Revisiting prognostic factors of gliomatosis cerebri in adult-type diffuse gliomas.

PURPOSE: There is lack of comprehensive analysis evaluating the impact of clinical, molecular, imaging, and surgical data on survival of patients with gliomatosis cerebri (GC). This study aimed to investigate prognostic factors of GC in adult-type diffuse glioma patients. METHODS: Retrospective chart and imaging review was performed in 99 GC patients from adult-type diffuse glioma (among 1,211 patients; 6 oligodendroglioma, 16 IDH-mutant astrocytoma, and 77 IDH-wildtype glioblastoma) from a single institution between 2005 and 2021. Predictors of overall survival (OS) of entire patients and IDH-wildtype glioblastoma patients were determined. RESULTS: The median OS was 16.7 months (95% confidence interval [CI] 14.2-22.2) in entire patients and 14.3 months (95% CI 12.2-61.9) in IDH-wildtype glioblastoma patients. In entire patients, KPS (hazard ratio [HR] = 0.98, P = 0.004), no 1p/19q codeletion (HR = 10.75, P = 0.019), MGMTp methylation (HR = 0.54, P = 0.028), and hemorrhage (HR = 3.45, P = 0.001) were independent prognostic factors on multivariable analysis. In IDH-wildtype glioblastoma patients, KPS (HR = 2.24, P = 0.075) was the only independent prognostic factor on multivariable analysis. In subgroup of IDH-wildtype glioblastoma with CE tumors, total resection of CE tumor did not remain as a significant prognostic factor (HR = 1.13, P = 0.685). CONCLUSIONS: The prognosis of GC patients is determined by its underlying molecular type and patient performance status. Compared with diffuse glioma without GC, aggressive surgery of CE tumor in GC patients does not improve survival.

Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study.

OBJECTIVES: To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting. MATERIALS AND METHODS: In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having newly developed BM from two tertiary university hospitals, which performed black-blood imaging between January 2020 and April 2021. Four neuroradiologists independently evaluated BM either with segmented masks and BM counts provided (with DLS) or not provided (without DLS) on a clinical trial imaging management system (CTIMS). To assess reading reproducibility, BM count agreement between the readers and the reference standard were calculated using limits of agreement (LoA). Readers' workload was assessed with reading time, which was automatically measured on CTIMS, and were compared between with and without DLS using linear mixed models considering the imaging center. RESULTS: In the validation cohort, the detection sensitivity and positive predictive value of the DLS were 90.2% (95% confidence interval [CI]: 88.1-92.2) and 88.2% (95% CI: 85.7-90.4), respectively. The difference between the readers and the reference counts was larger without DLS (LoA: -0.281, 95% CI: -2.888, 2.325) than with DLS (LoA: -0.163, 95% CI: -2.692, 2.367). The reading time was reduced from mean 66.9 s (interquartile range: 43.2-90.6) to 57.3 s (interquartile range: 33.6-81.0) (P <.001) in the with DLS group, regardless of the imaging center. CONCLUSION: Deep learning-based BM detection and counting with black-blood imaging improved reproducibility and reduced reading time, on multi-center validation.

Clinical factors and conventional MRI may independently predict progression-free survival and overall survival in adult pilocytic astrocytomas.

PURPOSE: Pilocytic astrocytoma (PA) is rare in adults, and only limited knowledge on the clinical course and prognosis has been available. The combination of clinical information and comprehensive imaging parameters could be used for accurate prognostic stratification in adult PA patients. This study was conducted to predict the prognostic factors from clinical information and conventional magnetic resonance imaging (MRI) features in adult PAs. METHODS: A total of 56 adult PA patients were enrolled in the institutional cohort. Clinical characteristics including age, sex, anaplastic PA, presence of neurofibromatosis type 1, Karnofsky performance status, extent of resection, and postoperative treatment were collected. MRI characteristics including major axis length, tumor location, presence of the typical 'cystic mass with enhancing mural nodule appearance', proportion of enhancing tumor, the proportion of edema, conspicuity of the nonenhancing margin, and presence of a cyst were evaluated. Univariable and multivariable Cox proportional hazard modeling were performed. RESULTS: The 5-year progression-free survival (PFS) and overall survival (OS) rates were 83.9% and 91.l%, respectively. On univariable analysis, older age, larger proportion of edema, and poor definition of nonenhancing margin were predictors of shorter PFS and OS, respectively (all Ps < .05). On multivariable analysis, older age (hazard ratio [HR] = 1.04, P = .014; HR = 1.14, P = .030) and poor definition of nonenhancing margin (HR = 3.66, P = .027; HR = 24.30, P = .024) were independent variables for shorter PFS and OS, respectively. CONCLUSION: Age and the margin of the nonenhancing part of the tumor may be useful biomarkers for predicting the outcome in adult PAs.

Clinical and diffusion parameters may noninvasively predict TERT promoter mutation status in grade II meningiomas.

BACKGROUND AND PURPOSE: Increasing evidence suggests that genomic and molecular markers need to be integrated in grading of meningioma. Telomerase reverse transcriptase promoter (TERTp) mutation is receiving attention due to its clinical relevance in the treatment of meningiomas. The predictive ability of conventional and diffusion MRI parameters for determining the TERTp mutation status in grade II meningiomas has yet been identified. MATERIAL AND METHODS: In this study, 63 patients with surgically confirmed grade II meningiomas (56 TERTp wildtype, 7 TERTp mutant) were included. Conventional imaging features were qualitatively assessed. The maximum diameter, volume of the tumors and histogram parameters from the apparent diffusion coefficient (ADC) were assessed. Independent clinical and imaging risk factors for TERTp mutation were investigated using multivariable logistic regression. The discriminative value of the prediction models with and without imaging features was evaluated. RESULTS: In the univariable regression, older age (odds ratio [OR] = 1.13, P = 0.005), larger maximum diameter (OR = 1.09, P = 0.023), larger volume (OR = 1.04, P = 0.014), lower mean ADC (OR = 0.02, P = 0.025), and lower ADC 10th percentile (OR = 0.01, P = 0.014) were predictors of TERTp mutation. In multivariable regression, age (OR = 1.13, P = 0.009) and ADC 10th percentile (OR = 0.01, P = 0.038) were independent predictors of variables for predicting the TERTp mutation status. The performance of the prediction model increased upon inclusion of imaging parameters (area under the curves of 0.86 and 0.91, respectively, without and with imaging parameters). CONCLUSION: Older age and lower ADC 10th percentile may be useful parameters to predict TERTp mutation in grade II meningiomas.

Stent Opening Visualization During Mechanical Thrombectomy; Relationship with the Retrieved Clot and Procedural Success.

PURPOSE: The angiographic visualization of the stent during mechanical thrombectomy (MT) may provide information regarding the characteristics of the underlying occluding clot, device-clot interaction, and recanalization. The purpose of this study was to evaluate the open stent sign in relation to the retrieved clot and recanalization. METHODOLOGY: 78 patients treated with the stent retriever for acute stroke were retrospectively reviewed. The open stent sign was defined as full opening (>80% of normal vessel diameter) of the stent on DSA after deployment across the occlusion. The retrieved clot was visually classified as red or non-red clots. The relationship between the open stent sign and the patient characteristics, recanalization, retrieved clot, and clinical outcome were analyzed. RESULTS: Overall successful recanalization and good outcome was achieved in 68 (87.2%) and 35 (44.9%) patients, respectively. Open stent sign was seen in 52 patients (66.7%). Occlusions showing positive open stent sign was associated with significantly higher first pass effect (44.2% vs 19.2%, p=0.044) and successful recanalization rate (94.2% vs 73.1%, p=0.013) compared to negative open stent sign. The open stent sign was associated with higher incidence of red clot (75.0% vs 38.9%, p=0.008). On multivariate analysis, the open stent sign (OR 22.721, 95% CI 1.953-264.372, p=0.013) was a predictor of successful recanalization. CONCLUSIONS: The visualization of the open stent during MT of acute ischemic stroke may provide added information in terms of clot characteristics and procedural success. The open stent sign is associated with red clots, higher first pass effect and successful recanalization.

Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance.

PURPOSE: Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal characteristics after an MR examination, such as susceptibility-weighted imaging or gradient echo imaging (GRE). In this paper, an efficient method for CMB detection in GRE scans is presented. MATERIALS AND METHODS: The proposed framework consists of the following phases: (1) pre-processing (skull extraction), (2) the first training with the ground truth labeled using CMB, (3) the second training with the ground truth labeled with CMB mimicking the same subjects, and (4) post-processing (cerebrospinal fluid (CSF) filtering). The proposed technique was validated on a dataset of 1133 CBMs that consisted of 5284 images for training and 1737 images for testing. We applied a two-stage approach using a region-based CNN method based on You Only Look Once (YOLO) to investigate a novel CMB detection technique. RESULTS: The sensitivity, precision, F1-score and false positive per person (FPavg) were evaluated as 80.96, 60.98, 69.57 and 6.57, 59.69, 62.70, 61.16 and 4.5, 66.90, 79.75, 72.76 and 2.15 for YOLO with a single label, YOLO with double labels, and YOLO + CSF filtering, respectively, and YOLO + CSF filtering showed the highest precision performance, F1-score and lowest FPavg. CONCLUSIONS: Using proposed framework, we developed an optimized CMB learning model with low false positives and a balanced performance in clinical practice.

Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation.

Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.

Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland.

PURPOSE: This study was conducted to evaluate the diagnostic performance of machine learning in differentiating follicular adenoma from carcinoma using preoperative ultrasonography (US). METHODS: In this retrospective study, preoperative US images of 348 nodules from 340 patients were collected from two tertiary referral hospitals. Two experienced radiologists independently reviewed each image and categorized the nodules according to the 2015 American Thyroid Association guideline. Categorization of a nodule as highly suspicious was considered a positive diagnosis for malignancy. The nodules were manually segmented, and 96 radiomic features were extracted from each region of interest. Ten significant features were selected and used as final input variables in our in-house developed classifier models based on an artificial neural network (ANN) and support vector machine (SVM). The diagnostic performance of radiologists and both classifier models was calculated and compared. RESULTS: In total, 252 nodules from 245 patients were confirmed as follicular adenoma and 96 nodules from 95 patients were diagnosed as follicular carcinoma. As measures of diagnostic performance, the average sensitivity, specificity, and accuracy of the two experienced radiologists in discriminating follicular adenoma from carcinoma on preoperative US images were 24.0%, 84.0%, and 64.8%, respectively. The sensitivity, specificity, and accuracy of the ANN and SVM-based models were 32.3%, 90.1%, and 74.1% and 41.7%, 79.4%, and 69.0%, respectively. The kappa value of the two radiologists was 0.076, corresponding to slight agreement. CONCLUSION: Machine learning-based classifier models may aid in discriminating follicular adenoma from carcinoma using preoperative US.

Intranodular Vascularity May Be Useful in Predicting Malignancy in Thyroid Nodules with the Intermediate Suspicion Pattern of the 2015 American Thyroid Association Guidelines.

The aim of the study described here was to determine whether vascularity patterns on Doppler ultrasonography (US) differentiate benign and malignant thyroid nodules with the intermediate suspicion pattern based on the 2015 American Thyroid Association guidelines. A total of 411 benign or malignant thyroid nodules from 406 patients with intermediate-suspicion US features were retrospectively collected. Univariate and multivariate logistic regression analyses with the generalized estimating equation were used to identify factors predicting malignancy, and odds ratios with 95% confidence intervals were calculated. The vascularity patterns significantly differed between the benign (353 of 411, 85.9%) and malignant (58 of 411, 14.1%) nodules (p = 0.005). Only intranodular vascularity was significantly associated with malignancy on univariate analysis (p = 0.006) and was an independent predictor of malignancy on multivariate analysis (p = 0.004). In conclusion, intranodular vascularity on Doppler US may be useful for predicting malignancy in thyroid nodules with the intermediate-suspicion pattern.

Core-Needle Biopsy Does Not Show Superior Diagnostic Performance to Fine-Needle Aspiration for Diagnosing Thyroid Nodules.

PURPOSE: To compare the diagnostic performances of fine-needle aspiration (FNA) and core-needle biopsy (CNB) for thyroid nodules according to nodule size. MATERIALS AND METHODS: This retrospective study included 320 thyroid nodules from 320 patients who underwent both FNA and CNB at outside clinics and proceeded with surgery in our institution between July 2012 and May 2019. According to nodule size, the diagnostic performances of FNA and CNB were calculated using various combinations of test-negatives and test-positives defined by the Bethesda categories and were compared using the generalized estimated equation and the Delong method. RESULTS: There were 279 malignant nodules in 279 patients and 41 benign nodules in 41 patients. The diagnostic performance of FNA was mostly not different from CNB regardless of nodule size, except for negative predictive value, which was better for FNA than CNB when applying Criteria 1 and 2. When applying Criteria 3, the specificity and positive predictive value of FNA were superior to CNB regardless of size. When applying Criteria 4, diagnostic performance did not differ between FNA and CNB regardless of size. After applying Criteria 5, diagnostic performance did not differ between FNA and CNB in nodules ≥2 cm. However, in nodules ≥1 cm and all nodules, the sensitivity, accuracy, and negative predictive value of CNB were better than those of FNA. CONCLUSION: CNB did not show superior diagnostic performance to FNA for diagnosing thyroid nodules.

Optimisation of the MR protocol in pregnant women with suspected acute appendicitis.

PURPOSE: To investigate the optimal magnetic resonance (MR) imaging protocol in pregnant women suspected of having acute appendicitis. MATERIALS AND METHODS: One hundred and forty-six pregnant women with suspected appendicitis were included. MR images were reviewed by two radiologists in three separate sessions. In session 1, only axial single-shot turbo spin echo (SSH-TSE) T2-weighted images (WI) were included with other routine sequences. In sessions 2 and 3, coronal and sagittal T2WI were sequentially added. The visibility of the appendix and diagnostic confidence of appendicitis were evaluated in each session using a 5-point grading scale. If diseases other than appendicitis were suspected, specific diagnosis with a 5-point confidence scale was recorded. Diagnostic performance for appendicitis and other diseases were evaluated. RESULTS: Twenty-five patients (17.1%) were diagnosed with appendicitis. Among the patients with normal appendix, 28 were diagnosed with other disease. Diagnostic performance including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve values for diagnosing appendicitis and other diseases showed no significant difference among sets for both reviewers (p>0.05). CONCLUSION: Diagnostic performance of MR in pregnant patients with suspected appendicitis can be preserved with omission of sagittal or both coronal and sagittal SSH-T2WI. KEY POINTS: • Diagnostic performance of appendicitis is preserved with omission of sagittal/coronal T2WIs. • Diagnosis of other disease may be sufficient with axial T2WIs only. • Careful serial omission of sagittal and coronal T2WIs can be considered.

T1 bright appendix sign to exclude acute appendicitis in pregnant women.

OBJECTIVE: To evaluate the diagnostic value of the T1 bright appendix sign for the diagnosis of acute appendicitis in pregnant women. MATERIAL AND METHODS: This retrospective study included 125 pregnant women with suspected appendicitis who underwent magnetic resonance (MR) imaging. The T1 bright appendix sign was defined as a high intensity signal filling more than half length of the appendix on T1-weighted imaging. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the T1 bright appendix sign for normal appendix identification were calculated in all patients and in those with borderline-sized appendices (6-7 mm). RESULTS: The T1 bright appendix sign was seen in 51% of patients with normal appendices, but only in 4.5% of patients with acute appendicitis. The overall sensitivity, specificity, PPV, and NPV of the T1 bright appendix sign for normal appendix diagnosis were 44.9%, 95.5%, 97.6%, and 30.0%, respectively. All four patients with borderline sized appendix with appendicitis showed negative T1 bright appendix sign. CONCLUSION: The T1 bright appendix sign is a specific finding for the diagnosis of a normal appendix in pregnant women with suspected acute appendicitis. KEY POINTS: • Magnetic resonance imaging is increasingly used in emergency settings. • Acute appendicitis is the most common cause of acute abdomen. • Magnetic resonance imaging is widely used in pregnant population. • T1 bright appendix sign can be a specific sign representing normal appendix.

Differentiation of the Follicular Neoplasm on the Gray-Scale US by Image Selection Subsampling along with the Marginal Outline Using Convolutional Neural Network.

We conducted differentiations between thyroid follicular adenoma and carcinoma for 8-bit bitmap ultrasonography (US) images utilizing a deep-learning approach. For the data sets, we gathered small-boxed selected images adjacent to the marginal outline of nodules and applied a convolutional neural network (CNN) to have differentiation, based on a statistical aggregation, that is, a decision by majority. From the implementation of the method, introducing a newly devised, scalable, parameterized normalization treatment, we observed meaningful aspects in various experiments, collecting evidence regarding the existence of features retained on the margin of thyroid nodules, such as 89.51% of the overall differentiation accuracy for the test data, with 93.19% of accuracy for benign adenoma and 71.05% for carcinoma, from 230 benign adenoma and 77 carcinoma US images, where we used only 39 benign adenomas and 39 carcinomas to train the CNN model, and, with these extremely small training data sets and their model, we tested 191 benign adenomas and 38 carcinomas. We present numerical results including area under receiver operating characteristic (AUROC).