Differential diagnosis of gallbladder neoplastic polyps and cholesterol polyps with radiomics of dual modal ultrasound: a pilot study

A Comparative Study of Two Radiomics-Based Blood Flow Modes with Thyroid Imaging Reporting and Data System in Predicting Malignancy of Thyroid Nodules and Reducing Unnecessary Fine-Needle Aspiration Rate

RATIONALE AND OBJECTIVES

We aimed to compare superb microvascular imaging (SMI)-based radiomics methods, and contrast-enhanced ultrasound (CEUS)-based radiomics methods to the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for classifying thyroid nodules (TNs) and reducing unnecessary fine-needle aspiration biopsy (FNAB) rate.

MATERIALS AND METHODS

This retrospective study enrolled a dataset of 472 pathologically confirmed TNs. Radiomics characteristics were extracted from B-mode ultrasound (BMUS), SMI, and CEUS images, respectively. After eliminating redundant features, four radiomics scores (Rad-scores) were constructed. Using multivariable logistic regression analysis, four radiomics prediction models incorporating Rad-score and corresponding US features were constructed and validated in terms of discrimination, calibration, decision curve analysis, and unnecessary FNAB rate.

RESULTS

The diagnostic performance of the BMUS + SMI radiomics method was better than ACR TI-RADS (area under the curve [AUC]: 0.875 vs. 0.689 for the training cohort, 0.879 vs. 0.728 for the validation cohort) (P < 0.05), and comparable with BMUS + CEUS radiomics method (AUC: 0.875 vs. 0.878 for the training cohort, 0.879 vs. 0.865 for the validation cohort) (P > 0.05). Decision curve analysis showed that the BMUS+SMI radiomics method could achieve higher net benefits than the BMUS radiomics method and ACR TI-RADS when the threshold probability was between 0.13 and 0.88 in the entire cohort. When applying the BMUS+SMI radiomics method, the unnecessary FNAB rate reduced from 43.4% to 13.9% in the training cohort and from 45.6% to 18.0% in the validation cohorts in comparison to ACR TI-RADS.

CONCLUSION

The dual-modal SMI-based radiomics method is convenient and economical and can be an alternative to the dual-modal CEUS-based radiomics method in helping radiologists select the optimal clinical strategy for TN management.

Discrimination between benign and malignant gallbladder lesions on enhanced CT imaging using radiomics

BACKGROUND

Gallbladder cancer is a rare but aggressive malignancy that is often diagnosed at an advanced stage and is associated with poor outcomes.

PURPOSE

To develop a radiomics model to discriminate between benign and malignant gallbladder lesions using enhanced computed tomography (CT) imaging.

MATERIAL AND METHODS

All patients had a preoperative contrast-enhanced CT scan, which was independently analyzed by two radiologists. Regions of interest were manually delineated on portal venous phase images, and radiomics features were extracted. Feature selection was performed using mRMR and LASSO methods. The patients were randomly divided into training and test groups at a ratio of 7:3. Clinical and radiomics parameters were identified in the training group, three models were constructed, and the models' prediction accuracy and ability were evaluated using AUC and calibration curves.

RESULTS

In the training group, the AUCs of the clinical model and radiomics model were 0.914 and 0.968, and that of the nomogram model was 0.980, respectively. There were statistically significant differences in diagnostic accuracy between nomograms and radiomics features (P <0.05). There was no significant difference in diagnostic accuracy between the nomograms and clinical features (P >0.05) or between the clinical features and radiomics features (P >0.05). In the testing group, the AUC of the clinical model and radiomics model were 0.904 and 0.941, and that of the nomogram model was 0.948, respectively. There was no significant difference in diagnostic accuracy between the three groups (P >0.05).

CONCLUSION

It was suggested that radiomics analysis using enhanced CT imaging can effectively discriminate between benign and malignant gallbladder lesions.

Preliminary study on diagnosis of gallbladder neoplastic polyps based on contrast-enhanced ultrasound and grey scale ultrasound radiomics

Objective

Neoplastic gallbladder polyps (GPs), including adenomas and adenocarcinomas, are considered absolute indications for surgery; however, the distinction of neoplastic from non-neoplastic GPs on imaging is often challenging. This study thereby aimed to develop a CEUS radiomics nomogram, and evaluate the role of a combined grey-scale ultrasound and CEUS model for the prediction and diagnosis of neoplastic GPs.

Methods

Patients with GPs of ≥ 1 cm who underwent CEUS between January 2017 and May 2022 were retrospectively enrolled. Grey-scale ultrasound and arterial phase CEUS images of the largest section of the GPs were used for radiomics feature extraction. Features with good reproducibility in terms of intraclass correlation coefficient were selected. Grey-scale ultrasound and CEUS Rad-score models were first constructed using the Mann-Whitney U and LASSO regression test, and were subsequently included in the multivariable logistic regression analysis as independent factors for construction of the combined model.

Results

A total of 229 patients were included in our study. Among them, 118 cholesterol polyps, 68 adenomas, 33 adenocarcinomas, 6 adenomyomatoses, and 4 inflammatory polyps were recorded. A total of 851 features were extracted from each patient. Following screening, 21 and 15 features were retained in the grey-scale and CEUS models, respectively. The combined model demonstrated AUCs of 0.88 (95% CI: 0.83 - 0.93) and 0.84 (95% CI: 0.74 - 0.93) in the training and testing set, respectively. When applied to the whole dataset, the combined model detected 111 of the 128 non-neoplastic GPs, decreasing the resection rate of non-neoplastic GPs to 13.3%.

Conclusion

Our proposed combined model based on grey-scale ultrasound and CEUS radiomics features carries the potential as a non-invasive, radiation-free, and reproducible tool for the prediction and identification of neoplastic GPs. Our model may not only guide the treatment selection for GPs, but may also reduce the surgical burden of such patients.

Advances in the management of gallbladder polyps: establishment of predictive models and the rise of gallbladder-preserving polypectomy procedures

Gallbladder polyps are a common biliary tract disease whose treatment options have yet to be fully established. The indication of "polyps ≥ 10 mm in diameter" for cholecystectomy increases the possibility of gallbladder excision due to benign polyps. Compared to enumeration of risk factors in clinical guidelines, predictive models based on statistical methods and artificial intelligence provide a more intuitive representation of the malignancy degree of gallbladder polyps. Minimally invasive gallbladder-preserving polypectomy procedures, as a combination of checking and therapeutic approaches that allow for eradication of lesions and preservation of a functional gallbladder at the same time, have been shown to maximize the benefits to patients with benign polyps. Despite the reported good outcomes of predictive models and gallbladder-preserving polypectomy procedures, the studies were associated with various limitations, including small sample sizes, insufficient data types, and unknown long-term efficacy, thereby enhancing the need for multicenter and large-scale clinical studies. In conclusion, the emergence of predictive models and minimally invasive gallbladder-preserving polypectomy procedures has signaled an ever increasing attention to the role of the gallbladder and clinical management of gallbladder polyps.

Application and prospects of AI-based radiomics in ultrasound diagnosis

Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.