Differentiating Benign From Malignant Cystic Renal Masses: A Feasibility Study of Computed Tomography Texture-Based Machine Learning Algorithms.

OBJECTIVE: The Bosniak classification attempts to predict the likelihood of renal cell carcinoma (RCC) among cystic renal masses but is subject to interobserver variability and often requires multiphase imaging. Artificial intelligence may provide a more objective assessment. We applied computed tomography texture-based machine learning algorithms to differentiate benign from malignant cystic renal masses. METHODS: This is an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study of 147 patients (mean age, 62.4 years; range, 28-89 years; 94 men) with 144 cystic renal masses (93 benign, 51 RCC); 69 were pathology proven (51 RCC, 18 benign), and 75 were considered benign based on more than 4 years of stability at follow-up imaging. Using a single image from a contrast-enhanced abdominal computed tomography scan, mean, SD, mean value of positive pixels, entropy, skewness, and kurtosis radiomics features were extracted. Random forest, multivariate logistic regression, and support vector machine models were used to classify each mass as benign or malignant with 10-fold cross validation. Receiver operating characteristic curves assessed algorithm performance in the aggregated test data. RESULTS: For the detection of malignancy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were 0.61, 0.87, 0.72, 0.80, and 0.79 for the random forest model; 0.59, 0.87, 0.71, 0.79, and 0.80 for the logistic regression model; and 0.55, 0.86, 0.68, 0.78, and 0.76 for the support vector machine model. CONCLUSION: Computed tomography texture-based machine learning algorithms show promise in differentiating benign from malignant cystic renal masses. Once validated, these may serve as an adjunct to radiologists' assessments.

Radiation dose of fluoroscopy-guided versus ultralow-dose CT-fluoroscopy-guided lumbar spine epidural steroid injections.

OBJECTIVE: Compare radiation dose of lumbar spine epidural steroid injections (ESIs) performed under fluoroscopy guidance and ultralow-dose CT-fluoroscopy guidance. MATERIALS AND METHODS: Retrospective review of consecutive lumbar ESIs performed using fluoroscopy, between May 2017 and April 2019, and using ultralow-dose CT-fluoroscopy, between August 2019 and February 2021, was performed. Ultralow-dose CT-fluoroscopy technique omits a planning CT scan, utilizes CT-fluoroscopy, and minimizes radiation dose parameters. Patient characteristics (age, sex, height, weight, body mass index (BMI)), procedural characteristics (anatomic level, type of ESI, procedure time, pain reduction, complications, trainee participation), and radiation dose were compared. Chi-square tests and two-sample t-tests were performed for statistical analysis. RESULTS: One hundred and forty-seven patients (mean age 55.8 ± 16.7; 85 women) underwent ESIs using fluoroscopy. Sixty-six patients (mean age 60.9 ± 16.7; 33 women) underwent ESIs using ultralow-dose CT-fluoroscopy. The effective dose for the fluoroscopy group was 0.30 mSv ± 0.34, compared to 0.15 mSV ± 0.11 for ultralow-dose CT-fluoroscopy (p < 0.001). The average age in the CT-fluoroscopy group was older (p = 0.04), and there was more trainee participation in the fluoroscopy group (p < 0.001); otherwise there was no statistically significant difference in patient or procedural characteristics between the conventional fluoroscopy group and the ultralow-dose CT-fluoroscopy group. There was no statistically significant difference in immediate post-procedure pain reduction between the groups (p = 0.16). Four intrathecal injections occurred only in the fluoroscopy group, though this difference was not significant (p = 0.18). CONCLUSION: Ultralow-dose CT-fluoroscopy technique for image-guided lumbar spine ESIs can lower radiation dose compared to fluoroscopy-guided technique.

Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.

Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain. Purpose To demonstrate the validity of fully automated, deep learning BC analysis from abdominal CT examinations, to define demographically adjusted BC reference curves, and to illustrate the advantage of use of these curves compared with standard methods, along with their biologic significance in predicting survival. Materials and Methods After external validation and equivalency testing with manual segmentation, a fully automated deep learning BC analysis pipeline was applied to a cross-sectional population cohort that included any outpatient without a cardiovascular disease or cancer who underwent abdominal CT examination at one of three hospitals in 2012. Demographically adjusted population reference curves were generated for each BC area. The z scores derived from these curves were compared with sex-specific thresholds for sarcopenia by using χ2 tests and used to predict 2-year survival in multivariable Cox proportional hazards models that included weight and body mass index (BMI). Results External validation showed excellent correlation (R = 0.99) and equivalency (P < .001) of the fully automated deep learning BC analysis method with manual segmentation. With use of the fully automated BC data from 12 128 outpatients (mean age, 52 years; 6936 [57%] women), age-, race-, and sex-normalized BC reference curves were generated. All BC areas varied significantly with these variables (P < .001 except for subcutaneous fat area vs age [P = .003]). Sex-specific thresholds for sarcopenia demonstrated that age and race bias were not present if z scores derived from the reference curves were used (P < .001). Skeletal muscle area z scores were significantly predictive of 2-year survival (P = .04) in combined models that included BMI. Conclusion Fully automated body composition (BC) metrics vary significantly by age, race, and sex. The z scores derived from reference curves for BC parameters better capture the demographic distribution of BC compared with standard methods and can help predict survival. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Summers in this issue.

Simplified Universal Grading of Lumbar Spine MRI Degenerative Findings: Inter-Reader Agreement of Non-Radiologist Spine Experts.

OBJECTIVE: 1) To describe a simplified multidisciplinary grading system for the most clinically relevant lumbar spine degenerative changes. 2) To measure the inter-reader variability among non-radiologist spine experts in their use of the classification system for interpretation of a consecutive series of lumbar spine magnetic resonance imaging (MRI) examinations. METHODS: ATS multidisciplinary and collaborative standardized grading of spinal stenosis, foraminal stenosis, lateral recess stenosis, and facet arthropathy was developed. Our institution's picture archiving and communication system was searched for 50 consecutive patients who underwent non-contrast MRI of the lumbar spine for chronic back pain, radiculopathy, or symptoms of spinal stenosis. Three fellowship-trained spine subspecialists from neurosurgery, orthopedic surgery, and physiatry interpreted the 50 exams using the classification at the L4-L5 and L5-S1 levels. Inter-reader agreement was assessed with Cohen's kappa coefficient. RESULTS: For spinal stenosis, the readers demonstrated substantial agreement (κ = 0.702). For foraminal stenosis and facet arthropathy, the three readers demonstrated moderate agreement (κ = 0.544, and 0.557, respectively). For lateral recess stenosis, there was fair agreement (κ = 0.323). CONCLUSIONS: A simplified universal grading system of lumbar spine MRI degenerative findings is newly described. Use of this multidisciplinary grading system in the assessment of clinically relevant degenerative changes revealed moderate to substantial agreement among non-radiologist spine physicians. This standardized grading system could serve as a foundation for interdisciplinary communication.

The role of computed tomography angiography as initial imaging tool for acute hemorrhage in the head and neck.

OBJECTIVES: Acute hemorrhage in the head and neck (AHNH) is life-threatening due to asphyxiation and hemorrhagic shock. When conservative measures fail, some patients benefit from endovascular therapy (EVT). While CTA is routinely used to localize bleeding and plan EVT in gastrointestinal hemorrhage, the diagnostic value of CTA in AHNH and role of CTA in treatment-planning are uncertain. METHODS: We retrospectively reviewed neck CTAs from June 2015 to October 2018 indicated for AHNH. When performed, digital subtraction angiography (DSA) findings and EVT were documented. Extravasation or pseudoaneurysm on DSA was considered positive for bleed localization. RESULTS: Thirty CTA exams were performed for AHNH in 18 patients (mean age = 56.6, male% = 55.6%). Eleven out of 30 exams (36.7%) had immediate DSA follow-up within 24 h. Etiologies of hemorrhage included malignancy 11/18 (61.1%) and coagulopathy (4/18, 22.2%) among others. CTA reports identified definite or possible source of bleeding in 7/30 (23.3%) exams. Seven out of 7 (100%) patients with definite or possible source of bleeding on CTA underwent DSA and 4/23 (17.4%) patients underwent DSA despite negative CTA. With DSA as the gold standard, CTA had a sensitivity of 70% and a specificity of 100%. CONCLUSIONS: CTA has high specificity and reasonable sensitivity for detecting arterial source of bleeding in patients presenting with AHNH. Patients with negative CTA may avoid catheter angiography in most cases; however, false-negative CTA should not preclude angiography in high-risk patients.

Computed tomography window blending in maxillofacial imaging.

PURPOSE: The purpose of this study was to demonstrate the ability of a custom window blending algorithm to depict multicompartmental disease processes of the maxillofacial region in a single image, using routine computed tomography (CT) DICOM data. METHODS: Five cases were selected from case files demonstrating trauma, infection, and malignancy of the maxillofacial region on routine CT examinations. Images were processed with a modified Relative Attenuation-Dependent Image Overlay (RADIO) window-blending algorithm in Adobe Photoshop controlled by ExtendScript. RESULTS: The modified RADIO algorithm was able to demonstrate pertinent multicompartmental imaging findings in each of the examinations, allowing simultaneous visualization of clinically relevant bone and soft tissue findings in a single image, without needing to change window and level settings. CONCLUSION: A custom window blending algorithm can demonstrate a range of multicompartmental pathology in the maxillofacial region in a single image.

Predictive Features for Anterior Mediastinal Mass Diagnoses.

OBJECTIVE: We set out to evaluate a set of demographic and computed tomography imaging features for diagnosing anterior mediastinal masses. METHODS: We identified 223 patients with anterior mediastinal masses, which we divided into training and validation sets. One radiologist evaluated computed tomography imaging features on the training set. Then, predictive features were identified, and 3 radiologists evaluated these on the validation set. A naive Bayesian classifier based on the features was compared with the radiologists' first-choice diagnosis. RESULTS: Internal mammary lymphadenopathy and mediastinal encasement were strongly associated with lymphomas. Low attenuation and midline location were strongly associated with benign lesions, and older age was associated with thymic epithelial neoplasms. The average accuracy of the 3 radiologists' diagnoses was 78%, compared with 71% for the classifier. CONCLUSIONS: Nine demographic and imaging features were found to be helpful in diagnosing anterior mediastinal masses. By using these features, radiologists can suggest the diagnosis with fair accuracy.

CT-guided transforaminal epidural steroid injections: do needle position and degree of foraminal stenosis affect the pattern of epidural flow?

OBJECTIVE: To determine the effect of needle position and foraminal stenosis on contrast flow directionality during CT-guided transforaminal epidural steroid injections (TFESI). MATERIALS AND METHODS: One hundred five consecutive CT-guided injections were performed in 68 patients (mean age, 65.5 years) from January 1 to December 31 2017, all with preceding MRI. Two readers independently reviewed CT images to assess needle position and to determine direction of contrast flow, which was defined as central or peripheral. The MRIs were independently reviewed by the readers to determine the degree of foraminal stenosis. Inter-reader agreement for both was evaluated with the kappa statistic. Analyses were performed to determine effect of needle position, degree of foraminal stenosis, and volume of contrast injected with directionality of contrast flow, and association between contrast flow directionality with immediate post-procedural pain scores. RESULTS: Central direction of contrast flow was demonstrated in 41/78 (52.6%) of cases with posterolateral needle position, and 20/27 (74.1%) with central or anteromedial needle position (p = 0.07). There was no difference in direction of contrast flow with high-grade versus absence of high-grade neuroforaminal narrowing, or with volume of contrast injected. There was no difference in immediate post-procedure pain scores regardless of contrast flow directionality. CONCLUSIONS: Needle position is not significantly associated with contrast flow directionality during CT-guided TFESI, although there was a trend towards relatively decreased central flow with posterolateral positioning. Degree of foraminal stenosis and volume of injected contrast did not affect contrast flow directionality. There was no difference in immediate post-procedural pain scores with either direction of contrast flow.

Color postprocessing of conventional CT images: preliminary results in assessment of nondisplaced proximal femoral fractures.

PURPOSE: The purpose of this study was to demonstrate the diagnostic performance and effect on reader confidence of a custom computed tomography (CT) color postprocessing algorithm for assessment of nondisplaced proximal femoral fractures. MATERIALS AND METHODS: Four radiologists, including two PGY-3 radiology residents and two emergency radiologists, independently interpreted 30 CT examinations of the hip and/or pelvis performed for trauma, consisting of a total of 15 cases positive for nondisplaced hip fracture and 15 age and sex-matched controls. Images were reviewed first with conventional CT images and after at least 8 weeks, all images were reviewed again with the addition of coronal color postprocessed images. Sensitivity and specificity were compared with McNemar's test, and diagnostic confidence was compared with paired t tests. RESULTS: There was no significant difference in diagnostic performance between conventional and postprocessed images, although there was nominally increased sensitivity and decreased specificity with the postprocessed images: for all readers, the sensitivity and specificity for conventional images was 88.3 and 95.0%, compared to 93.3% (p = 0.25) and 88.3% (p = 0.14) for postprocessed images. Three of four readers (including both attending radiologists) reported an increase in confidence with postprocessed images for cases negative for fracture (10-point confidence scale of 7.25 for conventional images, compared to 8.2 for postprocessed images for all readers, p = 0.0053). There was no difference in diagnostic confidence for cases positive for fracture. CONCLUSIONS: A custom color CT postprocessing algorithm did not demonstrate a significant difference in diagnostic performance for assessment of nondisplaced proximal femoral fractures within the limitations of a relatively small sample size; however, postprocessing increases confidence of experienced readers in cases negative for fracture.

Diagnosis of Normal-Pressure Hydrocephalus: Use of Traditional Measures in the Era of Volumetric MR Imaging.

Purpose To assess the diagnostic performance of the callosal angle (CA) and Evans index (EI) measures and to determine their role versus automated volumetric methods in clinical radiology. Materials and Methods Magnetic resonance (MR) examinations performed before surgery (within 1-5 months of the MR examination) in 36 shunt-responsive patients with normal-pressure hydrocephalus (NPH; mean age, 75 years; age range, 58-87 years; 26 men, 10 women) and MR examinations of age- and sex-matched patients with Alzheimer disease (n = 34) and healthy control volunteers (n = 36) were studied. Three blinded observers independently measured EI and CA for each patient. Volumetric segmentation of global gray matter, white matter, ventricles, and hippocampi was performed by using software. These measures were tested by using multivariable logistic regression models to determine which combination of metrics is most accurate in diagnosis. Results The model that used CA and EI demonstrated 89.6%-93.4% accuracy and average area under the curve of 0.96 in differentiating patients with NPH from patients without NPH (ie, Alzheimer disease and healthy control). The regression model that used volumetric predictors of gray matter and white matter was 94.3% accurate. Conclusion CA and EI may serve as a screening tool to help the radiologist differentiate patients with NPH from patients without NPH, which would allow for designation of patients for further volumetric assessment. © RSNA, 2017.

Standardized Classification of Lumbar Spine Degeneration on Magnetic Resonance Imaging Reduces Intra- and Inter-subspecialty Variability.

BACKGROUND AND PURPOSE: To determine the efficacy of standardized definitions of degenerative change in reducing variability in interpretation of lumbar spine magnetic resonance imaging within and between groups of subspecialty-trained neuroradiologists (NR) and musculoskeletal radiologists (MSK). MATERIALS AND METHODS: Six radiologists, three from both NR and MSK groups were trained on a standardized classification system of degenerative change. After an 11-month washout period, they independently re-interpreted fifty exams at the L4-L5 and L5-S1 levels. Responses were converted to a six-point ordinal scale for the assessment of neural foraminal stenosis and spinal canal stenosis (SCS), three-point scale for lateral recess stenosis, and four-point scale for facet osteoarthritis (FO). Intra-subspecialty and inter-subspecialty analysis was performed using the weighted Cohen's kappa with a binary matrix of all reader pairs. RESULTS: Inter-subspecialty agreement improved from k=0.527 (moderate) to k=0.602 (substantial) for neural foraminal stenosis, from k=0.540 (moderate) to k=0.652 (substantial) for SCS, from k=0.0818 (slight) to k=0.337 (fair) for lateral recess stenosis, and from k=0.176 (slight) to k=0.495 (moderate) for FO. The NR group demonstrated improved intra-subspecialty agreement for the assessment of SCS, from k=0.368 (fair) to k=0.638 (substantial). The MSK group demonstrated improved intra-subspecialty agreement for the assessment of FO, from k=0.134 (slight) to k=0.413 (moderate). Intra-subspecialty agreement was similar for other parameters before and after training. CONCLUSIONS: As result of the standardized definitions training, the NR and MSK groups each improved in one of the four parameters, while inter-subspecialty variability improved in all four parameters. These definitions may be useful in clinical practice across radiology subspecialties.

Robustness and performance of radiomic features in diagnosing cystic renal masses.

PURPOSE: We study the inter-reader variability in manual delineation of cystic renal masses (CRMs) presented in computerized tomography (CT) images and its effect on the classification performance of a machine learning algorithm in distinguishing benign from potentially malignant CRMs. In addition, we assessed whether the inclusion of higher-order robust radiomic features improves the classification performance over the use of first-order features. METHODS: 230 CRMs were independently delineated by two radiologists. Through a combination of random fluctuations, dilation, and erosion operations over the original region of interests (ROIs), we generated four additional sets of synthetic ROIs to capture the inter-reader variability realistically, as confirmed by dice coefficient measurements and visual assessment. We then identified the robust features based on the intra-class coefficient (ICC > 0.85) across these datasets. We applied a tenfold stratified cross-validation (CV) to train and test the performance of the random forest model for the classification of CRMs into benign and potentially malignant. RESULTS: The mean area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were 0.87, 0.82, 0.90, 0.85, and 0.93, respectively. With the usage of first-order features alone, the corresponding values were nearly identical. CONCLUSION: AUC ranged for the robust and uncorrelated features from 0.83 ± 0.09 to 0.93 ± 0.04 and for the first-order features from 0.84 ± 0.09 to 0.91 ± 0.04. Our study indicates that the first-order features alone are sufficient for the classification of CRMs, and that inclusion of higher-order features does not necessarily improve performance.

Quantifying Impact of Disruption to Radiology Education During the COVID-19 Pandemic and Implications for Future Training.

PURPOSE: The aim of this study was to assess the impact on radiology resident education due to the COVID-19 pandemic in order to inform future educational planning. METHODS: During a 10-week study period from March 16 to May 22, 2020, changes to educational block-weeks (BW) of first through fourth year residents (R1-4) were documented as disrupted in the setting of the COVID-19 pandemic. The first 5 weeks and the second 5 weeks were evaluated separately for temporal differences. Overall and mean disrupted BW per resident were documented. Wilcoxon rank-sum tests were used to assess pairwise differences between classes with Bonferroni-adjusted P-values, as well as differences in the early versus later phase of the pandemic. RESULTS: Of 373 BW, 56.6% were assigned to virtual curriculum, 39.4% radiology clinical duties, 2.9% illness, and 1.1% reassignment. Scheduling intervention affected 6.2 ± 2.3 (range 1-10) mean BW per resident over the 10-week study period. The R3 class experienced the largest disruption, greater than the R2 classes, and statistically significantly more than the R1 and R4 classes (both P < 0.05). The second half of the pandemic caused statistically significantly more schedule disruptions than the first half (P = 0.009). DISCUSSION: The impact of COVID-19 pandemic varied by residency class year, with the largest disruption of the R3 class and the least disruption of the R4 class. To optimize future educational opportunities, shifting to a competency-based education paradigm may help to achieve proficiency without extending the length of the training program.

Stratification of cystic renal masses into benign and potentially malignant: applying machine learning to the bosniak classification.

PURPOSE: To create a CT texture-based machine learning algorithm that distinguishes benign from potentially malignant cystic renal masses as defined by the Bosniak Classification version 2019. METHODS: In this IRB-approved, HIPAA-compliant study, 4,454 adult patients underwent renal mass protocol CT or CT urography from January 2011 to June 2018. Of these, 257 cystic renal masses were included in the final study cohort. Each mass was independently classified using Bosniak version 2019 by three radiologists, resulting in 185 benign (Bosniak I or II) and 72 potentially malignant (Bosniak IIF, III or IV) masses. Six texture features: mean, standard deviation, mean of positive pixels, entropy, skewness, kurtosis were extracted using commercial software TexRAD (Feedback PLC, Cambridge, UK). Random forest (RF), logistic regression (LR), and support vector machine (SVM) machine learning algorithms were implemented to classify cystic renal masses into the two groups and tested with tenfold cross validations. RESULTS: Higher mean, standard deviation, mean of positive pixels, entropy, skewness were statistically associated with the potentially malignant group (P ≤ 0.0015 each). Sensitivity, specificity, positive predictive value, negative predictive value, and area under curve of RF model was 0.67, 0.91, 0.75, 0.88, 0.88; of LR model was 0.63, 0.93, 0.78, 0.86, 0.90, and of SVM model was 0.56, 0.91, 0.71, 0.84, 0.89, respectively. CONCLUSION: Three CT texture-based machine learning algorithms demonstrated high discriminatory capability in distinguishing benign from potentially malignant cystic renal masses as defined by the Bosniak Classification version 2019. If validated, CT texture-based machine learning algorithms may help reduce interreader variability when applying the Bosniak classification.

A Comparative Retrospective Study of Immunotherapy RANO Versus Standard RANO Criteria in Glioblastoma Patients Receiving Immune Checkpoint Inhibitor Therapy.

OBJECTIVES: Real-time assessment of treatment response in glioblastoma (GBM) patients on immune checkpoint blockade (ICB) remains challenging because inflammatory effects of therapy may mimic progressive disease, and the temporal evolution of these inflammatory findings is poorly understood. We compare GBM patient response during ICB as assessed with the Immunotherapy Response Assessment in Neuro-Oncology (iRANO) and the standard Response Assessment in Neuro-Oncology (RANO) radiological criteria. METHODS: 49 GBM patients (seven newly diagnosed and 42 recurrent) treated with ICBs at a single institution were identified. Tumor burden was quantified on serial MR scans according to RANO criteria during ICB. Radiographic response assessment by iRANO and RANO were compared. RESULTS: 82% (40/49) of patients received anti-PD-1, 16% (8/49) received anti-PD-L1, and 2% (1/49) received anti-PD-1 and anti-CTLA4 treatment. Change in tumor burden and best overall response ranged from -100 to +557% (median: +48%). 12% (6/49) of patients were classified as concordant non-progressors by both RANO and iRANO (best response: one CR, one PR, and four SD). Another12% (6/49) had discordant assessments: 15% (6/41) of RANO grade progressive disease (PD) patients had iRANO grade of progressive disease unconfirmed (PDU). The final classification of these discordant patients was pseudoprogression (PsP) in three of six, PD in two of six, and PDU in one of six who went off study before the iRANO assessment of PDU. iRANO delayed diagnosis of PD by 42 and 93 days in the two PD patients. 76% (37/49) patients were classified as concordant PD by both RANO and iRANO. 12% (6/49) of all patients were classified as PsP, starting at a median of 12 weeks (range, 4-30 weeks) after ICB initiation. CONCLUSIONS: Standard RANO and iRANO have high concordance for assessing PD in patients within 6 months of ICB initiation. iRANO was beneficial in 6% (3/49) cases later proven to be PsP, but delayed confirmation of PD by <3 months in 4% (2/49). PsP occurred in 12% of patients, starting at up to 7 months after initiation of ICB. Further study to define the utility of modified RANO compared with iRANO in ICB GBM patients is needed.

Beyond business as usual: Radiology residency educational response to the COVID-2019 pandemic.

The COVID-19 pandemic has disrupted standard hospital operations and diagnostic radiology resident education at academic medical centers across the country. Deferment of elective surgeries and procedures coupled with a shift of resources toward increased inpatient clinical needs for the care of COVID-19 patients has resulted in substantially decreased imaging examinations at many institutions. Additionally, both infection control and risk mitigation measures have resulted in minimal on-site staffing of both trainees and staff radiologists at many institutions. As a result, residents have been placed in nonstandard learning environments, including working from home, engaging in a virtual curriculum, and participating in training sessions in preparation for potential reassignment to other patient care settings. Typically, for residents to gain the necessary knowledge, skills, and experience to practice independently upon graduation, radiology training programs must provide an optimal balance between resident education and clinical obligations. We describe our experience adapting to the challenges in educational interruptions and clinical work reassignments of 41 interventional and diagnostic radiology residents at a large academic center. We highlight opportunities for collaboration and teamwork in creatively adjusting and planning for the short and long-term impact of the pandemic on resident education. This experience shows how the residency educational paradigm was shifted during a pandemic and can serve as a template to address future disruptions.

Cross-Residency Radiologic/Pathologic Correlation Curriculum: Teaching Correlation of Surgical Specimens With Imaging.

The College of American Pathologists expects pathologists to attain competency in radiologic/pathologic correlation, including correlation of histopathologic findings with imaging findings. While pathology residents appreciate the importance of radiologic/pathologic correlation, their lack of experience and confidence in interpreting imaging studies deters them from obtaining specimen radiographs and reviewing preoperative imaging studies. Formal training in this domain is lacking. A cross-residency curriculum was developed to help pathology residents build basic skills in the correlation of surgical specimens with preoperative imaging and specimen radiographs. Didactic sessions were prepared by 3 pairs of radiology and pathology residents with guidance from radiology and pathology attendings in the subspecialty areas of breast, musculoskeletal, and head and neck. The authors describe the development, implementation, and assessment of the curriculum. A total of 20 pathology residents attended the sessions, with 7 completing both the pre- and postintervention surveys. These residents gained confidence in their ability to interpret specimen radiographs and to select specimens to evaluate with radiography. They gained an appreciation of the importance of collaboration with radiologists in evaluating specimens and of viewing preoperative imaging studies to guide gross examination and dissection. They reported obtaining specimen radiographs and viewing preoperative imaging studies more frequently after attending the sessions. Innovative solutions such as this cross-residency educational initiative offer a potential solution to fulfill the radiologic/pathologic correlation competency standard for pathology residents and may be replicable by other residency programs and academic institutions.

Frequency and Evolution of New Postoperative Enhancement on 3 Tesla Intraoperative and Early Postoperative Magnetic Resonance Imaging.

BACKGROUND: Intraoperative magnetic resonance imaging (IO-MRI) provides real-time assessment of extent of resection of brain tumor. Development of new enhancement during IO-MRI can confound interpretation of residual enhancing tumor, although the incidence of this finding is unknown. OBJECTIVE: To determine the frequency of new enhancement during brain tumor resection on intraoperative 3 Tesla (3T) MRI. To optimize the postoperative imaging window after brain tumor resection using 1.5 and 3T MRI. METHODS: We retrospectively evaluated 64 IO-MRI performed for patients with enhancing brain lesions referred for biopsy or resection as well as a subset with an early postoperative MRI (EP-MRI) within 72 h of surgery (N = 42), and a subset with a late postoperative MRI (LP-MRI) performed between 120 h and 8 wk postsurgery (N = 34). Three radiologists assessed for new enhancement on IO-MRI, and change in enhancement on available EP-MRI and LP-MRI. Consensus was determined by majority response. Inter-rater agreement was assessed using percentage agreement. RESULTS: A total of 10 out of 64 (16%) of the IO-MRI demonstrated new enhancement. Seven of 10 patients with available EP-MRI demonstrated decreased/resolved enhancement. One out of 42 (2%) of the EP-MRI demonstrated new enhancement, which decreased on LP-MRI. Agreement was 74% for the assessment of new enhancement on IO-MRI and 81% for the assessment of new enhancement on the EP-MRI. CONCLUSION: New enhancement occurs in intraoperative 3T MRI in 16% of patients after brain tumor resection, which decreases or resolves on subsequent MRI within 72 h of surgery. Our findings indicate the opportunity for further study to optimize the postoperative imaging window.

Intra- and Intersubspecialty Variability in Lumbar Spine MRI Interpretation: A Multireader Study Comparing Musculoskeletal Radiologists and Neuroradiologists.

BACKGROUND AND PURPOSE: The purpose of this study is to assess the differences in degenerative spine MRI reporting between subspecialty-trained attending neuroradiologists and musculoskeletal radiologists (MSK) at a single institution, academic medical center. MATERIALS AND METHODS: Fifty consecutive outpatient noncontrast lumbar spine examinations were selected from the Picture Archiving and Communication System. Three MSK and 3 neuroradiologists (NR) independently reviewed and interpreted the exams at the L4-L5 and L5-S1 levels in the same manner as in clinical practice. The assessment of neural foraminal stenosis (NFS) and spinal canal stenosis (SCS) was converted to a 5-point ordinal scale. The assessment of lateral recess stenosis (LRS) and facet osteoarthritis (FO) was recorded as present/absent. Intersubspecialty and intrasubspecialty analysis was performed using Cohen's kappa coefficient with a binary matrix of all reader pairs. RESULTS: There was moderate intersubspecialty agreement (k = 0.527) for NFS and SCS (k = 0.540). Intersubspecialty agreement was slight for LRS (k = 0.0818) and FO (k = 0.176). The MSK group demonstrated greater intrasubspecialty agreement in assessment of NFS and SCS compared to the NR group, with nonoverlapping confidence intervals. The NR group demonstrated greater nominal intrasubspecialty agreement in the assessment of both LRS and FO, although with nonoverlapping confidence intervals. CONCLUSION: There is moderate intersubspecialty agreement between MSK radiologists and neuroradiologists in reporting the severity of NFS and SCS, although MSK radiologists demonstrated greater intrasubspecialty agreement. There is slight intersubspecialty agreement for LRS and FO. The demonstration of differences in inter-reader agreement is a crucial first step to attempt to ameliorate these variabilities.

CT-Guided Percutaneous Spine Biopsy Specimen Adequacy, Pathology Concordance, and Negative Predictive Value with Battery-Powered Drill and Manual Approaches.

OBJECTIVE: Determine computed tomography-guided percutaneous spine biopsy specimen adequacy, pathology-imaging concordance, and negative predictive value with battery-powered drill vs manual approach. MATERIALS AND METHODS: One-hundred-fourteen consecutive computed tomography-guided percutaneous spine biopsies in 109 patients (age: 61.1 ± 15.4 years; range: 17-90 years; males: 55, 50.5%; females: 54, 49.5%) performed at a single institution from September 2013 through January 2017 were retrospectively reviewed. Specimen adequacy was recorded. Imaging-pathology concordance was assessed. Chi-square tests compared specimen adequacy and imaging-pathology concordance obtained with a battery-powered drill vs manual approach. Negative predictive values were calculated. RESULTS: Battery-powered drill yielded slightly better, but not statistically significant, specimen adequacy (96% vs 90% overall, P = 0.270; 96% vs 89% for suspected neoplasm, P = 0.278; 95% vs 90% for suspected infection, P = 0.514), pathology-imaging concordance (82% vs 74% overall, P = 0.301; 92% vs 77% for suspected neoplasm, P = 0.107; 71% vs 65% for suspected infection, P = 0.602), and negative predictive value (65% vs 41% overall; 75% vs 33% for suspected neoplasm; 58% vs 33% for suspected infection). Four battery-powered drill procedures were technically unsuccessful. CONCLUSIONS: Use of a battery-powered drill appears to yield similar to slightly better spine biopsy specimens than a manual approach, but also appears to carry a greater risk of technical failure. The battery-powered drill may be particularly helpful for procedures with complex approaches, but trajectory planning remains of paramount importance.