Cervical lymphadenopathy: can the histogram analysis of apparent diffusion coefficient help to differentiate between lymphoma and squamous cell carcinoma in patients with unknown clinical primary tumor?

Differentiation of cancer of unknown primary and lymphoma in head and neck metastatic poorly differentiated cancer using 18 F-FDG PET/CT tumor metabolic heterogeneity index

OBJECTIVE

To explore the value of 18 F-FDG PET/CT tumor metabolic heterogeneity index (HI) and establish and validate a nomogram model for distinguishing head and neck cancer of unknown primary (HNCUP) from lymphoma with head and neck metastatic poorly differentiated cancer.

METHODS

This retrospective analysis was conducted on 1242 patients with cervical metastatic poorly differentiated cancer. 108 patients, who were clinically and pathologically confirmed as HNCUP or lymphoma, were finally enrolled. Two independent sample t-tests and χ 2 test were used to compare the clinical and imaging features. Binary logistic regression was used to screen for independent predictive factors.

RESULTS

Among the 108 patients), 65 patients were diagnosed with HNCUP and 43 were lymphoma. Gender ( P  = 0.001), SUV max ( P  < 0.001), SUV mean ( P  < 0.001), TLG ( P  = 0.012), and HI ( P  < 0.001) had statistical significance in distinguishing HNCUP and lymphoma. Female ( OR  = 4.546, P  = 0.003) and patients with HI ≥ 2.37 ( OR  = 3.461, P  = 0.047) were more likely to be diagnosed as lymphoma.

CONCLUSION

For patients with cervical metastatic poorly differentiated cancer, gender and HI were independent predictors of pathological type. For such patients, clinical attention should be paid to avoid misdiagnosing lymphoma as HNCUP, which may delay treatment.

Artificial Intelligence and Deep Learning of Head and Neck Cancer

Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.

Compartmental models for diffusion weighted MRI reveal widespread brain changes in HIV-infected patients

Diffusion tensor imaging (DTI) has been used to explore changes in the brain of subjects with human immunodeficiency virus (HIV) infection. However, DTI notoriously suffers from low specificity. Neurite orientation dispersion and density imaging (NODDI) is a compartmental model able to provide specific microstructural information with additional sensitivity/specificity. In this study we use both the NODDI and the DTI models to evaluate microstructural differences between 35 HIV-positive patients and 20 healthy controls. Diffusion-weighted imaging was acquired using three b-values (0, 1000 and 2500 s/mm²). Both DTI and NODDI models were fitted to the data, obtaining estimates for fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), neurite density index (NDI) and orientation dispersion index (ODI), after which we performed group comparisons using Tract-based spatial statistics (TBSS). While significant group effects were found in in FA, MD, RD, AD and NDI, NDI analysis uncovered a much wider involvement of brain tissue in HIV infection as compared to DTI. In region-of interest (ROI)-based analysis, NDI estimates from the right corticospinal tract produced excellent performance in discriminating the two groups (AUC = 0.974, sensitivity = 90%; specificity =97%).

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.

Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review

The presence of axillary lymph nodes metastases in breast cancer is the most significant prognostic factor, with a great impact on morbidity, disease-related survival and management of oncological therapies; for this reason, adequate imaging evaluation is strictly necessary. Physical examination is not enough sensitive to assess breast cancer nodal status; axillary ultrasonography (US) is commonly used to detect suspected or occult nodal metastasis, providing exclusively morphological evaluation, with low sensitivity and positive predictive value. Currently, sentinel lymph node biopsy (SLNB) and/or axillary dissection are the milestone for the diagnostic assessment of axillary lymph node metastases, although its related morbidity. The impact of magnetic resonance imaging (MRI) in the detection of nodal metastases has been widely investigated, as it continues to represent the most promising imaging modality in the breast cancer management. In particular, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values represent additional reliable non-contrast sequences, able to improve the diagnostic accuracy of breast cancer MRI evaluation. Several studies largely demonstrated the usefulness of implementing DWI/ADC MRI in the characterization of breast lesions. Herein, in the light of our clinical experience, we perform a review of the literature regarding the diagnostic performance and accuracy of ADC value as potential pre-operative tool to define metastatic involvement of nodal structures in breast cancer patients. For the purpose of this review, PubMed, Web of Science, and SCOPUS electronic databases were searched with different combinations of "axillary lymph node", "breast cancer", "MRI/ADC", "breast MRI" keywords. All original articles, reviews and metanalyses were included.

Parotid gland tumors: comparison of conventional and diffusion-weighted MRI findings with histopathological results

OBJECTIVES

The aim of this study was to investigate the relationship between pathological classification of parotid gland tumors and conventional MRI - diffusion-weighted imaging findings and also contribute the possible effect of apparent diffusion coefficient (ADC) to diagnosis.

METHODS

60 patients with parotid masses diagnosed using histopathology and/or cytology were enrolled in this retrospective study. All patients were evaluated using a 1.5 T MRI. Demographic features, conventional MRI findings, and ADC values (mean, minimum, maximum, and relative) were recorded. MRI findings and ADC values were compared between benign-malignant groups and pleomorphic adenoma vs Warthin's tumor groups.

RESULTS

60 tumors (48 benign, 12 malignant) were evaluated in a total of 60 patients (39 males, 21 females). The mean age was 59 (±14, 18-86) years old; the mean lesion size was 26 (±10, 11-61) mm. On the texture of conventional MRI, T2 dominantly hyperintense/with hypointensity signal was seen in 87% of pleomorphic adenomas and T2 dominantly hypointense/with hyperintesity signal was encountered in 64% of all Warthin's tumors. Seven (28%) Warthin's tumors were misdiagnosed as pleomorphic adenomas and two others (8%) as malignant tumors. The commonly used mean ADC value was 1.6 ± 0.6 × 10^-3 mm² s^-1 for benign tumors, 0.8 ± 0.3 × 10^-3 mm² s^-1 for malign tumors, 1 (0.9-1.8) × 10^-3 mm² s^-1 for Warthin's tumors, and 1.9 ± 0.3 × 10^-3 mm² s^-1 for pleomorphic adenomas. There was a statistically significant difference in ADC values between benign-malignant tumors and pleomorphic adenomas-Warthin's tumors.

CONCLUSIONS

Warthin's tumor may occasionally be misdiagnosed as pleomorphic adenoma and malignant tumor because of variable morphologic features. In addition to benign-malignant differentiation, the added ADC measurement may also be useful for differentiating Warthin's tumors from pleomorphic adenomas.

Narrative review of multiparametric ultrasound in parotid gland evaluation

Disorders affecting parotid gland represent a heterogeneous group comprising congenital, inflammatory and neoplastic diseases which show a focal or diffuse pattern of appearance. The differentiation of neoplastic from non-neoplastic conditions of parotid glands is pivotal for the diagnostic imaging. Frequently there is evidence of overlapping between the clinical and the imaging appearance of the various pathologies. The parotid gland is also often object of study with the combination of different techniques [ultrasound-computed tomography-magnetic resonance imaging (US-CT-MRI), ex.]. Compared to other dominant methods of medical imaging, US has several advantages providing images in real-time at lower cost, and without harmful use of ionizing radiation and of contrast enhancement. B-mode US, and the microvascular pattern color Doppler are usually used as first step evaluation of parotid lesions. Elastography and contrast-enhanced US (CEUS) has opened further possible perspectives to improve the differentiation between benign and malignant parotid lesions. The characterization of the parotid tumors plays a crucial role for their treatment planning and for the prediction of possible surgical complications. We present, here an updated review of the most recurrent pathologies of parotid gland focusing on the diagnostic power of multiparametric US including CEUS and ultrasound elastography (USE); limitations, advantages and the main key-points will be presented.

Texture analysis of routine T2 weighted fat-saturated images can identify head and neck paragangliomas - A pilot study

PURPOSE

To evaluate the role of the first and second-order texture parameters obtained from T2-weighted fat-saturated DIXON images in differentiating paragangliomas from other neck masses, and to develop a statistical model to classify them.

METHOD

We retrospectively evaluated 38 paragangliomas, 18 nerve-sheath tumours and 14 other miscellaneous neck lesions obtained from an IRB approved study conducted between January 2016 and June 2019; using a composite gold standard of histopathology, cytology and DOTANOC PET CT (A total of 70 lesions in 63 patients). Fat-suppressed T2weighted-DIXON axial images were used. First and second-order texture-parameters were calculated from the original and filtered images. Feature selection using F-statistics and collinearity analysis provided 14 texture parameters for further analysis. Mann-Whitney-U test was used to compare between the groups and p-values were adjusted for multiple comparisons. ROC curve analysis was used to obtain optimal cut-offs.

RESULTS

A total of ten texture features were found to be significantly different between paragangliomas and non-paraganglioma lesions. Minimum from the histogram of grey levels was lower in paragangliomas with a cut off of ≤113.462 obtaining 62.9 % sensitivity and 77.27 % specificity in differentiating paragangliomas from non-paragangliomas. Logistic regression model was trained (n-49) using forward feature selection, which when evaluated on the validation set(n-21)- obtained an AUC of 0.855(95 %CI, 0.633 to 0.968) with a positive likelihood ratio of 4.545 (95 %CI, 1.298-15.923) in differentiating paragangliomas from non-paragangliomas.

CONCLUSION

Texture analysis of a routine imaging sequence can identify paragangliomas with high accuracy. Further development of texture analysis would enable better imaging workflow, resource utilisation and imaging cost reductions.

Differentiation between nasopharyngeal carcinoma and lymphoma at the primary site using whole-tumor histogram analysis of apparent diffusion coefficient maps

INTRODUCTION

To determine the value of whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating nasopharyngeal carcinoma (NPC) from lymphoma (NPL) at the primary site METHOD AND MATERIALS: One hundred forty-seven patients with nasopharyngeal tumors (89 NPCs and 38 NPLs) who had undergone magnetic resonance imaging (MRI) and diffusion-weighted imaging were retrospectively analyzed. ADC histogram-derived parameters were compared between the NPC and NPL groups by using the Mann-Whitney U test. Receiver operating characteristic (ROC) curves of the histogram parameters were plotted for diagnostic accuracy. Sensitivity and specificity were calculated for each histogram parameter.

RESULTS

In whole-tumor histogram analysis, the mean, median, and 10th and 25th percentiles of ADC were all significantly higher in NPC than NPL (P = 0.045, P = 0.035, P = 0.005, and P = 0.016, respectively). Uniformity was significantly higher in NPC than NPL (P = 0.001). Skewness was significantly lower in NPC than NPL (P = 0.039). For the conventional ROI-based method, ADC_mean values were significantly higher in NPC than in NPL (P = 0.009). The ROC curve analysis showed that uniformity yielded the largest area under the curve (AUC = 0.768) for differentiating NPC from NPL among all ADC metrics, followed by 10th percentiles of ADC (AUC = 0.725); sensitivity and specificity were 76.5% and 71.4%, respectively.

CONCLUSION

Whole-tumor histogram analysis of ADC maps could be helpful for differentiating NPC from NPL.