Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer.

BACKGROUND: In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy. OBJECTIVE: This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status. METHODS: A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models. RESULTS: For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548-0.982), 0.801 (0.597-1.000), and 0.779 (0.567-0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548-0.982), 0.757 (0.538-0.977), and 0.779 (0.567-0.992), respectively. CONCLUSIONS: Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.

Effects of Model-Based Iterative Reconstruction in Low-Dose Paranasal Computed Tomography: A Comparison with Filtered Back Projection and Hybrid Iterative Reconstruction.

Iterative reconstruction (IR) improves image quality compared with filtered back projection (FBP). This study investigated the usefulness of model-based IR (forward-projected model-based iterative reconstruction solution [FIRST]) in comparison with FBP and hybrid IR (adaptive iterative dose reduction three-dimensional processing [AIDR 3D]) in low-dose paranasal CT. Twenty-four patients with paranasal sinusitis who underwent standard-dose CT (120 kV) and low-dose CT (100 kV) scanning before and after medical treatment were enrolled. Standard-dose CT scans were reconstructed with FBP (FBP120), and low-dose CT scans with FBP (FBP100), AIDR 3D, and FIRST. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in three anatomical structures and effective doses were compared using Mann-Whitney U test. Two radiologists independently evaluated the visibility of 16 anatomical structures, overall image quality, and artifacts. Effective doses in lowdose CT were significantly reduced compared with those in standard-dose CT (0.24 vs 0.43 mSv, p<0.001). FIRST achieved significantly higher SNR (p<0.01, respectively) and CNR (p<0.001, respectively) of evaluated structures and significant improvement in overall image quality (p<0.001), artifacts (p<0.001), and visibility related to muscles (p<0.05) compared to FBP120, FBP100, and AIDR 3D. FIRST allowed radiation-dose reduction, while maintaining objective and subjective image quality in low-dose paranasal CT.

Predicting the aggravation of coronavirus disease-19 pneumonia using chest computed tomography scans.

Presently, coronavirus disease-19 (COVID-19) is spreading worldwide without an effective treatment method. For COVID-19, which is often asymptomatic, it is essential to adopt a method that does not cause aggravation, as well as a method to prevent infection. Whether aggravation can be predicted by analyzing the extent of lung damage on chest computed tomography (CT) scans was examined. The extent of lung damage on pre-intubation chest CT scans of 277 patients with COVID-19 was assessed. It was observed that aggravation occurred when the CT scan showed extensive damage associated with ground-glass opacification and/or consolidation (p < 0.0001). The extent of lung damage was similar across the upper, middle, and lower fields. Furthermore, upon comparing the extent of lung damage based on the number of days after onset, a significant difference was found between the severe pneumonia group (SPG) with intubation or those who died and non-severe pneumonia group (NSPG) ≥3 days after onset, with aggravation observed when ≥14.5% of the lungs exhibited damage at 3-5 days (sensitivity: 88.2%, specificity: 72.4%) and when ≥20.1% of the lungs exhibited damage at 6-8 days (sensitivity: 88.2%, specificity: 69.4%). Patients with aggravation suddenly developed hypoxemia after 7 days from the onset; however, chest CT scans obtained in the paucisymptomatic phase without hypoxemia indicated that subsequent aggravation could be predicted based on the degree of lung damage. Furthermore, in subjects aged ≥65 years, a significant difference between the SPG and NSPG was observed in the extent of lung damage early beginning from 3 days after onset, and it was found that the degree of lung damage could serve as a predictor of aggravation. Therefore, to predict and improve prognosis through rapid and appropriate management, evaluating patients with factors indicating poor prognosis using chest CT is essential.

Serum gasdermin D levels are associated with the chest computed tomography findings and severity of COVID-19.

BACKGROUND: The role of programmed cell death, especially pyroptosis and apoptosis, in unfavorable immune responses in COVID-19 remains to be elucidated. METHODS: We conducted a cross-sectional analysis to investigate the association between the serum gasdermin D (GSDMD) levels, a pyroptotic marker, and caspase-cleaved cytokeratin 18 fragment (M30), an apoptotic marker, and the clinical status and abnormal chest computed tomography (CT) findings in patients with COVID-19. RESULTS: In this study, 46 patients diagnosed with COVID-19 were divided into the following three groups according to the disease severity: mild to moderate group (n = 10), severe group (n = 14), and critical group (n = 22). The serum GSDMD levels were higher in the critical group than in the mild to moderate group (P = 0.016). In contrast, serum M30 levels were lower in the critical group than in the severe group (P = 0.048). Patients who required mechanical ventilation or died had higher serum GSDMD levels than those who did not (P = 0.007). Area of consolidation only and of ground glass opacity plus consolidation positively correlated with serum GSDMD levels (r = 0.56, P < 0.001 and r = 0.53, P < 0.001, respectively). CONCLUSION: Higher serum GSDMD levels are associated with critical respiratory status and the consolidation area on chest CT in patients with COVID-19, suggesting that excessive activation of pyroptosis may affect the clinical manifestations in patients with COVID-19.

Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography.

To investigate the usefulness of texture analysis to discriminate between cervical lymph node (LN) metastasis from cancer of unknown primary (CUP) and cervical LN involvement of malignant lymphoma (ML) on unenhanced computed tomography (CT). Cervical LN metastases in 17 patients with CUP and cervical LN involvement in 17 patients with ML were assessed by 18F-FDG PET/CT. The texture features were obtained in the total cross-sectional area (CSA) of the targeted LN, following the contour of the largest cervical LN on unenhanced CT. Values for the max standardized uptake value (SUVmax) and the mean SUV value (SUVmean), and 34 texture features were compared using a Mann-Whitney U test. The diagnostic accuracy and area under the curve (AUC) of the combination of the texture features were evaluated by support vector machine (SVM) with nested cross-validation. The SUVmax and SUVmean did not differ significantly between cervical LN metastases from CUP and cervical LN involvement from ML. However, significant differences of 9 texture features of the total CSA were observed (p = 0.001 - 0.05). The best AUC value of 0.851 for the texture feature of the total CSA were obtained from the correlation in the gray-level co-occurrence matrix features. SVM had the best AUC and diagnostic accuracy of 0.930 and 84.8%. Radiomics analysis appears to be useful for differentiating cervical LN metastasis from CUP and cervical LN involvement of ML on unenhanced CT.

Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study.

OBJECTIVES: This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy. METHODS: Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training (N = 49) and test (N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy. The DL models that extracted imaging features on pre- and intra-treatment DWI and ADC maps were trained to predict the local recurrence within a 2-year follow-up. In the test group, each DL model was analyzed for recurrence prediction. Additionally, the Kaplan-Meier and multivariable Cox regression analyses were performed to evaluate the prognostic significance of the DL models and clinical variables. RESULTS: The highest area under the receiver operating characteristics curve and accuracy for predicting the local recurrence in the DL model were 0.767 and 81.0%, respectively, using intra-treatment DWI (DWIintra). The log-rank test showed that DWIintra was significantly associated with PFS (p = 0.013). DWIintra was an independent prognostic factor for PFS in multivariate analysis (p = 0.023). CONCLUSION: DL models using DWIintra may have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. The model-related findings may contribute to determining the therapeutic strategy in the early stage of the treatment. KEY POINTS: • Deep learning models using intra-treatment diffusion-weighted imaging have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. • The findings from these models may contribute to determining the therapeutic strategy at the early stage of the treatment.

Therapeutic efficacy of selective intra-arterial chemoradiotherapy with docetaxel and nedaplatin for fixed bulky nodal disease in head and neck cancer of unknown primary.

PURPOSE: Fixed bulky nodal disease in patients with head and neck cancer of unknown primary (HNCUP) remains difficult to treat. This retrospective study evaluated the therapeutic efficacy of selective intra-arterial chemoradiotherapy with docetaxel and nedaplatin for fixed bulky nodal disease in HNCUP. METHODS: Data from seven consecutive patients with fixed bulky nodal disease in HNCUP who had undergone selective intra-arterial chemoradiotherapy were analyzed. Whole pharyngeal mucosa and all bilateral nodal areas were irradiated (total dose 50 Gy), and bulky nodal lesions were provided an additional 20 Gy. Intra-arterial chemotherapy used a combination of nedaplatin (80 mg/m2) and docetaxel (60 mg/m2). Outcome measures were local control, disease-free survival, overall survival, and adverse events. Statistical analyses were performed using the Kaplan-Meier method. RESULTS: Median follow-up period was 24 months (range 9-64). All patients had extracapsular extension (N3b) on imaging and clinical findings. Symptoms due to bulky disease were neck discomfort (100%), tumor bleeding (43%), tracheal obstruction (14%), and carotid sinus syndrome (28%). Median value for maximum diameter of cervical disease was 84 mm (range 70-107), and 3-year local control, disease-free survival, and overall survival rates were 100, 54, and 64%, respectively. Symptoms due to bulky disease disappeared in all patients after intra-arterial chemoradiotherapy. Grade 4 leukopenia occurred in two patients (28%) as an acute adverse event. No other serious acute adverse events were observed. CONCLUSION: Selective intra-arterial chemoradiotherapy with docetaxel and nedaplatin can potentially achieve both favorable local control and survival in in HNCUP with fixed bulky nodal disease.

Therapeutic efficacy of selective intraarterial chemoradiotherapy with docetaxel and nedaplatin for human papilloma virus-negative oropharyngeal cancer.

OBJECTIVE: Human papilloma virus-negative oropharyngeal cancer has not achieved satisfactory outcomes compared with those of human papilloma virus-positive oropharyngeal cancer. This study evaluated the therapeutic efficacy of selective intraarterial chemoradiotherapy with the docetaxel and nedaplatin regimen for human papilloma virus-negative oropharyngeal cancer. METHODS: Twenty-two consecutive patients with human papilloma virus-negative oropharyngeal cancer who had undergone selective intraarterial chemoradiotherapy were retrospectively analyzed. The primary tumor and whole neck were irradiated (50 Gy). Subsequently, the primary site and metastatic lymph nodes were boosted by 20 Gy. The intraarterial chemotherapy regimen comprised a combination of nedaplatin (80 mg/m2) and docetaxel (60 mg/m2), which was initially administered at the start of radiotherapy and was given every 4 weeks for three sessions. Each intraarterial dose of an anticancer agent was determined according to the percentage of the tumor volume supplied by the target artery to the total tumor volume, which was intraoperatively measured via cone-beam computed tomography. The outcome measures were locoregional control, disease-free survival, and overall survival rates and adverse events. Statistical analyses were performed using the Kaplan-Meier method. RESULTS: The median follow-up period was 59 (range, 15-103) months. The T stage was T1/T2 in 5 patients (23%), T3 in 5 patients (23%), and T4 in 12 patients (54%). Cervical lymph node metastasis was staged as ≥N2c in 7 (32%) patients. Complete response was achieved in all patients at the first imaging examination after intraarterial chemoradiotherapy. The 5-year locoregional control, disease-free survival, and overall survival rates were 96% (95% confidence interval, 0.72-0.99), 91% (95% confidence interval, 0.68-0.98), and 100% (95% confidence interval, not available), respectively. Regarding serious acute adverse events, grade 4 laryngeal edema and leukopenia were observed in 1 (5%) and 11 patients (50%), respectively. No other serious acute adverse events were observed. CONCLUSION: Selective intraarterial chemoradiotherapy with docetaxel and nedaplatin has the potential to achieve favorable locoregional control, disease-free survival, and overall survival rates in human papilloma virus-negative oropharyngeal cancer.

Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography.

OBJECTIVE: Discriminating metastatic from benign cervical lymph nodes (LNs) in oral squamous cell carcinoma (OSCC) patients using pretreatment computed tomography (CT) has been controversial. This study aimed to investigate whether CT-based texture analysis with machine learning can accurately identify cervical lymph node metastasis in OSCC patients. METHODS: Twenty-three patients (with 201 cervical LNs [150 benign, 51 metastatic] at levels I-V) who underwent preoperative contrast-enhanced CT and subsequent cervical neck dissection were enrolled. Histopathologically proven LNs were randomly divided into the training cohort (70%; n = 141, at levels I-V) and validation cohort (30%; n = 60, at level I/II). Twenty-five texture features and the nodal size of targeted LNs were analyzed on the CT scans. The nodal-based sensitivities, specificities, diagnostic accuracy rates, and the area under the curves (AUCs) of the receiver operating characteristic curves of combined features using a support vector machine (SVM) at levels I/II, I, and II were evaluated and compared with two radiologists and a dentist (readers). RESULTS: In the validation cohort, the AUCs (0.820 at level I/II, 0.820 at level I, and 0.930 at level II, respectively) of the radiomics approach were superior to three readers (0.798-0.816, 0.773-0.798, and 0.825-0.865, respectively). The best models were more specific at levels I/II and I and accurate at each level than each of the readers (p < .05). CONCLUSIONS: Machine learning-based analysis with contrast-enhanced CT can be used to noninvasively differentiate between benign and metastatic cervical LNs in OSCC patients. KEY POINTS: • The best algorithm in the validation cohort can noninvasively differentiate between benign and metastatic cervical LNs at levels I/II, I, and II. • The AUCs of the model at each level were superior to those of multireaders. • Significant differences in the specificities at level I/II and I and diagnostic accuracy rates at each level between the model and multireaders were found.

Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma.

Differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma (ML) remains challenging on cross-sectional images. The aim of this study is to investigate the usefulness of texture features on unenhanced CT for differentiating between nasopharyngeal cancer and nasopharyngeal ML. Thirty patients with nasopharyngeal tumors, including 17 nasopharyngeal cancers and 13 nasopharyngeal MLs, were underwent 18F-FDG PET/CT. All nasopharyngeal cancers and 7 of 13 nasopharyngeal MLs were confirmed by endoscopic biopsy. On unenhanced CT, 34 texture features were analyzed following lesion segmentation in the maximum area of the target lesion. The Mann-Whitney U test and areas under the curve (AUCs) were used for analysis and to compare the maximum standardized uptake values (SUV)max, SUVmean, and 34 texture features. A support vector machine (SVM) was constructed to evaluate the diagnostic accuracy and AUCs of combinations of texture features, with 50 repetitions of 5-fold cross-validation. Differences between the SUVmax and SUVmean for nasopharyngeal cancers and nasopharyngeal MLs were not significant. Significant differences of texture features were seen, as follows: 1 histogram feature (p = 0.038), 3 gray-level co-occurrence matrix features (p < 0.05), and 1 neighborhood gray-level different matrix feature (NGLDM) (p = 0.003). Coarseness in NGLDM provided the highest diagnostic accuracy and largest AUC of 76.7% and 0.82, respectively. SVM evaluation of the combined texture features obtained the highest accuracy of 81.3%, with an AUC of 0.80. Combined texture features can provide useful information for discriminating between nasopharyngeal cancer and nasopharyngeal ML on unenhanced CT.

Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma.

We investigated the value of deep learning (DL) in differentiating between benign and metastatic cervical lymph nodes (LNs) using pretreatment contrast-enhanced computed tomography (CT). This retrospective study analyzed 86 metastatic and 234 benign (non-metastatic) cervical LNs at levels I-V in 39 patients with oral squamous cell carcinoma (OSCC) who underwent preoperative CT and neck dissection. LNs were randomly divided into training (70%), validation (10%), and test (20%) sets. For the validation and test sets, cervical LNs at levels I-II were evaluated. Convolutional neural network analysis was performed using Xception architecture. Two radiologists evaluated the possibility of metastasis to cervical LNs using a 4-point scale. The area under the curve of the DL model and the radiologists' assessments were calculated and compared at levels I-II, I, and II. In the test set, the area under the curves at levels I-II (0.898) and II (0.967) were significantly higher than those of each reader (both, p < 0.05). DL analysis of pretreatment contrast-enhanced CT can help classify cervical LNs in patients with OSCC with better diagnostic performance than radiologists' assessments alone. DL may be a valuable diagnostic tool for differentiating between benign and metastatic cervical LNs.

Combination of compressed sensing and parallel imaging for T2-weighted imaging of the oral cavity in healthy volunteers: comparison with parallel imaging.

OBJECTIVE: Compressed sensing (CS) and parallel imaging (PI) are magnetic resonance (MR) imaging acceleration techniques. Image quality of two-dimensional fast spin echo imaging of the oral cavity using CS or combined CS and PI has not been evaluated. The aim of this study was to compare the acquisition time and image quality between T2-weighted imaging (T2WI) with CS and PI (CSPI-T2WI) and T2WI with PI (PI-T2WI) of the oral cavity. MATERIALS AND METHODS: Twenty healthy volunteers who underwent CSPI-T2WI and PI-T2WI of the oral cavity on a 3 T MR scanner were enrolled in the study. Contrast ratios of fat/muscle and bone/muscle on CSPI-T2WI and PI-T2WI were measured. Overall image quality, 4 kinds of artifacts, and visualization of 18 anatomical structures were independently evaluated by two radiologists with grading scales. The quantitative and qualitative measurements were compared between CSPI-T2WI and PI-T2WI by using the Wilcoxon signed-rank test. RESULTS: Mean acquisition time of CSPI-T2WI and PI-T2WI was 72 s and 136 s, respectively (p < .001). CSPI-T2WI showed a significantly higher contrast ratio of fat/muscle than PI-T2WI (p < .01). There were no significant differences in the overall image quality, artifacts, and visualization of anatomical structures between CSPI-T2WI and PI-T2WI. CONCLUSIONS: CSPI-T2WI of the oral cavity in healthy volunteers can provide a reduction in acquisition time without impaired image quality compared to PI-T2WI. KEY POINTS: • The acquisition time of T2WI with the combined CS and PI provided a 47% reduction in acquisition time compared with T2WI with PI. • T2WI with the combined CS and PI did not show impaired image quality compared with T2WI with PI. • Combined CS and PI can be a useful technology to evaluate the oral cavity with high-speed acquisition.

Achieving high spatial and temporal resolution with perfusion MRI in the head and neck region using golden-angle radial sampling.

OBJECTIVES: Conventional perfusion-weighted MRI sequences often provide poor spatial or temporal resolution. We aimed to overcome this problem in head and neck protocols using a golden-angle radial sparse parallel (GRASP) sequence. METHODS: We prospectively included 58 patients for examination on a 3.0-T MRI using a study protocol. GRASP (A) was applied to a volumetric interpolated breath-hold examination (VIBE) with 135 reconstructed pictures and high temporal (2.5 s) and spatial resolution (0.94 × 0.94 × 3.00 mm). Additional sequences of matching temporal resolution (B: 2.5 s, 1.88 × 1.88 × 3.00 mm), with a compromise between temporal and spatial resolution (C: 7.0 s, 1.30 × 1.30 × 3.00 mm) and with matching spatial resolution (D: 145 s, 0.94 × 0.94 × 3.00 mm), were subsequently without GRASP. Instant inline-image reconstructions (E) provided one additional series of averaged contrast information throughout the entire acquisition duration of A. Overall diagnostic image quality, edge sharpness and contrast of soft tissues, vessels and lesions were subjectively rated using 5-point Likert scales. Objective image quality was measured as contrast-to-noise ratio in D and E. RESULTS: Overall, the anatomic and pathologic image quality was substantially better with the GRASP sequence for the temporally (A/B/C, all p < 0.001) and spatially resolved comparisons (D/E, all p < 0.002 except lesion edge sharpness with p = 0.291). Image artefacts were also less likely to occur with GRASP. Differences in motion, aliasing and truncation were mainly significant, but pulsation and fat suppression were comparable. In addition, the contrast-to-noise ratio of E was significantly better than that of D (pD-E < 0.001). CONCLUSIONS: High temporal and spatial resolution can be obtained synchronously using a GRASP-VIBE technique for perfusion evaluation in head and neck MRI. KEY POINTS: • Golden-angle radial sparse parallel (GRASP) sampling allows for temporally resolved dynamic acquisitions with a very high image quality. • Very low-contrast structures in the head and neck region can benefit from using the GRASP sequence. • Inline-image reconstruction of dynamic and static series from one single acquisition can replace the conventional combination of two acquisitions, thereby saving examination time.

CT texture analysis of mediastinal lymphadenopathy: Combining with US-based elastographic parameter and discrimination between sarcoidosis and lymph node metastasis from small cell lung cancer.

OBJECTIVES: To investigate the potential of computed tomography (CT)-based texture analysis and elastographic data provided by endobronchial ultrasonography (EBUS) for differentiating the mediastinal lymphadenopathy by sarcoidosis and small cell lung cancer (SCLC) metastasis. METHODS: Sixteen patients with sarcoidosis and 14 with SCLC were enrolled. On CT images showing the largest mediastinal lymph node, a fixed region of interest was drawn on the node, and texture features were automatically measured. Among the 30 patients, 19 (12 sarcoidosis and 7 SCLC) underwent endobronchial ultrasound transbronchial needle aspiration, and the fat-to-lesion strain ratio (FLR) was recorded. Texture features and FLRs were compared between the 2 patient groups. Logistic regression analysis was performed to evaluate the diagnostic accuracy of these measurements. RESULTS: Of the 31 texture features, the differences between 11 texture features of CT ROIs in the patients with sarcoidosis versus patients with SCLC were significant. Among them, the grey-level run length matrix with high gray-level run emphasis (GLRLM-HGRE) showed the greatest difference (P<0.01). Differences between FLRs were significant (P<0.05). Logistic regression analysis together with receiver operating characteristic curve analysis demonstrated that the FLR combined with the GLRLM-HGRE showed a high diagnostic accuracy (100% sensitivity, 92% specificity, 0.988 area under the curve) for discriminating between sarcoidosis and SCLC. CONCLUSION: Texture analysis, particularly combined with the FLR, is useful for discriminating between mediastinal lymphadenopathy caused by sarcoidosis from that caused by metastasis from SCLC.

Predictive Value of VIBE using Subtraction to Evaluate Idiopathic Facial Palsy after Starting Therapy.

PURPOSE: To determine the predictive value of 3-dimensional spoiled gradient-echo volumetric interpolated breath-hold examination (VIBE) using subtraction to evaluate the short-term effect of therapy for facial palsy. MATERIALS AND METHODS: We included 97 patients with idiopathic facial palsy (52 male, 45 female; aged 50.7 ±â€Š19.4 years) who underwent MR imaging with a contrast agent after starting therapy. The mean interval between onset and therapy was 1.55 ±â€Š1.69 days, between therapy and MR imaging was 3.19 ±â€Š2.78 days, and between MR imaging and assessment of the therapeutic effect was 3.50 ±â€Š0.71 days. The degree of therapeutic effect was determined using a 4-grade scale based on the House-Brackmann scale for grading facial nerve function. Two radiologists reviewed VIBE with pre- and postcontrast subtraction using the 4-point scale. We evaluated the diagnostic performance and compared the degree of therapeutic effect and enhancement of facial nerves that were divided into 5 segments bilaterally. RESULTS: We identified 98 facial palsy initially and significant enhancement in 55 facial nerves after the start of therapy and residual palsy in 87. Sensitivity for all facial palsy was 62.0 %, specificity was 90.9 %, positive predictive value was 98.2 %, negative predictive value was 23.3 %, and accuracy was 65.3 %. Eleven patients recovered completely, 1 showed significant enhancement, and the remaining 10 did not show significant enhancement of the facial nerve. CONCLUSION: VIBE has a potential to predict the prognostic outcome and assess facial palsy after the start of therapy. KEY POINTS: · Three-dimensional spoiled gradient-echo volumetric interpolated breath-hold examination (VIBE) using subtraction can be useful to predict residual facial palsy after initial therapy.. · Strong enhancement of the facial nerve on VIBE using subtraction was associated with residual facial palsy after the start of therapy.. · Patients with a favorable prognosis did not show strong enhancement.. CITATION FORMAT: · Tomita H, Detmar K, Nakajima Y et al. Predictive Value of VIBE using Subtraction to Evaluate Idiopathic Facial Palsy after Starting Therapy. Fortschr Röntgenstr 2020; 192: 1183 - 1189.

CT radiomics analysis of lung cancers: Differentiation of squamous cell carcinoma from adenocarcinoma, a correlative study with FDG uptake.

BACKGROUND AND PURPOSE: Pulmonary squamous cell carcinoma (SCC) was reported to have significantly higher histogram_kurtosis obtained by unenhanced computed tomography (CT) and a greater maximum standardized uptake value (SUVmax) determined by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) than pulmonary adenocarcinoma (AD). The first study aim was to investigate whether CT radiomics features could differentiate SCC from AD. The second aim was to investigate correlations between CT radiomics features and SUVmax. MATERIALS AND METHODS: Forty solid lung tumors < 3 cm in diameter pathologically proven to be SCC (n = 18) or AD (n = 22) were included. The SUVmax was determined by 18F-FDG-PET/CT and 42 CT radiomics features were obtained from unenhanced CT. A Mann-Whitney U test was used to compare the SUVmax and each CT radiomics feature to differentiate SCC from AD. The area under the curve (AUC) was calculated from receiver operating characteristic (ROC) analysis. A binary logistic regression analysis was performed for a combination of SUVmax with each CT radiomics feature. A Spearman rank correlation analysis was performed to determine correlations between SUVmax and CT radiomics features. RESULTS: Twenty-six of 42 CT radiomics features were significantly different between groups. The best AUC of the CT radiomics features was 0.81 for both the GLRLM_SRHGE and GLZLM_HGZE. The AUC value improved when the shape_sphericity feature was combined with SUVmax (AUC = 0.92). The CT radiomics features exhibiting strongest correlations with the SUVmax were different in both AD and SCC groups. CONCLUSION: CT radiomics features correlated with tumor structural characteristics of SCC and AD, and were closely related to metabolic information from FDG-PET/CT.

Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions.

RESULTS: The 34 nodules comprised 14 benign nodules and 20 malignant nodules. Iodine content and Hounsfield unit curve slopes did not differ significantly between benign and malignant thyroid nodules (P = 0.480-0.670). However, significant differences in the texture features of monochromatic images were observed between benign and malignant nodules: histogram mean and median, co-occurrence matrix contrast, gray-level gradient matrix (GLGM) skewness, and mean gradients and variance of gradients for GLGM at 80 keV (P = 0.014-0.044). The highest AUC was 0.77, for the histogram mean and median of images acquired at 80 keV. CONCLUSIONS: Texture features extracted from monochromatic images using DECT, specifically acquired at high keV, may be a promising diagnostic approach for thyroid nodules. A further large study for incidental thyroid nodules using DECT texture analysis is required to validate our results.

A case of medial pterygoid muscle metastasis of lung cancer presenting with trismus.

A man in his 60s with severe trismus was referred to our hospital. Based on computed tomography (CT), positron emission tomography/computed tomography (PET/CT), and biopsy, his initial diagnosis at another hospital was oropharyngeal cancer with cervical lymph node and distant metastases. After the review of the contrast-enhanced CT and reevaluated PET/CT images, we updated his diagnosis to left primary lung cancer that had metastasized to the cervical lymph nodes, bones, and skeletal muscles including the right medial pterygoid muscle. Since metastasis from primary lung cancer to the contralateral cervical lymph node is relatively rare, cervical lymph node metastases were thought to have originated from the metastatic lesion in the right medial pterygoid muscle. As metastases in the masticator muscles from lung cancer are rare, it is sometimes necessary to differentiate from primary head and neck lesions. Here, we report a rare case of lung cancer with the onset of trismus due to metastasis to the masticator muscle and perineural spread along the mandibular nerve.

Correlation between heart size and emphysema in patients with chronic obstructive pulmonary disease: CT-based analysis using inspiratory and expiratory scans.

The objective of this study was to investigate the relationship between the extent of emphysema and heart size in patients with chronic obstructive pulmonary disease (COPD) using inspiratory and expiratory chest computed tomography (CT). This retrospective study was approved by the institutional review board and informed consent was waived. We measured lung volume (LV), low attenuation area percent (%LAA; less than or equal to -950 HU), maximum cardiac area, and maximum transverse cardiac diameter on inspiratory/expiratory chest CT in 60 patients with COPD. Spearman rank correlation analysis was used to determine the correlations between the heart and lung CT measurements, and the correlations between these measurements and spirometric values. On inspiratory CT, the maximum transverse cardiac diameter was negatively correlated with LV ( ρ = -0.42; p < 0.01) and %LAA ( ρ = -0.43; p < 0.001). Furthermore, on expiratory CT, the maximum cardiac area was negatively correlated with LV ( ρ = -0.35; p < 0.01) and %LAA ( ρ = -0.37; p < 0.01), and there was a negative correlation between transverse cardiac diameter and %LAA ( ρ = -0.34; p < 0.01). Although inspiratory cardiac size was not correlated with any of the spirometric values, the maximum cardiac area and transverse diameter on expiratory scans were significantly correlated with the reduced airflow values on spirometry ( p < 0.01). In patients with COPD, the transverse cardiac diameter decreased as the emphysema progressed. A smaller cardiac area on expiratory CT suggested the presence of large LVs, emphysema, and airflow limitation in COPD.

Magnetic Resonance Imaging of a Urethral Caruncle and the Pathologic Correlation: A Report of 3 Cases.

A urethral caruncle is the most common disease of the urethra in postmenopausal women. A definitive diagnosis can usually be reached based on physical examination. Cross-sectional imaging is performed when malignant urethral tumor is suspected, such as a urethral carcinoma. No articles have discussed the detailed imaging of urethral caruncles. We present 3 patients with symptomatic urethral caruncles who underwent magnetic resonance imaging preoperatively.