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Purpose: To evaluate nnU-net's performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI. Methods: We collected T1-weighted (T1), T2-weighted and T1-weighted contrast-enhanced images with/without fat saturation (T2_FS/T2_nFS, T1c_FS/T1c_nFS) of OAL from four institutions. Two radiologists manually annotated lesions as the ground truth using ITK-SNAP. A deep learning framework, nnU-net, was developed and trained using two models. Model 1 was trained on T1, T2, and T1c, while Model 2 was trained exclusively on T1 and T2. A 5-fold cross-validation was utilized in the training process. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), sensitivity, and positive prediction value (PPV). Volumetric assessment was performed using Bland-Altman plots and Lin's concordance correlation coefficient (CCC). Results: A total of 147 patients from one center were selected as training set and 33 patients from three centers were regarded as test set. For both Model 1 and 2, nnU-net demonstrated outstanding segmentation performance on T2_FS with DSC of 0.80–0.82, PPV of 84.5–86.1%, and sensitivity of 77.6–81.2%, respectively. Model 2 failed to detect 19 cases of T1c, whereas the DSC, PPV, and sensitivity for T1_nFS were 0.59, 91.2%, and 51.4%, respectively. Bland–Altman plots revealed minor tumor volume differences with 0.22–1.24 cm3 between nnU-net prediction and ground truth on T2_FS. The CCC were 0.96 and 0.93 in Model 1 and 2 for T2_FS images, respectively. Conclusion: The nnU-net offered excellent performance in automated segmentation and volumetric assessment in MRI of OAL, particularly on T2_FS images. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to develop a multisequence MRI-based volumetric histogram metrics model for predicting pathological complete response (pCR) in advanced head and neck squamous cell carcinoma (HNSCC) patients undergoing neoadjuvant chemo-immunotherapy (NCIT) and compare its predictive performance with AJCC staging and RECIST 1.1 criteria. Methods: Twenty-four patients with locally advanced HNSCC from a prospective phase II trial were enrolled for analysis. All patients underwent pre- and post-NCIT MRI examinations from which whole-tumor histogram features were extracted, including T1WI, T2WI, enhanced T1WI (T1Gd), diffusion-weighted imaging (DWI) sequences, and their corresponding apparent diffusion coefficient (ADC) maps. The pathological results divided the patients into pathological complete response (pCR) and non-pCR (N-pCR) groups. Delta features were calculated as the percentage change in histogram features from pre- to post-treatment. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. Results: Eleven of 24 patients achieved pCR. Pre_T2_original_firstorder_Minimum, Post_ADC_original_firstorder_MeanAbsoluteDeviation, and Delta_T1Gd_original_firstorder_Skewness were associated with achieving pCR after NCIT. The Combined_Model demonstrated the best predictive performance (AUC 0.95), outperforming AJCC staging (AUC 0.52) and RECIST 1.1 (AUC 0.72). The Pre_Model (AUC 0.83) or Post-Model (AUC 0.83) had a better predictive ability than AJCC staging. Conclusion: Multisequence MRI-based volumetric histogram analysis can non-invasively predict the pCR status of HNSCC patients undergoing NCIT. The use of histogram features extracted from pre- and post-treatment MRI exhibits promising predictive performance and offers a novel quantitative assessment method for evaluating pCR in HNSCC patients receiving NCIT. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the feasibility of diffusion-weighted imaging (DWI) in combination with conventional MRI features to differentiate sinonasal malignant melanoma (SNMM) from sinonasal squamous cell carcinoma (SNSCC). Methods: A total of 37 patients with SNMM and 44 patients with SNSCC were retrospectively reviewed. Conventional MRI features and apparent diffusion coefficients (ADCs) were evaluated independently by two experienced head and neck radiologists. ADCs were obtained from two different regions of interest (ROIs) including maximum slice (MS) and small solid sample (SSS). Multivariate logistic regression analysis was performed to identify significant MR imaging features in discriminating between SNMM and SNSCC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance. Results: SNMMs were more frequently located in the nasal cavity, with well-defined border, T1 Septate Pattern (T1-SP) and heterogeneous T1 hyperintensity, whereas SNSCCs were more frequently located in the paranasal sinus, with homogenous T1 isointensity, ill-defined border, reticular or linear T2 hyperintensity, and pterygopalatine fossa or orbital involvement (all p < 0.05). The mean ADCs of SNMM (MS ADC, 0.85 × 10−3mm2/s; SSS ADC, 0.69 × 10−3mm2/s) were significantly lower than those of SNSCC (MS ADC, 1.05 × 10−3mm2/s; SSS ADC, 0.82 × 10−3mm2/s) (p < 0.05). With a combination of location, T1 signal intensity, reticular or linear T2 hyperintensity, and a cut-off MS ADC of 0.87 × 10−3mm2/s, the sensitivity, specificity, and AUC were 97.3%, 68.2%, and 0.89, respectively. Conclusion: DWI combined with conventional MRI can effectively improve the diagnostic performance in differentiating SNMM from SNSCC. [ABSTRACT FROM AUTHOR]

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Purpose: Among head and neck cancers, hypopharyngeal squamous cell carcinoma (HSCC) shows the highest malignancy, which is associated with histologic grading. This study was designed to investigate whether quantitative parameters derived from 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) can preoperatively estimate the histologic grade of HSCC. Methods: 18F-FDG PET/MRI of neck was successfully performed in 21 patients with histologically proven HSCC including poorly differentiated group (ten patients) and well-moderately differentiated group (eleven patients). Quantitative parameters derived from FDG-PET, diffusion-weighted imaging (DWI), and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) were calculated based on volume of interest drawn on the tumor and compared between two groups. The efficacy of quantitative parameters for the estimation of histologic grades of HSCC was evaluated. Results: There were statistically significant differences in mean value of standard uptake value (SUV), apparent diffusion coefficient (ADC), and Ktrans derived from 18F-FDG PET/MRI of HSCC between two groups (p < 0.05). There was no statistically significant difference in other quantitative parameters derived from 18F-FDG PET/MRI of HSCC between two groups. The area under the curve (AUC) of the combination of SUVmean, ADCmean, and Ktrans in the estimation of histologic grade of HSCC was 0.936 with sensitivity of 90.0% and specificity of 81.8%. Conclusion: The combination of SUVmean, ADCmean, and Ktrans derived from 18F-FDG PET/MRI can accurately predict the histologic grade of HSCC preoperatively. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate a dual-energy CT (DECT)–based radiomics nomogram from multicenter trials for predicting the histological differentiation of head and neck squamous cell carcinoma (HNSCC). Methods: A total of 178 patients (112 in the training and 66 in the validation cohorts) from eight institutions with histologically proven HNSCCs were included in this retrospective study. Radiomics-signature models were constructed from features extracted from virtual monoenergetic images (VMI) and iodine-based material decomposition images (IMDI), reconstructed from venous-phase DECT images. Clinical factors were also assessed to build a clinical model. Multivariate logistic regression analysis was used to develop a nomogram combining the radiomics signature models and clinical model for predicting poorly differentiated HNSCC and moderately well-differentiated HNSCC. The predictive performance of the clinical model, radiomics signature models, and nomogram was compared. The calibration degree of the nomogram was also assessed. Results: The tumor location, VMI-signature, and IMDI-signature were associated with the degree of HNSCC differentiation, and areas under the ROC curves (AUCs) were 0.729, 0.890, and 0.833 in the training cohort and 0.627, 0.859, and 0.843 in the validation cohort, respectively. The nomogram incorporating tumor location and two radiomics-signature models yielded the best performance in training (AUC = 0.987) and validation (AUC = 0.968) cohorts with a good calibration degree. Conclusion: The nomogram that integrated the DECT-based radiomics-signature models and tumor location showed good performance in predicting histological differentiation degree of HNSCC, providing a novel combination for predicting HNSCC differentiation. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate whether imaging features on conventional magnetic resonance imaging (MRI) can differentiate sinonasal extranodal natural killer/T cell lymphomas (ENKTL) from diffuse large B cell lymphoma (DLBCL). Methods: Consecutively, pathology-proven 59 patients with ENKTL and 27 patients with DLBCL in the sinonasal region were included in this study. Imaging features included tumor side, location, margin, pre-contrast T1 and T2 signal intensity and homogeneity, post-contrast enhancement degree and homogeneity, septal enhancement pattern, internal necrosis, mass effect, and adjacent involvements. These imaging features for each ENKTL or DLBCL on total 86 MRI scans were indicated independently by two experienced head and neck radiologists. The MRI-based performance in differential diagnosis of the two types of lymphomas was evaluated by multivariate logistic regression analysis. Results: All ENKTLs were located in the nasal cavity, with ill-defined margin, heterogeneous signal intensity, internal necrosis, marked enhancement of solid component on MRI, whereas DLBCLs were more often located in the paranasal sinuses, with MR homogenous intensity, mild enhancement, septal enhancement pattern, and intracranial or orbital involvements (all P < 0.05). Using a combination of location, internal necrosis and septal enhancement pattern of the tumor in multivariate logistic regression analysis, sensitivity, specificity, and accuracy in differential diagnosis of ENKTL and DLBCL were 100%, 79.4%, and 91.9%, respectively, for radiologist 1, and were 98.3%, 81.5%, and 93.0%, respectively, for radiologist 2. Conclusion: MRI can effectively differentiate ENKTL from DLBCL in the sinonasal region with a high diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the diagnostic performance of the quantitative parameters derived from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI in differentiating lacrimal gland pleomorphic adenomas (LGPAs) from lacrimal gland malignant epithelial tumors (LGMETs). Methods: Seventy-seven cases with LG epithelial tumors confirmed by histopathology (47 LGPAs and 30 LGMETs) underwent DWI and DCE-MRI. The quantitative parameters including the apparent diffusion coefficient (ADC), the volume transfer constant (Ktrans), the efflux rate constant from the extravascular extracellular space (EES) to blood plasma (Kep), and the extravascular extracellular volume fraction (Ve) were used to differentiate LGPAs from LGMETs. Independent-samples t test was conducted to compare these parameters. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Compared with LGPAs, LGMETs had significantly lower ADC value (1.090 ± 0.169mm2/s) (P < 0.001), higher Ktrans value (0.892 ± 0.517/min) (P = 0.001), and Kep value (1.300 ± 1.131/min) (P = 0.002). ADC as a diagnostic index showed a better diagnostic efficacy in predicting malignant tumors (AUC 0.914, sensitivity 90.0%, specificity 85.1%, and accuracy 87.0%) than Ktrans and Kep alone. The combination of ADC and Ktrans presented the optimal diagnostic performance for the differentiation (AUC 0.938, sensitivity 93.3%, specificity 87.2%, accuracy 89.6%). Conclusion: The quantitative parameters including ADC, Ktrans, and Kep derived from DWI and DCE-MRI might be potential imaging biomarkers in differentiating LGPAs from LGMETs. The combination of ADC and Ktrans is superior to other quantitative parameters in distinguishing LGPAs from LGMETs. [ABSTRACT FROM AUTHOR]

Introduction: Perfusion-CT (PCT) processing involves deconvolution, a mathematical operation that computes the perfusion parameters from the PCT time density curves and an arterial curve. Delay-sensitive deconvolution does not correct for arrival delay of contrast, whereas delay-insensitive deconvolution does. The goal of this study was to compare delay-sensitive and delay-insensitive deconvolution PCT in terms of delineation of the ischemic core and penumbra. Methods: We retrospectively identified 100 patients with acute ischemic stroke who underwent admission PCT and CT angiography (CTA), a follow-up vascular study to determine recanalization status, and a follow-up noncontrast head CT (NCT) or MRI to calculate final infarct volume. PCT datasets were processed twice, once using delay-sensitive deconvolution and once using delay-insensitive deconvolution. Regions of interest (ROIs) were drawn, and cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) in these ROIs were recorded and compared. Volume and geographic distribution of ischemic core and penumbra using both deconvolution methods were also recorded and compared. Results: MTT and CBF values are affected by the deconvolution method used ( p < 0.05), while CBV values remain unchanged. Optimal thresholds to delineate ischemic core and penumbra are different for delay-sensitive (145 % MTT, CBV 2 ml × 100 g × min) and delay-insensitive deconvolution (135 % MTT, CBV 2 ml × 100 g × min for delay-insensitive deconvolution). When applying these different thresholds, however, the predicted ischemic core ( p = 0.366) and penumbra ( p = 0.405) were similar with both methods. Conclusion: Both delay-sensitive and delay-insensitive deconvolution methods are appropriate for PCT processing in acute ischemic stroke patients. The predicted ischemic core and penumbra are similar with both methods when using different sets of thresholds, specific for each deconvolution method. [ABSTRACT FROM AUTHOR]