Diagnostic performance and inter-operator variability of apparent diffusion coefficient analysis for differentiating pleomorphic adenoma and carcinoma ex pleomorphic adenoma: comparing one-point measurement and whole-tumor measurement including radiomics approach

Differential diagnosis between pleomorphic adenoma and carcinoma ex pleomorphic adenoma: focus on clinical and MR findings

BACKGROUND

In parotid tumors, preoperative differential diagnosis between pleomorphic adenoma (PA) and carcinoma ex pleomorphic adenoma (CXPA) remains challenging.

OBJECTIVES

The present study aimed to evaluate the clinical characteristics of CXPA and PA and to identify factors associated with the differential diagnosis of these conditions.

MATERIAL AND METHODS

We reviewed the medical records of 6 CXPA and 127 PA patients treated surgically at our institution between 2008 and 2024.

RESULT

Univariate analysis revealed significant differences in spontaneous pain (p = .044), facial nerve palsy (p = .045), and subjective enlargement (p < .01) between the CXPA and PA patients. Magnetic resonance imaging (MRI) revealed that the proportions of masses with ill-defined margins, localization in the deep lobe, and low apparent diffusion coefficient (ADC) were significantly higher in patients with CXPA than in those with PA (p < .01, p = .048, and p < .01, respectively). Multivariate analysis identified that spontaneous pain and masses with ill-defined margins were associated with CXPA (p = .047 and p = .013, respectively).

CONCLUSION

Spontaneous pain and masses with ill-defined margins could be suggestive of CXPA rather than PA. Low ADC levels indicate the possibility of CXPA.

The Role of MRI Radiomics Using T2-Weighted Images and the Apparent Diffusion Coefficient Map for Discriminating Between Warthin's Tumors and Malignant Parotid Gland Tumors

BACKGROUND/OBJECTIVES

Differentiating between benign and malignant parotid gland tumors (PGT) is essential for establishing the treatment strategy, which is greatly influenced by the tumor's histology. The objective of this study was to evaluate the role of MRI-based radiomics in the differentiation between Warthin's tumors (WT) and malignant tumors (MT), two entities that proved to present overlapping imaging features on conventional and functional MRI sequences.

METHODS

In this retrospective study, a total of 106 PGT (66 WT, 40 MT) with confirmed histology were eligible for radiomic analysis, which were randomly split into a training group (79 PGT; 49 WT; 30 MT) and a testing group (27 PGT; 17 WT, 10 MT). The radiomic features were extracted from 3D segmentations of PGT performed on the following sequences: PROPELLER T2-weighted images and the ADC map, using a dedicated software. First- and second-order features were derived for each lesion, using original and filtered images.

RESULTS

After employing several feature reduction techniques, including LASSO regression, three final radiomic parameters were identified to be the most significant in distinguishing between the two studied groups, with fair AUC values that ranged between 0.703 and 0.767. All three radiomic features were used to construct a Radiomic Score that presented the highest diagnostic performance in distinguishing between WT and MT, achieving an AUC of 0.785 in the training set, and 0.741 in the testing set.

CONCLUSIONS

MRI-based radiomic features have the potential to serve as promising novel imaging biomarkers for discriminating between Warthin's tumors and malignant tumors in the parotid gland. Nevertheless, it is still to prove how radiomic features can consistently achieve higher diagnostic performance, and if they can outperform alternative imaging methods, ideally in larger, multicentric studies.

Data-centric artificial intelligence in oncology: a systematic review assessing data quality in machine learning models for head and neck cancer

Machine learning models have been increasingly considered to model head and neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of the disease. As the concept of data-centric artificial intelligence is still incipient in healthcare systems, little is known about the data quality of the models proposed for clinical utility. This is important as it supports the generalizability of the models and data standardization. Therefore, this study overviews the quality of structured and unstructured data used for machine learning model construction in head and neck cancer. Relevant studies reporting on the use of machine learning models based on structured and unstructured custom datasets between January 2016 and June 2022 were sourced from PubMed, EMBASE, Scopus, and Web of Science electronic databases. Prediction model Risk of Bias Assessment (PROBAST) tool was used to assess the quality of individual studies before comprehensive data quality parameters were assessed according to the type of dataset used for model construction. A total of 159 studies were included in the review; 106 utilized structured datasets while 53 utilized unstructured datasets. Data quality assessments were deliberately performed for 14.2% of structured datasets and 11.3% of unstructured datasets before model construction. Class imbalance and data fairness were the most common limitations in data quality for both types of datasets while outlier detection and lack of representative outcome classes were common in structured and unstructured datasets respectively. Furthermore, this review found that class imbalance reduced the discriminatory performance for models based on structured datasets while higher image resolution and good class overlap resulted in better model performance using unstructured datasets during internal validation. Overall, data quality was infrequently assessed before the construction of ML models in head and neck cancer irrespective of the use of structured or unstructured datasets. To improve model generalizability, the assessments discussed in this study should be introduced during model construction to achieve data-centric intelligent systems for head and neck cancer management.

CT-based delta radiomics in predicting the prognosis of stage IV gastric cancer to immune checkpoint inhibitors

Introduction

To explore the prognostic value of CT-based delta radiomics in predicting the prognosis of patients with stage IV gastric cancer treated with immune checkpoint inhibitors (ICI).

Materials and methods

Forty-two patients with stage IV gastric cancer, who had received ICI monotherapy, were enrolled in this retrospective study. Baseline and first follow-up CT scans were analyzed. Intratumoral and peritumoral regions of interest (ROI) were contoured, enabling the extraction of 192 features from each ROI. The intraclass correlation coefficients were used to select features with high stability. The least absolute shrinkage and selection operator was used to select features with high weights for predicting patient prognosis. Kaplan-Meier analysis and log-rank test were performed to explore the association between features and progression free survival (PFS). Cox regression analyses were used to identify predictors for PFS. The C-index was used to assess the prediction performance of features.

Results

Two radiomics features of ΔVintra_ZV and postVperi_Sphericity were identified from intratumoral and peritumoral regions, respectively. The Kaplan-Meier analysis revealed significant differences in PFS between patients with low and high feature value (ΔVintra_ZV: P=0.000; postVperi_Sphericity: P=0.012), and the multivariable cox analysis demonstrated that ΔVintra_ZV was independent predictor for PFS (HR, 1.911; 95% CI: 1.163-3.142; P=0.011), with C-index of 0.705.

Conclusions

Based on CT scans at baseline and first follow-up, the delta radiomics features could efficiently predict the PFS of gastric cancer patients treated with ICI therapy.

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.

MR Imaging Findings of Carcinoma Ex Pleomorphic Adenoma Related to Extracapsular Invasion and Prognosis

BACKGROUND AND PURPOSE

MR imaging can reflect the pathologic progression of carcinoma ex pleomorphic adenoma (CXPA). This study aimed to identify the imaging findings related to extracapsular invasion of CXPA. Additionally, the pathologic background of these findings was investigated.

MATERIALS AND METHODS

This retrospective study included 37 patients with histologically confirmed CXPA. Three radiologists independently evaluated whether the CXPA showed the following characteristic MR imaging findings: border, capsule, the corona sign on fat-saturated T2WI and contrast-enhanced fat-saturated T1WI, and the black ring sign. The corona sign appeared larger on fat-saturated and/or contrast-enhanced fat-saturated T1WI than on T1WI. The black ring sign was defined as an intratumoral nodule with a thick low-intensity rim on T2WI. Interreader agreement of the visual assessment was performed using κ analysis, and MR imaging and histopathologic findings were also correlated. Kaplan-Meier survival and the log-rank test were used to estimate the 3-year disease-free survival.

RESULTS

MR imaging findings, especially peritumoral findings, showed a significant difference between invasive and noninvasive CXPA. The reliability was poor for the border and capsule. In contrast, it was good for the corona sign on fat-saturated and contrast-enhanced fat-saturated T1WI and the black ring sign. Pathologically, the corona sign reflected the invasiveness of the tumor and inflammatory cells, while the black ring sign reflected hyalinization or fibrosis. The corona sign also showed a significant difference in the 3-year disease-free survival.

CONCLUSIONS

MR imaging findings, including the corona and black ring signs, reliably differentiated invasive and noninvasive CXPA. The corona sign can be used as a prognostic factor for CXPA.

Apparent Diffusion Coefficient Map–Based Radiomics Features for Differential Diagnosis of Pleomorphic Adenomas and Warthin Tumors From Malignant Tumors

Purpose

The magnetic resonance imaging (MRI) findings may overlap due to the complex content of parotid gland tumors and the differentiation level of malignant tumor (MT); consequently, patients may undergo diagnostic lobectomy. This study assessed whether radiomics features could noninvasively stratify parotid gland tumors accurately based on apparent diffusion coefficient (ADC) maps.

Methods

This study examined diffusion-weighted imaging (DWI) obtained with echo planar imaging sequences. Eighty-eight benign tumors (BTs) [54 pleomorphic adenomas (PAs) and 34 Warthin tumors (WTs)] and 42 MTs of the parotid gland were enrolled. Each case was randomly divided into training and testing cohorts at a ratio of 7:3 and then was compared with each other, respectively. ADC maps were digitally transferred to ITK SNAP (www.itksnap.org). The region of interest (ROI) was manually drawn around the whole tumor margin on each slice of ADC maps. After feature extraction, the Synthetic Minority Oversampling TEchnique (SMOTE) was used to remove the unbalance of the training dataset. Then, we applied the normalization process to the feature matrix. To reduce the similarity of each feature pair, we calculated the Pearson correlation coefficient (PCC) value of each feature pair and eliminated one of them if the PCC value was larger than 0.95. Then, recursive feature elimination (RFE) was used to process feature selection. After that, we used linear discriminant analysis (LDA) as the classifier. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the ADC.

Results

The LDA model based on 13, 8, 3, and 1 features can get the highest area under the ROC curve (AUC) in differentiating BT from MT, PA from WT, PA from MT, and WT from MT on the validation dataset, respectively. Accordingly, the AUC and the accuracy of the model on the testing set achieve 0.7637 and 73.17%, 0.925 and 92.31%, 0.8077 and 75.86%, and 0.5923 and 65.22%, respectively.

Conclusion

The ADC-based radiomics features may be used to assist clinicians for differential diagnosis of PA and WT from MTs.

Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence-based image analysis

Radiogenomics is a new field of medical science that integrates two omics, radiomics and genomics, and may bring a major paradigm shift in traditional personalized medicine strategies that require tumor tissue samples. In addition, the acquisition of the data does not require special imaging equipment or special imaging conditions, and it is possible to use image information from computed tomography, magnetic resonance imaging, positron emission tomography-computed tomography in clinical practice, so the versatility and cost-effectiveness of radiogenomics are expected. So far, the field of radiogenomics has developed, especially in the fields of brain tumors and breast cancer, but recently, reports of radiogenomic research on gastroenterological cancer are increasing. This review provides an overview of radiogenomic research methods and summarizes the current radiogenomic research in gastroenterological cancer. In addition, the application of artificial intelligence is considered to be indispensable for the integrated analysis of enormous omics information in the future, and the future direction of this research, including the latest technologies, will be discussed.

Carcinoma ex pleomorphic adenoma of major salivary glands: CT and MR imaging findings

OBJECTIVES

To describe the CT and MR imaging characteristics of primary carcinoma ex pleomorphic adenoma (Ca-ex-PA) in major salivary glands and present more information for recognizing this malignancy.

METHODS

212 patients with primary Ca-ex-PA in major salivary glands (169 in the parotid gland, 36 in the submandibular gland, 7 in the sublingual gland) underwent CT and MR imaging (plain and contrast-enhanced scans) prior to surgical management and histopathological examination. The CT and MR imaging findings of this condition were retrospectively reviewed and correlated with their pathological types: non-invasive carcinoma (Type I, 37 cases), minimally invasive carcinoma (Type II, 18 cases), and widely invasive carcinoma (TypeIII, 157 cases). The binary logistic regression analysis was used to analyze the independent influencing factors of morphology and boundary for differentiating between Type I/II and Type III of Ca-ex-PA, and the sensitivity, specificity and positive predictive value were calculated. Differences in apparent diffusion coefficient (ADC) values between Type I/II and Type III of Ca-ex-PA were calculated by independent sample t-tests.

RESULTS

On CT and MR imaging, there were 190/212 cases (89.6%) identified as lobular, 203/212 cases (95.8%) with enhancement, and 173/212 cases (81.6%) with inhomogeneous after contrast administration.Calcification within the mass was shown in 76 of 192 cases (39.6%) on plain CT examination. Of 55 neoplasms with Type I and II, 38 (69.1%) were presented as round or oval and 42 (76.4%) as well-defined margins. Of 157 neoplasms with Type III, 103 (65.6%) were presented as irregular form and 110 (70.1%) as uneven margins or with partial uneven margins.The sensitivity, specificity and positive predictive value for distinguishing Type I/II and Type III tumors according to the morphology and boundary were 78.34%, 63.64% and 86.01%, respectively. The mean ADC value of Ca-ex-PA (22 cases) in major salivary glands was about 0.93 × 10^-3 mm² s^-1, and there was no significant difference in mean ADC value between Type I/II and Type III of this neoplasm. Cervical lymph node metastasis and distance metastasis were found in 67 patients (31.6%, Type III) and 32 patients (15.1%, Type I in 1; Type II in 1; and Type III in 30), respectively.

CONCLUSIONS

Most Ca-ex-PA is characterized by an irregular, lobular, and inhomogeneous enhanced neoplasm with uneven margin or partial uneven margin on CT and MR imaging, which is frequently corresponding with Type III. And a round or oval mass with well-defined margin usually correlates with Type I and II. Morphology and boundary are important basis for distinguishing Type I/II and Type III tumors. Calcification within the neoplasm shown on CT may be regarded as a specific sign for indicating this malignancy. Low ADC value is an important manifestation of this neoplasm.Ca-ex-PA with Type III is more likely to have cervical lymph node metastasis and distant metastasis.