Multiparametric approach with synthetic MR imaging for diagnosing salivary gland lesions

PURPOSE

To determine whether synthetic MR imaging can distinguish between benign and malignant salivary gland lesions.

METHODS

The study population included 44 patients with 33 benign and 11 malignant salivary gland lesions. All MR imaging was obtained using a 3 Tesla system. The QRAPMASTER pulse sequence was used to acquire images with four TI values and two TE values, from which quantitative images of T1 and T2 relaxation times and proton density (PD) were generated. The Mann-Whitney U test was used to compare T1, T2, PD, and ADC values among the subtypes of salivary gland lesions. ROC analysis was used to evaluate diagnostic capability between malignant tumors (MTs) and either pleomorphic adenomas (PAs) or Warthin tumors (WTs). We further calculated diagnostic accuracy for distinguishing malignant from benign lesions when combining these parameters.

RESULTS

PAs demonstrated significantly higher T1, T2, PD, and ADC values than WTs (all p < 0.001). Compared to MTs, PAs had significantly higher T1, T2, and ADC values (all p < 0.001), whereas WTs had significantly lower T1, T2, and PD values (p < 0.001, p = 0.008, and p = 0.003, respectively). T2 and ADC were most effective in differentiating between MTs and PAs (AUC = 0.928 and 0.939, respectively), and T1 and PD values for differentiating between MTs and WTs (AUC = 0.915 and 0.833, respectively). Combining T1 with T2 or ADC achieved accuracy of 86.4% in distinguishing between malignant and benign tumors. Similarly, combining PD with T2 or ADC reached accuracy of 86.4% for differentiating between malignant and benign tumors.

CONCLUSIONS

Utilizing a combination of synthetic MRI parameters may assist in differentiating malignant from benign salivary gland lesions.

Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis

OBJECTIVES

To investigate the usefulness of machine learning (ML) models using pretreatment 18F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS).

MATERIALS AND METHODS

This retrospective study included 47 patients with CS who underwent 18F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 18F-FDG-PET-based radiomic features and the visibility of right ventricle 18F-FDG uptake were used to predict ACEs using seven different ML algorithms (namely, decision tree, random forest [RF], neural network, k-nearest neighbors, Naïve Bayes, logistic regression, and support vector machine [SVM]) with tenfold cross-validation and the synthetic minority over-sampling technique. The ML models were constructed using the top four features ranked by the decrease in Gini impurity. The AUCs and accuracies were used to compare predictive performances.

RESULTS

Patients who developed ACEs presented with a significantly higher surface area and gray level run length matrix run length non-uniformity (GLRLM_RLNU), and lower neighborhood gray-tone difference matrix_coarseness and sphericity than those without ACEs (each, p < 0.05). In the training cohort, all seven ML algorithms had a good classification performance with AUC values of > 0.80 (range: 0.841-0.944). In the testing cohort, the RF algorithm had the highest AUC and accuracy (88.9% [8/9]) with a similar classification performance between training and testing cohorts (AUC: 0.945 vs 0.889). GLRLM_RLNU was the most important feature of the modeling process of this RF algorithm.

CONCLUSION

ML analyses using 18F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.

Iodine contrast volume reduction in preoperative transcatheter aortic valve implantation computed tomography: Comparison with 64- and 256-multidetector row computed tomography

INTRODUCTION

This study aimed to compare the vascular enhancement and radiation dose in preoperative transcatheter aortic valve implantation (TAVI) computed tomography (CT) with a reduced contrast medium (CM) using volume scans in 256-multidetector row CT (MDCT) with a standard CM using 64-MDCT.

METHODS

This study included 78 patients with preoperative TAVI CT with either 64- or 256-MDCT. The CM was injected at 1.5 mL/kg in the 64-MDCT group and 1.0 mL/kg in the 256-MDCT group. We compared vascular enhancement of the aortic root and access routes, image quality (IQ) scores, and radiation dose in both groups.

RESULTS

Despite the reduced CM (by 33 %) in the 256-MDCT group, the mean vascular enhancement of the right and left subclavian arteries was significantly higher than that in the 64-MDCT group [284 and 267 Hounsfield units (HU) vs. 376 and 359 HU; p < 0.05]; however, no significant differences in the mean vascular enhancement in the ascending aorta, abdominal aorta at the celiac level, and bilateral common femoral arteries were observed between the two groups (p > 0.05 for all). The median IQ scores at the aortic root were higher in the 256-MDCT group than in the 64-MDCT group (3 vs. 4; p < 0.05), and those at the femoral access routes were comparable (4 vs. 4; p = 0.33). The mean effective dose was significantly reduced by 30 % in the 256-MDCT group (23.6 vs. 16.3 mSv; p < 0.05).

CONCLUSION

In preoperative TAVI CT, volume scans using 256-MDCT provide comparable or better vascular enhancement and IQ with a 30 % reduction in CM and radiation dose than those using 64-MDCT.

IMPLICATIONS FOR PRACTICE

Volume scan using 256-MDCT for preoperative TAVI CT may reduce CM and radiation dose in TAVI patients with renal dysfunction.

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.

Differentiating primary central nervous system lymphoma from glioblastoma by time-dependent diffusion using oscillating gradient

BACKGROUND

This study aimed to elucidate the impact of effective diffusion time setting on apparent diffusion coefficient (ADC)-based differentiation between primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBMs) and to investigate the usage of time-dependent diffusion magnetic resonance imaging (MRI) parameters.

METHODS

A retrospective study was conducted involving 21 patients with PCNSLs and 66 patients with GBMs using diffusion weighted imaging (DWI) sequences with oscillating gradient spin-echo (Δeff = 7.1 ms) and conventional pulsed gradient (Δeff = 44.5 ms). In addition to ADC maps at the two diffusion times (ADC7.1 ms and ADC44.5 ms), we generated maps of the ADC changes (cADC) and the relative ADC changes (rcADC) between the two diffusion times. Regions of interest were placed on enhancing regions and non-enhancing peritumoral regions. The mean and the fifth and 95th percentile values of each parameter were compared between PCNSLs and GBMs. The area under the receiver operating characteristic curve (AUC) values were used to compare the discriminating performances among the indices.

RESULTS

In enhancing regions, the mean and fifth and 95th percentile values of ADC44.5 ms and ADC7.1 ms in PCNSLs were significantly lower than those in GBMs (p = 0.02 for 95th percentile of ADC44.5 ms, p = 0.04 for ADC7.1 ms, and p < 0.01 for others). Furthermore, the mean and fifth and 95th percentile values of cADC and rcADC were significantly higher in PCNSLs than in GBMs (each p < 0.01). The AUC of the best-performing index for ADC7.1 ms was significantly lower than that for ADC44.5 ms (p < 0.001). The mean rcADC showed the highest discriminating performance (AUC = 0.920) among all indices. In peritumoral regions, no significant difference in any of the three indices of ADC44.5 ms, ADC7.1 ms, cADC, and rcADC was observed between PCNSLs and GBMs.

CONCLUSIONS

Effective diffusion time setting can have a crucial impact on the performance of ADC in differentiating between PCNSLs and GBMs. The time-dependent diffusion MRI parameters may be useful in the differentiation of these lesions.

Oscillating Gradient Diffusion-Weighted MRI for Risk Stratification of Uterine Endometrial Cancer

BACKGROUND

Oscillating gradient diffusion-weighted imaging (DWI) enables elucidation of microstructural characteristics in cancers; however, there are limited data to evaluate its utility in patients with endometrial cancer.

PURPOSE

To investigate the utility of oscillating gradient DWI for risk stratification in patients with uterine endometrial cancer compared with conventional pulsed gradient DWI.

STUDY TYPE

Retrospective.

SUBJECTS

Sixty-three women (mean age: 58 [range: 32-85] years) with endometrial cancer.

FIELD STRENGTH/SEQUENCE

3 T MRI including DWI using oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) research sequences.

ASSESSMENT

Mean value of the apparent diffusion coefficient (ADC) values for OGSE (ADCOGSE ) and PGSE (ADCPGSE ) as well as the ADC ratio (ADCOGSE /ADCPGSE ) within endometrial cancer were measured using regions of interest. Prognostic factors (histological grade, deep myometrial invasion, lymphovascular invasion, International Federation of Gynecology and Obstetrics [FIGO] stage, and prognostic risk classification) were tabulated.

STATISTICAL TESTS

Interobserver agreement was analyzed by calculating the intraclass correlation coefficient. The associations of ADCOGSE , ADCPGSE , and ADCOGSE /ADCPGSE with prognostic factors were examined using the Kendall rank correlation coefficient, Mann-Whitney U test, and receiver operating characteristic (ROC) curve. A P value of <0.05 was statistically significant.

RESULTS

Compared with ADCOGSE and ADCPGSE , ADCOGSE /ADCPGSE was significantly and strongly correlated with histological grade (observer 1, τ = 0.563; observer 2, τ = 0.456), FIGO stage (observer 1, τ = 0.354; observer 2, τ = 0.324), and prognostic risk classification (observer 1, τ = 0.456; observer 2, τ = 0.385). The area under the ROC curves of ADCOGSE /ADCPGSE for histological grade (observer 1, 0.92, 95% confidence intervals [CIs]: 0.83-0.98; observer 2, 0.84, 95% CI: 0.73-0.92) and prognostic risk (observer 1, 0.80, 95% CI: 0.68-0.89; observer 2, 0.76, 95% CI: 0.63-0.86) were significantly higher than that of ADCOGSE and ADCPGSE .

DATA CONCLUSION

The ADC ratio obtained via oscillating gradient and pulsed gradient DWIs might be useful imaging biomarkers for risk stratification in patients with endometrial cancer.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer

PURPOSE

To develop and identify machine learning (ML) models using pretreatment clinical and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]-FDG-PET)-based radiomic characteristics to predict disease recurrences in patients with breast cancers who underwent surgery.

PROCEDURES

This retrospective study included 112 patients with 118 breast cancer lesions who underwent [18F]-FDG-PET/ X-ray computed tomography (CT) preoperatively, and these lesions were assigned to training (n=95) and testing (n=23) cohorts. A total of 12 clinical and 40 [18F]-FDG-PET-based radiomic characteristics were used to predict recurrences using 7 different ML algorithms, namely, decision tree, random forest (RF), neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine (SVM) with a 10-fold cross-validation and synthetic minority over-sampling technique. Three different ML models were created using clinical characteristics (clinical ML models), radiomic characteristics (radiomic ML models), and both clinical and radiomic characteristics (combined ML models). Each ML model was constructed using the top ten characteristics ranked by the decrease in Gini impurity. The areas under ROC curves (AUCs) and accuracies were used to compare predictive performances.

RESULTS

In training cohorts, all 7 ML algorithms except for logistic regression algorithm in the radiomics ML model (AUC = 0.760) achieved AUC values of >0.80 for predicting recurrences with clinical (range, 0.892-0.999), radiomic (range, 0.809-0.984), and combined (range, 0.897-0.999) ML models. In testing cohorts, the RF algorithm of combined ML model achieved the highest AUC and accuracy (95.7% (22/23)) with similar classification performance between training and testing cohorts (AUC: training cohort, 0.999; testing cohort, 0.992). The important characteristics for modeling process of this RF algorithm were radiomic GLZLM_ZLNU and AJCC stage.

CONCLUSIONS

ML analyses using both clinical and [18F]-FDG-PET-based radiomic characteristics may be useful for predicting recurrence in patients with breast cancers who underwent surgery.

The usefulness of machine-learning-based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer

OBJECTIVE

To examine whether machine learning (ML) analyses involving clinical and 18F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer.

METHODS

This retrospective study included 49 patients with laryngeal cancer who underwent18F-FDG-PET/CT before treatment, and these patients were divided into the training (n = 34) and testing (n = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N stage, Union for International Cancer Control stage, and treatment) and 40 18F-FDG-PET-based radiomic features were used to predict disease progression and survival. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were used for predicting disease progression. Two ML algorithms (cox proportional hazard and random survival forest [RSF] model) considering for time-to-event outcomes were used to assess progression-free survival (PFS), and prediction performance was assessed by the concordance index (C-index).

RESULTS

Tumor size, T stage, N stage, GLZLM_ZLNU, and GLCM_Entropy were the five most important features for predicting disease progression.In both cohorts, the naïve Bayes model constructed by these five features was the best performing classifier (training: AUC = 0.805; testing: AUC = 0.842). The RSF model using the five features (tumor size, GLZLM_ZLNU, GLCM_Entropy, GLRLM_LRHGE and GLRLM_SRHGE) exhibited the highest performance in predicting PFS (training: C-index = 0.840; testing: C-index = 0.808).

CONCLUSION

ML analyses involving clinical and 18F-FDG-PET-based radiomic features may help predict disease progression and survival in patients with laryngeal cancer.

ADVANCES IN KNOWLEDGE

ML approach using clinical and 18F-FDG-PET-based radiomic features has the potential to predict prognosis of laryngeal cancer.

Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI

BACKGROUND

This study was designed to investigate the use of time-dependent diffusion magnetic resonance imaging (MRI) parameters in distinguishing between glioblastomas and brain metastases.

METHODS

A retrospective study was conducted involving 65 patients with glioblastomas and 27 patients with metastases using a diffusion-weighted imaging sequence with oscillating gradient spin-echo (OGSE, 50 Hz) and a conventional pulsed gradient spin-echo (PGSE, 0 Hz) sequence. In addition to apparent diffusion coefficient (ADC) maps from two sequences (ADC50Hz and ADC0Hz), we generated maps of the ADC change (cADC): ADC50Hz - ADC0Hz and the relative ADC change (rcADC): (ADC50Hz - ADC0Hz)/ ADC0Hz × 100 (%).

RESULTS

The mean and the fifth and 95th percentile values of each parameter in enhancing and peritumoral regions were compared between glioblastomas and metastases. The area under the receiver operating characteristic curve (AUC) values of the best discriminating indices were compared. In enhancing regions, none of the indices of ADC0Hz and ADC50Hz showed significant differences between metastases and glioblastomas. The mean cADC and rcADC values of metastases were significantly higher than those of glioblastomas (0.24 ± 0.12 × 10-3mm2/s vs. 0.14 ± 0.03 × 10-3mm2/s and 23.3 ± 9.4% vs. 14.0 ± 4.7%; all p < 0.01). In peritumoral regions, no significant difference in all ADC indices was observed between metastases and glioblastomas. The AUC values for the mean cADC (0.877) and rcADC (0.819) values in enhancing regions were significantly higher than those for ADC0Hz5th (0.595; all p < 0.001).

CONCLUSIONS

The time-dependent diffusion MRI parameters may be useful for differentiating brain metastases from glioblastomas.

A case of glioblastoma harboring non-amplified epidermal growth factor receptor variant III: Critical molecular detection using RNA-based panel analysis

Amplification of the epidermal growth factor receptor gene (EGFR) and its variants are the most commonly detected pathogenic gene alterations in glioblastoma. Herein, we report a case of molecularly defined glioblastoma harboring an EGFR variant III (EGFRvIII) without EGFR amplification. The initial histological diagnosis was isocitrate dehydrogenase (IDH)-wildtype low-grade glioma, due to an absence of anaplasia, necrosis, and microvascular proliferation, and a low Ki-67 labeling index. DNA-based next-generation sequencing (NGS) panel analysis revealed a TERTp promoter mutation but no EGFR mutation or amplification, supporting the diagnosis of "molecular glioblastoma." However, RNA-based NGS panel analysis revealed mRNA expression of EGFRvIII. Therefore, the final integrative diagnosis was glioblastoma with non-amplified EGFRvIII. Our report suggests that non-amplified EGFRvIII might be an early molecular event in glioblastoma tumorigenesis. In addition to the usual DNA-based analysis, RNA-based analysis is required to identify exon-skipping EGFR variants without EGFR amplification.

Extracellular volume fraction derived from equilibrium contrast-enhanced CT as a diagnostic parameter in anterior mediastinal tumors

PURPOSE

To assess the usefulness of extracellular volume (ECV) fraction derived from equilibrium contrast-enhanced CT (CECT) for diagnosing anterior mediastinal tumors.

METHOD

This study included 161 histologically confirmed anterior mediastinal tumors (55 low-risk thymomas, 57 high-risk thymomas, 32 thymic carcinomas, and 17 malignant lymphomas) that were assessed by pretreatment CECT. ECV fraction was calculated using measurements obtained within the lesion and the aorta on unenhanced and equilibrium phase CECT. ECV fraction was compared among anterior mediastinal tumors using one-way ANOVA or t-test. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the ability of ECV fraction to differentiate thymic carcinomas/lymphomas from thymomas.

RESULTS

ECV fraction differed significantly among the anterior mediastinal tumors (p < 0.001). ECV fraction of thymic carcinomas was significantly higher than those of low-risk thymomas, high-risk thymomas, and lymphomas (p < 0.001, p < 0.001, and p = 0.006, respectively). ECV fraction of lymphomas was significantly higher than that of low-risk thymomas (p < 0.001). ECV fraction was significantly higher in thymic carcinomas/lymphomas than in thymomas (40.1 % vs. 27.7 %, p < 0.001). The optimal cutoff value to differentiate thymic carcinomas/lymphomas from thymomas was 38.5 % (AUC, 0.805; 95 %CI, 0.736-0.863).

CONCLUSIONS

ECV fraction derived from equilibrium CECT is helpful in diagnosing anterior mediastinal tumors. High ECV fraction is indicative of thymic carcinomas/lymphomas, particularly thymic carcinomas.

Cine MR feature tracking analysis for diagnosing thymic epithelial tumors: a feasibility study

BACKGROUND

To assess the feasibility of the cine MR feature tracking technique for the evaluation of cardiovascular-induced morphological deformation in the diagnosis of thymic epithelial tumors (TETs).

METHODS

Our study population consisted of 43 patients with pathologically proven TETs including 10 low-grade thymomas, 23 high-grade thymomas, and 10 thymic carcinomas. Cine MR images were acquired using a balanced steady-state free precession sequence with short periods of breath-hold in the axial and oblique planes in the slice with the largest lesion cross-sectional area. The tumor margin was manually delineated in the diastolic phase and was automatically tracked for all other cardiac phases. The change rates of the long-to-short diameter ratio (∆LSR) and tumor area (∆area) associated with pulsation were compared between the three pathological groups using the Kruskal-Wallis H test and the Mann-Whitney U test. A receiver-operating characteristic (ROC) curve analysis was performed to assess the ability of each parameter to differentiate thymic carcinomas from thymomas.

RESULTS

∆LSR and ∆area were significantly different among the three groups in the axial plane (p = 0.028 and 0.006, respectively) and in the oblique plane (p = 0.034 and 0.043, respectively). ∆LSR and ∆area values were significantly lower in thymic carcinomas than in thymomas in the axial plane (for both, p = 0.012) and in the oblique plane (p = 0.015 and 0.011, respectively). The area under the ROC curves for ∆LSR and ∆area for the diagnosis of thymic carcinoma ranged from 0.755 to 0.764.

CONCLUSIONS

Evaluation of morphological deformation using cine-MR feature tracking analysis can help diagnose histopathological subtypes of TETs and identify thymic carcinomas preoperatively.

Left atrial CT volume and CHA2DS2-VASc score predict early pulmonary vein stump thrombus after left upper lobectomy

The purpose of this study is to clarify the feasibility of left atrial (LA) volume measurement and CHA₂DS₂-VASc score for predicting the development of pulmonary vein (PV) stump thrombus after left upper lobectomy (LUL). The study population comprised 50 patients who underwent LUL for pulmonary lesions. All patients were evaluated for the development of PV stump thrombus at 7 days after LUL. LA volume was measured using preoperative CT and the CHA₂DS₂-VASc score was evaluated. LA volume and CHA₂DS₂-VASc score were compared between patients with and without the development of PV stump thrombus using the Mann-Whitney U test. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the accuracy of prediction of PV stump thrombus development. PV stump thrombus was detected in 17 (33.4%) of the 50 patients. LA volume was significantly greater in patients who developed PV stump thrombus than in those without thrombus (79.7 ± 19.4 vs. 66.6 ± 17.0 mL, p = 0.040). CHA₂DS₂-VASc score was significantly higher in patients with PV stump thrombosis than in those without thrombus (3.4 ± 1.5 vs. 2.5 ± 1.5, p = 0.039). Area under the ROC curve values for predicting PV stump thrombus were 0.679, 0.676, and 0.714 for LA volume, CHA₂DS₂-VASc score, and their combination, respectively. In conclusion, LA volume measured using preoperative CT and CHA₂DS₂-VASc score may help predict the development of PV stump thrombus after LUL.

Feasibility of iodine concentration and extracellular volume fraction measurement derived from the equilibrium phase dual-energy CT for differentiating thymic epithelial tumors

PURPOSE

To assess the diagnostic feasibility of iodine concentration (IC) and extracellular volume (ECV) fraction measurement using the equilibrium phase dual-energy CT (DECT) for the evaluation of thymic epithelial tumors (TETs).

MATERIALS AND METHODS

This study included 33 TETs (11 low-risk thymomas, 11 high-risk thymomas, and 11 thymic carcinomas) that were assessed by pretreatment DECT. IC was measured during the equilibrium phases and ECV fraction was calculated using IC of the thymic lesion and the aorta. IC and ECV fraction were compared among TET subtypes using the Kruskal-Wallis H test and Mann-Whitney U test. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the ability of IC and ECV fraction to diagnose thymic carcinoma.

RESULTS

IC during the equilibrium phase and ECV fraction differed among the three TET groups (both p < 0.001). IC during the equilibrium phase and ECV fraction was significantly higher in thymic carcinomas than in thymomas (1.9 mg/mL vs. 1.2 mg/mL, p < 0.001; 38.2% vs. 25.9%, p < 0.001; respectively). The optimal cutoff values of IC during the equilibrium phase and of ECV fraction to diagnose thymic carcinoma were 1.5 mg/mL (AUC, 0.955; sensitivity, 100%; specificity, 90.9%) and 26.8% (AUC, 0.888; sensitivity, 100%; specificity, 72.7%), respectively.

CONCLUSION

IC and ECV fraction measurement using DECT are helpful in diagnosing TETs. High IC during the equilibrium phase and high ECV fraction are suggestive of thymic carcinoma.

Alterations in EGFR and PDGFRA are associated with the localization of contrast-enhancing lesions in glioblastoma

Background

Glioblastoma (GBM) is a malignant brain tumor, with radiological and genetic heterogeneity. We examined the association between radiological characteristics and driver gene alterations.

Methods

We analyzed the driver genes of 124 patients with IDH wild-type GBM with contrast enhancement using magnetic resonance imaging. We used a next-generation sequencing panel to identify mutations in driver genes and matched them with radiological information. Contrast-enhancing lesion localization of GBMs was classified into 4 groups based on their relationship with the subventricular zone (SVZ) and cortex (Ctx).

Results

The cohort included 69 men (55.6%) and 55 women (44.4%) with a mean age of 66.4 ± 13.3 years. EGFR and PDGFRA alterations were detected in 28.2% and 22.6% of the patients, respectively. Contrast-enhancing lesion touching both the SVZ and Ctx was excluded because it was difficult to determine whether it originated from the SVZ or Ctx. Contrast-enhancing lesions touching the SVZ but not the Ctx had significantly worse overall survival than non-SVZ lesions (441 days vs. 897 days, P = .002). GBM touching only the Ctx had a better prognosis (901 days vs. 473 days, P < .001) than non-Ctx lesions and was associated with EGFR alteration (39.4% vs. 13.2%, P = .015). Multiple contrast lesions were predominant in PDGFRA alteration and RB1-wild type (P = .036 and P = .031, respectively).

Conclusions

EGFR alteration was associated with cortical lesions. And PDGFRA alteration correlated with multiple lesions. Our results suggest that clarifying the association between driver genes and tumor localization may be useful in clinical practice, including prognosis prediction.

Estimation of undistorted images in brain echo-planar images with distortions using the conjugate gradient method with anatomical regularization

PURPOSE

Although echo-planar imaging (EPI) is widely used for diffusion magnetic resonance (MR) imaging, EPI images suffer from susceptibility-induced geometric distortions. We herein propose a new estimation method for undistorted EPI images using anatomical T₁ -weighted images (T₁ WIs) based on the physics of MR imaging.

METHODS

Our proposed method estimates the undistorted EPI image in the image domain while estimating the magnetic field inhomogeneity map using the conjugate gradient method with anatomical regularization. Our method synthesizes the distorted image to match the measured EPI image containing geometric distortions by alternately updating the undistorted EPI image and the magnetic field inhomogeneity map. We evaluated our proposed method and compared it with a nonrigid registration-based distortion correction method using simulated data and using real data. In the evaluation of the estimation of the magnetic field inhomogeneity map, we used the normalized root-mean-squared error (NRMSE) between the estimated results and the ground truth. In the evaluation of the estimation of undistorted images, we used mutual information (MI) between the undistorted EPI image and the anatomical T₁ WI.

RESULTS

Using the simulated data, the means and standard deviations of the NRMSE values in the nonrigid registration-based method and proposed method were 1.29 ± 0.63 and 0.64 ± 0.30, respectively. The MI values in the proposed method were larger than those in the nonrigid registration-based method in all evaluated slices. For the real data, the proposed method improved the distortion, and the MI values in the proposed method were larger than those in the nonrigid registration-based method. In the estimation of the magnetic field inhomogeneity map, the NRMSE values in our method were smaller than those in the nonrigid registration-based method.

CONCLUSIONS

We demonstrated that our proposed method can estimate the regions with compressed distortions that are not well represented by the nonrigid registration-based methods. The results suggest that the proposed method could be useful in analyses combining EPI images with T₁ WIs.

Application of 123I-MIBG myocardial maximum standardized uptake value to characterize cardiac function in patients with pheochromocytoma: comparison with echocardiography

Purpose

This study examined the usefulness of the maximum standardized uptake value (SUVmax) of myocardial [123I]-metaiodobenzylguanidine ([123I]-MIBG) to characterize myocardial function by comparing it with echocardiographic parameters in patients with pheochromocytoma.

Materials and methods

This study included 18 patients with pheochromocytoma who underwent both planar and [123I]-MIBG single-photon emission computed tomography/computed tomography scans and echocardiography before surgery. Myocardial [123I]-MIBG visibility and SUVmax were compared with echocardiographic parameters related to systolic and diastolic functions. The Mann–Whitney U test, Fisher exact test, or Spearman rank correlation assessed differences or relationships between two quantitative variables.

Results

On visual analysis, 6 patients showed normal myocardial [123I]-MIBG uptake, whereas 12 patients showed decreased myocardial [123I]-MIBG uptake. No patients showed systolic dysfunction. A significant difference was observed in the incidence of diastolic dysfunction between the groups with normal and decreased uptake (p = 0.009), and left ventricular (LV) diastolic dysfunction was observed in 9 (75%) of 12 patients with decreased myocardial uptake. The myocardial SUVmax was significantly lower in 9 patients with LV diastolic dysfunction than in 9 patients with normal cardiac function (1.67 ± 0.37 vs. 3.03 ± 1.38, p = 0.047). Myocardial SUVmax was positively correlated with septal e′ (early diastolic velocity of septal mitral annulus) (ρ = 0.51, p = 0.031) and negatively correlated with the septal E/e′ ratio (early mitral E-velocity to early diastolic velocity of septal mitral annulus; ρ =  − 0.64, p = 0.004), respectively.

Conclusions

LV diastolic dysfunction was inversely related to myocardial [123I]-MIBG uptake. Myocardial [123I]-MIBG SUVmax may be useful for characterizing cardiac function in patients with pheochromocytoma.

Second abstract.

The semiquantitative analysis using the myocardial SUVmax in 123I-MIBG SPECT/CT was found to be potentially useful for characterizing cardiac function in patients with pheochromocytoma.

Value of Dual-Energy Computed Tomography for Detecting Small Pancreatic Ductal Adenocarcinoma

OBJECTIVE

The aim of the study is to evaluate the usefulness of virtual monoenergetic imaging (VMI) generated from dual-energy computed tomography (DECT) in detecting small pancreatic ductal adenocarcinomas (PDACs).

METHODS

This study included 82 patients pathologically diagnosed with small PDAC (≤30 mm) and 20 without pancreatic tumors who underwent triple-phase contrast-enhanced DECT. To assess diagnostic performance for small PDAC detection via a receiver operating characteristic analysis, 3 observers reviewed 2 image sets (conventional computed tomography [CT] set and combined image set [conventional CT + 40-keV VMI from DECT]). The tumor-to-pancreas contrast-to-noise ratio was compared between conventional CT and 40-keV VMI from DECT.

RESULTS

The area under the receiver operating characteristic curve of the 3 observers were 0.97, 0.96, and 0.97 in conventional CT set and 0.99, 0.99, and 0.99 in combined image set (P = 0.017-0.028), respectively. The combined image set yielded a better sensitivity than the conventional CT set (P = 0.001-0.023), without a loss of specificity (all P > 0.999). The tumor-to-pancreas contrast-to-noise ratios of 40-keV VMI from DECT were approximately threefold higher than those of conventional CT at all phases.

CONCLUSIONS

The addition of 40-keV VMI from DECT to conventional CT had better sensitivity for detecting small PDACs without compromising specificity.

Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study

BACKGROUND

To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic ¹⁸F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10-60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40-60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon's rank sum test, McNemar's test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables.

RESULTS

Both readers similarly rated 51 scans as positive (scores = 1-3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001).

CONCLUSIONS

Short-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF.

I-131 false-positive uptake in a thymic cyst with expression of the sodium-iodide symporter: A case report

RATIONALE

I-131 radioiodine false-positive findings in postoperative patients with differentiated thyroid cancer (DTC) should be recognized to avoid unnecessary therapies.

PATIENT CONCERNS AND DIAGNOSES

A 50-year-old man underwent I-131 therapy 3 times, including the initial ablative therapy after total thyroidectomy for papillary thyroid cancer. The initial I-131 posttherapeutic whole-body scintigraphy showed 2 cervical and one superior mediastinal focal I-131 positive uptake lesions. The serum thyroglobulin level was negative every time when the radioiodine therapy was performed. Although the 2 cervical positive uptake lesions disappeared after the second therapy, the superior mediastinal I-131 positive uptake persisted even after the third therapy, and this lesion was suspicion of I-131 therapy-resistant node metastasis.

INTERVENTIONS AND OUTCOMES

The lesion was resected, and the pathological diagnosis with immune-histochemical analysis was a thymic cyst with thymic epithelial cells having a weak expression of the sodium-iodide symporter (NIS).

LESSONS

The false-positive result may be attributed to the NIS expression in the thymic cyst epithelial cells. It is necessary to include a thymic cyst in the differential diagnosis, when I-131 uptake is noted in the superior mediastinal region on I-131 posttherapeutic scans of patients with postoperative DTC. Although the I-131 positive uptake in a thymic cyst may be influenced by the I-131 administered dose and scan timing after I-131 administration, the NIS expression may be essential to the false-positive uptake in a thymic cyst.

Differentiation of hemangioblastoma from brain metastasis using MR amide proton transfer imaging

BACKGROUND AND PURPOSE

Differentiation between hemangioblastoma and brain metastasis remains a challenge in neuroradiology using conventional MRI. Amide proton transfer (APT) imaging can provide unique molecular information. This study aimed to evaluate the usefulness of APT imaging in differentiating hemangioblastomas from brain metastases and compare APT imaging with diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging.

METHODS

This retrospective study included 11 patients with hemangioblastoma and 20 patients with brain metastases. Region-of-interest analyses were employed to obtain the mean, minimum, and maximum values of APT signal intensity, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV), and these indices were compared between hemangioblastomas and brain metastases using the unpaired t-test and Mann-Whitney U test. Their diagnostic performances were evaluated using receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC). AUCs were compared using DeLong's method.

RESULTS

All MRI-derived indices were significantly higher in hemangioblastoma than in brain metastasis. ROC analysis revealed the best performance with APT-related indices (AUC = 1.000), although pairwise comparisons showed no significant difference between the mean ADC and mean rCBV.

CONCLUSIONS

APT imaging is a useful and robust imaging tool for differentiating hemangioblastoma from metastasis.

The efficacy of 18F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors

OBJECTIVE

To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (18F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs).

METHODS

This retrospective study included 79 TET [27 low-risk thymomas (types A, AB and B1), 31 high-risk thymomas (types B2 and B3) and 21 thymic carcinomas] patients who underwent pre-therapeutic 18F-FDG-PET/CT. High-risk TETs (high-risk thymomas and thymic carcinomas) were 52 patients. The 107 PET-based radiomic features, including SUV-related parameters [maximum SUV (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)] and 1024 deep-learning features extracted from the convolutional neural network were used to predict the pathological risk subtypes of TETs using six different machine-learning algorithms. The area under the curves (AUCs) were calculated to compare the predictive performances.

RESULTS

SUV-related parameters yielded the following AUCs for predicting thymic carcinomas: SUVmax 0.713, MTV 0.442, and TLG 0.479 or high-risk TETs: SUVmax 0.673, MTV 0.533, and TLG 0.539. The best-performing algorithm was the logistic regression model for predicting thymic carcinomas (AUC 0.900, accuracy 81.0%), and the random forest (RF) model for high-risk TETs (AUC 0.744, accuracy 72.2%). The AUC was significantly higher in the logistic regression model than three SUV-related parameters for predicting thymic carcinomas, and in the RF model than MTV and TLG for predicting high-risk TETs (each; p < 0.05).

CONCLUSION

18F-FDG-PET-based radiomic analysis using a machine-learning approach may be useful for predicting the pathological risk subtypes of TETs.

ADVANCES IN KNOWLEDGE

Machine-learning approach using 18F-FDG-PET-based radiomic features has the potential to predict the pathological risk subtypes of TETs.

Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients

PURPOSE

To examine the usefulness of machine learning to predict prognosis in cervical cancer using clinical and radiomic features of 2-deoxy-2-[¹⁸F]fluoro-D-glucose (¹⁸F-FDG) positron emission tomography/computed tomography (CT) (¹⁸F-FDG-PET/CT).

METHODS

This retrospective study included 50 cervical cancer patients who underwent ¹⁸F-FDG-PET/CT before treatment. Four clinical (age, histology, stage, and treatment) and 41 ¹⁸F-FDG-PET-based radiomic features were ranked and a subset of useful features for association with disease progression was selected based on decrease of the Gini impurity. Six machine learning algorithms (random forest, neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine) were compared using the areas under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis.

RESULTS

The five top predictors of disease progression were: stage, surface area, metabolic tumor volume, gray-level run length non-uniformity (GLRLM_RLNU), and gray-level non-uniformity for run (GLRLM_GLNU). The naive Bayes model was the best-performing classifier for predicting disease progression (AUC = 0.872, accuracy = 0.780, F1 score = 0.781, precision = 0.788, and recall = 0.780). In the naive Bayes model, 5-year PFS was significantly higher in predicted non-progression than predicted progression (80.1% vs. 9.1%, p < 0.001) and was only the independent factor for PFS in multivariate analysis (HR, 6.89; 95% CI, 1.92-24.69; p = 0.003).

CONCLUSION

A machine learning approach based on clinical and pretreatment ¹⁸F-FDG PET-based radiomic features may be useful for predicting tumor progression in cervical cancer patients.

18F-FDG PET/CT Imaging of G-CSF-Producing Dedifferentiated Liposarcoma

ABSTRACT

Granulocyte colony-stimulating factor (G-CSF)-producing tumors are malignant tumors associated with a poor prognosis, and G-CSF-producing liposarcoma is particularly rare. We report a case of G-CSF-producing dedifferentiated liposarcoma. 18F-FDG PET/CT showed abnormal 18F-FDG uptake throughout the bone marrow and in the primary site. When a diffuse bone marrow 18F-FDG uptake was observed on 18F-FDG PET/CT, G-CSF-producing dedifferentiated liposarcoma should be included in the differential diagnosis.

Effect of injection duration on contrast enhancement during cardiac computed tomography angiography in newborns and infants

INTRODUCTION

To investigate how changing the injection duration at cardiac computed tomography angiography (CCTA) affects contrast enhancement in newborns and infants.

METHODS

Included were 142 newborns and infants with confirmed congenital heart disease who underwent CCTA between January 2015 and December 2018. In group 1 (n = 71 patients), the injection duration was 8 s; in group 2 (n = 71) it was 16 s. Our findings were assessed by one-to-one matching analysis to estimate the propensity score of each patient. We compare the CT number for the pulmonary artery (PA), ascending aorta (AAO), left superior vena cava (SVC), AAO and PA enhancement ratio, and the scores for visualization between the two groups.

RESULTS

In group 1, median CT number and ranges was 345 (211-591) HU in the AAO, 324 (213-567) HU in the PA, and 62 (1-70) HU in the SVC. These values were 465 (308-669) HU, 467 (295-638) HU, and 234 (67-443) HU, respectively, in group 2 (p < 0.05). The median score for volume-rendering visualization on 3D images of the CCTA was 2 in group 1 and 3 in group 2; the score for visualization of the left SVC of the maximum intensity projection images was 2 in group 1 and 3 in group 2 (p < 0.05). The CT number for the AAO and PA enhancement ratio was 15.2 in group 1 and 9.2 in group 2 (p < 0.05).

CONCLUSION

The 16-sec injection protocol yielded significantly higher CT numbers for the AAO, PA, and the SVC than the 8-sec injection protocol; the visualization scores were also significantly higher in group 2.

IMPLICATIONS FOR PRACTICE

In newborns and infants, the longer injection time for CCTA yields stable and higher contrast enhancement at identical CM concentrations.

Extracellular volume fraction with MRI: As an alternative predictive biomarker to dynamic contrast-enhanced MRI for chemotherapy response of pancreatic ductal adenocarcinoma

PURPOSE

To assess the feasibility of extracellular volume (ECV) fraction determined with equilibrium contrast-enhanced MRI for prediction of treatment response to chemotherapy in pancreatic ductal adenocarcinoma (PDAC) in comparison with dynamic contrast-enhanced MRI (DCE-MRI), and to clarify the association between ECV fraction and DCE-MRI-derived pharmacokinetic parameters.

METHODS

This retrospective study included 58 consecutive patients with histologically confirmed PDAC who underwent DCE-MRI before systemic chemotherapy. Tumor pharmacokinetic parameters, including the volume transfer coefficient (K^trans), rate constant (k_ep), and extracellular extravascular volume fraction (v_e) of DCE-MRI, and ECV fraction determined with equilibrium contrast-enhanced MRI were compared between the response and non-response groups. The correlation of tumor ECV fraction with each DCE-MRI-derived pharmacokinetic parameter was examined using Spearman's rank correlation coefficient.

RESULTS

Tumor K^trans, v_e, and ECV fraction were significantly higher in the response group than in the non-response group (all, P < 0.001), whereas no significant difference was found in k_ep (P = 0.119). Tumor ECV fraction showed the highest area under receiver operating characteristic curve of 0.918, with a sensitivity of 89.3%, specificity of 90.0%, and accuracy of 89.7% (cut off, >37.6%). The ECV fraction showed a significant positive correlation with K^trans (Spearman's coefficient = 0.66, P < 0.001) and v_e (Spearman's coefficient = 0.79, P < 0.001).

CONCLUSIONS

ECV fraction determined with equilibrium contrast-enhanced MRI was as useful as DCE-MRI-derived pharmacokinetic parameters for predicting treatment response to chemotherapy in patients with PDAC.

Enhancement rate of venous phase to portal venous phase computed tomography and its correlation with ultrasound elastography determination of liver fibrosis

INTRODUCTION

This study aimed to compare the correlation between the computed tomography (CT) enhancement rate of the venous to portal venous phase (VP-ER) and the extracellular volume (ECV) fraction with shear-wave ultrasound elastography (USE) findings in patients with liver fibrosis.

METHODS

We included 450 patients with clinically suspected liver cirrhosis who underwent triphasic dynamic CT studies and USE. We compared the USE results with the unenhanced CT phase, with enhancement in the hepatic artery phase (HAP), portal venous phase (PVP), and venous phase (VP), and with the ECV fraction and the VP-ER. We also compared the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the ECV fraction and VP-ER with that of the values obtained with USE.

RESULTS

The VP-ER was the most highly correlated with the liver stiffness value determined with USE (Pearson's correlation coefficient: r = 0.37), followed by enhancement in the PVP (r = -0.25), CT number on unenhanced CT scans (r = -0.22), the ECV fraction (r = 0.19), enhancement in the VP (r = 0.059), and enhancement in the HAP (r = -0.023) (all p < 0.01). The VP-ER showed a significantly higher AUC than the ECV fraction (0.75 vs 0.62) when the liver stiffness was >15 kPa in USE studies (p = 0.04).

CONCLUSION

Compared to the ECV fraction, the VP-ER is more useful for predicting all degrees of liver fibrosis on routine triphasic dynamic CT images.

IMPLICATIONS FOR PRACTICE

Although improvement is needed, the VP-ER has a higher diagnostic ability for liver fibrosis than the ECV fraction in clinical practice.

Efficacy of the spiral flow generating extended tube during paediatric CCTA

INTRODUCTION

To compare the computed tomography (CT) number for paediatric cardiac computed tomography angiography (CCTA) and visualisation score of the three-dimensional (3D) images using the conventional T-shaped extended tube (T-tube) and spiral flow-generating extended tube (spiral-tube) connected between the contrast injector and cannula.

METHODS

In total, 108 patients suspected to have congenital heart disease (CHD) were considered for inclusion. We utilised the T-tube for intravenous contrast and spiral-tube in 54 patients each. Observers individually inspected randomized volume rendering images of the internal thoracic artery, each acquired from the with or without spiral-tube groups, using a four-point scale. We compared the mean CT number of the ascending aorta (AAO) and pulmonary artery (PA), contrast noise ratio (CNR), CT number for the AAO and PA enhancement ratio, and the visualisation scores between the groups.

RESULTS

There were no significant differences in patient characteristics between the with or without spiral-tube groups (p > 0.05). The mean CT number ±standard deviation for the AAO and PA, and the CNR without or with spiral-tube groups were 441.2 ± 89.2 and 489.8 ± 86.1 HU for the AAO, 436.3 ± 100.6 and 475.3 ± 85.2 HU for the PA, and 9.5 ± 2.2 and 10.8 ± 2.4 for the CNR, respectively (p < 0.05). In the spiral-tube group, the CT number, CNR, and visualisations score of the 3D images were significantly higher for the AAO and PA than those in the T-tube group (p < 0.05).

CONCLUSION

The spiral-tube proved to be beneficial in improving the CT number for the AAO and PA, CNR, and visualisation score compared with the conventional T-tube during paediatric CCTA.

IMPLICATIONS FOR PRACTICE

The spiral-tube may allow the visualisation of smaller blood vessels than those visualised by the conventional T-tube for paediatric patients in CCTA.

Deep learning with convolutional neural network for estimation of the characterisation of coronary plaques: Validation using IB-IVUS

INTRODUCTION

Deep learning approaches have shown high diagnostic performance in image classifications, such as differentiation of malignant tumors and calcified coronary plaque. However, it is unknown whether deep learning is useful for characterizing coronary plaques without the presence of calcification using coronary computed tomography angiography (CCTA). The purpose of this study was to compare the diagnostic performance of deep learning with a convolutional neural network (CNN) with that of radiologists in the estimation of coronary plaques.

METHODS

We retrospectively enrolled 178 patients (191 coronary plaques) who had undergone CCTA and integrated backscatter intravascular ultrasonography (IB-IVUS) studies. IB-IVUS diagnosed 81 fibrous and 110 fatty or fibro-fatty plaques. We manually captured vascular short-axis images of the coronary plaques as Portable Network Graphics (PNG) images (150 × 150 pixels). The display window level and width were 100 and 700 Hounsfield units (HU), respectively. The deep-learning system (CNN; GoogleNet Inception v3) was trained on 153 plaques; its performance was tested on 38 plaques. The area under the curve (AUC) obtained by receiver operating characteristic analysis of the deep learning system and by two board-certified radiologists was compared.

RESULTS

With the CNN, the AUC and the 95% confidence interval were 0.83 and 0.69-0.96, respectively; for radiologist 1 they were 0.61 and 0.42-0.80; for radiologist 2 they were 0.68 and 0.51-0.86, respectively. The AUC for CNN was significantly higher than for radiologists 1 (p = 0.04); for radiologist 2 it was not significantly different (p = 0.22).

CONCLUSION

DL-CNN performed comparably to radiologists for discrimination between fatty and fibro-fatty plaque on CCTA images.

IMPLICATIONS FOR PRACTICE

The diagnostic performance of the CNN and of two radiologists in the assessment of 191 ROIs on CT images of coronary plaques whose type corresponded with their IB-IVUS characterization was comparable.

Consistency of Pituitary Adenoma: Prediction by Pharmacokinetic Dynamic Contrast-Enhanced MRI and Comparison with Histologic Collagen Content

Simple Summary

Transsphenoidal resection of hard pituitary adenomas have a particularly high risk of residual tumor and complications. Therefore, prediction of tumor consistency is valuable for planning pituitary adenoma surgery. We prospectively examined whether quantitative pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is useful for predicting consistency of pituitary adenoma in 49 participants. We found that the measure of volume of extravascular extracellular space per unit volume of tissue derived from DCE-MRI could predict the consistency of pituitary adenomas. Furthermore, the volume of extravascular extracellular space per unit volume of tissue was significantly positively correlated with histopathologic collagen content of the adenoma. Our results suggest that volume of extravascular extracellular space per unit volume of tissue derived from quantitative pharmacokinetic analysis of DCE-MRI has a predictive value for consistency of pituitary adenomas.

Abstract

Prediction of tumor consistency is valuable for planning transsphenoidal surgery for pituitary adenoma. A prospective study was conducted involving 49 participants with pituitary adenoma to determine whether quantitative pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is useful for predicting consistency of adenomas. Pharmacokinetic parameters in the adenomas including volume of extravascular extracellular space (EES) per unit volume of tissue (v_e), blood plasma volume per unit volume of tissue (v_p), volume transfer constant between blood plasma and EES (K^trans), and rate constant between EES and blood plasma (k_ep) were obtained. The pharmacokinetic parameters and the histologic percentage of collagen content (PCC) were compared between soft and hard adenomas using Mann–Whitney U test. Pearson’s correlation coefficient was used to correlate pharmacokinetic parameters with PCC. Hard adenomas showed significantly higher PCC (44.08 ± 15.14% vs. 6.62 ± 3.47%, p < 0.01), v_e (0.332 ± 0.124% vs. 0.221 ± 0.104%, p < 0.01), and K^trans (0.775 ± 0.401/min vs. 0.601 ± 0.612/min, p = 0.02) than soft adenomas. Moreover, a significant positive correlation was found between v_e and PCC (r = 0.601, p < 0.01). The v_e derived using DCE-MRI may have predictive value for consistency of pituitary adenoma.

Application of a machine learning approach to characterization of liver function using 99mTc-GSA SPECT/CT

PURPOSE

To assess the utility of a machine-learning approach for predicting liver function based on technetium-99 m-galactosyl serum albumin (^99mTc-GSA) single photon emission computed tomography (SPECT)/CT.

METHODS

One hundred twenty-eight patients underwent a ^99mTc-GSA SPECT/CT-based liver function evaluation. All were classified into the low liver-damage or high liver-damage group. Four clinical (age, sex, background liver disease and histological type) and 8 quantitative ^99mTc-GSA SPECT/CT features (receptor index [LHL15], clearance index [HH15], liver-SUVmax, liver-SUVmean, heart-SUVmax, metabolic volume of liver [MVL], total lesion GSA [TL-GSA, liver-SUVmean × MVL] and SUVmax ratio [liver-SUVmax/heart-SUVmax]) were obtained. To predict high liver damage, a machine learning classification with features selection based on Gini impurity and principal component analysis (PCA) were performed using a support vector machine and a random forest (RF) with a five-fold cross-validation scheme. To overcome imbalanced data, stratified sampling was used. The ability to predict high liver damage was evaluated using a receiver operating characteristic (ROC) curve analysis.

RESULTS

Four indices (LHL15, HH15, heart SUVmax and SUVmax ratio) yielded high areas under the ROC curves (AUCs) for predicting high liver damage (range: 0.89-0.93). In a machine learning classification, the RF with selected features (heart SUVmax, SUVmax ratio, LHL15, HH15, and background liver disease) and PCA model yielded the best performance for predicting high liver damage (AUC = 0.956, sensitivity = 96.3%, specificity = 90.0%, accuracy = 91.4%).

CONCLUSION

A machine-learning approach based on clinical and quantitative ^99mTc-GSA SPECT/CT parameters might be useful for predicting liver function.

Visualization of incidentally imaged pituitary gland on three-dimensional arterial spin labeling of the brain

OBJECTIVE

To evaluate the visualization of incidentally imaged normal pituitary gland on three-dimensional (3D) pseudo continuous arterial spin labeling (PCASL) perfusion imaging of the brain.

METHODS

Ninety-three patients with a normal pituitary gland who underwent 3D PCASL for suspected brain diseases were retrospectively included. Visualization of the pituitary gland on PCASL cerebral blood flow (CBF) maps was assessed independently by two observers using a three-point grading system: Grade 1, pituitary CBF ≤ CBF of the cerebral white matter (WM); Grade 2, CBF of WM < pituitary CBF ≤ CBF of the cortical gray matter (GM); and Grade 3, CBF of GM < pituitary CBF. The interobserver agreement of visual grading was determined using weighted κ statistic. The associations of visual grades with age, sex, and pituitary volume were assessed using multivariate logistic regression. Pituitary glands were divided equally into three groups (small, medium, and large) according to their volume for categorization.

RESULTS

The interobserver agreement for visual rating was excellent (weighted κ = 0.823). Of the 93 cases, Grades 1, 2, and 3 included 17 (18.3%), 41 (44.1%), and 35 cases (37.6%), respectively. Medium and large pituitary volume were significantly associated with Grade 3 visualization (p = 0.0153, OR = 4.8323; 95% CI: 1.3525, 17.2649 and p = 0.0009; OR = 9.0299; 95% CI: 2.4663, 33.0614, respectively), whereas there was no significant association for age or sex.

CONCLUSION

The normal pituitary gland is often visualized with higher CBF than cortical GM on 3D PCASL, especially in individuals with larger pituitary volume.

ADVANCES IN KNOWLEDGE

Appearance of the normal pituitary gland on 3D PCASL has been documented for the first time.

Correlation between amide proton transfer-related signal intensity and diffusion and perfusion magnetic resonance imaging parameters in high-grade glioma

Amide proton transfer (APT) imaging is a magnetic resonance (MR) molecular imaging technique that is sensitive to mobile proteins and peptides in living tissue. Studies have shown that APT-related signal intensity (APTSI) parallels with the malignancy grade of gliomas, allowing the preoperative assessment of tumor grades. An increased APTSI in malignant gliomas has been attributed to cytosolic proteins and peptides in proliferating tumor cells; however, the exact underlying mechanism is poorly understood. To get an insight into the mechanism of high APTSI in malignant gliomas, we investigated the correlations between APTSI and several MR imaging parameters including apparent diffusion coefficient (ADC), relative cerebral blood volume and pharmacokinetic parameters obtained in the same regions-of-interest in 22 high-grade gliomas. We found a significant positive correlation between APTSI and ADC (ρ = 0.625 and 0.490 for observers 1 and 2, respectively; p < 0.001 for both), which is known to be inversely correlated with cell density. Multiple regression analysis revealed that ADC was significantly associated with APTSI (p < 0.001 for both observers). Our results suggest possible roles of extracellular proteins and peptides in high APTSI in malignant gliomas.

A pilot study on EORTC or PERCIST for the prediction of progression-free survival with nivolumab therapy in advanced or metastatic gastric cancers: A STROBE-compliant article

Recent breakthrough results from immune checkpoint inhibitors (ICIs) have paved the way to a new era of cancer immunotherapy, and have thus led to a paradigm shift of cancer treatment. In particular, inhibition of the antiprogrammed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) axis with ICI, including nivolumab and pembrolizumab, has been emerging as a novel treatment strategy for advanced gastric cancers. An accurate noninvasive assessment of the response to ICI is important for the management of patients with advanced or metastatic gastric cancer.To examine whether the European Organization for Research and Treatment of Cancer (EORTC) and PET Response Criteria in Solid Tumors (PERCIST) are valuable for predicting progression-free survival (PFS) in patients with advanced or metastatic gastric cancers treated with nivolumab.Six patients with advanced or metastatic gastric cancers who underwent 18F-FDG-PET/computed tomography (CT) scans before, and from 2 to 6 months after initiation of nivolumab therapy between September 2017 and August 2019, were evaluated retrospectively. The correlation between tumor progression and EORTC or PERCIST was assessed with the Fisher's exact test. The PFS was assessed with the Kaplan-Meier method.Two patients were alive without progression, and the remaining 4 patients exhibited tumor progression. Two patients without progression were classified as partial metabolic response (PMR) patients based on EORTC or PERCIST, while the other 4 patients with progression were classified as progressive metabolic disease (PMD) patients based on EORTC (P = .067), or stable metabolic disease (SMD) patients, or PMD patients based on PERCIST (P = .067).The mean and median PFS of all patients was 12.7 months (95% confidence interval [CI], 4.9-20.4 months) and 5 months (95%CI, 4.0-11.0 months). Two EORTC or PERCIST PMR patients showed significantly longer median PFS compared with 4 non-PMR patients (not reached vs 4.0 months, P = .044). Three PERCIST PMR or SMD patients also showed significantly longer median PFS compared with 3 PMD patients (not reached vs 4.0 months, P = .022). These results suggest that EORTC or PERCIST has the potential to predict PFS of patients with advanced or metastatic gastric cancers treated by nivolumab and further studies are needed to determine its value in larger study populations.

Diagnostic performance of computed tomography digital subtraction angiography of the lower extremities during haemodialysis in patients with suspected peripheral artery disease

INTRODUCTION

With intra-arterial digital subtraction angiography (DSA) considered as the gold standard, we compared the diagnostic value of computed tomography angiography (CTA) and computed tomography-digital subtraction angiography (CT-DSA in hemodialysis (HD) patients suspected of having lower limb peripheral artery disease (PAD).

METHODS

In this retrospective study, we enrolled 220 HD patients with suspected PAD. CT-DSA images were obtained by subtracting unenhanced images from enhanced images. The research team calculated the area under the curve (AUC), sensitivity, specificity, positive and negative predictive value (PPV, NPV), and recorded the diagnostic accuracy between the CTA and CT-DSA images using the DSA as gold standard. Visual evaluation of calcifications in the peripheral arteries were also compared between CTA and CT-DSA images.

RESULTS

At the above-knee level, the CTA AUC [95% confidence interval (CI)] was 0.68 (CI 0.64-0.72), sensitivity and specificity were 60 and 81%, PPV and NPV were 85 and 53%, and accuracy was 67%. Below the knee, these values were 0.66 (CI 0.62-0.70), 71 and 79%, 79 and 47%, and 66%. For CT-DSA, above-knee, the AUC [95% CI] was 0.88 (CI 0.85-0.91), sensitivity and specificity were 84 and 92%, PPV and NPV were 89 and 97%, and accuracy was 93%. Below the knee, these values were 0.95 (CI 0.93-0.97), 95 and 93%, 96 and 83%, and 93%. The scores for the visualization of calcification in the peripheral arteries was significantly higher for CT-DSA than CTA (p < 0.05).

CONCLUSIONS

CT-DSA helps to assess stenotic PAD with high calcification in the lower extremities of HD patients.

IMPLICATIONS FOR PRACTICE

On CT-DSA images, the severity of vascular calcification can be assessed for HD patients suspected of PAD of the lower extremities.

Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer

PURPOSE

To examine the prognostic significance of pretreatment 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]-FDG) positron emission tomography (PET)-based radiomic features using a machine learning approach in patients with endometrial cancers.

PROCEDURES

Included in this retrospective study were 53 patients with endometrial cancers who underwent [¹⁸F]-FDG PET/X-ray computed tomography (CT) before treatment. Since two different PET scanners were used, post-reconstruction harmonization was performed for all PET parameters using the ComBat harmonization method. Four clinical (age, histological type, stage, and treatment method) and 40 [¹⁸F]-FDG PET-based radiomic features were ranked, and a subset of useful features was selected based on the decrease in the Gini impurity in terms of associations with disease progression. The machine learning algorithms (random forest, neural network, k-nearest neighbors (kNN), naive Bayes, logistic regression, and support vector machine) were compared using the areas under the receiver operating characteristic curve (AUC) and validated by the random sampling method. Progression-free survival (PFS) and overall survival (OS) were assessed by the Cox regression analysis.

RESULTS

The five best predictors of disease progression were coarseness, gray-level run length nonuniformity, stage, treatment method, and gray-level zone length nonuniformity. The kNN model obtained the best performance classifier for predicting the disease progression (AUC =0.890, accuracy =0.849, F1 score =0.848, precision =0.857, and recall =0.849). Coarseness which was the first ranked radiomic feature was selected for survival analyses, and only coarseness remained as a significant and independent factor for both PFS (hazard ratios (HR), 0.65; 95 % confidence interval [CI], 0.49-0.86; p=0.003) and OS (HR, 0.52; 95 % CI, 0.36-0.76; p<0.001) at multivariate Cox regression analysis.

CONCLUSIONS

[¹⁸F]-FDG PET-based radiomic analysis using a machine learning approach may be useful for predicting tumor progression and prognosis in patients with endometrial cancers.

Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies

INTRODUCTION

We compared the diagnostic performance of morphological methods such as the major axis, the minor axis, the volume and sphericity and of machine learning with texture analysis in the identification of lymph node metastasis in patients with thyroid cancer who had undergone contrast-enhanced CT studies.

METHODS

We sampled 772 lymph nodes with histology defined tissue types (84 metastatic and 688 benign lymph nodes) that were visualised on CT images of 117 patients. A support vector machine (SVM), free programming software (Python), and the scikit-learn machine learning library were used to discriminate metastatic-from benign lymph nodes. We assessed 96 texture and 4 morphological features (major axis, minor axis, volume, sphericity) that were reported useful for the differentiation between metastatic and benign lymph nodes on CT images. The area under the curve (AUC) obtained by receiver operating characteristic analysis of univariate logistic regression and SVM classifiers were calculated for the training and testing datasets.

RESULTS

The AUC for all classifiers in training and testing datasets was 0.96 and 0.86, at the SVM for machine learning. When we applied conventional methods to the training and testing datasets, the AUCs were 0.63 and 0.48 for the major axis, 0.70 and 0.44 for the minor axis, 0.66 and 0.43 for the volume, and 0.69 and 0.54 for sphericity, respectively. The SVM using texture features yielded significantly higher AUCs than univariate logistic regression models using morphological features (p = 0.001).

CONCLUSION

For the identification of metastatic lymph nodes from thyroid cancer on contrast-enhanced CT images, machine learning combined with texture analysis was superior to conventional diagnostic methods with the morphological parameters.

IMPLICATIONS FOR PRACTICE

Our findings suggest that in patients with thyroid cancer and suspected lymph node metastasis who undergo contrast-enhanced CT studies, machine learning using texture analysis is high diagnostic value for the identification of metastatic lymph nodes.

Pulmonary hamartoma: Feasibility of dual-energy CT detection of intranodular fat

We have reported 2 cases of pulmonary hamartoma focusing on detecting intranodular fat, which is one of CT features suggestive of pulmonary hamartoma, using dual-energy CT analyses. For patient 1, a 73-year-old man was pointed out to have a nodular opacity on chest radiograph of pretreatment workup for retinal detachment. In patient 2, a 66-year-old woman with uterine carcinoma admitted for preoperative assessment. Both patients underwent dual-energy CT examination and the pulmonary lesions exhibited a downward-sloping curve at lower X-ray energies on attenuation curve of virtual monochromatic images, which suggested fatty tissue. Dual-energy CT analysis can help diagnose pulmonary hamartoma with detection of intralesional fatty tissue.

Usefulness of dual-layer spectral CT in follow-up examinations: diagnosing recurrent squamous cell carcinomas in the head and neck

PURPOSE

To evaluate the usefulness of dual-energy analyses using dual-layer spectral CT (DLSCT) for diagnosing recurrent lesions of head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

The study population comprised 62 patients with a history of HNSCC. Attenuation values on conventional 120-kVp images and 40-keV virtual monochromatic images (VMIs) and iodine concentration (IC) were compared between recurrent lesions and post-treatment changes or non-recurrent nodes using the Mann-Whitney U test. Receiver-operating characteristic (ROC) analysis was used to assess the ability of attenuation values and IC to diagnose recurrent lesions.

RESULTS

Attenuation values for 120-kVp and 40-keV images and IC of local recurrent lesions were significantly higher than those of post-treatment changes (p < 0.001), whereas recurrent nodes showed significantly lower attenuation values for both 120 kVp and 40 keV and IC than non-recurrent nodes (p < 0.001). Area under the ROC curves for 120-kVp images, 40-keV images, and IC to diagnose local recurrences were 0.912, 0.992, and 0.984, respectively, and those to diagnose recurrent nodes were 0.819, 0.922, and 0.934, respectively.

CONCLUSIONS

Dual-energy images using DLSCT, particularly 40-keV VMIs and IC, may help in diagnosing recurrent lesions of HNSCC.

Application of adrenal maximum standardized uptake value to 131I-6β-iodomethyl-19-norcholesterol SPECT/CT for characterizing unilateral hyperfunctioning adrenocortical masses

PURPOSE

To evaluate the maximum standardized uptake value (SUVmax) by ¹³¹I-6β-iodomethyl-19-norcholesterol (NP-59) single-photon emission computed tomography (SPECT)/computed tomography (CT) for characterizing unilateral hyperfunctioning adrenocortical masses.

METHODS

Ten patients underwent NP-59 SPECT/CT to evaluate the following unilateral adrenocortical hyperfuncting masses: three with Cushing's syndrome (CS), three with subclinical CS, and four with primary aldosteronism (PA). Visual and quantitative or semiquantitative analyses (noncontrast CT HU [Hounsfield units], lesion SUVmax, contralateral SUVmax, and SUVmax ratio [lesion SUVmax/contralateral adrenal SUVmax]) were performed. The Mann-Whitney U test or Chi-squared test was used appropriately to assess differences between quantitative variables or compare categorical data. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

All adrenal tumors were diagnosed as cortical adenomas. On visual analysis, unilateral uptake was noted in three patients with CS and one patient with subclinical CS, whereas bilateral uptake was noted in four patients with PA and two patients with subclinical CS (p = 0.046). No significant difference was observed in CT HU (p = 0.055). The lesion SUVmax and SUVmax ratio were significantly higher and the contralateral SUVmax was significantly lower in six patients with CS than in four patients with PA (each, p < 0.05). The area under the ROC curve and accuracy for differentiating between CS and PA were, respectively, 0.92 and 90.0 % for the lesion SUVmax, 1.00 and 100 % for the contralateral SUVmax, and 0.92 and 90.0 % for the SUVmax ratio.

CONCLUSIONS

Quantitative or semiquantitative analysis using the adrenal SUVmax in adrenocortical NP-59 SPECT/CT has potential for characterizing unilateral hyperfunctioning adrenocortical masses.

Clinical Outcomes of Proton Beam Therapy for Ground-Glass Opacity-Type Lung Cancer

Purpose

Surgery is the standard treatment for early-stage non-small cell lung cancer (NSCLC), including ground-glass opacity (GGO)-type lung cancer. However, some patients are inoperable or refuse to undergo surgery. To explore whether proton beam therapy (PBT) can be an alternative to surgical resection in these patients, this study aimed to examine the retrospective treatment outcomes of patients with GGO-type lung cancer who underwent PBT.

Patients and Methods

Patients with stage I NSCLC and GGOs who underwent PBT at the Medipolis Proton Therapy and Research Center (Kagoshima, Japan) between April 2011 and September 2015 were included. Patients were treated with a total dose of 66 GyE delivered in 10 fractions. Survival curves were calculated using the Kaplan–Meier method, and treatment-related adverse events (AEs) were assessed.

Results

A total of 48 patients (median age: 70.9 ± 9.2 years; men: 54.2%) were analyzed, among whom 53 tumors were observed. The 3-year overall survival rate after PBT was 91.7% (95% confidence interval [CI], 79.3–96.8%), the 3-year disease-free survival rate was 85.4% (95% CI: 71.8–92.8%), and the 3-year local control rate among 53 tumors was 92.5% (95% CI: 81.1–97.1%). During the 3-year follow-up period, 4 patients died, and 3 survived despite recurrence or metastasis. Common AEs were radiation pneumonitis (89.6%), rib fracture (27.1%), and cough (27.1%). None of the patients developed grade ≥3 treatment-related AEs.

Conclusion

The results of this study suggest that PBT may be a promising alternative for patients with GGO-type lung cancer when surgical resection is not feasible, with excellent survival outcomes and tolerable treatment-related AEs.

Visual enhancement pattern during the delayed phase of enhanced CT as an independent prognostic factor in stage IV pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has substantial heterogeneity in biophysical features and in outcomes of patients. Identifying reliable pretreatment imaging biomarkers for PDAC with distant metastases (stage IV) is a key imperative. Our objective was to determine whether visual tumor enhancement pattern on enhanced computed tomography (CT) can be used as a prognostic factor in stage IV PDAC treated with chemotherapy.

METHODS

This is a retrospective cohort study of 133 patients with stage IV PDAC who underwent multiphasic enhanced CT before systemic chemotherapy. The enhancement pattern of PDAC was qualitatively categorized as hypoattenuation, isoattenuation, or hyperattenuation on each of the pancreatic, portal venous, and delayed phases. The effects of clinical prognostic factors and the visual tumor enhancement pattern on progression-free survival (PFS) and overall survival (OS) were assessed in univariate and multivariate analyses using Cox proportional hazards models.

RESULTS

On univariate analysis, the number of metastatic organs and the visual tumor enhancement pattern during the delayed phase were significantly associated with PFS (p = 0.003 and < 0.001, respectively) and OS (p = 0.005 and < 0.001, respectively). Multivariate analysis identified the number of metastatic organs (PFS, p = 0.021; OS, p = 0.041) and visual tumor enhancement pattern during the delayed phase (PFS, p < 0.001; OS, p < 0.001) as independent predictors of PFS and OS.

CONCLUSION

Visual enhancement pattern of PDAC on delayed phase enhanced CT appears to be associated with outcomes and could be a useful prognostic factor in stage IV PDAC, despite the need to add the delayed phase to CT protocol for pancreatic disease.

Combined signal averaging and compressed sensing: impact on quality of contrast-enhanced fat-suppressed 3D turbo field-echo imaging for pharyngolaryngeal squamous cell carcinoma

PURPOSE

To determine whether combined signal averaging and compressed sensing (CS averaging) improves the image quality of contrast-enhanced fat-suppressed T1-weighted three-dimensional turbo field-echo (FS T1W 3D-TFE) for evaluation of pharyngolaryngeal squamous cell carcinoma (PLSCC).

METHODS

This retrospective study included 27 patients with PLSCC. In all patients, contrast-enhanced FS T1W 3D-TFE imaging with CS averaging (number of excitations, 7) and that without CS averaging (number of excitations, 1) were obtained during the same acquisition time. Overall image quality, mucosal enhancement, vessel clarity, motion artifact, lesion conspicuity, and lesion edge sharpness were qualitatively evaluated using a 5-point scale. Images with and without CS averaging were compared using the Wilcoxon signed-rank test. Signal-to-noise ratio (SNR) of the lesion and the muscle structure were compared between the two imaging methods using a paired t-test.

RESULTS

Compared with the images without CS averaging, those with CS averaging showed significantly better overall image quality (p = 0.002), mucosal enhancement (p = 0.009), vessel clarity (p = 0.003), muscle edge clarity (p = 0.002), lesion conspicuity (p = 0.002), and lesion edge sharpness (p = 0.001); and less motion artifact (p < 0.001). The SNRs of the lesion and of the muscle structure were significantly higher for images with CS averaging than those without CS averaging (p < 0.001).

CONCLUSION

CS averaging improves the image quality of contrast-enhanced FS T1W 3D-TFE MR images for evaluation of PLSCC without requiring additional acquisition time.

Survivin-responsive conditionally replicating adenovirus for patients with advanced sarcoma demonstrated potent and long-term efficacy and high safety in a phase I clinical trial

11512

Background: Whereas one of oncolytic viruses (OVs), inducing selective tumor killing and systemic anti-tumor immunity, was approved by FDA in 2015, the best OV that more safely and efficiently treats intractable cancers has not been successfully developed. By our platform technology to efficiently construct next-generation OVs, i.e., “conditionally replicating adenoviruses (CRAs) that target and/or treat tumor cells with multiple factors” (m-CRAs), we identified that among candidates, survivin-responsive m-CRAs (Surv.m-CRAs) exhibited the most potent antitumor efficacy and cancer selectivity ( i.e., safety) in preclinical studies ( Cancer Res, 2005 et al.). Here we present the data of First-In-Human phase I clinical trial of Surv.m-CRA-1 for musculoskeletal tumors (MST). Methods: This single-arm, open label study included 9 patients with unresectable and advanced MST. Patients underwent a single intratumoral injection of either 1×10^10 viral particle (vp) (low), 1×10^11 vp (mid) or 1×10^12 vp (high). The primary endpoints were safety and tolerability. The secondary endpoints included the local control of treated tumor at one month, defined by RECIST and Choi criteria, analysis of dissemination of Surv.m-CRA-1, serum cytokine and adenoviral antibody. Long-term follow-up was done in some patients. Results: Four patients (44.4%) had grade 3 or higher adverse events, including lymphopenia, leukocytopenia and mildly elevated liver transaminase in 2, 1 and 1 patient, respectively. Virus excretions, including second peak of viremia from viral replication in tumor, were observed in 1, 2 and 3 patients of low, mid and high dose, respectively. Out of 9 patients, 5 PR, 3 SD and 1 PD by Choi, and 8 SD and 1 PD by RECIST were observed. During follow-up, another 1 and 2 patients became PR by Choi and RECIST, respectively. Of note, long-term PR (over 2 years) after a single injection of Surv.m-CRA-1 was achieved in two chordoma cases in low dose. Conclusions: Surv.m-CRA-1 was well tolerated and showed antitumor activity for prolonged periods against advanced MST. We about to start Phase I/II study of multiple injections of Surv.m-CRA-1 for advanced solid tumors in two-arms for musculoskeletal tumors and pancreatic cancer. Clinical trial information: R000026464 .

Quantitative pharmacokinetic analysis of high-temporal-resolution dynamic contrast-enhanced MRI to differentiate the normal-appearing pituitary gland from pituitary macroadenoma

PURPOSE

To evaluate the usefulness of high-temporal-resolution dynamic contrast-enhanced (DCE) MRI and quantitative pharmacokinetic analysis to differentiate the normal-appearing pituitary gland from a pituitary macroadenoma.

MATERIALS AND METHODS

Twenty-seven patients with macroadenomas underwent preoperative DCE-MRI with a temporal resolution of 5 s using compressed sensing to obtain pharmacokinetic parameters. Two independent observers localized the normal-appearing pituitary gland on post-contrast T1-weighted images before and after referring to the corresponding K^trans maps. Agreements between the localizations and intraoperative findings were evaluated using the kappa statistics. The Mann-Whitney U test was used to compare the pharmacokinetic parameters of the normal-appearing pituitary gland and adenoma.

RESULTS

For both observers, the agreement between the MRI-based localization and the intraoperative findings increased after referring to the K^trans maps (observer 1, 0.930-1; observer 2, 0.636-0.855). The normal-appearing pituitary gland had significantly higher K^trans [/min] (1.50 ± 0.80 vs 0.58 ± 0.49, P < 0.0001), k_ep [/min] (3.19 ± 1.29 vs 2.15 ± 1.18, P = 0.0049), and v_e (0.43 ± 0.15 vs 0.25 ± 0.17, P = 0.0003) than adenoma.

CONCLUSION

High-temporal-resolution DCE-MRI and quantitative pharmacokinetic analysis help accurately localize the normal-appearing pituitary gland in patients with macroadenomas. The normal-appearing pituitary gland was characterized by higher K^trans, k_ep, and v_e than macroadenoma. Dynamic contrast-enhanced MRI with high-temporal-resolution using compressed sensing was used for quantitative pharmacokinetic analysis of pituitary macroadenomas. An observer study, the use of K^trans maps improved accuracy in localizing the normal-appearing pituitary gland. As compared to an adenoma, the normal-appearing pituitary gland had significantly higher K^trans, k_ep, and v_e values.