Tumor blood flow and apparent diffusion coefficient histogram analysis for differentiating malignant salivary tumors from pleomorphic adenomas and Warthin's tumors

A Combination of Amide Proton Transfer, Tumor Blood Flow, and Apparent Diffusion Coefficient Histogram Analysis Is Useful for Differentiating Malignant from Benign Intracranial Tumors in Young Patients: A Preliminary Study

PURPOSE

To evaluate the amide proton transfer (APT), tumor blood flow (TBF), and apparent diffusion coefficient (ADC) combined diagnostic value for differentiating intracranial malignant tumors (MTs) from benign tumors (BTs) in young patients, as defined by the 2021 World Health Organization classification of central nervous system tumors.

METHODS

Fifteen patients with intracranial MTs and 10 patients with BTs aged 0-30 years underwent MRI with APT, pseudocontinuous arterial spin labeling (pCASL), and diffusion-weighted imaging. All tumors were evaluated through the use of histogram analysis and the Mann-Whitney U test to compare 10 parameters for each sequence between the groups. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

The APT maximum, mean, 10th, 25th, 50th, 75th, and 90th percentiles were significantly higher in MTs than in BTs; the TBF minimum (min) was significantly lower in MTs than in BTs; TBF kurtosis was significantly higher in MTs than in BTs; the ADC min, 10th, and 25th percentiles were significantly lower in MTs than in BTs (all p < 0.05). The APT 50th percentile (0.900), TBF min (0.813), and ADC min (0.900) had the highest area under the curve (AUC) values of the parameters in each sequence. The AUC for the combination of these three parameters was 0.933.

CONCLUSIONS

The combination of APT, TBF, and ADC evaluated through histogram analysis may be useful for differentiating intracranial MTs from BTs in young patients.

Pseudocontinuous Arterial Spin Labeling: Clinical Applications and Usefulness in Head and Neck Entities

Simple Summary

Conventional imaging methods, such as ultrasonography, computed tomography, and magnetic resonance imaging may be inadequate to accurately diagnose lesions of the head and neck because they vary widely. Recently, the arterial spin labeling technique, especially pseudocontinuous arterial spin labeling (pCASL) with the three-dimensional (3D) readout method, has been dramatically developed to improve diagnostic performance for lesion differentiation, which can show prominent blood flow characteristics. Here, we demonstrate the clinical usefulness of 3D pCASL for diagnosing various entities, including inflammatory lesions, hypervascular lesions, and neoplasms in the head and neck, for evaluating squamous cell carcinoma (SCC) treatment responses, and for predicting SCC prognosis.

Abstract

As functional magnetic resonance imaging, arterial spin labeling (ASL) techniques have been developed to provide quantitative tissue blood flow measurements, which can improve the performance of lesion diagnosis. ASL does not require contrast agents, thus, it can be applied to a variety of patients regardless of renal impairments and contrast agent allergic reactions. The clinical implementation of head and neck lesions is limited, although, in recent years, ASL has been increasingly utilized in brain lesions. Here, we review the development of the ASL techniques, including pseudocontinuous ASL (pCASL). We compare readout methods between three-dimensional (3D) turbo spin-echo and 2D echo planar pCASL for the clinical applications of pCASL to head and neck lesions. We demonstrate the clinical usefulness of 3D pCASL for diagnosing various entities, including inflammatory lesions, hypervascular lesions, and neoplasms; for evaluating squamous cell carcinoma (SCC) treatment responses, and for predicting SCC prognosis.

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.