The diagnostic role of ultrasonography, computed tomography, magnetic resonance imaging, positron emission tomography/computed tomography, and real-time elastography in the differentiation of benign and malignant salivary gland tumors: a meta-analysis

Lymph node surgery for salivary gland cancer: REFCOR recommendations by the formal consensus method

OBJECTIVE

To determine the indications for neck dissection in the management of parotid, submandibular or minor salivary gland cancers depending on the clinical situation: i.e., clinical lymph node involvement (cN+) or not (cN0); low or high risk of occult nodal metastasis; diagnosis of malignancy before, during or after surgery.

MATERIAL AND METHODS

The French Network of Rare Head and Neck Tumors (REFCOR) formed a steering group which drafted a narrative review of the literature published on Medline and proposed recommendations. The level of adherence to the recommendations was then assessed by a rating group according to the formal consensus method.

RESULTS

In cN+ salivary gland cancer, ipsilateral neck dissection is recommended. In cN0 salivary gland cancer, ipsilateral neck dissection is recommended, except for tumors at low risk of occult nodal metastasis. If definitive pathology reveals a high risk of occult nodal involvement, additional neck treatment is recommended: ipsilateral neck dissection or elective nodal irradiation.

CONCLUSION

The rate of occult lymph node involvement, and therefore the indication for elective neck dissection, depends primarily on the pathologic grade of the salivary gland cancer.

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on ultrasound: a retrospective study

BACKGROUND

To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis.

METHODS

A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases' characteristics were conducted.

RESULTS

The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values.

CONCLUSIONS

The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.

Diagnostic imaging of salivary gland cancers: REFCOR recommendations by the formal consensus method

OBJECTIVE

To define the indications for each imaging modality in the screening, characterization, extension and follow-up of salivary gland tumors.

MATERIAL AND METHODS

The French Network of Rare Head and Neck Tumors (REFCOR) formed a steering group who drafted a narrative review of the literature published on Medline and proposed recommendations. The level of adherence to the recommendations was then assessed by a rating group, according to the formal consensus method.

RESULTS

If a swelling of a salivary gland is palpable for 3 weeks, an ultrasound scan is recommended to confirm a tumoral lesion and rule out differential diagnoses. For a salivary gland tumor, MRI is recommended with diffusion-weighted and dynamic contrast-enhanced techniques. In the case of histologically proven malignancy or a highly suspicious lesion, a CT scan of the neck and chest is recommended to assess the tumor, lymph nodes and metastases. FDG-PET is not currently recommended in routine clinical practice for initial diagnosis, assessment of extension, evaluation of response to treatment, staging of recurrence, or follow-up of salivary gland tumors.

CONCLUSION

Assessing salivary tumors is based on MRI. Extension assessment is based on neck and chest CT.

Sonographic and Cytological Evaluation of Salivary Gland Tumors

INTRODUCTION

Salivary gland tumours are relatively uncommon, but they have a multifaceted clinical presentation and varied morphological configuration. The investigations required for these tumours remain debatable. We conducted a study to determine the accuracy of various modalities used in salivary gland tumours.

METHODS

We enrolled 72 subjects, consisting of 44 females and 28 males, with a mean age of 40.93 ± 16.51 years (range: 15 to 79 years), suffering from various salivary gland tumours. The tumour distribution included 42 parotid gland tumours (58.33%), followed by 21 submandibular gland tumours (29.16%), three sublingual gland tumours (4.16%), and six minor salivary gland tumours (8.33%). These individuals were subjected to clinical examination, sonography, and fine needle aspiration cytology as per indications. The results of each modality were compared to surgical pathology to find sensitivity and accuracy.

RESULTS

The clinical examination was found to be least sensitive (83.8%) as compared to FNAC (97.6%), and ultrasound (100%). Ultrasound had the highest diagnostic accuracy (86.2%) as compared to clinical examination (80.6%) and FNAC (82.6%).

CONCLUSION

Although sonography was found to have the highest sensitivity and accuracy as compared to fine needle aspiration cytology and clinical examination, the difference was subtle, as both sonography and fine needle aspiration cytology had a statistically significant correlation with histopathology.

ACR Appropriateness Criteria® Staging and Post-Therapy Assessment of Head and Neck Cancer

Imaging of head and neck cancer at initial staging and as part of post-treatment surveillance is a key component of patient care as it guides treatment strategy and aids determination of prognosis. Head and neck cancer includes a heterogenous group of malignancies encompassing several anatomic sites and histologies, with squamous cell carcinoma the most common. Together this comprises the seventh most common cancer worldwide. At initial staging comprehensive imaging delineating the anatomic extent of the primary site, while also assessing the nodal involvement of the neck is necessary. The treatment of head and neck cancer often includes a combination of surgery, radiation, and chemotherapy. Post-treatment imaging is tailored for the evaluation of treatment response and early detection of local, locoregional, and distant recurrent tumor. Cross-sectional imaging with CT or MRI is recommended for the detailed anatomic delineation of the primary site. PET/CT provides complementary metabolic information and can map systemic involvement. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Ultrasonography characteristics of cystic components in primary salivary gland tumors

OBJECTIVES

The present study aimed to characterize the ultrasonography (US) features of cystic components in salivary gland tumors (SGTs).

MATERIALS AND METHODS

A total of 207 patients (218 lesions) with pathologically confirmed primary SGTs were analyzed. Preoperative US revealed the presence of cystic components in lesions. Lesion size, shape, margin, and US findings of the cystic components, including number, distribution, margin, occupying rate, and internal characteristics, were evaluated.

RESULTS

Similarities were observed between the US performance of benign SGTs (B-SGTs) and malignant SGTs (M-SGTs) with cystic components. Differences in sex and age of patients, number, distribution, and internal characteristics of cystic components were statistically significant. For SGTs with cystic components, the proportions of M-SGTs to ill-defined margins (P = 0.002), eccentric distribution (P = 0.019), and none of the internal characteristics (P = 0.019) were significantly higher than those of B-SGTs. Younger age (P = 0.001), eccentric distribution (P = 0.034) and ill-defined margin (P < 0.001) were risk factors for diagnosing M-SGTs. Cystic component features needed to be combined with lesion indicators (border and shape) to improve diagnostic sensitivity.

CONCLUSIONS

US features of the B-SGTs and M-SGTs were significantly different. Cystic component is of interest in the US-related differential diagnosis of B-SGT and M-SGT.

CLINICAL RELEVANCE

Cystic components are potentially valuable in the differential diagnosis of B-SGTs and M-SGTs on US.

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study

OBJECTIVES

To develop deep learning-assisted diagnosis models based on CT images to facilitate radiologists in differentiating benign and malignant parotid tumors.

METHODS

Data from 573 patients with histopathologically confirmed parotid tumors from center 1 (training set: n = 269; internal-testing set: n = 116) and center 2 (external-testing set: n = 188) were retrospectively collected. Six deep learning models (MobileNet V3, ShuffleNet V2, Inception V3, DenseNet 121, ResNet 50, and VGG 19) based on arterial-phase CT images, and a baseline support vector machine (SVM) model integrating clinical-radiological features with handcrafted radiomics signatures were constructed. The performance of senior and junior radiologists with and without optimal model assistance was compared. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the clinical benefit of using the optimal model.

RESULTS

MobileNet V3 had the best predictive performance, with sensitivity increases of 0.111 and 0.207 (p < 0.05) in the internal- and external-testing sets, respectively, relative to the SVM model. Clinical benefit and overall efficiency of junior radiologist were significantly improved with model assistance; for the internal- and external-testing sets, respectively, the AUCs improved by 0.128 and 0.102 (p < 0.05), the sensitivity improved by 0.194 and 0.120 (p < 0.05), the NRIs were 0.257 and 0.205 (p < 0.001), and the IDIs were 0.316 and 0.252 (p < 0.001).

CONCLUSIONS

The developed deep learning models can assist radiologists in achieving higher diagnostic performance and hopefully provide more valuable information for clinical decision-making in patients with parotid tumors.

KEY POINTS

• The developed deep learning models outperformed the traditional SVM model in predicting benign and malignant parotid tumors. • Junior radiologist can obtain greater clinical benefits with assistance from the optimal deep learning model. • The clinical decision-making process can be accelerated in patients with parotid tumors using the established deep learning model.

Evaluation of Fine Needle Aspiration Cytopathology in Salivary Gland Tumors under Milan System: Challenges, Misdiagnosis Rates, and Clinical Recommendations

(1) Background: Salivary gland tumors are rare in the head and neck. To determine the need and extent of surgical intervention, fine needle aspiration (FNA) is a widely accepted tool to approach salivary gland lesions. However, the FNA cytology varies between entities, while the lack of uniform terminology makes diagnosis more challenging. Since establishing the Milan system for reporting salivary gland cytopathology (MSRSGC) has become an increasingly accepted reporting standard, further examination and detailed recommendations were needed. (2) Methods: Between April 2013 and October 2021, 375 cases with FNA and salivary gland resection were retrospectively collected. All FNA specimens were reclassified according to the criteria of MSRSGC. After surgical excision, the FNA data were compared with the histological diagnosis to estimate the risk of malignancy (ROM), the risk of neoplasm (RON), and the diagnostic accuracy for each diagnostic category. (3) Results: Our cohort's distribution of ROM and RON was similar to the MSRSGC's recommendation. Carcinoma ex pleomorphic adenoma (CXPA) has the highest rate (66.7%) of misdiagnosed as a nonneoplastic lesion or benign salivary gland tumor. Pleomorphic adenoma (PA) and Warthin's tumor were the most common benign salivary gland tumors, while the cytology diagnosis of Warthin's tumor seems more challenging than PAs. (4) Conclusions: Despite the convenience and effectiveness of MSRSGC, we suggest close follow-up, re-biopsy, or surgical removal for salivary lesions even in Milan IVA-Benign for possibly missing FNA of malignancy, mixed lesions, or prevention of malignant transformation.

New trends in diagnosing and treating ovarian cancer using nanotechnology

Ovarian cancer stands as the fifth most prevalent cancer among women, causing more mortalities than any other disease of the female reproductive system. There are numerous histological subtypes of ovarian cancer, each of which has distinct clinical characteristics, risk factors, cell origins, molecular compositions, and therapeutic options. Typically, it is identified at a late stage, and there is no efficient screening method. Standard therapies for newly diagnosed cancer are cytoreductive surgery and platinum-based chemotherapy. The difficulties of traditional therapeutic procedures encourage researchers to search for other approaches, such as nanotechnology. Due to the unique characteristics of matter at the nanoscale, nanomedicine has emerged as a potent tool for creating novel drug carriers that are more effective and have fewer adverse effects than traditional treatments. Nanocarriers including liposomes, dendrimers, polymer nanoparticles, and polymer micelles have unique properties in surface chemistry, morphology, and mechanism of action that can distinguish between malignant and normal cells, paving the way for targeted drug delivery. In contrast to their non-functionalized counterparts, the development of functionalized nano-formulations with specific ligands permits selective targeting of ovarian cancers and ultimately increases the therapeutic potential. This review focuses on the application of various nanomaterials to the treatment and diagnosis of ovarian cancer, their advantages over conventional treatment methods, and the effective role of controlled drug delivery systems in the therapy of ovarian cancer.

Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors

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.

Patterns of care analysis for salivary gland cancer: a survey within the German Society of Radiation Oncology (DEGRO) and recommendations for daily practice

Background

Salivary gland cancer (SGC) is rare and a heterogeneous type of cancer. Prospective randomized trials are lacking. No guideline focusing on standard procedures of radiotherapy (RT) in the treatment of SGC exists. Therefore, we surveyed the members of the German Society of Radiation Oncology (DEGRO) to gain information about current therapeutic strategies of SGC.

Methods

An anonymous questionnaire was designed and made available on the online platform umfrageonline.com. The corresponding link was sent to all DEGRO members who provided their user data for contact purposes. Alternatively, a PDF printout version was sent. Frequency distributions of responses for each question were calculated. The data were also analyzed by type of institution.

Results

Sixty-seven responses were received, including answers from 21 university departments, 22 non-university institutions, and 24 radiation oncology practices. Six participants reported that their departments (practice: n = 5, non-university hospital: n = 1) did not treat SGC, and therefore the questionnaire was not completed. Concerning radiation techniques, target volume definition, and concomitant chemotherapy, treatment strategies varied greatly among the participants. Comparing university vs. non-university institutions, university hospitals treat significantly more patients with SGC per year and initiated more molecular pathological diagnostics.

Conclusion

SGC represents a major challenge for clinicians, as reflected by the inhomogeneous survey results regarding diagnostics, RT approaches, and systemic therapy. Future prospective, multicenter clinical trials are warranted to improve and homogenize treatment of SGC and to individualize treatment according to histologic subtypes and risk factors.

Supplementary Information

The online version of this article (10.1007/s00066-021-01833-x) contains supplementary material, which is available to authorized users.

Qualitative and Quantitative Diagnosis in Head and Neck Cancer

The diagnosis is the art of determining the nature of a disease, and an accurate diagnosis is the true cornerstone on which rational treatment should be built. Within the workflow in the management of head and neck tumours, there are different types of diagnosis. The purpose of this work is to point out the differences and the aims of the different types of diagnoses and to highlight their importance in the management of patients with head and neck tumours. Qualitative diagnosis is performed by a pathologist and is essential in determining the management and can provide guidance on prognosis. The evolution of immunohistochemistry and molecular biology techniques has made it possible to obtain more precise diagnoses and to identify prognostic markers and precision factors. Quantitative diagnosis is made by the radiologist and consists of identifying a mass lesion and the estimation of the tumour volume and extent using imaging techniques, such as CT, MRI, and PET. The distinction between the two types of diagnosis is clear, as the methodology is different. The accurate establishment of both diagnoses plays an essential role in treatment planning. Getting the right diagnosis is a key aspect of health care, and it provides an explanation of a patient’s health problem and informs subsequent decision. Deep learning and radiomics approaches hold promise for improving diagnosis.

Application of ultrasound and contrast-enhanced ultrasound to distinguish salivary focal inflammatory masses from malignant masses: A retrospective observational study

BACKGROUND

The aim was to retrospectively analyze the ultrasonographic and clinical characteristics of focal inflammatory masses and malignant masses of salivary gland by using B-mode ultrasound (US) and contrast-enhanced ultrasound (CEUS) for differential analysis.

METHODS

The features of US and CEUS were retrospectively analyzed for 19 cases of focal salivary inflammatory masses and 45 cases of malignant salivary masses. All cases were confirmed by pathohistological examination.

RESULTS

On B-mode US, the incidence of expansive growth patterns of malignant salivary masses (44.4%, 20/45) was significantly higher than that of focal salivary inflammatory masses (15.8%, 3/19) (p = 0.029). The rate of lymphadenopathy surrounding salivary glands of malignant salivary masses (42.2%, 19/45) was significantly higher than that of focal salivary inflammatory masses (15.8%, 3/19) (p = 0.042). On CEUS, clear enhancement margins were more common in malignant salivary masses (44.4%, 20/45) compared to focal salivary inflammatory masses (15.8%, 3/19) (p = 0.029); Rapid washout was more common in malignant salivary masses (82.2%, 37/45) than focal salivary inflammatory masses (31.6%, 6/19) (p <  0.001). Rapid washout on CEUS and craniocaudal diameter were independent predictive factors in differentiating salivary inflammatory masses and malignant masses according to binary logistic regression analysis. US and CEUS achieved a sensitivity of 80.0%, a specificity of 78.9%and an accuracy of 80.0%for discrimination between salivary inflammatory masses and malignant masses.

CONCLUSION

Therefore, a multimodal ultrasonographic pathway combining clinical manifestations, B-mode US and CEUS was needed to differentiate between salivary focal inflammatory masses and malignancies to avoid unnecessary biopsies.

The current value of quantitative shear wave sonoelastography in parotid gland tumors

Background

The preoperative differentiation between salivary gland tumor entities using computed tomography, magnetic resonance imaging (MRI) and ultrasound (US) is still limited. Biopsies are often regarded as indispensable for properly characterizing these various lesions. The aim of this study was to analyze the value of acoustic radiation force impulse (ARFI) sonoelastography as an US differentiation tool when examining parotid gland (PG) lesions.

Methods

We included 104 patients with PG masses in this study, employing two different US devices using quantitative ARFI-sonoelastography (Siemens Acuson-S3000, n=59; Siemens Acuson-Sequoia, n=45). The ability of sonoelastographic measurements to differentiate between different neoplasms was compared and analyzed for both US machines.

Results

Quantitative shear wave sonoelastography is limited in its ability to reliably differentiate between tumor entities of the PG as a stand-alone parameter. Measurement results were unsystematically distributed and not transferable between the two US devices. A significant differentiation of benign and malignant lesions was not possible with either US machine (S3000: P=0.770, Sequoia: P=0.382). A differentiation between pleomorphic adenomas (PA) and Warthin tumors was only possible with the Acuson S3000 system (P=0.001, Spearman-Rho =0.492, sensitivity 73.9%, specificity 65.0%).

Conclusions

A reliable identification and differentiation of PG tumors as well as clinical treatment decisions cannot be made with the sole use of ARFI-sonoelastography. The results emphasize the device-dependence and high error-proneness of this US technique when examining lesions of the PG.

Controversies in the Workup and Surgical Management of Parotid Neoplasms

OBJECTIVE

Parotid neoplasms are a rare heterogeneous group of tumors with varied clinical presentation and behavior. Here we provide an evidence-based review of the contemporary approach to evaluation and surgical management of parotid tumors.

DATA SOURCE

PubMed and Web of Science Databases.

REVIEW METHODS

Searches of the PubMed and Web of Science databases were performed on subjects related to the diagnosis and surgical management of parotid neoplasms. Particular emphasis was placed on the following areas: evaluation of parotid tumors, including imaging workup and the utility of fine-needle aspiration; extent of surgery of the primary lesion, including the extent of parotidectomy as well as oncologic management of the facial nerve; the extent of surgery of involved and at-risk cervical lymphatics; and parotid bed reconstruction. Articles published from 2014 to the present were prioritized, supplementing with information from prior studies in areas where data are lacking.

CONCLUSION

A summary of the literature in these areas is outlined to provide an evidence-based approach to evaluation and management of parotid neoplasms.

IMPLICATIONS FOR PRACTICE

While data are available to help guide many aspects of workup and management of parotid neoplasms, further research is needed to refine protocols for this heterogeneous group of diseases.