Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

The value of T1- and FST2-Weighted-based radiomics nomogram in differentiating pleomorphic adenoma and Warthin tumor

PURPOSE

To establish a radiomics nomogram based on MRI radiomics features combined with clinical characteristics for distinguishing pleomorphic adenoma (PA) from warthin tumor (WT).

METHODS

294 patients with PA (n = 159) and WT (n = 135) confirmed by histopathology were included in this study between July 2017 and June 2023. Clinical factors including clinical data and MRI features were analyzed to establish clinical model. 10 MRI radiomics features were extracted and selected from T1WI and FS-T2WI, used to establish radiomics model and calculate radiomics scores (Rad-scores). Clinical factors and Rad-scores were combined to serve as crucial parameters for combined model. Through Receiver operator characteristics (ROC) curve and decision curve analysis (DCA), the discriminative values of the three models were qualified and compared, the best-performing combined model was visualized in the form of a radiomics nomogram.

RESULTS

The combined model demonstrated excellent discriminative performance for PA and WT in the training set (AUC=0.998) and testing set (AUC=0.993) and performed better compared with the clinical model and radiomics model in the training set (AUC=0.996, 0.952) and testing model (AUC=0.954, 0.849). The DCA showed that the combined model provided more overall clinical usefulness in distinguishing parotid PA from WT than another two models.

CONCLUSION

An analytical radiomics nomogram based on MRI radiomics features, incorporating clinical factors, can effectively distinguish between PA and WT.

Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study

Purpose

To evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA).

Methods

Data of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models' performance was evaluated using the area under the curve (AUC).

Results

There were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798).

Conclusion

Misdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT.

CT radiomics for predicting the prognosis of patients with stage II rectal cancer during the three-year period after surgery, chemotherapy and radiotherapy

Objective

Pre-treatment enhanced CT image data were used to train and build models to predict the efficacy of non-small cell lung cancer after conventional radiotherapy and chemotherapy using two classification algorithms, Logistic Regression (LR) and Gaussian Naive Baye (GNB).

Methods

In this study, we used pre-treatment enhanced CT image data for region of interest (ROI) sketching and feature extraction. We utilized the least absolute shrinkage and selection operator (LASSO) mutual confidence method for feature screening. We pre-screened logistic regression (LR) and Gaussian naive Bayes (GNB) classification algorithms and trained and modeled the screened features. We plotted 5-fold and 10-fold cross-validated receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC). We performed DeLong's test for validation and plotted calibration curves and decision curves to assess model performance.

Results

A total of 102 patients were included in this study, and after a comparative analysis of the two models, LR had only slightly lower specificity than GNB, and higher sensitivity, accuracy, AUC value, precision, and F1 value than GNB (training set accuracy: 0.787, AUC value: 0.851; test set accuracy: 0.772, AUC value: 0.849), and the LR model has better performance in both the decision curve and the calibration curve.

Conclusion

CT can be used for efficacy prediction after radiotherapy and chemotherapy in NSCLC patients. LR is more suitable for predicting whether NSCLC prognosis is in remission without considering the computing speed.

Radiomic nomogram for discriminating parotid pleomorphic adenoma from parotid adenolymphoma based on grayscale ultrasonography

Objectives

To differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features.

Methods

This retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman's correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test.

Results

To differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965-1) and 0.910 (95% CI: 0.830-0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness.

Conclusion

A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.

Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review

Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23-334 benign and 8-56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin's tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.

Risk Stratification and Overall Survival Prediction in Advanced Gastric Cancer Patients Based on Whole-Volume MRI Radiomics

BACKGROUND

The prognosis of advanced gastric cancer (AGC) patients has attracted much attention, but there is a lack of evaluation method. MRI-based radiomics has the potential to evaluate AGC patients' prognosis.

PURPOSE

To identify and validate the risk stratification and overall survival (OS) in AGC patients using MRI-based radiomics.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 233 patients (168 males, 63.6 ± 11.1 years; 65 females, 59.7 ± 11.8 years) confirmed AGC were collected. The data were randomly divided into a training (164) and validation set (69).

SEQUENCE

A 3.0 T, axial T2-weighted, diffusion-weighted imaging, and contrast-enhanced T1-weighted (CE-T1WI).

ASSESSMENT

Radiologist 1 segmented 233 patients and radiologist 2 segmented randomly 50 patients on CE-T1WI. The risk score (RS) was summed by each sample based on the radiomics features and correlation coefficients. Patients were followed up for 7-67 months (median 41; 138 dead and 95 alive).

STATISTICAL TESTS

The intraclass correlation coefficient (ICC) and Kappa value were calculated. Differences in survival analysis were assessed by Kaplan-Meier curves and log-rank test. Cox-regression analysis was performed to identify the radiomics features and clinical indicators associated with OS. The calibration curves were built to assess the model. A two-tailed P value < 0.05 was considered statistically significant.

RESULTS

Integrated with age, lymphovascular invasion (LVI) and RS, a survival combined model was built. The area under the curve (AUC) for predicting 3-year and 5-year OS was 0.765 and 0.788 in the training set, 0.757 and 0.729 in the validation set. There was no significant difference between the radiomics model and survival combined model for 3-year (0.690 vs. 0.757, P = 0.425) and 5-year OS (0.687 vs. 729, P = 0.412) in the validation set. The calibration curves showed a high degree of fit for the survival combined model.

DATA CONCLUSION

This study established a survival combined model that might help AGC patients in future clinical decision-making.

EVIDENCE LEVEL

33 TECHNICAL EFFICACY: Stage 5.

Semiquantitative magnetic resonance imaging parameters for differentiating parotid pleomorphic adenoma from Warthin tumor

Background

Accurately distinguishing between pleomorphic adenoma (PA) and Warthin tumor (WT) is beneficial for their respective management. Preoperative magnetic resonance imaging (MRI) can provide valuable information due to its excellent soft tissue contrast. This study explored the value of semiquantitative contrast-enhanced MRI parameters in the differential diagnosis of PA and WT.

Methods

Data from 106 patients, 62 with PA and 44 with WT (confirmed by histopathology) were retrospectively and consecutively analyzed. The tumor-to-spinal cord contrast ratios (TSc-CR) based on the mean, maximum, and minimum signal intensity (T1-mean TSc-CR, T1-max TSc-CR, and T1-min TSc-CR, respectively) in the early and delayed phases were calculated on contrast-enhanced T1-weighted images as semiquantitative parameters, and then compared between PA and WT. Receiver operating characteristic (ROC) curve analysis and areas under the curve (AUCs) were used to determine the performance of these parameters in the differential diagnosis of PA from WT.

Results

Except T1-min TSc-CR in the early phase, all semiquantitative MRI parameters differed significantly between PA and WT (all P<0.05). T1-max TSc-CR showed higher sensitivity {70.45% [95% confidence interval (CI): 0.548-0.832]} and specificity [70.97% (95% CI: 0.581-0.818)] and had a higher AUC [0.707 (95% CI: 0.610-0.791)] in the early phase when using a cutoff value of 1.89. T1-max TSc-CR showed higher sensitivity [88.64% (95% CI: 0.754-0.962)], specificity [72.58% (95% CI: 0.598-0.831)], and AUC [0.854 (95% CI: 0.772-0.915)] in the delayed phase when using a cutoff value of 2.33. The sensitivity, specificity, and AUC were improved to 90.91% (95% CI: 0.783-0.975), 93.55% (95% CI: 0.843-0.982), and 0.960 (95% CI: 0.903-0.988), respectively, after combination of all semiquantitative parameters in the early and delayed phases. The two radiologists had excellent interobserver agreement on TSc-CRs [all interclass correlation coefficient (ICC) >0.75].

Conclusions

Semiquantitative parameters using TSc-CR are valuable in distinguishing PA from WT, and a combination of these parameters can improve the differential diagnostic efficiency.

Support Tools in the Differential Diagnosis of Salivary Gland Tumors through Inflammatory Biomarkers and Radiomics Metrics: A Preliminary Study

BACKGROUND

The purpose of this study was to investigate how the systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR), and radiomic metrics (quantitative descriptors of image content) extracted from MRI sequences by machine learning increase the efficacy of proper presurgical differentiation between benign and malignant salivary gland tumors.

METHODS

A retrospective study of 117 patients with salivary gland tumors was conducted between January 2015 and November 2022. Univariate analyses with nonparametric tests and multivariate analyses with machine learning approaches were used.

RESULTS

Inflammatory biomarkers showed statistically significant differences (p < 0.05) in the Kruskal-Wallis test based on median values in discriminating Warthin tumors from pleomorphic adenoma and malignancies. The accuracy of NLR, PLR, SII, and SIRI was 0.88, 0.74, 0.76, and 0.83, respectively. Analysis of radiomic metrics to discriminate Warthin tumors from pleomorphic adenoma and malignancies showed statistically significant differences (p < 0.05) in nine radiomic features. The best multivariate analysis result was obtained from an SVM model with 86% accuracy, 68% sensitivity, and 91% specificity for six features.

CONCLUSIONS

Inflammatory biomarkers and radiomic features can comparably support a pre-surgical differential diagnosis.

CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors

Objective

This study assessed whether radiomics features could stratify parotid gland tumours accurately based on only noncontrast CT images and validated the best classifier of different radiomics models.

Methods

In this single-centre study, we retrospectively recruited 249 patients with a diagnosis of pleomorphic adenoma (PA), Warthin tumour (WT), basal cell adenoma (BCA) or malignant parotid gland tumours (MPGTs) from June 2020 to August 2022. Each patient was randomly classified into training and testing cohorts at a ratio of 7:3, and then, pairwise comparisons in different parotid tumour groups were performed. CT images were transferred to 3D-Slicer software and the region of interest was manually drawn for feature extraction. Feature selection methods were performed using the intraclass correlation coefficient, t test and least absolute shrinkage and selection operator. Five common classifiers, namely, random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbours (KNN) and general Bayesian network (Gnb), were selected to build different radiomics models. The receiver operating characteristic curve, area under the curve (AUC), accuracy, sensitivity, specificity and F-1 score were used to assess the prediction performances of these models. The calibration of the model was calculated by the Hosmer–Lemeshow test. DeLong’s test was utilized for comparing the AUCs.

Results

The radiomics model based on the RF, SVM, Gnb, LR, LR and RF classifiers obtained the highest AUC in differentiating PA from MPGTs, WT from MPGTs, BCA from MPGTs, PA from WT, PA from BCA, and WT from BCA, respectively. Accordingly, the AUC and the accuracy of the model for each classifier were 0.834 and 0.71, 0.893 and 0.79, 0.844 and 0.79, 0.902 and 0.88, 0.602 and 0.68, and 0.861 and 0.94, respectively.

Conclusion

Our study demonstrated that noncontrast CT-based radiomics could stratify refined pathological types of parotid tumours well but could not sufficiently differentiate PA from BCA. Different classifiers had the best diagnostic performance for different parotid tumours. Our study findings add to the current knowledge on the differential diagnosis of parotid tumours.

A Rare Case of Pleomorphic Adenoma in the Internal Auditory Canal

Pleomorphic adenoma is a common tumor of the salivary gland tumor, but it is rare in the ear. To the authors' knowledge, this patient is the first reported case of pleomorphic adenoma arising in the internal auditory canal. There have been few documents reporting the manifestations and the therapy strategy of this disease. This study clearly demonstrated the experience in treating pleomorphic adenoma, including the characteristics of the clinical manifestation, the key procedures of surgery, and key points of the diagnosis. The translabyrinthine approach under the microscope performed to remove this tumor is a good option for resecting this type of tumor.

Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid

OBJECTIVES

The differentiation of Warthin tumor and pleomorphic adenoma before treatment is crucial for clinical strategies. The aim of this study was to develop and test a T2-weighted-based radiomics model for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

METHODS

A total of 117 patients, including 61 cases of Warthin tumor and 56 cases of pleomorphic adenoma, were retrospectively enrolled from two centers between January 2010 and June 2022. The training set included 82 cases, and the validation set included 35 cases. From T2-weighted images, 971 radiomics features were extracted. Seven radiomics features remained after a two-step selection process. We used the seven radiomics features and clinical factors through multivariable logistic regression to build radiomics and clinical models, respectively. A radiomics-clinical model was also built that combined the independent clinical predictors with the radiomics features. Through ROC curves, the three models were evaluated and compared.

RESULTS

In the radiomics model, AUCs were 0.826 and 0.796 in training and validation sets, respectively. In the clinical model, the AUCs were 0.923 and 0.926 in the training and validation sets, respectively. Decision curve analysis revealed that the radiomics-clinical model had the best diagnostic performance for distinguishing Warthin tumor from pleomorphic adenoma of the parotid gland (AUC = 0.962 and 0.934 for the training and validation sets, respectively).

CONCLUSION

The radiomics-clinical model performed well in differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

KEY POINTS

• The clinical model outperformed the radiomics model in distinguishing pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics features extracted from T2-weighted images could help differentiate pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics-clinical model was superior to the radiomics and the clinical models for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author's professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author's professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year.

Distinguishing Parotid Polymorphic Adenoma and Warthin Tumor Based on the CT Radiomics Nomogram: A Multicenter Study

RATIONALE AND OBJECTIVES

To develop, validate, and test a comprehensive radiomics prediction model to distinguish parotid polymorphic adenomas (PAs) and warthin tumors (WTs) using clinical data and enhanced computed tomography (CT) from a multicenter cohort.

MATERIALS AND METHODS

A total of 267 patients with PAs (n =172) or WTs (n = 95) from two hospitals were randomly divided into training (n =188) and validation (n =79) datasets. Radiomics features were extracted from the enhanced CT (arterial phase) followed by dimensionality reduction. Clinical and CT features were combined to establish a prediction model. A radiomics nomogram was constructed by combining RadScore and clinical factors. Moreover, an independent dataset of 31 patients from a third hospital was employed to test the model. Thus, the performance of the nomogram, radiomics signature, and clinical models was evaluated on the training, validation, and the independent testing datasets. Receiver operating characteristic (ROC) curves were used to compare the performance, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the model.

RESULTS

A total of 15 radiomics features were selected from CT data as the imaging markers to generate RadScores, and demographics or clinical data like age, sex, and smoking factors combined with RadScores were used to distinguish PAs and WTs based on multivariate logistic regression analyses. The results showed that radiomics nomograms combining clinical factors and RadScores provided satisfactory predictive values for distinguishing PAs from WTs, with areas under ROC curves (AUC) of 0.979, 0.922, and 0.903 for the training, validation, and the independent testing datasets, respectively. Decision curve analysis revealed that the radiomics nomogram outperformed the clinical factor models in terms of accuracy and effectiveness.

CONCLUSION

CT-based radiomics nomograms combining RadScores and clinical factors can be used to identify PAs and WTs, which may help tumor management by clinicians.

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram

Objectives

We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs).

Materials and Methods

This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong’s test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models.

Results

The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967).

Conclusions

Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.