Effect of deep learning-based reconstruction on high-resolution three-dimensional T2-weighted fast asymmetric spin-echo imaging in the preoperative evaluation of cerebellopontine angle tumors.

PURPOSE: We aimed to evaluate the effect of deep learning-based reconstruction (DLR) on high-spatial-resolution three-dimensional T2-weighted fast asymmetric spin-echo (HR-3D T2-FASE) imaging in the preoperative evaluation of cerebellopontine angle (CPA) tumors. METHODS: This study included 13 consecutive patients who underwent preoperative HR-3D T2-FASE imaging using a 3 T MRI scanner. The reconstruction voxel size of HR-3D T2-FASE imaging was 0.23 × 0.23 × 0.5 mm. The contrast-to-noise ratios (CNRs) of the structures were compared between HR-3D T2-FASE images with and without DLR. The observers' preferences based on four categories on the tumor side on HR-3D T2-FASE images were evaluated. The facial nerve in relation to the tumor on HR-3D T2-FASE images was assessed with reference to intraoperative findings. RESULTS: The mean CNR between the tumor and trigeminal nerve and between the cerebrospinal fluid and trigeminal nerve was significantly higher for DLR images than non-DLR-based images (14.3 ± 8.9 vs. 12.0 ± 7.6, and 66.4 ± 12.0 vs. 53.9 ± 8.5, P < 0.001, respectively). The observer's preference for the depiction and delineation of the tumor, cranial nerves, vessels, and location relation on DLR HR-3D T2FASE images was superior to that on non-DLR HR-3D T2FASE images in 7 (54%), 6 (46%), 6 (46%), and 6 (46%) of 13 cases, respectively. The facial nerves around the tumor on HR-3D T2-FASE images were visualized accurately in five (38%) cases with DLR and in four (31%) without DLR. CONCLUSION: DLR HR-3D T2-FASE imaging is useful for the preoperative assessment of CPA tumors.

Long-term outcomes of multidisciplinary treatment combining surgery and stereotactic radiotherapy with Novalis for craniopharyngioma.

Craniopharyngiomas are difficult to resect completely, recurrence is frequent, and hypothalamic/pituitary function may be affected after surgery. Therefore, the ideal treatment for craniopharyngiomas is local control with preservation of hypothalamic and pituitary functions. The purpose of this study is to retrospectively evaluate the long-term efficacy and adverse events of stereotactic radiotherapy (SRT) with Novalis for craniopharyngioma. This study included 23 patients with craniopharyngiomas who underwent surgery between 2006 and 2021 and underwent SRT as their first irradiation after surgery. The median post-irradiation observation period was 88 months, with the overall survival rates of 100 % at 10 years and 85.7 % at 20 years. One patient died of adrenal insufficiency 12 years after irradiation. The local control rate of the cystic component was 91.3 % at 5 years, 83.0 % at 15 years, with no increase in the solid component. No delayed impairment of visual or pituitary function due to irradiation was observed. No new hypothalamic dysfunction was observed after radiation therapy. No delayed adverse events such as brain necrosis, cerebral artery stenosis, cerebral infarction, or secondary brain tumors were also observed. SRT was safe and effective over the long term in patients irradiated in childhood as well as adults, with no local recurrence or adverse events. We believe that surgical planning for craniopharyngioma with stereotactic radiotherapy in mind is effective in maintaining a good prognosis and quality of life.

A Rare Case of Adult Poorly Differentiated Chordoma of the Skull Base With Rapid Progression and Systemic Metastasis: A Review of the Literature.

Chordoma is a rare tumor that arises from chordal tissue during fetal life. Recently, the concept of poorly differentiated chordoma, a subtype of chordoma characterized by loss of SMARCB1/INI1 with a poorer prognosis than conventional chordomas, was established. It predominantly occurs in children and is rare in adults. Here, we report a rare adult case of poorly differentiated chordoma of the skull base with a unique course that rapidly systemically metastasized and had the shortest survival time of any adult chordoma reported to date. The patient was a 32-year-old male with a chief complaint of diplopia. MRI showed a widespread neoplastic lesion with the clivus as the main locus. Endoscopic extended transsphenoidal tumor resection was performed. Pathological findings showed that the tumor was malignant, and immunohistochemistry revealed a Ki-67 labeling index of 80%, diffusely positive brachyury, and loss of INI1 expression. The final diagnosis was poorly differentiated chordoma. Postoperatively, the residual tumor in the right cavernous sinus showed rapid growth. The patient was promptly treated with gamma knife three fractions. The residual tumor regressed, but the tumor developed systemic metastasis in a short period, and the patient died seven months after diagnosis. This report of a rapidly progressing and fatal adult poorly differentiated chordoma shows the highest Ki-67 labeling index reported to date. Prompt multidisciplinary treatment should be considered when the Ki-67 labeling index is high.

Postoperative disappearance of leptomeningeal enhancement around the brainstem in glioblastoma.

PURPOSE: Leptomeningeal enhancement (LME) suggests leptomeningeal dissemination (LMD) of tumor cells, which is a complication of end-stage glioblastoma, and is associated with a poor prognosis. However, magnetic resonance imaging (MRI) occasionally indicates the disappearance of peri-brainstem LME after surgical resection of glioblastoma. Since preoperative LMD may affect treatment indications, we aimed to analyze the clinical significance of preoperative LME of the brainstem in glioblastoma. METHODS: We retrospectively collected clinical and radiological data from consecutive patients with glioblastoma and preoperative LME of the brainstem, who were treated at our hospital between 2017 and 2020. RESULTS: Among 112 patients with glioblastoma, nine (8%) showed preoperative LME of the brainstem. In comparison with tumors without LME, tumor size was significantly associated with the preoperative LME of the brainstem (p = 0.016). In addition, there was a trend toward significance for a relationship between deep tumor location and preoperative LME of the brainstem (p = 0.058). Notably, among six patients who underwent surgical resection for glioblastoma with LME of the brainstem, four showed significant radiological disappearance of the LME on postoperative MRI. This suggests that the LME did not result from LMD in these cases. Moreover, these four patients lived longer than would be expected from the presence of LMD. However, this LME disappearance was not observed after biopsy or chemoradiotherapy. CONCLUSIONS: These findings suggest that preoperative LME does not necessarily indicate the presence of untreatable LMD; moreover, LME may disappear after surgical tumor resection. Thus, transient preoperative LME could be attributed to other mechanisms, including impaired venous flow due to intratumoral arteriovenous shunts, which can be resolved by reducing the tumor burden.

Inhalational Anesthesia Reduced Transient Neurological Events After Revascularization Surgery for Moyamoya Disease.

BACKGROUND AND OBJECTIVES: The choice between inhalational and total intravenous anesthesia (TIVA) in revascularization surgery for Moyamoya disease (MMD) remains a topic of debate. Anesthesia methods have changed with the advent of new anesthetics. This study investigated whether modern anesthesia methods affected the development of neurological symptoms after revascularization surgery for MMD. METHODS: This single-center retrospective study included 63 adult patients (82 hemispheres) with MMD treated with direct and indirect bypass surgeries at our hospital between 2013 and 2022. Patients were divided into inhalational anesthesia (IA) and TIVA groups based on the anesthesia maintenance method. Baseline patient characteristics; postoperative neurological symptoms, including hyperperfusion syndrome, cerebral infarction, and transient neurological events (TNEs); and cortical hyperintensity belt (CHB) sign scores (5-point scale from 0 to 4) on postoperative magnetic resonance imaging were compared between the two groups. The operation methods, anesthetics, and intraoperative hemodynamic and ventilatory parameters were compared between patients with and without TNEs. RESULTS: The IA and TIVA groups comprised 39 and 43 hemispheres, respectively. The frequency of postoperative hyperperfusion syndrome and cerebral infarction did not differ between the groups, but the number of TNEs in the IA group (5/39; 13%) was significantly lower than that in the TIVA group (16/43; 37%). Multivariate logistic regression analysis revealed that TNEs were associated with TIVA (odds ratio, 3.91; 95% CI, 1.24-12.35; P = .02). The median [IQR] postoperative CHB sign score in the IA group (2 [1-3]) was significantly lower than that in the TIVA group (4 [3-4]). CONCLUSION: The IA group had fewer postoperative TNEs and lower CHB sign scores than the TIVA group. Although further studies are needed, this study provides insights into the prevention of TNEs with IA and reconsideration of the optimal anesthesia for MMD.

Deep learning-based reconstruction can improve the image quality of low radiation dose head CT.

OBJECTIVES: To evaluate the image quality of deep learning-based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images. METHODS: This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured. RESULTS: The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively. CONCLUSION: DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time. KEY POINTS: • For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR. • The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s). • Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.

Importance of Age and Noncontrast-Enhancing Tumor as Biomarkers for Isocitrate Dehydrogenase-Mutant Glioblastoma: A Multicenter Study.

PURPOSE: This study aimed to investigate the most useful clinical and magnetic resonance imaging (MRI) parameters for differentiating isocitrate dehydrogenase (IDH)-mutant and -wildtype glioblastomas in the 2016 World Health Organization Classification of Tumors of the Central Nervous System. METHODS: This multicenter study included 327 patients with IDH-mutant or IDH-wildtype glioblastoma in the 2016 World Health Organization classification who preoperatively underwent MRI. Isocitrate dehydrogenase mutation status was determined by immunohistochemistry, high-resolution melting analysis, and/or IDH1/2 sequencing. Three radiologists independently reviewed the tumor location, tumor contrast enhancement, noncontrast-enhancing tumor (nCET), and peritumoral edema. Two radiologists independently measured the maximum tumor size and mean and minimum apparent diffusion coefficients of the tumor. Univariate and multivariate logistic regression analyses with an odds ratio (OR) were performed. RESULTS: The tumors were IDH-wildtype glioblastoma in 306 cases and IDH-mutant glioblastoma in 21. Interobserver agreement for both qualitative and quantitative evaluations was moderate to excellent. The univariate analyses revealed a significant difference in age, seizure, tumor contrast enhancement, and nCET ( P < 0.05). The multivariate analysis revealed significant difference in age for all 3 readers (reader 1, odds ratio [OR] = 0.960, P = 0.012; reader 2, OR = 0.966, P = 0.048; reader 3, OR = 0.964, P = 0.026) and nCET for 2 readers (reader 1, OR = 3.082, P = 0.080; reader 2, OR = 4.500, P = 0.003; reader 3, OR = 3.078, P = 0.022). CONCLUSIONS: Age and nCET are the most useful parameters among the clinical and MRI parameters for differentiating IDH-mutant and IDH-wildtype glioblastomas.

Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging.

The application of machine learning (ML) and deep learning (DL) in radiology has expanded exponentially. In recent years, an extremely large number of studies have reported about the hepatobiliary domain. Its applications range from differential diagnosis to the diagnosis of tumor invasion and prediction of treatment response and prognosis. Moreover, it has been utilized to improve the image quality of DL reconstruction. However, most clinicians are not familiar with ML and DL, and previous studies about these concepts are relatively challenging to understand. In this review article, we aimed to explain the concepts behind ML and DL and to summarize recent achievements in their use in the hepatobiliary region.

Performance of Machine Learning Methods Based on Multi-Sequence Textural Parameters Using Magnetic Resonance Imaging and Clinical Information to Differentiate Malignant and Benign Soft Tissue Tumors.

RATIONALE AND OBJECTIVES: To evaluate the performance of a machine learning method to differentiate malignant from benign soft tissue tumors based on textural features on multiparametric magnetic resonance imaging (mpMRI). MATERIALS AND METHODS: We enrolled 163 patients with soft tissue tumors whose diagnosis was pathologically proven (71 malignant, 92 benign). All patients underwent mpMRI. Twelve histographic and textural parameters were assessed on T1-weighted imaging (T1WI), T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced T1WI imaging. We compared mean signals of all sequences from the malignant and benign tumors using Welch's t-test. Prediction models were developed via a machine learning technique (support vector machine) using textural features of each sequence, clinical information (sex + age + tumor size), and the combined model incorporating all features. Areas under the receiver operating characteristic curves (AUCs) of these models were calculated using fivefold cross validation. RESULTS: The diagnostic ability of clinical information model (AUC 0.85) was not inferior to the model with textural features of each sequence (AUC 0.79-0.84). The combined model showed the highest diagnostic ability (AUC 0.89). The AUC of the combined model (0.89) was comparable to those of two board-certified radiologists (0.89 and 0.87). CONCLUSIONS: Machine learning methods based on textural features on mpMRI and clinical information offer adequate diagnostic performance to differentiate between malignant and benign soft tissue tumors.

Arterial Spin Labeling for Pediatric Central Nervous System Diseases: Techniques and Clinical Applications.

Dynamic susceptibility contrast (DSC) and arterial spin labeling (ASL) are techniques used to evaluate brain perfusion using MRI. DSC requires dynamic image acquisition with a rapid administration of gadolinium-based contrast agent. In contrast, ASL obtains brain perfusion information using magnetically labeled blood water as an endogenous tracer. For the evaluation of brain perfusion in pediatric neurological diseases, ASL has a significant advantage compared to DSC, CT, and single-photon emission CT/positron emission tomography because of the lack of radiation exposure and contrast agent administration. However, in ASL, optimization of several parameters, including the type of labeling, image acquisition, background suppression, and postlabeling delay, is required, because they have a significant effect on the quantification of cerebral blood flow (CBF).In this article, we first review recent technical developments of ASL and age-dependent physiological characteristics in pediatric brain perfusion. We then review the clinical implementation of ASL in pediatric neurological diseases, including vascular diseases, brain tumors, acute encephalopathy with biphasic seizure and late reduced diffusion (AESD), and migraine. In moyamoya disease, ASL can be used for brain perfusion and vessel assessment in pre- and post-treatment. In arteriovenous malformations, ASL is sensitive to detect small degrees of shunt. Furthermore, in vascular diseases, the implementation of ASL-based time-resolved MR angiography is described. In neoplasms, ASL-derived CBF has a high diagnostic accuracy for differentiation between low- and high-grade pediatric brain tumors. In AESD and migraine, ASL may allow for accurate early diagnosis and provide pathophysiological information.

Effects of tube voltage and iodine contrast medium on radiation dose of whole-body CT.

BACKGROUND: The low-tube-voltage scan generally needs a higher tube current than the conventional 120 kVp to maintain the image noise. In addition, the low-tube-voltage scan increases the photoelectric effect, which increases the radiation absorption in organs. PURPOSE: To compare the organ radiation dose caused by iodine contrast medium between low tube voltage with low contrast medium and that of conventional 120-kVp protocol with standard contrast medium. MATERIAL AND METHODS: After the propensity-matching analysis, 66 patients were enrolled including 33 patients with 120 kVp and 600 mgI/kg and 33 patients with 80 kVp and 300 mgI/kg (50% iodine reduction). The pre- and post-contrast phases were assessed in all patients. The Monte Carlo simulation tool was used to simulate the radiation dose. The computed tomography (CT) numbers for 10 organs and the organ doses were measured. The organ doses were normalized by the volume CT dose index, and the 120-kVp protocol was compared with the 80-kVp protocol. RESULTS: On contrast-enhanced CT, there were no significant differences in the mean CT numbers of the organs between 80-kVp and 120-kVp protocols except for the pancreas, kidneys, and small intestine. The normalized organ doses at 80 kVp were significantly lower than those of 120 kVp in all organs (e.g. liver, 1.6 vs. 1.9; pancreas, 1.5 vs. 1.8; spleen, 1.7 vs. 2.0) on contrast-enhanced CT. CONCLUSION: The low tube voltage with low-contrast-medium protocol significantly reduces organ doses at the same volume CT dose index setting compared with conventional 120-kVp protocol with standard contrast medium on contrast-enhanced CT.

Worse prognosis for IDH wild-type diffuse gliomas with larger residual biological tumor burden.

OBJECTIVE: The association of overall survival (OS) with tumor burden, including contrast enhanced (CE) volume on CE T1-weighted images, fluid-attenuated inversion recovery (FLAIR) hyperintense volume, and 3, 4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) hypermetabolic volume, in isocitrate dehydrogenase (IDH) wild-type gliomas remains unclear. This study aimed to assess the association between biological tumor burden in pre- and post-operative status and OS in IDH wild-type gliomas, and evaluated which volume was the best predictor of OS. METHODS: Thirty-four patients with treatment-naïve IDH wild-type gliomas (WHO grade II 6, III 15, IV 13) were retrospectively included. Three pre-operative tumor regions of interest (ROIs) were segmented based on the CE, FLAIR hyperintense, and FDOPA hypermetabolic regions. Resected ROIs were segmented from the post-operative images. Residual CE, FLAIR hyperintense, and FDOPA hypermetabolic ROIs were created by subtracting resected ROIs from pre-operative ROIs. Cox regression analysis was conducted to investigate the association of OS with the volume of each ROI, and Akaike information criterion was used to assess the fitness. RESULTS: Residual CE volume had a significant association with OS [hazard ratio (HR) = 1.26, p = 0.039], but this effect disappeared when controlling for tumor grade. Residual FDOPA hypermetabolic volume best fit the regression model and was significantly associated with OS (HR = 1.18, p = 0.008), even when controlling for tumor grade. FLAIR hyperintense volume showed no significant association with OS. CONCLUSION: Residual FDOPA hypermetabolic burden predicted OS for IDH wild-type gliomas, regardless of the tumor grade. Furthermore, removing hypermetabolic and CE regions may improve the prognosis.

Preferential tumor localization in relation to 18F-FDOPA uptake for lower-grade gliomas.

PURPOSE: Although tumor localization and 3,4-dihydroxy-6-18F-fluoro-L-phenylalanine (FDOPA) uptake may have an association, preferential tumor localization in relation to FDOPA uptake is yet to be investigated in lower-grade gliomas (LGGs). This study aimed to identify differences in the frequency of tumor localization between FDOPA hypometabolic and hypermetabolic LGGs using a probabilistic radiographic atlas. METHODS: Fifty-one patients with newly diagnosed LGG (WHO grade II, 29; III, 22; isocitrate dehydrogenase wild-type, 21; mutant 1p19q non-codeleted,16; mutant codeleted, 14) who underwent FDOPA positron emission tomography (PET) were retrospectively selected. Semiautomated tumor segmentation on FLAIR was performed. Patients with LGGs were separated into two groups (FDOPA hypometabolic and hypermetabolic LGGs) according to the normalized maximum standardized uptake value of FDOPA PET (a threshold of the uptake in the striatum) within the segmented regions. Spatial normalization procedures to build a 3D MRI-based atlas using each segmented region were validated by an analysis of differential involvement statistical mapping. RESULTS: Superimposition of regions of interest showed a high number of hypometabolic LGGs localized in the frontal lobe, while a high number of hypermetabolic LGGs was localized in the insula, putamen, and temporal lobe. The statistical mapping revealed that hypometabolic LGGs occurred more frequently in the superior frontal gyrus (close to the supplementary motor area), while hypermetabolic LGGs occurred more frequently in the insula. CONCLUSION: Radiographic atlases revealed preferential frontal lobe localization for FDOPA hypometabolic LGGs, which may be associated with relatively early detection.

Differentiating IDH status in human gliomas using machine learning and multiparametric MR/PET.

BACKGROUND: The purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) images using an unsupervised, two-level clustering approach followed by support vector machine in order to classify the isocitrate dehydrogenase (IDH) status of gliomas. METHODS: Sixty-two treatment-naïve glioma patients who underwent FDOPA PET and MRI were retrospectively included. Contrast enhanced T1-weighted images, T2-weighted images, fluid-attenuated inversion recovery images, apparent diffusion coefficient maps, and relative cerebral blood volume maps, and FDOPA PET images were used for voxel-wise feature extraction. An unsupervised two-level clustering approach, including a self-organizing map followed by the K-means algorithm was used, and each class label was applied to the original images. The logarithmic ratio of labels in each class within tumor regions was applied to a support vector machine to differentiate IDH mutation status. The area under the curve (AUC) of receiver operating characteristic curves, accuracy, and F1-socore were calculated and used as metrics for performance. RESULTS: The associations of multiparametric imaging values in each cluster were successfully visualized. Multiparametric images with 16-class clustering revealed the highest classification performance to differentiate IDH status with the AUC, accuracy, and F1-score of 0.81, 0.76, and 0.76, respectively. CONCLUSIONS: Machine learning using an unsupervised two-level clustering approach followed by a support vector machine classified the IDH mutation status of gliomas, and visualized voxel-wise features from multiparametric MRI and FDOPA PET images. Unsupervised clustered features may improve the understanding of prioritizing multiparametric imaging for classifying IDH status.

Virtual Monochromatic Image Quality from Dual-Layer Dual-Energy Computed Tomography for Detecting Brain Tumors.

OBJECTIVE: To evaluate the usefulness of virtual monochromatic images (VMIs) obtained using dual-layer dual-energy CT (DL-DECT) for evaluating brain tumors. MATERIALS AND METHODS: This retrospective study included 32 patients with brain tumors who had undergone non-contrast head CT using DL-DECT. Among them, 15 had glioblastoma (GBM), 7 had malignant lymphoma, 5 had high-grade glioma other than GBM, 3 had low-grade glioma, and 2 had metastatic tumors. Conventional polychromatic images and VMIs (40-200 keV at 10 keV intervals) were generated. We compared CT attenuation, image noise, contrast, and contrast-to-noise ratio (CNR) between tumor and white matter (WM) or grey matter (GM) between VMIs showing the highest CNR (optimized VMI) and conventional CT images using the paired t test. Two radiologists subjectively assessed the contrast, margin, noise, artifact, and diagnostic confidence of optimized VMIs and conventional images on a 4-point scale. RESULTS: The image noise of VMIs at all energy levels tested was significantly lower than that of conventional CT images (p < 0.05). The 40-keV VMIs yielded the best CNR. Furthermore, both contrast and CNR between the tumor and WM were significantly higher in the 40 keV images than in the conventional CT images (p < 0.001); however, the contrast and CNR between tumor and GM were not significantly different (p = 0.47 and p = 0.31, respectively). The subjective scores assigned to contrast, margin, and diagnostic confidence were significantly higher for 40 keV images than for conventional CT images (p < 0.01). CONCLUSION: In head CT for patients with brain tumors, compared with conventional CT images, 40 keV VMIs from DL-DECT yielded superior tumor contrast and diagnostic confidence, especially for brain tumors located in the WM.

Voxelwise and Patientwise Correlation of 18F-FDOPA PET, Relative Cerebral Blood Volume, and Apparent Diffusion Coefficient in Treatment-Naïve Diffuse Gliomas with Different Molecular Subtypes.

Our purpose was to identify correlations between 18F-fluorodihydroxyphenylalanine (18F-FDOPA) uptake and physiologic MRI, including relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC), in gliomas with different molecular subtypes and to evaluate their prognostic values. Methods: Sixty-eight treatment-naïve glioma patients who underwent 18F-FDOPA PET and physiologic MRI were retrospectively selected (36 with isocitrate dehydrogenase wild-type [IDHwt], 16 with mutant 1p/19q noncodeleted [IDHm-noncodel], and 16 with mutant codeleted [IDHm-codel]). Fluid-attenuated inversion recovery hyperintense areas were segmented and used as regions of interest. For voxelwise and patientwise analyses, Pearson correlation coefficients (rvoxelwise and rpatientwise) between the normalized SUV (nSUV), rCBV, and ADC were evaluated. Cox regression analysis was performed to investigate the associations between overall survival and rvoxelwise, maximum or median nSUV, median rCBV, or median ADC. Results: For IDHwt and IDHm-noncodel gliomas, nSUV demonstrated significant positive correlations with rCBV (rvoxelwise = 0.25 and 0.31, respectively; rpatientwise = 0.50 and 0.70, respectively) and negative correlations with ADC (rvoxelwise = -0.19 and -0.19, respectively; rpatientwise = -0.58 and -0.61, respectively) in both voxelwise and patientwise analyses. IDHm-codel gliomas demonstrated a significant positive correlation between nSUV and ADC only in voxelwise analysis (rvoxelwise = 0.18). In Cox regression analysis, rvoxelwise between nSUV and rCBV (hazard ratio, 28.82) or ADC (hazard ratio, 0.085) had significant associations with overall survival for only IDHwt gliomas. Conclusion: IDHm-codel gliomas showed distinctive patterns of correlations between amino acid PET and physiologic MRI. Stronger correlations between nSUV and rCBV or ADC may result in a worse prognosis for IDHwt gliomas.

Multiparametric MR-PET measurements in hypermetabolic regions reflect differences in molecular status and tumor grade in treatment-naïve diffuse gliomas.

PURPOSE: To assess whether hypermetabolically-defined regions of interest (ROIs) on 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) could be used to evaluate physiological features and whether there are measurable differences between molecular subtypes and tumor grades. METHODS: Sixty-eight treatment-naïve glioma patients who underwent FDOPA PET and magnetic resonance imaging (MRI) were retrospectively included. Fluid-attenuated inversion recovery hyperintense regions (FLAIRROI) were segmented. FDOPA hypermetabolic regions (FDOPAROI, tumor-to-striatum ratios > 1) within FLAIRROI were extracted. Normalized maximum standardized uptake value (nSUVmax), volume of each ROI, and median relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) within FLAIRROI or FDOPAROI were calculated. Imaging metrics were compared using Students t or Mann-Whitney U tests. Area under the curve (AUC) of receiver-operating characteristic curves were used to determine whether imaging metrics within FLAIRROI or FDOPAROI can discriminate different molecular statuses or grades. RESULTS: Using either FLAIRROI or FDOPAROI, the nSUVmax and rCBV were significantly higher and the ADC was lower in isocitrate dehydrogenase (IDH) wild-type than mutant gliomas, and in higher-grade gliomas (HGGs) than lower-grade gliomas (LGGs). The FDOPAROI volume was significantly higher in 1p19q codeleted than non-codeleted gliomas, and in HGGs than LGGs. Although not significant, imaging metrics extracted by FDOPAROI discriminated molecular status and tumor grade more accurately than those extracted by FLAIRROI (AUC of IDH status, 0.87 vs. 0.82; 1p19q status, 0.78 vs. 0.73; grade, 0.87 vs. 0.76). CONCLUSION: FDOPA hypermetabolic ROI may extract useful imaging features of gliomas, which can illuminate biological differences between different molecular status or tumor grades.

Focal cortical dysplasia imaging discrepancies between MRI and FDG-PET: Unique association with temporal lobe location.

PURPOSE: Although magnetic resonance imaging (MRI) and 18F-2-fluorodeoxyglucose-positron emission tomography (FDG-PET) are used for pre-surgical assessment of focal cortical dysplasia (FCD), they often disagree. This study aimed to identify factors that contribute to discrepancies in FCD imaging between MRI and FDG-PET. METHODS: Sixty-two patients (mean age, 18.9 years) with a FCD type I or II were retrospectively selected. These patients were visually categorized into two groups: 1) extent of PET abnormality larger than MRI abnormality and 2) vice versa or equivalent. Predictive factors of these two groups were analyzed by multivariate logistic regression. The extent of hypometabolic transient zone surrounding FCDs and their mean standardized uptake values were measured and compared by the Mann-Whitney U-test. RESULTS: FCDs were detected on MRI and PET in 46 and 55 patients, respectively, whereas no abnormality was detected in 4 patients. The PET hypometabolic areas were larger than the MRI abnormal areas in 26 patients (88 % in the temporal lobe), whereas the PET hypometabolic areas were equivalent or smaller than the MRI abnormal areas in 32 patients (69 % in the frontal lobe). The temporal lobe location was an independent predictor for differentiating the two groups (OR = 35.2, 95 % CI = 6.81-168.0, P < .001). The temporal lobe lesions had significantly wider transient zones and lower standardized uptake values than those in the other lobes (P < .001, both). CONCLUSION: The discrepancies between MRI and FDG-PET findings of FCD were associated with temporal lobe location.

Usefulness of Virtual Monochromatic Dual-Layer Computed Tomographic Imaging for Breast Carcinoma.

OBJECTIVE: This study aimed to evaluate virtual monochromatic images (VMIs) obtained using dual-layer dual-energy computed tomography (CT) for breast carcinoma. METHODS: We retrospectively enrolled 28 patients with breast cancer who were pathologically diagnosed using dual-layer dual-energy CT. Virtual monochromatic images (40-200 keV) were generated. We compared CT number, image noise, contrast, and contrast-to-noise ratio (CNR) between VMIs with the highest CNR and conventional CT images. We performed qualitative image analysis between VMIs at optimized energy and conventional CT images. RESULTS: Image noise of VMIs was not significantly different from that of the conventional CT images. As the x-ray energy decreased, CNR increased. The 40-keV VMIs were highest CNR and higher than that of the conventional CT images. In qualitative image analysis, the 40-keV images were significantly higher than conventional CT images. CONCLUSION: Both qualitative and quantitative analyses showed that the image quality of VMIs at 40 keV was significantly higher than that of conventional CT images.

An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases.

PURPOSE: To evaluate the performance of a machine learning method based on texture parameters in conventional magnetic resonance imaging (MRI) in differentiating glioblastoma (GB) from brain metastases (METs). MATERIALS AND METHODS: In this retrospective study conducted between November 2008 and July 2017, we included 73 patients diagnosed with GB (n = 73) and METs (n = 53) who underwent contrast-enhanced 3 T brain MRI. Twelve histogram and texture parameters were assessed on T2-weighted images (T2WIs), apparent diffusion coefficient maps (ADCs), and contrast-enhanced T1-weighted images (CE-T1WIs). A prediction model was developed for a machine learning method, and the area under the receiver operating characteristic curve of this model was calculated through 5-fold cross-validation. Furthermore, machine learning method's performance was compared with three board-certified radiologists' judgments. RESULTS: Univariate logistic regression model showed that the area under the curve (AUC) was highest with the standard value of T2WIs (0.78), followed by the maximum value of T2WIs (0.764), minimum value of T2WIs (0.738), minimum values of CE-T1WIs and contrast of T2WIs (0.733), and mean value of T2WIs (0.724). AUC calculated using the support vector machine was comparable to that calculated by the three radiologists (0.92 vs. 0.72, p < .01; 0.92 vs. 0.73, p < .01; and 0.92 vs. 0.86, p = .096). CONCLUSION: In differentiating GB from METs on the basis of texture parameters in MRI, the performance of the machine learning method based on convention MRI was superior to that of the univariate method, and comparable to that of the radiologists.