyroid nodules, in aspects of sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV).

MATERIALS AND METHODS

Four hundred seven Chinese patients from July 2019 to June 2022 were retrospectively recruited in this study. Cytological diagnoses were categorized into six categories based on the 2017 Bethesda System for Reporting Thyroid Cytopathology (BSRTC), and histological specimens were drawn a diagnosis by means of an analogy with the Bethesda system.

RESULTS

All patients incorporated were proceed with surgical excision (SE) and received final surgical diagnoses. The rate of malignancy (ROM) was comparable between two methods, with the exception that the test-negative category of the FNA group was significantly higher than that of the CNB group (39.25% vs. 23.86%, p = .022). Sensitivity and accuracy were higher in CNB group (82.50% and 87.83%, respectively) than that in FNA group (72.00% and 79.36%, respectively), as well as NPV (76.14% in CNB vs. 60.75% in FNA), but not in terms of the specificity and PPV (95.59% and 97.30% in FNA vs. 97.10% and 98.02% in CNB).

CONCLUSION

CNB displayed a higher sensitivity and accuracy than FNA in malignant lesions of thyroid. Both FNA and CNB exhibit excellent performance with the understanding that both need to be applied under the most appropriate conditions to maximize their benefits.

Image-guided intratumoral immunotherapy: Developing a clinically practical technology

Immunotherapy has revolutionized the contemporary oncology landscape, with durable responses possible across a range of cancer types. However, the majority of cancer patients do not respond to immunotherapy due to numerous immunosuppressive barriers. Efforts to overcome these barriers and increase systemic immunotherapy efficacy have sparked interest in the local intratumoral delivery of immune stimulants to activate the local immune response and subsequently drive systemic tumor immunity. While clinical evaluation of many therapeutic candidates is ongoing, development is hindered by a lack of imaging confirmation of local delivery, insufficient intratumoral drug distribution, and a need for repeated injections. The use of polymeric drug delivery systems, which have been widely used as platforms for both image guidance and controlled drug release, holds promise for delivery of intratumoral immunoadjuvants and the development of an in situ cancer vaccine for patients with metastatic cancer. In this review, we explore the current state of the field for intratumoral delivery and methods for optimizing controlled drug release, as well as practical considerations for drug delivery design to be optimized for clinical image guided delivery particularly by CT and ultrasound.