Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging

Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review

BACKGROUND

Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities.

STUDY DESIGN

A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another.

RESULTS

The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection.

CONCLUSION

Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.

MR Image Fusion-Based Parotid Gland Tumor Detection

The differentiation of benign and malignant parotid gland tumors is of major significance as it directly affects the treatment process. In addition, it is also a vital task in terms of early and accurate diagnosis of parotid gland tumors and the determination of treatment planning accordingly. As in other diseases, the differentiation of tumor types involves several challenging, time-consuming, and laborious processes. In the study, Magnetic Resonance (MR) images of 114 patients with parotid gland tumors are used for training and testing purposes by Image Fusion (IF). After the Apparent Diffusion Coefficient (ADC), Contrast-enhanced T1-w (T1C-w), and T2-w sequences are cropped, IF (ADC, T1C-w), IF (ADC, T2-w), IF (T1C-w, T2-w), and IF (ADC, T1C-w, T2-w) datasets are obtained for different combinations of these sequences using a two-dimensional Discrete Wavelet Transform (DWT)-based fusion technique. For each of these four datasets, ResNet18, GoogLeNet, and DenseNet-201 architectures are trained separately, and thus, 12 models are obtained in the study. A Graphical User Interface (GUI) application that contains the most successful of these trained architectures for each data is also designed to support the users. The designed GUI application not only allows the fusing of different sequence images but also predicts whether the label of the fused image is benign or malignant. The results show that the DenseNet-201 models for IF (ADC, T1C-w), IF (ADC, T2-w), and IF (ADC, T1C-w, T2-w) are better than the others, with accuracies of 95.45%, 95.96%, and 92.93%, respectively. It is also noted in the study that the most successful model for IF (T1C-w, T2-w) is ResNet18, and its accuracy is equal to 94.95%.

Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review

Purpose

A systematic review and meta-analysis were conducted to evaluate the diagnostic precision of radiomics in the differential diagnosis of parotid tumors, considering the increasing utilization of radiomics in tumor diagnosis. Although some researchers have attempted to apply radiomics in this context, there is ongoing debate regarding its accuracy.

Methods

Databases of PubMed, Cochrane, EMBASE, and Web of Science up to May 29, 2024 were systematically searched. The quality of included primary studies was assessed using the Radiomics Quality Score (RQS) checklist. The meta-analysis was performed utilizing a bivariate mixed-effects model.

Results

A total of 39 primary studies were incorporated. The machine learning model relying on MRI radiomics for diagnosis malignant tumors of the parotid gland, demonstrated a sensitivity of 0.80 [95% CI: 0.74, 0.86], SROC of 0.89 [95% CI: 0.27-0.99] in the validation set. The machine learning model based on MRI radiomics for diagnosis malignant tumors of the parotid gland, exhibited a sensitivity of 0.83[95% CI: 0.76, 0.88], SROC of 0.89 [95% CI: 0.17-1.00] in the validation set. The models also demonstrated high predictive accuracy for benign lesions.

Conclusion

There is great potential for radiomics-based models to improve the accuracy of diagnosing benign and malignant tumors of the parotid gland. To further enhance this potential, future studies should consider implementing standardized radiomics-based features, adopting more robust feature selection methods, and utilizing advanced model development tools. These measures can significantly improve the diagnostic accuracy of artificial intelligence algorithms in distinguishing between benign and malignant tumors of the parotid gland.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023434931.

Histogram Analysis of Apparent Diffusion Coefficient Maps Provides Genotypic and Pretreatment Phenotypic Information in Pediatric and Young Adult Rhabdomyosarcoma

INTRODUCTION

We evaluate the role of apparent diffusion coefficient (ADC) histogram metrics in stratifying pediatric and young adult rhabdomyosarcomas.

METHODS

We retrospectively evaluated baseline diffusion-weighted imaging (DWI) from 38 patients with rhabdomyosarcomas (Not otherwise specified: 2; Embryonal: 21; Spindle Cell: 2; Alveolar: 13, mean ± std dev age: 8.1 ± 7.76 years). The diffusion images were obtained on a wide range of 1.5 T and 3 T scanners at multiple sites. FOXO1 fusion status was available for 35 patients, nine of whom harbored the fusion. 13 patients were TNM stage 1, eight had stage 2 disease, nine were stage 3, and eight had stage 4 disease. 23 patients belonged to Clinical Group III and seven to Group IV, while two and five were CG I and II, respectively. Nine patients were classified as low risk, while 21 and five were classified as intermediate and high risk respectively. Histogram parameters of the apparent diffusion coefficient (ADC) map from the entire tumor were obtained based on manual tumor contouring. A two-tailed Mann-Whitney U test was used for all two-group, and the Kruskal-Wallis's test was used for multiple-group comparisons. Bootstrapped receiver operating characteristic (ROC) curves and areas under the curve (AUC) were generated for the statistically significant histogram parameters to differentiate genotypic and phenotypic parameters.

RESULTS

Alveolar rhabdomyosarcomas had a statistically significant lower 10th Percentile (586.54 ± 164.52, mean ± std dev, values are in ×10-6mm2/s) than embryonal rhabdomyosarcomas (966.51 ± 481.33) with an AUC of 0.85 (95%CI. 0.73-0.95) for differentiating the two. The 10th percentile was also significantly different between FOXO1 fusion-positive (553.87 ± 187.64) and negative (898.07 ± 449.38) rhabdomyosarcomas with an AUC of 0.83 (95% CI 0.71-0.94). Alveolar rhabdomyosarcomas also had statistically significant lower Mean, Median, and Root Mean Squared ADC histogram values than embryonal rhabdomyosarcomas. Four, five, and seven of the 18 histogram parameters evaluated demonstrated a statistically significant increase with higher TNM stage, clinical group, assignment, and pretreatment risk stratification, respectively. For example, Entropy had an AUC of 0.8 (95% CI. 0.67-0.92) for differentiating TNM stage 1 from ≥ stage 2 and 0.9 (95% CI. 0.8-0.98) for differentiating low from intermediate or high-risk stratification.

CONCLUSION

Our findings demonstrate the potential of ADC histogram metrics to predict clinically relevant variables for rhabdomyosarcoma, including FOXO1 fusion status, histopathology, Clinical Group, TNM staging, and risk stratification.

Current status of diffusion-weighted imaging in differentiating parotid tumors

Recently, diffusion-weighted imaging (DWI) is an essential magnetic resonance imaging (MRI) protocol for head and neck imaging in clinical practice as it plays an important role in lesion detection, tumor extension evaluation, differential diagnosis, therapeutic effect prediction, therapy evaluation, and recurrence diagnosis. Especially in the parotid gland, several studies have already attempted to achieve accurate differentiation between benign and malignant tumors using DWI. A conventional single-shot echo-planar-based DWI is widely used for head and neck imaging, whereas advanced DWI sequences, such as intravoxel incoherent motion, diffusion kurtosis imaging, periodically rotated overlapping parallel lines with enhanced reconstruction, and readout-segmented echo-planar imaging (readout segmentation of long variable echo-trains), have been used to characterize parotid tumors. The mean apparent diffusion coefficient values are easily measured and useful for assessing cellularity and histological characteristics, whereas advanced image analyses, such as histogram analysis, texture analysis, and machine and deep learning, have been rapidly developed. Furthermore, a combination of DWI and other MRI protocols has reportedly improved the diagnostic accuracy of parotid tumors. This review article summarizes the current state of DWI in differentiating parotid tumors.