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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. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs). Methods: This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves. Results: Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888). Conclusion: Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors. [ABSTRACT FROM AUTHOR]

ObjectiveThis study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs).MethodsThis retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves.ResultsAmong the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888).ConclusionMultiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.

Background: Layer-stacking irradiation (LSI) results in the accumulation of multiple small spread-out Bragg peaks along the beam direction. Although the superiority of LSI to conventional passive irradiation (CPI) regarding normal tissue sparing is theoretically evident, the clinical benefit of LSI has not been demonstrated. Here, we compared LSI with CPI using the same treatment planning-computed tomography images used for carbon ion radiotherapy (CIRT).Methods: Twenty-one parotid tumors were analyzed. The clinical target volume (CTV) 1 and CTV2 encompassed the parotid grand and the tumor, respectively. CTV1 and CTV2 received 36 Gy (RBE: relative biological effectiveness) in nine fractions and 64 Gy (RBE) in 16 fractions, respectively, using either LSI or CPI. CTV coverage was assessed by DX%, which is the dose covering at least X% of the target volume. Skin dose was assessed by SX, which is the skin surface area receiving at least X Gy (RBE).Results: For CTV1 and CTV2, there were no significant differences in D2% between LSI and CPI. D50% and D98% were slightly higher for CPI; however, the absolute difference between the two methods was

Background: Layer-stacking irradiation (LSI) results in the accumulation of multiple small spread-out Bragg peaks along the beam direction. Although the superiority of LSI to conventional passive irradiation (CPI) regarding normal tissue sparing is theoretically evident, the clinical benefit of LSI has not been demonstrated. Here, we compared LSI with CPI using the same treatment planning-computed tomography images used for carbon ion radiotherapy (CIRT). Methods: Twenty-one parotid tumors were analyzed. The clinical target volume (CTV) 1 and CTV2 encompassed the parotid grand and the tumor, respectively. CTV1 and CTV2 received 36 Gy (RBE: relative biological effectiveness) in nine fractions and 64 Gy (RBE) in 16 fractions, respectively, using either LSI or CPI. CTV coverage was assessed by DX%, which is the dose covering at least X% of the target volume. Skin dose was assessed by SX, which is the skin surface area receiving at least X Gy (RBE). Results: For CTV1 and CTV2, there were no significant differences in D2% between LSI and CPI. D50% and D98% were slightly higher for CPI; however, the absolute difference between the two methods was <3%. S10–S60 (in increments of 10) were significantly lower for LSI than for CPI (P < 0.001 for all parameters). LSI was associated with a significant trend toward dose reduction at the skin area irradiated with a higher dose by CPI (P < 0.001). Conclusions: LSI achieved better skin sparing than CPI without sacrificing target volume coverage in parotid tumor patients. [ABSTRACT FROM AUTHOR]

BackgroundThe recurrence of pleomorphic adenoma (PA) has been extensively debated, mostly in relation to the extent of parotidectomy.MethodsA systematic review was undertaken to clarify the surgical and pathological variables related to PA recurrence. Inclusion criteria were as follows: English literature, and prospective or retrospective studies. Exclusion criteria were as follows: single case reports, reviews, and lack of PA recurrence data.ResultsPathology-related variables associated with recurrence include the histological subtype, the thickness and incompleteness of the tumor capsule, pseudopodia, and satellite nodules. Surgery-related variables associated with recurrence are the presence of intact margins and tumor puncture or spillage. Other factors are the size of the tumor and the age of patient. Myxoid subtypes of PA tend to have incomplete and thinner capsules and to recur more frequently. Surgical variables related to recurrence include positive margins and tumor spillage.ConclusionMyxoid and/or large PA, especially in young patients, should be approached more cautiously to avoid recurrences.