Machine Learning-Assisted Diagnostic System for Indeterminate Thyroid Nodules

Ultrasound radiomics and genomics improve the diagnosis of cytologically indeterminate thyroid nodules

Background

Increasing numbers of cytologically indeterminate thyroid nodules (ITNs) present challenges for preoperative diagnosis, often leading to unnecessary diagnostic surgical procedures for nodules that prove benign. Research in ultrasound radiomics and genomic testing leverages high-throughput data and image or sequence algorithms to establish assisted models or testing panels for ITN diagnosis. Many radiomics models now demonstrate diagnostic accuracy above 80% and sensitivity over 90%, surpassing the performance of less experienced radiologists and, in some cases, matching the accuracy of experienced radiologists. Molecular testing panels have helped clinicians achieve accurate diagnoses of ITNs, preventing unnecessary diagnostic surgical procedures in 42%-61% of patients with benign nodules.

Objective

In this review, we examined studies on ultrasound radiomics and genomic molecular testing for cytological ITNs conducted over the past 5 years, aiming to provide insights for researchers focused on improving ITN diagnosis.

Conclusion

Radiomics models and molecular testing have enhanced diagnostic accuracy before surgery and reduced unnecessary diagnostic surgical procedures for ITN patients.

Assessment of the risk of malignancy in Bethesda III thyroid nodules: a comprehensive review

The increasing prevalence of thyroid cancer emphasizes the need for a thorough assessment of risk of malignancy in Bethesda III nodules. Various methods ranging commercial platforms of molecular genetic testing (including Afirma® GEC, Afirma® GSC, ThyroSeq® V3, RosettaGX®, ThyGeNEXT®/ThyraMIR®, ThyroidPRINT®) to radionuclide scans and ultrasonography have been investigated to provide a more nuanced comprehension of risk estimation. The integration of molecular studies and imaging techniques into clinical practice may provide clinicians with improved and personalized risk assessment. This integrated approach we feel may enable clinicians to carefully tailor interventions, thereby minimizing the likelihood of unnecessary thyroid surgeries and overall crafting the optimal treatment. By aligning with the evolving landscape of personalized healthcare, this comprehensive strategy ensures a patient-centric approach to thyroid nodule and thyroid cancer management.

The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study

OBJECTIVE

The aim of this study is to develop AI-assisted software incorporating a deep learning (DL) model based on static ultrasound images. The software aims to aid physicians in distinguishing between malignant and benign thyroid nodules with echogenic foci and to investigate how the AI-assisted DL model can enhance radiologists' diagnostic performance.

METHODS

For this retrospective study, a total of 2724 ultrasound (US) scans were collected from two independent institutions, encompassing 1038 echogenic foci nodules. All echogenic foci were confirmed by pathology. Three DL segmentation models (DeepLabV3+, U-Net, and PSPNet) were developed, with each model using two different backbones to extract features from the nodular regions with echogenic foci. Evaluation indexes such as Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA), and Dice coefficients were employed to assess the performance of the segmentation model. The model demonstrating the best performance was selected to develop the AI-assisted diagnostic software, enabling radiologists to benefit from AI-assisted diagnosis. The diagnostic performance of radiologists with varying levels of seniority and beginner radiologists in assessing high-echo nodules was then compared, both with and without the use of auxiliary strategies. The area under the receiver operating characteristic curve (AUROC) was used as the primary evaluation index, both with and without the use of auxiliary strategies.

RESULTS

In the analysis of Institution 2, the DeepLabV3+ (backbone is MobileNetV2 exhibited optimal segmentation performance, with MIoU = 0.891, MPA = 0.945, and Dice = 0.919. The combined AUROC (0.693 [95% CI 0.595-0.791]) of radiology beginners using AI-assisted strategies was significantly higher than those without such strategies (0.551 [0.445-0.657]). Additionally, the combined AUROC of junior physicians employing adjuvant strategies improved from 0.674 [0.574-0.774] to 0.757 [0.666-0.848]. Similarly, the combined AUROC of senior physicians increased slightly, rising from 0.745 [0.652-0.838] to 0.813 [0.730-0.896]. With the implementation of AI-assisted strategies, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of both senior physicians and beginners in the radiology department underwent varying degrees of improvement.

CONCLUSIONS

This study demonstrates that the DL-based auxiliary diagnosis model using US static images can improve the performance of radiologists and radiology students in identifying thyroid echogenic foci.

Diagnostic efficiency among Eu-/C-/ACR-TIRADS and S-Detect for thyroid nodules: a systematic review and network meta-analysis

Background

The performance in evaluating thyroid nodules on ultrasound varies across different risk stratification systems, leading to inconsistency and uncertainty regarding diagnostic sensitivity, specificity, and accuracy.

Objective

Comparing diagnostic performance of detecting thyroid cancer among distinct ultrasound risk stratification systems proposed in the last five years.

Evidence acquisition

Systematic search was conducted on PubMed, EMBASE, and Web of Science databases to find relevant research up to December 8, 2022, whose study contents contained elucidation of diagnostic performance of any one of the above ultrasound risk stratification systems (European Thyroid Imaging Reporting and Data System[Eu-TIRADS]; American College of Radiology TIRADS [ACR TIRADS]; Chinese version of TIRADS [C-TIRADS]; Computer-aided diagnosis system based on deep learning [S-Detect]). Based on golden diagnostic standard in histopathology and cytology, single meta-analysis was performed to obtain the optimal cut-off value for each system, and then network meta-analysis was conducted on the best risk stratification category in each system.

Evidence synthesis

This network meta-analysis included 88 studies with a total of 59,304 nodules. The most accurate risk category thresholds were TR5 for Eu-TIRADS, TR5 for ACR TIRADS, TR4b and above for C-TIRADS, and possible malignancy for S-Detect. At the best thresholds, sensitivity of these systems ranged from 68% to 82% and specificity ranged from 71% to 81%. It identified the highest sensitivity for C-TIRADS TR4b and the highest specificity for ACR TIRADS TR5. However, sensitivity for ACR TIRADS TR5 was the lowest. The diagnostic odds ratio (DOR) and area under curve (AUC) were ranked first in C-TIRADS.

Conclusion

Among four ultrasound risk stratification options, this systemic review preliminarily proved that C-TIRADS possessed favorable diagnostic performance for thyroid nodules.

Systematic review registration

https://www.crd.york.ac.uk/prospero, CRD42022382818.

Digital Health for Patients Undergoing Cardiac Surgery: A Systematic Review

Digital health interventions have shown promise in improving patient outcomes and experiences in various healthcare settings. However, their effectiveness in the context of cardiac surgery remains uncertain. This systematic review aims to evaluate the existing evidence on the use of digital health interventions for patients undergoing cardiac surgery. A comprehensive search of PubMed MEDLINE, Elsevier EMBASE, Elsevier Scopus databases, and ClinicalTrials.gov was conducted to identify relevant studies published up to the present. Studies that examined the effects of digital health interventions, including mobile applications and web-based interventions, on perioperative care and patient outcomes in cardiac surgery were included. The data were extracted and synthesized to provide a comprehensive overview of the findings. The search yielded 15 studies composed of 4041 patients, analyzing the feasibility and implementation of mobile or internet applications for patients undergoing cardiac surgery. The studies included the use of mobile applications (ManageMySurgery, SeamlessMD, mHeart, Telediaglog, ExSed, Soulage Tavie, Heart Health application, and Mayo Clinic Health Connection) and web-based interventions (Heartnet and Active Heart). The findings indicated that these digital health interventions were associated with improved patient engagement, satisfaction, and reduced healthcare utilization. Patients reported finding the interventions helpful in their recovery process, and there was evidence of enhanced symptom monitoring and timely intervention. The completion rates of modules varied depending on the phase of care, with higher engagement observed during the acute phase. Interest in using digital health applications was expressed by patients, regardless of age, gender, or complexity of the cardiac defect. The results demonstrated that web-based interventions resulted in improvements in mental health, quality of life, and eHealth literacy. This systematic review highlights the potential benefits of digital health interventions in the context of cardiac surgery. Further research, including randomized controlled trials, is needed to establish the effectiveness, feasibility, and generalizability of digital health interventions in cardiac surgery.

Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions

Background: The use of artificial intelligence (AI) in health care has grown exponentially with the promise of facilitating biomedical research and enhancing diagnosis, treatment, monitoring, disease prevention, and health care delivery. We aim to examine the current state, limitations, and future directions of AI in thyroidology. Summary: AI has been explored in thyroidology since the 1990s, and currently, there is an increasing interest in applying AI to improve the care of patients with thyroid nodules (TNODs), thyroid cancer, and functional or autoimmune thyroid disease. These applications aim to automate processes, improve the accuracy and consistency of diagnosis, personalize treatment, decrease the burden for health care professionals, improve access to specialized care in areas lacking expertise, deepen the understanding of subtle pathophysiologic patterns, and accelerate the learning curve of less experienced clinicians. There are promising results for many of these applications. Yet, most are in the validation or early clinical evaluation stages. Only a few are currently adopted for risk stratification of TNODs by ultrasound and determination of the malignant nature of indeterminate TNODs by molecular testing. Challenges of the currently available AI applications include the lack of prospective and multicenter validations and utility studies, small and low diversity of training data sets, differences in data sources, lack of explainability, unclear clinical impact, inadequate stakeholder engagement, and inability to use outside of the research setting, which might limit the value of their future adoption. Conclusions: AI has the potential to improve many aspects of thyroidology; however, addressing the limitations affecting the suitability of AI interventions in thyroidology is a prerequisite to ensure that AI provides added value for patients with thyroid disease.