Accurate prediction of pure uric acid urinary stones in clinical context via a combination of radiomics and machine learning.

PURPOSE: Oral chemolysis is an effective and non-invasive treatment for uric acid urinary stones. This study aimed to classify urinary stones into either pure uric acid (pUA) or other composition (Others) using non-contrast-enhanced computed tomography scans (NCCTs). METHODS: Instances managed at our institution from 2019 to 2021 were screened. They were labeled as either pUA or Others based upon composition analyses, and randomly split into training or testing data set. Several instances contained multiple NCCTs which were all collected. In each of NCCTs, individual urinary stone was treated as individual sample. From manually drawn volumes of interest, we extracted original and wavelet radiomics features for each sample. The most important features were then selected via the Least Absolute Shrinkage and Selection Operator for building the final model on a Support Vector Machine. Performance on the testing set was evaluated via accuracy, sensitivity, specificity, and area under the precision-recall curve (AUPRC). RESULTS: There were 302 instances, of which 118 had pUA urinary stones, generating 576 samples in total. From 851 original and wavelet radiomics features extracted for each sample, 10 most important features were ultimately selected. On the testing data set, accuracy, sensitivity, specificity, and AUPRC were 93.9%, 97.9%, 92.2%, and 0.958, respectively, for per-sample prediction, and 90.8%, 100%, 87.5%, and 0.902, respectively, for per-instance prediction. CONCLUSION: The machine learning algorithm trained with radiomics features from NCCTs can accurately predict pUA urinary stones. Our work suggests a potential assisting tool for stone disease treatment selection.

Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study.

BACKGROUND: The diagnostic performance of convolutional neural networks (CNNs) for diagnosing several types of skin neoplasms has been demonstrated as comparable with that of dermatologists using clinical photography. However, the generalizability should be demonstrated using a large-scale external dataset that includes most types of skin neoplasms. In this study, the performance of a neural network algorithm was compared with that of dermatologists in both real-world practice and experimental settings. METHODS AND FINDINGS: To demonstrate generalizability, the skin cancer detection algorithm (https://rcnn.modelderm.com) developed in our previous study was used without modification. We conducted a retrospective study with all single lesion biopsied cases (43 disorders; 40,331 clinical images from 10,426 cases: 1,222 malignant cases and 9,204 benign cases); mean age (standard deviation [SD], 52.1 [18.3]; 4,701 men [45.1%]) were obtained from the Department of Dermatology, Severance Hospital in Seoul, Korea between January 1, 2008 and March 31, 2019. Using the external validation dataset, the predictions of the algorithm were compared with the clinical diagnoses of 65 attending physicians who had recorded the clinical diagnoses with thorough examinations in real-world practice. In addition, the results obtained by the algorithm for the data of randomly selected batches of 30 patients were compared with those obtained by 44 dermatologists in experimental settings; the dermatologists were only provided with multiple images of each lesion, without clinical information. With regard to the determination of malignancy, the area under the curve (AUC) achieved by the algorithm was 0.863 (95% confidence interval [CI] 0.852-0.875), when unprocessed clinical photographs were used. The sensitivity and specificity of the algorithm at the predefined high-specificity threshold were 62.7% (95% CI 59.9-65.1) and 90.0% (95% CI 89.4-90.6), respectively. Furthermore, the sensitivity and specificity of the first clinical impression of 65 attending physicians were 70.2% and 95.6%, respectively, which were superior to those of the algorithm (McNemar test; p < 0.0001). The positive and negative predictive values of the algorithm were 45.4% (CI 43.7-47.3) and 94.8% (CI 94.4-95.2), respectively, whereas those of the first clinical impression were 68.1% and 96.0%, respectively. In the reader test conducted using images corresponding to batches of 30 patients, the sensitivity and specificity of the algorithm at the predefined threshold were 66.9% (95% CI 57.7-76.0) and 87.4% (95% CI 82.5-92.2), respectively. Furthermore, the sensitivity and specificity derived from the first impression of 44 of the participants were 65.8% (95% CI 55.7-75.9) and 85.7% (95% CI 82.4-88.9), respectively, which are values comparable with those of the algorithm (Wilcoxon signed-rank test; p = 0.607 and 0.097). Limitations of this study include the exclusive use of high-quality clinical photographs taken in hospitals and the lack of ethnic diversity in the study population. CONCLUSIONS: Our algorithm could diagnose skin tumors with nearly the same accuracy as a dermatologist when the diagnosis was performed solely with photographs. However, as a result of limited data relevancy, the performance was inferior to that of actual medical examination. To achieve more accurate predictive diagnoses, clinical information should be integrated with imaging information.

Effects of sitting posture and bolus volume on activation of swallowing-related muscles.

BACKGROUND: The pharyngeal phase is a particularly important clinical factor related to swallowing dysfunctions. Head and neck posture, as well as bolus volume, are important factors affecting the pharyngeal stages of normal swallowing. OBJECTIVE: The aim of our study was to identify the effects of sitting posture and bolus volume on the activation of swallowing-related muscles. MATERIALS AND METHODS: Twenty-four subjects participated in the study. The subjects were positioned in three sitting postures-slump sitting (SS), lumbo-pelvic upright sitting (LUS), and thoracic upright sitting (TUS). While sitting in the chair, the subject was instructed to swallow 10 and 20 mL of water. Surface electromyography (EMG) was used to measure the muscle activity of the supra-hyoid (SH) and infra-hyoid (IH) muscles. Also, sitting posture alignment (head, cervical and shoulder angle) was also performed. Data were analysed with a repeated measures analysis of variance (RMANOVA) using a generalised linear model. RESULTS: There was no significant difference in terms of the head angle (P = .395). However, significant differences were found in relation to the cervical angle (P < .001) and shoulder angle (P < .001). The TUS produced the lowest SH EMG activity (P = .001), in comparison to SS and LUS. The bolus volume for 20 mL showed greater SH and IH EMG activity (P < .001) than did the bolus volume for 10 mL. CONCLUSIONS: Correcting sitting posture from SS to TUS may better assist swallowing-related muscles with less effort, irrespective of the bolus volume.

Translation of Carbon-13 EPI for hyperpolarized MR molecular imaging of prostate and brain cancer patients.

PURPOSE: To develop and translate a metabolite-specific imaging sequence using a symmetric echo planar readout for clinical hyperpolarized (HP) Carbon-13 (13 C) applications. METHODS: Initial data were acquired from patients with prostate cancer (N = 3) and high-grade brain tumors (N = 3) on a 3T scanner. Samples of [1-13 C]pyruvate were polarized for at least 2 h using a 5T SPINlab system operating at 0.8 K. Following injection of the HP substrate, pyruvate, lactate, and bicarbonate (for brain studies) were sequentially excited with a singleband spectral-spatial RF pulse and signal was rapidly encoded with a single-shot echo planar readout on a slice-by-slice basis. Data were acquired dynamically with a temporal resolution of 2 s for prostate studies and 3 s for brain studies. RESULTS: High pyruvate signal was seen throughout the prostate and brain, with conversion to lactate being shown across studies, whereas bicarbonate production was also detected in the brain. No Nyquist ghost artifacts or obvious geometric distortion from the echo planar readout were observed. The average error in center frequency was 1.2 ± 17.0 and 4.5 ± 1.4 Hz for prostate and brain studies, respectively, below the threshold for spatial shift because of bulk off-resonance. CONCLUSION: This study demonstrated the feasibility of symmetric EPI to acquire HP 13 C metabolite maps in a clinical setting. As an advance over prior single-slice dynamic or single time point volumetric spectroscopic imaging approaches, this metabolite-specific EPI acquisition provided robust whole-organ coverage for brain and prostate studies while retaining high SNR, spatial resolution, and dynamic temporal resolution.

Comparison between 8- and 32-channel phased-array receive coils for in vivo hyperpolarized 13 C imaging of the human brain.

PURPOSE: To compare the performance of an 8-channel surface coil/clamshell transmitter and 32-channel head array coil/birdcage transmitter for hyperpolarized 13 C brain metabolic imaging. METHODS: To determine the field homogeneity of the radiofrequency transmitters, B1 + mapping was performed on an ethylene glycol head phantom and evaluated by means of the double angle method. Using a 3D echo-planar imaging sequence, coil sensitivity and noise-only phantom data were acquired with the 8- and 32-channel receiver arrays, and compared against data from the birdcage in transceiver mode. Multislice frequency-specific 13 C dynamic echo-planar imaging was performed on a patient with a brain tumor for each hardware configuration following injection of hyperpolarized [1-13 C]pyruvate. Signal-to-noise ratio (SNR) was evaluated from pre-whitened phantom and temporally summed patient data after coil combination based on optimal weights. RESULTS: The birdcage transmitter produced more uniform B1 + compared with the clamshell: 0.07 versus 0.12 (fractional error). Phantom experiments conducted with matched lateral housing separation demonstrated 8- versus 32-channel mean transceiver-normalized SNR performance: 0.91 versus 0.97 at the head center; 6.67 versus 2.08 on the sides; 0.66 versus 2.73 at the anterior; and 0.67 versus 3.17 on the posterior aspect. While the 8-channel receiver array showed SNR benefits along lateral aspects, the 32-channel array exhibited greater coverage and a more uniform coil-combined profile. Temporally summed, parameter-normalized patient data showed SNRmean,slice ratios (8-channel/32-channel) ranging 0.5-2.00 from apical to central brain. White matter lactate-to-pyruvate ratios were conserved across hardware: 0.45 ± 0.12 (8-channel) versus 0.43 ± 0.14 (32-channel). CONCLUSION: The 8- and 32-channel hardware configurations each have advantages in particular brain anatomy.

Impact of isolated basal ganglia infarction at pretreatment DWI on outcomes after endovascular thrombectomy in acute anterior circulation stroke.

PURPOSE: Acute infarction confined to the basal ganglia (BG) is occasionally observed on baseline imaging before endovascular thrombectomy. This study aimed to investigate the impact of isolated BG infarction revealed on pretreatment DWI in a large cohort of patients with acute anterior circulation stroke who underwent thrombectomy. METHODS: We retrospectively analyzed clinical and DWI data from 328 patients who underwent thrombectomy for emergent occlusions of the intracranial internal carotid artery or the middle cerebral artery. Characteristics and treatment outcomes were compared between patients with isolated BG infarction and those with non-isolated BG infarction. Binary logistic regression analyses were performed to identify independent predictors of good outcome (90-day mRS 0-2). RESULTS: Isolated BG infarction was found in 57 patients (17.4%). Patients with isolated BG infarction had a higher incidence of underlying severe intracranial atherosclerotic stenosis (21.1% vs. 10.7%, P = 0.032) than those with non-isolated BG infarction. Successful reperfusion occurred more frequently in patients with isolated BG infarction than those with non-isolated BG infarction (93% vs. 79%, odds ratio 3.529, 95% confidence interval 1.226-10.161, P = 0.014). On multivariate logistic regression analysis, independent predictors of good outcome were age, DWI-ASPECTS, and admission NIHSS score. There was no significant difference in the rate of good outcome between the two groups (54.4% vs. 42.8%, P = 0.110). CONCLUSION: Isolated BG infarction on pretreatment DWI may predict successful reperfusion after endovascular thrombectomy in patients with acute anterior circulation stroke. In addition, our study suggested a novel finding that isolated BG infarction was more frequently associated with underlying severe ICAS than non-isolated BG infarction.

Technique development of 3D dynamic CS-EPSI for hyperpolarized 13 C pyruvate MR molecular imaging of human prostate cancer.

PURPOSE: The purpose of this study was to develop a new 3D dynamic carbon-13 compressed sensing echoplanar spectroscopic imaging (EPSI) MR sequence and test it in phantoms, animal models, and then in prostate cancer patients to image the metabolic conversion of hyperpolarized [1-13 C]pyruvate to [1-13 C]lactate with whole gland coverage at high spatial and temporal resolution. METHODS: A 3D dynamic compressed sensing (CS)-EPSI sequence with spectral-spatial excitation was designed to meet the required spatial coverage, time and spatial resolution, and RF limitations of the 3T MR scanner for its clinical translation for prostate cancer patient imaging. After phantom testing, animal studies were performed in rats and transgenic mice with prostate cancers. For patient studies, a GE SPINlab polarizer (GE Healthcare, Waukesha, WI) was used to produce hyperpolarized sterile GMP [1-13 C]pyruvate. 3D dynamic 13 C CS-EPSI data were acquired starting 5 s after injection throughout the gland with a spatial resolution of 0.5 cm3 , 18 time frames, 2-s temporal resolution, and 36 s total acquisition time. RESULTS: Through preclinical testing, the 3D CS-EPSI sequence developed in this project was shown to provide the desired spectral, temporal, and spatial 5D HP 13 C MR data. In human studies, the 3D dynamic HP CS-EPSI approach provided first-ever simultaneously volumetric and dynamic images of the LDH-catalyzed conversion of [1-13 C]pyruvate to [1-13 C]lactate in a biopsy-proven prostate cancer patient with full gland coverage. CONCLUSION: The results demonstrate the feasibility to characterize prostate cancer metabolism in animals, and now patients using this new 3D dynamic HP MR technique to measure kPL , the kinetic rate constant of [1-13 C]pyruvate to [1-13 C]lactate conversion.

Investigating tumor perfusion by hyperpolarized 13 C MRI with comparison to conventional gadolinium contrast-enhanced MRI and pathology in orthotopic human GBM xenografts.

PURPOSE: Dissolution dynamic nuclear polarization (DNP) enables the acquisition of 13 C magnetic resonance data with a high sensitivity. Recently, metabolically inactive hyperpolarized 13 C-labeled compounds have shown to be potentially useful for perfusion imaging. The purpose of this study was to validate hyperpolarized perfusion imaging methods by comparing with conventional gadolinium (Gd)-based perfusion MRI techniques and pathology. METHODS: Dynamic 13 C data using metabolically inactive hyperpolarized bis-1,1-(hydroxymethyl)-[1-13 C]cyclopropane-d8 (HMCP) were obtained from an orthotopic human glioblastoma (GBM) model for the characterization of tumor perfusion and compared with standard Gd-based dynamic susceptibility contrast (DSC) MRI data and immunohistochemical analysis from resected brains. RESULTS: Distinct HMCP perfusion characteristics were observed within the GBM tumors compared with contralateral normal brain tissue. The perfusion parameters obtained from the hyperpolarized HMCP data in tumor were strongly correlated with normalized peak height measured from the DSC images. The results from immunohistochemical analysis supported these findings by showing a high level of vascular staining for tumor that exhibited high levels of hyperpolarized HMCP signal. CONCLUSION: The results from this study have demonstrated that hyperpolarized HMCP data can be used as an indicator of tumor perfusion in an orthotopic xenograft model for GBM. Magn Reson Med 77:841-847, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

1 H-13 C independently tuned radiofrequency surface coil applied for in vivo hyperpolarized MRI.

PURPOSE: To develop a lump-element double-tuned common-mode-differential-mode (CMDM) radiofrequency (RF) surface coil with independent frequency tuning capacity for MRS and MRI applications. METHODS: The presented design has two modes that can operate with different current paths, allowing independent frequency adjustment. The coil prototype was tested on the bench and then examined in phantom and in vivo experiments. RESULTS: Standard deviations of frequency and impedance fluctuations measured in one resonator, while changing the tuning capacitor of another resonator, were less than 13 kHz and 0.55 Ω. The unloaded S21 was -36 dB and -41 dB, while the unloaded Q factor was 260 and 287, for 13 C and 1 H, respectively. In vivo hyperpolarized 13 C MR spectroscopy data demonstrated the feasibility of using the CMDM coil to measure the dynamics of lactate, alanine, pyruvate and bicarbonate signal in a normal rat head along with acquiring 1 H anatomical reference images. CONCLUSION: Independent frequency tuning capacity was demonstrated in the presented lump-element double-tuned CMDM coil. This CMDM coil maintained intrinsically decoupled magnetic fields, which provided sufficient isolation between the two resonators. The results from in vivo experiments demonstrated high sensitivity of both the 1 H and 13 C resonators. Magn Reson Med 76:1612-1620, 2016. © 2015 International Society for Magnetic Resonance in Medicine.

Accelerated high-bandwidth MR spectroscopic imaging using compressed sensing.

PURPOSE: To develop a compressed sensing (CS) acceleration method with a high spectral bandwidth exploiting the spatial-spectral sparsity of MR spectroscopic imaging (MRSI). METHODS: Accelerations were achieved using blip gradients during the readout to perform nonoverlapped and stochastically delayed random walks in kx -ky -t space, combined with block-Hankel matrix completion for efficient reconstruction. Both retrospective and prospective CS accelerations were applied to (13) C MRSI experiments, including in vivo rodent brain and liver studies with administrations of hyperpolarized [1-(13) C] pyruvate at 7.0 Tesla (T) and [2-(13) C] dihydroxyacetone at 3.0 T, respectively. RESULTS: In retrospective undersampling experiments using in vivo 7.0 T data, the proposed method preserved spectral, spatial, and dynamic fidelities with R(2) ≥ 0.96 and ≥ 0.87 for pyruvate and lactate signals, respectively, 750-Hz spectral separation, and up to 6.6-fold accelerations. In prospective in vivo experiments, with 3.8-fold acceleration, the proposed method exhibited excellent spatial localization of metabolites and peak recovery for pyruvate and lactate at 7.0 T as well as for dihydroxyacetone and its metabolic products with a 4.5-kHz spectral span (140 ppm at 3.0 T). CONCLUSIONS: This study demonstrated the feasibility of a new CS approach to accelerate high spectral bandwidth MRSI experiments. Magn Reson Med 76:369-379, 2016. © 2016 Wiley Periodicals, Inc.

Dynamic hyperpolarized carbon-13 MR metabolic imaging of nonhuman primate brain.

PURPOSE: To investigate hyperpolarized (13) C metabolic imaging methods in the primate brain that can be translated into future clinical trials for patients with brain cancer. METHODS: (13) C coils and pulse sequences designed for use in humans were tested in phantoms. Dynamic (13) C data were obtained from a healthy cynomolgus monkey brain using the optimized (13) C coils and pulse sequences. The metabolite kinetics were estimated from two-dimensional localized (13) C dynamic imaging data from the nonhuman primate brain. RESULTS: Pyruvate and lactate signal were observed in both the brain and the surrounding tissues with the maximum signal-to-noise ratio of 218 and 29 for pyruvate and lactate, respectively. Apparent rate constants for the conversion of pyruvate to lactate and the ratio of lactate to pyruvate showed a difference between brain and surrounding tissues. CONCLUSION: The feasibility of using hyperpolarized [1-(13) C]-pyruvate for assessing in vivo metabolism in a healthy nonhuman primate brain was demonstrated using a hyperpolarized (13) C imaging experimental setup designed for studying patients with brain tumors. The kinetics of the metabolite conversion suggests that this approach may be useful in future studies of human neuropathology.

Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

OBJECTIVES: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF. MATERIALS AND METHODS: Patients with renal cell carcinoma who underwent surgery between April 2019 and February 2022 at Chonnam National University Hwasun Hospital were retrospectively screened, and 119 patients included. Twenty-one and seventeen patients were set aside for the internal and external test sets, respectively. Pre-operative T1-weighted MRI acquired at 60 s following a contrast injection (T1w-60) were collected. For each T1w-60 data, two regions of interest (ROIs) were manually drawn: the perinephric fat tissue and an aorta segment on the same level as the targeted kidney. Preprocessing steps included resizing voxels, N4 Bias Correction filtering, and aorta-based normalization. For each patient, 851 radiomics features were extracted from the ROI of perinephric fat tissue. Gender and BMI were added as clinical factors. Least Absolute Shrinkage and Selection Operator was adopted for feature selection. We trained and evaluated five models using a 4-fold cross validation. The final model was chosen based on the highest mean AUC across four folds. The performance of the final model was evaluated on the internal and external test sets. RESULTS: A total of 15 features were selected in the final set. The final model achieved the accuracy, sensitivity, specificity, and AUC of 81% (95% confidence interval, 61.9-95.2%), 72.7% (42.9-100%), 90% (66.7-100%), and 0.855 (0.615-1.0), respectively on the internal test set, and 88.2% (70.6-100%), 100% (100-100%), 80% (50%-100%), 0.971 (0.871-1.0), respectively on the external test set. CONCLUSIONS: Our study demonstrated the feasibility of machine learning algorithms trained with MRI-based radiomics features for APF prediction. Further studies with a multi-center approach are necessary to validate our findings.

Deep-learning model for evaluating histopathology of acute renal tubular injury.

Tubular injury is the most common cause of acute kidney injury. Histopathological diagnosis may help distinguish between the different types of acute kidney injury and aid in treatment. To date, a limited number of study has used deep-learning models to assist in the histopathological diagnosis of acute kidney injury. This study aimed to perform histopathological segmentation to identify the four structures of acute renal tubular injury using deep-learning models. A segmentation model was used to classify tubule-specific injuries following cisplatin treatment. A total of 45 whole-slide images with 400 generated patches were used in the segmentation model, and 27,478 annotations were created for four classes: glomerulus, healthy tubules, necrotic tubules, and tubules with casts. A segmentation model was developed using the DeepLabV3 architecture with a MobileNetv3-Large backbone to accurately identify the four histopathological structures associated with acute renal tubular injury in PAS-stained mouse samples. In the segmentation model for four structures, the highest Intersection over Union and the Dice coefficient were obtained for the segmentation of the "glomerulus" class, followed by "necrotic tubules," "healthy tubules," and "tubules with cast" classes. The overall performance of the segmentation algorithm for all classes in the test set included an Intersection over Union of 0.7968 and a Dice coefficient of 0.8772. The Dice scores for the glomerulus, healthy tubules, necrotic tubules, and tubules with cast are 91.78 ± 11.09, 87.37 ± 4.02, 88.08 ± 6.83, and 83.64 ± 20.39%, respectively. The utilization of deep learning in a predictive model has demonstrated promising performance in accurately identifying the degree of injured renal tubules. These results may provide new opportunities for the application of the proposed methods to evaluate renal pathology more effectively.

Evaluation of heterogeneous metabolic profile in an orthotopic human glioblastoma xenograft model using compressed sensing hyperpolarized 3D 13C magnetic resonance spectroscopic imaging.

High resolution compressed sensing hyperpolarized (13)C magnetic resonance spectroscopic imaging was applied in orthotopic human glioblastoma xenografts for quantitative assessment of spatial variations in (13)C metabolic profiles and comparison with histopathology. A new compressed sensing sampling design with a factor of 3.72 acceleration was implemented to enable a factor of 4 increase in spatial resolution. Compressed sensing 3D (13)C magnetic resonance spectroscopic imaging data were acquired from a phantom and 10 tumor-bearing rats following injection of hyperpolarized [1-(13)C]-pyruvate using a 3T scanner. The (13)C metabolic profiles were compared with hematoxylin and eosin staining and carbonic anhydrase 9 staining. The high-resolution compressed sensing (13)C magnetic resonance spectroscopic imaging data enabled the differentiation of distinct (13)C metabolite patterns within abnormal tissues with high specificity in similar scan times compared to the fully sampled method. The results from pathology confirmed the different characteristics of (13)C metabolic profiles between viable, non-necrotic, nonhypoxic tumor, and necrotic, hypoxic tissue.

Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.

Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals. Preoperative MRIs were analyzed for tumor and edema volumes, enhancement patterns, margins, and tumor-brain interfaces. Radiomics features were extracted, and machine learning models were employed to predict meningioma grades. A total of 212 patients were included. In the training group (Hospital 1), significant differences were observed between low-grade and high-grade meningiomas in terms of tumor volume (p = 0.012), edema volume (p = 0.004), enhancement (p = 0.001), margin (p < 0.001), and tumor-brain interface (p < 0.001). Five radiomics features were selected for model development. The prediction model for radiomics features demonstrated an average validation accuracy of 0.74, while the model for clinical imaging features showed an average validation accuracy of 0.69. When applied to external test data (Hospital 2), the radiomics model achieved an area under the receiver operating characteristics curve (AUC) of 0.72 and accuracy of 0.69, while the clinical imaging model achieved an AUC of 0.82 and accuracy of 0.81. An improved performance was obtained from the model constructed by combining radiomics and clinical imaging features. In the combined model, the AUC and accuracy for meningioma grading were 0.86 and 0.73, respectively. In conclusion, this study demonstrates the potential value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas. The combination of both radiomics and clinical imaging features achieved the highest AUC among the models. Therefore, the combined model of radiomics and clinical imaging features may offer a more effective tool for predicting clinical outcomes in meningioma patients.

Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography.

OBJECTIVE: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. MATERIALS AND METHODS: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. RESULTS: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. CONCLUSION: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

Hyperpolarized 13C MR spectroscopic imaging can be used to monitor Everolimus treatment in vivo in an orthotopic rodent model of glioblastoma.

Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor in humans. Because the phosphatidylinositol-3-kinase (PI3K) signaling pathway is activated in more than 88% of GBM, new drugs which target this pathway, such as the mTOR inhibitor Everolimus, are currently in clinical trials. Early tumor response to molecularly targeted treatments remains challenging to assess non-invasively, because it is often associated with tumor stasis or slower tumor growth. Innovative neuroimaging methods are therefore critically needed to provide metabolic or functional information that is indicative of targeted therapeutic action at early time points during the course of treatment. In this study, we demonstrated for the first time that hyperpolarized (HP) 13C magnetic resonance spectroscopic imaging (MRSI) can be used on a clinical MR system to monitor early metabolic response of orthotopic GBM tumors to Everolimus treatment through measurement of the HP lactate-to-pyruvate ratios. The study was performed on a highly invasive non-enhancing orthotopic GBM tumor model in rats (GS-2 tumors), which replicates many fundamental features of human GBM tumors. Seven days after initiation of treatment there was a significant drop in the HP lactate-to-pyruvate ratio from the tumor tissue in treated animals relative to day 0 (67%±27% decrease). In the control group, no significant changes in the HP lactate-to-pyruvate ratios were observed. Importantly, at the 7 day time point, conventional MR imaging (MRI) was unable to detect a significant difference in tumor size between control and treated groups. Inhibition of tumor growth by conventional MRI was observed from day 15 of treatment. This implies that the decrease in the HP lactate-to-pyruvate ratio could be detected before any treatment-induced inhibition of tumor growth. Using immunohistochemical staining to further examine tumor response to treatment, we found that the decrease in the HP lactate-to-pyruvate ratio was associated with a drop in expression of lactate dehydrogenase, the enzyme that catalyzes pyruvate to lactate conversion. Also evident was decreased staining for carbonic anhydrase IX (CA-IX), an indicator of hypoxia-inducible factor 1α (HIF-1α) activity, which, in turn, regulates expression of lactate dehydrogenase. To our knowledge, this study is the first report of the use of HP 13C MRSI at a clinical field strength to monitor GBM response to molecularly targeted treatments. It highlights the potential of HP lactate-to-pyruvate ratio as an early biomarker of response, thereby supporting further investigation of this non-invasive imaging approach for eventual clinical application.

Investigating the Feasibility of In Vivo Perfusion Imaging Methods for Spinal Cord Using Hyperpolarized [13C]t-Butanol and [13C,15N2]Urea.

PURPOSE: This study examined the feasibility of using two novel agents, hyperpolarized [13C]t-butanol and [13C,15N2]urea, for assessing in vivo perfusion of the intact spinal cord in rodents. Due to their distinct permeabilities to blood brain barrier (BBB), we hypothesized that [13C]t-butanol and [13C,15N2]urea exhibit unique 13C signal characteristics in the spinal cord. PROCEDURES: Dynamic 13C t-butanol MRI data were acquired from healthy Long-Evans rats using a symmetric, ramp-sampled, partial-Fourier 13C echo-planar imaging sequence after the injection of hyperpolarized [13C]t-butanol solution. In subsequent scans, dynamic 13C urea MRI data were acquired after the injection of hyperpolarized [13C,15N2]urea. The SNRs of t-butanol and urea were calculated for regions corresponding to spine, supratentorial brain, and blood vessels and plotted over time. Mean peak SNR and AUC were calculated from the dynamic plots for each region and compared between t-butanol and urea. RESULTS: In spine and supratentorial brain, the mean peak SNR and AUC of t-butanol were significantly higher than those of urea (p < 0.05). In contrast, urea was predominantly contained within vasculature and exhibited significantly higher levels of mean peak SNR and AUC compared to t-butanol in blood vessels (p < 0.05). CONCLUSION: This study has demonstrated the feasibility of using hyperpolarized [13C]t-butanol and [13C,15N2]urea for assessing in vivo perfusion in cervical spinal cord. Due to differences in blood-brain barrier permeability, t-butanol rapidly crossed the blood-brain barrier and diffused into spine and brain tissue, while urea predominantly remained in vasculature. The results from this study suggest that this technique may provide unique non-invasive imaging tracers that are able to directly monitor hemodynamic processes in the normal and injured spinal cord.

Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms.

The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated the potential of using deep learning models for differentiating between upgraded versus pure DCIS in DCIS diagnosed by core-needle biopsy. Preoperative axial dynamic contrast-enhanced magnetic resonance imaging (MRI) data from 352 lesions were used to train, validate, and test three different types of deep learning models. The highest performance was achieved by Recurrent Residual Convolutional Neural Network using Regions of Interest (ROIs) with an accuracy of 75.0% and area under the receiver operating characteristic curve (AUC) of 0.796. Our results suggest that the deep learning approach may provide an assisting tool to predict the histologic upgrade of DCIS and provide personalized treatment strategies to patients with underestimated invasive disease.