Computed tomography/magnetic resonance imaging for mandibular boundary invasion of oral squamous cell carcinoma assessment.

BACKGROUND: The range of mandibular invasion by a tumour needs to be determined accurately to minimize unnecessary damage to the mandible. This study aimed to compare tumour boundary lines on computed tomography/magnetic resonance (CT/MR) images with those from pathological findings during the preoperative assessment of mandibular invasion by oral squamous cell carcinoma (OSCC). By comparing the methods, the potential of CT/MR for this application could be further elucidated. METHODS: Eight patients with OSCC were imaged with CT/MR, mandibular specimens were collected, and the material site was measured. Haematoxylin-eosin staining was used for histopathological assessment. The presence and boundaries of bone invasion were evaluated. The CT/MR and histopathological boundaries of bone invasion were delineated and merged to compare and calculate the deviation of CT/MR and histopathological boundaries using the Fréchet distance. RESULTS: The mean Fréchet distance between the CT and pathological tumour boundaries was 2.69 mm (standard error 0.46 mm), with a minimum of 1.18 mm, maximum of 3.64 mm, median of 3.10 mm, and 95% confidence interval of 1.40-3.97 mm. The mean Fréchet distance between the tumour boundaries on the MR and pathological images was 3.07 mm (standard error 0.56 mm), with a minimum of 1.53 mm, maximum of 4.74 mm, median of 2.90 mm, and 95% confidence interval of 1.53-4.61 mm. CONCLUSIONS: CT/MR imaging can provide an effective preoperative assessment of mandibular invasion of OSCC. Pathology images can be positioned on CT/MR scans with the help of computer software to improve the accuracy of the findings. The introduction of the Fréchet distance to compare tumour boundary lines is conducive to computer image diagnosis of tumour invasion of jaw boundaries.

Characterization of parotid gland tumors: Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion - A pilot study.

PURPOSE: To investigate the diagnostic performance of histogram features of diffusion parameters in characterizating parotid gland tumors. METHOD: From December 2018 to January 2023, patients who underwent diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) were consecutively enrolled in this retrospective study. The histogram features of diffusion parameters, including apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusion kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (DP), and perfusion fraction (FP) were analyzed. The Mann-Whitney U test was used for comparison between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs). Receiver operating characteristic curve and logistic regression analysis were used to identify the differential diagnostic performance. The Spearman's correlation coefficient was used to analyze the correlation between diffusion parameters and Ki-67 labeling index. RESULTS: For diffusion MRI, twenty-three histogram features of diffusion parameters showed significant differences between BPGTs and MPGTs (all P < 0.05). Compared with the DWI model, the IVIM model and combined model had better diagnostic specificity (58 %, 94 %, and 88 %, respectively; both corrected P < 0.001) and accuracy (64 %, 89 %, and 86 %, respectively; both corrected P = 0.006). The combined model was superior to the single DWI model with improved IDI (IDI improvement 0.25). Significant correlations were found between Ki-67 and ADCmean, Dkmean, Kmean, and Dmean (r = -0.57 to 0.53; all P < 0.05). CONCLUSIONS: Whole-tumor histogram analysis of IVIM and combined diffusion model could further improve the diagnostic performance for differentiating BPGTs from MPGTs.

Magnetic resonance imaging radiomics-based prediction of clinically significant prostate cancer in equivocal PI-RADS 3 lesions in the transitional zone.

Purpose: This bi-institutional study aimed to establish a robust model for predicting clinically significant prostate cancer (csPCa) (pathological grade group ≥ 2) in PI-RADS 3 lesions in the transition zone by comparing the performance of combination models. Materials and methods: This study included 243 consecutive men who underwent 3-Tesla magnetic resonance imaging (MRI) and ultrasound-guided transrectal biopsy from January 2020 and April 2022 which is divided into a training cohort of 170 patients and a separate testing cohort of 73 patients. T2WI and DWI images were manually segmented for PI-RADS 3 lesions for the mean ADC and radiomic analysis. Predictive clinical factors were identified using both univariate and multivariate logistic models. The least absolute shrinkage and selection operator (LASSO) regression models were deployed for feature selection and for constructing radiomic signatures. We developed nine models utilizing clinical factors, radiological features, and radiomics, leveraging logistic and XGboost methods. The performances of these models was subsequently compared using Receiver Operating Characteristic (ROC) analysis and the Delong test. Results: Out of the 243 participants with a median age of 70 years, 30 were diagnosed with csPCa, leaving 213 without a csPCa diagnosis. Prostate-specific antigen density (PSAD) stood out as the only significant clinical factor (odds ratio [OR], 1.068; 95% confidence interval [CI], 1.029-1.115), discovered through the univariate and multivariate logistic models. Seven radiomic features correlated with csPCa prediction. Notably, the XGboost model outperformed eight other models (AUC of the training cohort: 0.949, and validation cohort: 0.913). However, it did not surpass the PSAD+MADC model (P > 0.05) in the training and testing cohorts (AUC, 0.949 vs. 0.888 and 0.913 vs. 0.854, respectively). Conclusion: The machine learning XGboost model presented the best performance in predicting csPCa in PI-RADS 3 lesions within the transitional zone. However, the addition of radiomic classifiers did not display any significant enhancement over the compound model of clinical and radiological findings. The most exemplary and generalized option for quantitative prostate evaluation was Mean ADC+PSAD.

Diffusion kurtosis imaging and dynamic contrast-enhanced MRI for the differentiation of parotid gland tumors.

OBJECTIVE: To assess the usefulness of combined diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced MRI (DCE-MRI) in the differentiation of parotid gland tumors. METHODS: Seventy patients with 80 parotid gland tumors who underwent DKI and DCE-MRI were retrospectively enrolled and divided into four groups: pleomorphic adenomas (PAs), Warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). DCE-MRI and DKI quantitative parameters were measured. The Kruskal-Wallis H test and post hoc test with Bonferroni correction and ROC curve were used for statistical analysis. RESULTS: WTs demonstrated the highest Kep value (median 1.89, interquartile range [1.46-2.31] min-1) but lowest Ve value (0.20, [0.15-0.25]) compared with PAs (Kep, 0.34 [0.21-0.55] min-1; Ve, 0.36 [0.24-0.43]), OBTs (Kep, 1.22 [0.27-1.67] min-1; Ve, 0.28 [0.25-0.41]), and MTs (Kep, 0.71 [0.50-1.23] min-1; Ve, 0.35 [0.26-0.45]) (all p < .05). MTs had the lower D value (1.10, [0.88-1.29] × 10-3 mm2/s) compared with PAs (1.81, [1.60-2.20] × 10-3 mm2/s) and OBTs (1.57, [1.32-1.89] × 10-3 mm2/s) (both p < .05). PAs had the lower Ktrans value (0.12, [0.07-0.18] min-1) compared with OBTs (0.28, [0.11-0.50] min-1) (p < .05). The cutoff values of combined Kep and Ve, D, and Ktrans to distinguish WTs, MTs, and PAs sequentially were 1.06 min-1, 0.28, 1.46 × 10-3 mm2/s, and 0.21 min-1, respectively (accuracy, 89% [71/80], 91% [73/80], 78% [62/80], respectively). CONCLUSION: The combined use of DKI and DCE-MRI may help differentiate parotid gland tumors. KEY POINTS: • The combined use of DKI and DCE-MRI could facilitate the understanding of the pathophysiological characteristics of parotid gland tumors. • A stepwise diagnostic diagram based on the combined use of DCE-MRI parameters and the diffusion coefficient is helpful for accurate preoperative diagnosis in parotid gland tumors and may further facilitate the clinical management of patients.

Quantitative dynamic contrast-enhanced MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging in differentiating parotid gland tumors.

PURPOSE: To evaluate the ability of quantitative dynamic contrast-enhanced (DCE)-MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging (RESOLVE-DWI) in differentiating parotid tumors (PTs) with different histological types. METHODS: In this retrospective study, 123 patients with 145 histologically proven PTs who underwent both RESOLVE-DWI and DCE-MRI were enrolled including 51 pleomorphic adenomas (PAs), 52 Warthin's tumors (WTs), 27 other benign neoplasms (OBNs), and 15 malignant tumors (MTs). Quantitative parameters of DCE-MRI (Ktrans, Kep, and Ve) and the apparent diffusion coefficient (ADC) of lesions were calculated and analyzed. Kruskal-Wallis tests with Dunn-Bonferroni correction, logistic regression analyses, and receiver operating characteristic curve were used for statistical analyses. RESULTS: PAs exhibited a lowest Ktrans among these four PTs. WTs demonstrated the highest Kep and lowest Ve values. WTs and MTs showed lower ADCmin values than PAs and OBNs. The combination of Kep and Ve provided 98.1% sensitivity, 85% specificity, and 98.7% accuracy for differentiating WTs from the other three PTs. The ADCmin cutoff value of ≤ 0.826 yielded 80.0% sensitivity, 92.3% specificity, and 90.3% accuracy for the differentiation of MTs from PAs and OBNs. Ktrans with a cutoff value of ≤ 0.185 achieved a sensitivity, specificity, and accuracy of 84.3, 70.4, and 79.5%, respectively, for discriminating PAs from OBNs. CONCLUSION: The combination of quantitative DCE-MRI and RESOLVE-DWI is beneficial for characterizing four histological types of PTs.

Magnetic resonance imaging differentiates locoregional flaps from free flaps after reconstructive surgical treatment of tongue cancer.

OBJECTIVE: The aim of this study was to compare magnetic resonance imaging (MRI) features of reconstruction with locoregional flaps (LRFs) with free flaps (FFs) after surgical treatment for tongue cancer. STUDY DESIGN: In total, 115 cases of postoperative tongue carcinoma (67 cases of LRF surgery and 48 cases of FF surgery) were retrospectively reviewed. All patients had undergone nonenhanced and contrast-enhanced MRI at 0-4, 5-12, and 13-48 months after surgery. Signal intensity, margins, maximal size, contrast enhancement, change in the hyoglossus and mylohyoid muscles, recurrence, lymph node metastasis, and complications were evaluated. RESULTS: Significant differences were found between LRF and FF for signal intensity (P < .001) in all 3 periods, with LRF mostly isointense with muscle on T1-weighted images (T1WIs) and FF producing mixed hyperintensity with muscular striations in all cases in T1WIs and T2-weighted images (T2 WIs). Margin definition was similar between groups in the early period, but sharp margins were more common in FF after 4 months (P ≤ .018). LRF was significantly smaller than FF in all periods (P ≤ .017). Both mylohyoid and hyoglossus enlargements were common in the early period in both groups, but all cases became atrophic later. CONCLUSIONS: MRI can differentiate LRFs from FFs in a variety of parameters after flap reconstructive surgery for tongue cancer.

Differentiating intracranial solitary fibrous tumor/hemangiopericytoma from meningioma using diffusion-weighted imaging and susceptibility-weighted imaging.

PURPOSE: Intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) and meningioma are difficult to distinguish owing to their overlapping imaging manifestation on routine magnetic resonance imaging. The purpose of this study was to assess whether SFT/HPC can be differentiated from meningioma with diffusion-weighted imaging (DWI) and susceptibility-weighted imaging (SWI). METHODS: We retrospectively reviewed DWI, SWI, conventreional MR, and CT imaging features of 16 patients with SFT/HPC and 96 patients with meningioma. The apparent diffusion coefficient (ADC) value, normalized ADC (nADC) value, and degree of intratumoral susceptibility signal intensity (ITSS) were compared between SFT/HPCs and meningiomas using two-sample t tests, and among SFT/HPCs, low-grade and high-grade meningioma were tested using one-way analysis of variance (ANOVA). Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to determine the differentiation capacity. RESULTS: The ADC value, nADC value, and the degree of ITSS in SFT/HPC were significantly higher than those in low-grade and high-grade meningiomas (all p < 0.05). The threshold value of > 1.15 for nADC provided 75.00% sensitivity and 60.42% specificity for differentiating SFT/HPC from meningioma. Compared with nADC, the degree of ITSS had a moderate sensitivity (62.50%) and a higher specificity (85.42%) using the threshold value of > 1.00. Furthermore, combining DWI and SWI can achieve a relatively high differentiation capacity with a sensitivity of 81.25% and specificity of 78.12%. CONCLUSIONS: The nADC ratios and ITSS are useful for differentiating SFT/HPC from meningioma. Combining ITSS and nADC value appears to be a promising option for differential diagnosis.