Correlation between computed tomography imaging and histopathology in pleomorphic adenoma of parotid gland

Predicting High-risk Capsular Features in Pleomorphic Adenoma of the Parotid Gland Through a Nomogram Model Based on ADC Imaging

RATIONALE AND OBJECTIVES

Based on Apparent Diffusion Coefficient (ADC) images, a nomogram model is established to accurately predict the high-risk capsular characteristics associated with pleomorphic adenoma of the parotid gland (PAP) recurrence.

MATERIALS AND METHODS

This retrospective study analyzed 190 patients with PAPs. Significant clinical radiological factors were identified through univariate difference analysis and multivariate regression analysis. The optimal threshold was determined by analyzing the average ADC value of the entire tumor, using the best Youden index and sensitivity analysis, and tumor subregions were delineated accordingly. Three radiomic models were constructed for the whole tumor and for high/low ADC areas, with the best model determined through statistical analysis. Ultimately, a nomogram model was constructed by combining the independent predictive factor of high-risk capsular features with the optimal radiomic predictive score. Model performance was comprehensively assessed by the area under the receiver operating characteristic curve (ROC AUC), accuracy, sensitivity, and specificity.

RESULTS

The best ADC division threshold as 1.25 × 10-3 mm2/s. Multivariate analysis identified High-ADC Zone Volume Percentage as an independent predictor for PAPs with high-risk capsular characteristics. The radiomic model based on the low ADC tumor subregion was optimal (AUC 0.899). The nomogram model, combining independent predictors and optimal imaging studies predictive score, demonstrated high performance (AUC 0.909). Decision curve analysis confirmed the nomogram's clinical applicability.

CONCLUSION

The nomogram model constructed from ADC quantitative imaging can predict PAPs patients with high-risk capsular features. These patients require intraoperative preventive measures to avoid tumor spillage and residuals, as well as extended postoperative follow-up.

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast-enhanced CT imaging: A multicenter study

OBJECTIVES

To investigate the added value of extracellular volume fraction (ECV) and arterial enhancement fraction (AEF) derived from enhanced CT to conventional image and clinical features for differentiating between pleomorphic adenoma (PA) and atypical parotid adenocarcinoma (PCA) pre-operation.

METHODS

From January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT-derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong's test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application.

RESULTS

Age and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145).

CONCLUSIONS

ECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.

Enhanced CT combined with texture analysis for differential diagnosis of pleomorphic adenoma and adenolymphoma

OBJECTIVE

This study sought to evaluate the worth of the general characteristics of enhanced CT images and the histogram parameters of each stage in distinguishing pleomorphic adenoma (PA) and adenolymphoma (AL).

METHODS

The imaging features and histogram parameters of preoperative enhanced CT images in 20 patients with PA and 29 patients with AL were analyzed. Tumor morphology and histogram parameters of PA and AL were compared. Area under the curve (AUC), sensitivity, and subject operational feature specificity (ROC) analysis were used to determine the differential diagnostic effect of single-stage or multi-stage parameter combinations.

RESULTS

The difference in CT value and net enhancement value of arterial phase (AP) were significant (p < 0.05); Flat sweep phase (FSP), AP mean, percentiles, 10th, 50th, 90th, 99th and arterial period variance and venous phase (VP) kurtosis in the nine histogram parameters of each period (p < 0.05). An analysis of the ROC curve revealed a maximum area beneath the curve (AUC) in the 90th percentile of FSP for a single-parameter differential diagnosis to be 0.870. The diagnostic efficacy of the mean value of FSP + The 90th percentile of AP + Kurtosis of VP was the best in multi-parameter combination diagnosis, with an AUC of 0.925, and the sensitivity and specificity of 0.900 and 0.850, respectively.

CONCLUSION

The histogram analysis of enhanced CT images is valuable for the differentiation of PA and AL. Moreover, the combination of single-stage parameters or multi-stage parameters can improve the differential diagnosis efficiency.

CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study

OBJECTIVE

Capsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential diagnosis between parotid PA with and without complete capsule.

METHODS

Data of 260 patients (166 patients with PA from institution 1 (training set) and 94 patients (test set) from institution 2) were retrospectively analyzed. Three Volume of interest (VOIs) were defined in the CT images of each patient: tumor volume of interest (VOI_tumor), VOI_peritumor, and VOI_{intra-plus peritumor}. Radiomics features were extracted from each VOI and used to train nine different machine learning algorithms. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC).

RESULTS

The results showed that the radiomics models based on features from VOI_{intra-plus peritumor} achieved higher AUCs compared to models based on features from VOI_tumor. The best performing model was Linear discriminant analysis, which achieved an AUC of 0.86 in the tenfold cross-validation and 0.869 in the test set. The model was based on 15 features, including shape-based features and texture features.

CONCLUSIONS

We demonstrated the feasibility of combining artificial intelligence with CT-based peritumoral radiomics features can be used to accurately predict capsular characteristics of parotid PA. This may assist in clinical decision-making by preoperative identification of capsular characteristics of parotid PA.

The Diagnostic Value of Fine Needle Aspiration Biopsy in Parotid Tumors

The aim of this study was to investigate the diagnostic accuracy rates of the patients who underwent an operation for parotid mass, by comparing their fine needle aspiration biopsy (FNAB) cytology results with the final pathology. A total of 136 patient files of those who applied to Otorhinolaryngology clinic due to parotid mass and underwent parotidectomy procedure between 2010 and 2020 at a tertiary center were scanned retrospectively. Database on patient age, gender, preoperative FNAB results, and final surgical histopathology results was created. The mean age of the patients was 48.26 ± 17.37 Superficial parotidectomy was performed to 108 (79.4%) and total parotidectomy to 28 (20.6%) of the patients. The sensitivity of FNAB was found as 85.2%, specificity as 96.2%, positive predictive value as 85.2%, negative predictive value as 96.2% and accuracy as 94.0%. It is found that FNAB has the high specificity and high negative predictive value with high diagnostic accuracy on detecting preoperative malignancy in parotid gland. We think that FNAB is a significant, necessary and safe method in the diagnosis of parotid lesions in preoperative sense.

Surgical Treatment of a Giant Pleomorphic Adenoma of the Submandibular Gland: A Case Report

Pleomorphic adenomas (PAs) are the most common benign salivary neoplasms. PAs are generally slow-growing but may sometimes become aggressive and grow rapidly within a short period of time. Here, we report the case of an 83-year-old Chinese woman with an anterior neck mass that had been growing over the past 30 years. She felt uncomfortable because the mass had grown quite rapidly in the past year. The final diagnosis of a PA of the left submandibular gland was confirmed by histopathological and immunohistochemical examinations after surgical resection. Our patient recalled a history of an excision of a neck mass 40 years prior to presentation at another hospital. Based on our imaging findings and surgical findings, we speculate that the neck mass 40 years prior may also have been a PA. Our case reminds us the rare recurrence possibility of PAs, and early and thorough resection may have a good prognosis. In addition, to the best of our knowledge, this is the largest PA of the submandibular gland reported to date.

Recurrent pleomorphic adenoma of the parotid gland: A comparison of radiographic and pathologic tumor burden

PURPOSE

To compare recurrent pleomorphic adenoma tumor burden as detected on magnetic resonance and computerized tomography imaging with postoperative histopathology.

MATERIALS AND METHODS

44 patients were identified at a tertiary medical center between 2000 and 2015. Patients were included if they had viewable preoperative imaging and a postoperative diagnosis of recurrent pleomorphic adenoma. Primary outcomes were differences in the number and size of lesions detected on imaging and pathology.

RESULTS

The size in greatest dimension between pathology and imaging was not significant on aggregate MRI + CT (p = 0.78), MRI (p = 0.41), or CT (p = 0.69). There were more lesions found on pathology compared to both aggregate MRI + CT (p = 0.003) and CT alone (p = 0.014). The number of lesions between MRI and pathology failed to reach significance (p = 0.06). On univariate analysis, the interval between imaging and pathology (recurrent surgery) did not significantly affect the number of lesions detected (p = 0.18). On multivariable analysis, CT as the primary imaging modality and >1 recurrence was independently associated with greater inaccuracy with respect to number of lesions detected (p = 0.006; p = 0.008).

CONCLUSION

The size of the largest lesion on pathology can be accurately determined with imaging. Compared to MRI, CT scans significantly underpredict the number of lesions found on pathology. MRI should be prioritized unless contraindications exist. These findings will help guide imaging choice, preoperative planning, and patient counseling.