Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach.

OBJECTIVES: To evaluate the performance of machine learning-augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas. METHODS: Forty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning-augmented radiomics analyses. RESULTS: Though only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22-0.58) and 0.44 (95% CI 0.26-0.62) for RF and AdaBoost, respectively. CONCLUSION: Overall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures.

Educational, psychosocial, and clinical impact of SARS-CoV-2 (COVID-19) pandemic on medical students in the United States.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic altered education, exams, and residency applications for United States medical students. AIM: To determine the specific impact of the pandemic on US medical students and its correlation to their anxiety levels. METHODS: An 81-question survey was distributed via email, Facebook and social media groups using REDCapTM. To investigate risk factors associated with elevated anxiety level, we dichotomized the 1-10 anxiety score into low (≤ 5) and high (≥ 6). This cut point represents the 25th percentile. There were 90 (29%) shown as low anxiety and 219 (71%) as high anxiety. For descriptive analyses, we used contingency tables by anxiety categories for categorical measurements with chi square test, or mean ± STD for continuous measurements followed by t-test or Wilcoxson rank sum test depending on data normality. Least Absolute Shrinkage and Selection Operator was used to select important predictors for the final multivariate model. Hierarchical Poisson regression model was used to fit the final multivariate model by considering the nested data structure of students clustered within State. RESULTS: 397 medical students from 29 states were analyzed. Approximately half of respondents reported feeling depressed since the pandemic onset. 62% of participants rated 7 or higher out of 10 when asked about anxiety levels. Stressors correlated with higher anxiety scores included "concern about being unable to complete exams or rotations if contracting COVID-19" (RR 1.34; 95%CI: 1.05-1.72, P = 0.02) and the use of mental health services such as a "psychiatrist" (RR 1.18; 95%CI: 1.01-1.3, P = 0.04). However, those students living in cities that limited restaurant operations to exclusively takeout or delivery as the only measure of implementing social distancing (RR 0.64; 95%CI: 0.49-0.82, P < 0.01) and those who selected "does not apply" for financial assistance available if needed (RR 0.83; 95%CI: 0.66-0.98, P = 0.03) were less likely to have a high anxiety. CONCLUSION: COVID-19 significantly impacted medical students in numerous ways. Medical student education and clinical readiness were reduced, and anxiety levels increased. It is vital that medical students receive support as they become physicians. Further research should be conducted on training medical students in telemedicine to better prepare students in the future for pandemic planning and virtual healthcare.

Imaging of Benign and Malignant Breast Lesions Using Contrast-Enhanced Ultrasound: A Pictorial Essay.

ABSTRACT: Contrast-enhanced ultrasound is a promising noninvasive imaging technique for evaluating benign and malignant breast lesions, as contrast provides information about perfusion and microvasculature. Contrast-enhanced ultrasound is currently off-label use in the breast in the United States, but its clinical and investigational use in breast imaging is gaining popularity. It is important for radiologists to be familiar with the imaging appearances of benign and malignant breast masses using contrast-enhanced ultrasound. This pictorial essay illustrates enhancement patterns of various breast masses from our own experience. Pathologies include subtypes of invasive breast cancer, fibroadenomas, papillary lesions, fibrocystic change, and inflammatory processes. Contrast-enhanced ultrasound pitfalls and limitations are discussed.

Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics.

AIMS: We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses. METHODS: 131 women with suspicious breast masses (BI-RADS 4a, 4b, or 4c) who underwent CEUS examinations (using intravenous injection of perflutren lipid microsphere or sulfur hexafluoride lipid-type A microspheres) prior to ultrasound-guided biopsies were retrospectively identified. Post biopsy pathology showed 115 benign and 16 malignant masses. From the cine clip of the CEUS exams obtained using the built-in GE scanner software, breast masses and adjacent normal tissue were then manually segmented using the ImageJ software. One frame representing each of the four phases: precontrast, early, peak, and delay enhancement were selected post segmentation from each CEUS clip. 112 radiomic metrics were extracted from each segmented tissue normalized breast mass using custom Matlab® code. Linear and nonlinear machine learning (ML) methods were used to build the prediction model to distinguish benign from malignant masses. tenfold cross-validation evaluated model performance. Area under the curve (AUC) was used to quantify prediction accuracy. RESULTS: Univariate analysis found 35 (38.5%) radiomic variables with p < 0.05 in differentiating between benign from malignant masses. No feature selection was performed. Predictive models based on AdaBoost reported an AUC = 0.72 95% CI (0.56, 0.89), followed by Random Forest with an AUC = 0.71 95% CI (0.56, 0.87). CONCLUSIONS: CEUS based texture metrics can distinguish between benign and malignant breast masses, which can, in turn, lead to reduced unnecessary breast biopsies.

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes. METHODS: In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis. RESULTS: Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified. CONCLUSIONS: Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management. ADVANCES IN KNOWLEDGE: We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.

Accuracy of contrast-enhanced spectral mammography compared with MRI for invasive breast cancers: Prospective study in population of predominantly underrepresented minorities.

OBJECTIVES: This prospective study compares contrast-enhanced spectral mammography (CESM) with contrast-enhanced breast MRI in assessing the extent of newly diagnosed breast cancer in a multiethnic cohort. METHODS: This study includes 41 patients with invasive breast cancer detected by mammography or conventional ultrasound imaging from May 2017 to March 2020. CESM and MRI scans were performed prior to any treatment. Results are compared with each other and to histopathology. Detection of the malignant lesion was assessed by sensitivity, specificity, PPV, NPV. Consistency of malignant tumor size measurement was compared between modalities using Intraclass Correlation Coefficient (ICC). RESULTS: In a multiethnic cohort with over 65% Hispanic and African-American women, the sensitivity of detecting malignant lesions for CESM is 93.1% (77.23%, 99.15%) and MRI is 96.55% (82.24%, 99.91%). The PPV for CESM 96.43% (81.65%, 99.91%) is better compared to MRI 82.35% (65.47%, 93.24%). CESM is as effective as MRI in evaluating index cancers and multifocal/multicentric/contralateral disease. CESM has greater specificity and PPV since MRI tends to overcall benign lesions. There is a good agreement of tumor size between CESM to surgery and MRI to surgery with ICC of 0.85 (95% CI 0.69, 0.93) and 0.87 (95% CI 0.74, 0.94), respectively. There is good agreement of malignancy detection between CESM and MRI with Kappa of 0.74 (95% CI 0.52, 0.95). CONCLUSIONS: CESM is an effective imaging modality for evaluating the extent of disease in newly diagnosed invasive breast cancers and a good alternative to MRI in a multiethnic population.

Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis.

The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various tested programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray-level discretization approaches. Since these software programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and UpSet diagrams and quantified using two ad hoc metrics. Morphology features earned lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be interchangeable. Further studies are needed to standardize the workflow of radiomic feature extraction.

Addressing ethnic disparities in imaging utilization and clinical outcomes for COVID-19.

PURPOSE: Racial and ethnic disparities have exacerbated during the COVID-19 pandemic as the healthcare system is overwhelmed. While Hispanics are disproportionately affected by COVID-19, little is known about ethnic disparities in the hospital settings. This study investigates imaging utilization and clinical outcomes between Hispanic and non-Hispanic COVID-19 patients in the Emergency Department (ED) and during hospitalization. METHODS: Through retrospective chart review, we included 331 symptomatic COVID-19 patients (mean age 53.2 years) at a metropolitan healthcare system from March to June 2020. Poisson regression was used to compare diagnostic imaging utilization and clinical outcomes between Hispanic and non-Hispanic patients. RESULTS: After adjusting for confounders, no statistically significant difference was found between Hispanic and non-Hispanic patients for the number of weekly chest X-rays. Results were categorized into four clinical outcomes: ED management (0.16 ± 0.05 vs. 0.14 ± 0.8, p:0.79); requiring inpatient management (1.31 ± 0.11 vs. 1.46 ± 0.16, p:0.43); ICU admission without invasive ventilation (1.4 ± 0.17 vs. 1.35 ± 0.26, p:0.86); and ICU admission and ventilator support (3.29 ± 0.22 vs. 3.59 ± 0.37, p:0.38). There were no statistically significant relative differences in adjusted prevalence rate between ethnic groups for all clinical outcomes (p > 0.05). There was a statistically significant longer adjusted length of stay (days) in non-Hispanics for two subcohorts: inpatient management (8.16 ± 0.31 vs. 9.72 ± 0.5, p < 0.01) and ICU admission without invasive ventilation (10.39 ± 0.57 vs. 13.45 ± 1.13, p < 0.01). CONCLUSIONS: For Hispanic and non-Hispanic COVID-19 patients in the ED or hospitalized, there were no statistically significant differences in imaging utilization and clinical outcomes.

Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors.

OBJECTIVES: Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning. METHODS: Our retrospective study identified 128 histologically diagnosed benign (n = 36) and malignant (n = 92) soft tissue lesions. 3D ROIs were manually drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study. One thousand seven hundred eight radiomics features were extracted from each ROI. Univariate analyses with supportive ROC analyses were conducted to evaluate the discriminative power of predictive models constructed using Real Adaptive Boosting (Adaboost) and Random Forest (RF) machine learning approaches. RESULTS: Univariate analyses demonstrated that 36.89% of individual radiomics varied significantly between benign and malignant lesions at the p ≤ 0.05 level. Adaboost and RF performed similarly well, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81), respectively, after 10-fold cross-validation. Restricting the machine learning models to only sequences extracted from T2FS and STIR sequences maintained comparable performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84), respectively. CONCLUSION: Machine learning decision classifiers constructed from MRI-based radiomics features show promising ability to preoperatively discriminate between benign and malignant soft tissue masses. Our approach maintains applicability even when the dataset is restricted to T2FS and STIR fluid-sensitive sequences, which may bolster practicality in clinical application scenarios by eliminating the need for complex co-registrations for multisequence analysis. KEY POINTS: • Predictive models constructed from MRI-based radiomics data and machine learning-augmented approaches yielded good discriminative power to correctly classify benign and malignant lesions on preoperative scans, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81) for Real Adaptive Boosting (Adaboost) and Random Forest (RF), respectively. • Restricting the models to only use metrics extracted from T2 fat-saturated (T2FS) and Short-Tau Inversion Recovery (STIR) sequences yielded similar performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84) for Adaboost and RF, respectively. • Radiomics-based machine learning decision classifiers constructed from multicentric data more closely mimic the real-world practice environment and warrant additional validation ahead of prospective implementation into clinical workflows.

Management Impact of 68Ga-DOTATATE PET/CT in Neuroendocrine Tumors.

PURPOSE: The goal of our retrospective single tertiary academic medical center investigation was to examine the added diagnostic value and clinical impact of 68Ga-DOTATATE PET/CT in the therapeutic management of patients with neuroendocrine tumors (NETs). METHODS: Imaging database was queried for all "PET-DOTATATE" examinations performed at our tertiary care academic institution using MONTAGE™. The patient's clinical history and recent prior imaging were reviewed. The additional diagnostic value and clinical management impact of 68Ga-DOTATATE were assessed through retrospective chart review. RESULTS: A total of 81 68Ga-DOTATATE PET/CT scans in 74 patients were found, and 11 patients were excluded from analysis as they had no prior imaging available for comparison, with resultant analysis cohort of 63 patients. Six patients had 2 or more 68Ga-DOTATATE PET/CT examinations. The most common primary diagnosis was undifferentiated NET (63.5%), followed by carcinoid (27.0%), paraganglioma (4.8%), insulinoma (3.2%), and pheochromocytoma (1.6%). The primary sites of disease from the most to the least common were the pancreas (36.5%), small bowel (22.2%), unknown primary (15.9%), lung (6.3%), large bowel (6.3%), and mesentery (4.8%), and other locations accounted for 7.9%. In patients who had prior imaging available for comparison, there were new lesions identified on 68Ga-DOTATATE PET/CT in 21 patients (33.3%) that were not identified on other prior imaging modalities. Of these patients, 5 underwent subsequent MRI and 1 had a repeat 68Ga-DOTATATE PET/CT to further characterize new lesions seen. Moreover, 15 patients (23.8%) had a change in treatment plan, including altering medical therapy in 9 patients, change in planned extent of surgical management in 5 patients, and cancelation of a planned primary tumor resection in 1 patient with metastatic disease. CONCLUSION: Our retrospective cohort demonstrated that 68Ga-DOTATATE PET/CT improves lesion detection over conventional imaging in 33.3% and impacts the therapeutic management in 23.8% of patients with NET.

Machine learning based predictors for COVID-19 disease severity.

Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical ventilation. Among the algorithms considered, the Random Forest classifier performed the best with [Formula: see text] for predicting ICU need and [Formula: see text] for predicting the need for mechanical ventilation. We also determined the most influential features in making this prediction, and concluded that all three categories of data are important. We determined the relative importance of blood panel profile data and noted that the AUC dropped by 0.12 units when this data was not included, thus indicating that it provided valuable information in predicting disease severity. Finally, we generated RF predictors with a reduced set of five features that retained the performance of the predictors trained on all features. These predictors, which rely only on quantitative data, are less prone to errors and subjectivity.

Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.

OBJECTIVE: The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity. MATERIALS AND METHODS: A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps. RESULTS: A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family. CONCLUSION: The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.

Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

OBJECTIVES: Using a radiomics framework to quantitatively analyze tumor shape and texture features in three dimensions, we tested its ability to objectively and robustly distinguish between benign and malignant renal masses. We assessed the relative contributions of shape and texture metrics separately and together in the prediction model. MATERIALS AND METHODS: Computed tomography (CT) images of 735 patients with 539 malignant and 196 benign masses were segmented in this retrospective study. Thirty-three shape and 760 texture metrics were calculated per tumor. Tumor classification models using shape, texture, and both metrics were built using random forest and AdaBoost with tenfold cross-validation. Sensitivity analyses on five sub-cohorts with respect to the acquisition phase were conducted. Additional sensitivity analyses after multiple imputation were also conducted. Model performance was assessed using AUC. RESULTS: Random forest classifier showed shape metrics featuring within the top 10% performing metrics regardless of phase, attaining the highest variable importance in the corticomedullary phase. Convex hull perimeter ratio is a consistently high-performing shape feature. Shape metrics alone achieved an AUC ranging 0.64-0.68 across multiple classifiers, compared with 0.67-0.75 and 0.68-0.75 achieved by texture-only and combined models, respectively. CONCLUSION: Shape metrics alone attain high prediction performance and high variable importance in the combined model, while being independent of the acquisition phase (unlike texture). Shape analysis therefore should not be overlooked in its potential to distinguish benign from malignant tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics. KEY POINTS: • Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors. • Shape metrics alone can attain high prediction performance and demonstrate high variable importance in the combined shape and texture radiomics model. • Any future radiomics platform powered by machine learning should harness both shape and texture metrics, especially since tumor shape (unlike texture) is independent of the acquisition phase and more robust from the imaging variations.

Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country.

Background: The novel Severe Acute Respiratory Syndrome Coronavirus-2 has led to a global pandemic in which case fatality rate (CFR) has varied from country to country. This study aims to identify factors that may explain the variation in CFR across countries. Methods: We identified 24 potential risk factors affecting CFR. For all countries with over 5000 reported COVID-19 cases, we used country-specific datasets from the WHO, the OECD, and the United Nations to quantify each of these factors. We examined univariable relationships of each variable with CFR, as well as correlations among predictors and potential interaction terms. Our final multivariable negative binomial model included univariable predictors of significance and all significant interaction terms. Results: Across the 39 countries under consideration, our model shows COVID-19 case fatality rate was best predicted by time to implementation of social distancing measures, hospital beds per 1000 individuals, percent population over 70 years, CT scanners per 1 million individuals, and (in countries with high population density) smoking prevalence. Conclusion: Our model predicted an increased CFR for countries that waited over 14 days to implement social distancing interventions after the 100th reported case. Smoking prevalence and percentage population over the age of 70 years were also associated with higher CFR. Hospital beds per 1000 and CT scanners per million were identified as possible protective factors associated with decreased CFR.

Impacts of the Coronavirus Disease 2019 (COVID-19) pandemic on healthcare workers: A nationwide survey of United States radiologists.

BACKGROUND: Efforts to reduce nosocomial spread of COVID-19 have resulted in unprecedented disruptions in clinical workflows and numerous unexpected stressors for imaging departments across the country. Our purpose was to more precisely evaluate these impacts on radiologists through a nationwide survey. METHODS: A 43-item anonymous questionnaire was adapted from the AO Spine Foundation's survey and distributed to 1521 unique email addresses using REDCap™ (Research Electronic Data Capture). Additional invitations were sent out to American Society of Emergency Radiology (ASER) and Association of University Radiologists (AUR) members. Responses were collected over a period of 8 days. Descriptive analyses and multivariate modeling were performed using SAS v9.4 software. RESULTS: A total of 689 responses from radiologists across 44 different states met the criteria for inclusion in the analysis. As many as 61% of respondents rated their level of anxiety with regard to COVID-19 to be a 7 out of 10 or greater, and higher scores were positively correlated the standardized number of COVID-19 cases in a respondent's state (RR = 1.11, 95% CI: 1.02-1.21, p = 0.01). Citing the stressor of "personal health" was a strong predictor of higher anxiety scores (RR 1.23; 95% CI: 1.13-1.34, p < 0.01). By contrast, participants who reported needing no coping methods were more likely to self-report lower anxiety scores (RR 0.4; 95% CI: 0.3-0.53, p < 0.01). CONCLUSION: COVID-19 has had a significant impact on radiologists across the nation. As these unique stressors continue to evolve, further attention must be paid to the ways in which we may continue to support radiologists working in drastically altered practice environments and in remote settings.

Responding to coronavirus disease 2019: LA County hospital experience.

The coronavirus disease 2019 (COVID-19) has rapidly spread across the world since first being identified in Wuhan, China, in late 2019. In order to prepare for the surge of patients and the corresponding increase in radiology exams, clear and detailed policies need to be implemented by hospitals and radiology departments. In this article, we highlight the experiences and policies at LAC+USC Medical Center, the largest single provider of healthcare in LA County. Our policies aim to reduce the risk of transmission, guide patient management and workflow, preserve and effectively allocate resources, and be responsive to changing dynamics. We hope this communication may help other institutions in dealing with this pandemic as well as future outbreaks.

Salvage Therapies After 18F-Fluciclovine Detected Prostate Cancer Recurrences.

BACKGROUND: F-Fluciclovine is the most recent prostate cancer (PCa)-directed PET radiotracer approved by the US Food and Drug Administration for detection of recurrent PCa. We report the treatments and outcomes of patients at our institution with PCa recurrences detected on F-fluciclovine PET/CT. METHODS: We identified men with recurrent PCa detected on F-fluciclovine PET/CT performed between 2017 and 2018 who were previously treated definitively and analyzed their patterns of care and cancer-specific outcomes. RESULTS: We identified 28 men with recurrent PCa detected on F-fluciclovine PET/CT. Twenty-three were initially treated with surgery and 13 also received postoperative radiation therapy (RT). Five patients were initially treated with definitive radiation. After surgery, the median time to F-fluciclovine PET/CT was 67 months (median prostate-specific antigen [PSA] of 1.63 ng/mL). After RT, the median time to F-fluciclovine PET/CT was 95 months with median PSA of 13.31 ng/mL. Six men recurred locally, 9 recurred in the pelvic nodes, 9 had distant nodal recurrences, and 4 had osseous metastases. Of the patients initially treated with surgery, 4 received salvage radiation and 3 received androgen deprivation therapy (ADT). Of the patients initially treated with surgery and postoperative RT, 3 received salvage pelvic nodal dissection, 4 received salvage radiation, and 2 received ADT. Of the patients initially treated with radiation, 4 received salvage ADT. All had PSA decline after salvage therapy. CONCLUSIONS: F-fluciclovine PET/CT can localize PCa recurrences, and subsequent salvage therapies appear effective with decreasing PSA. Longer follow-up will reveal if these diagnostic tests and subsequent therapies will improve PCa survival.

Quantitative magnetic resonance imaging (q-MRI) for the assessment of soft-tissue sarcoma treatment response: a narrative case review of technique development.

Soft-tissue sarcomas are a heterogeneous class of tumors that exhibit varying degrees of cellularity and cystic degeneration in response to neoadjuvant chemotherapy. This creates unique challenges in the radiographic assessment of treatment response when relying on conventional markers such as tumor diameter (RECIST criteria). In this case series, we provide a narrative discussion of technique development for whole tumor volume quantitative magnetic resonance imaging (q-MRI), highlighting cases from a small pilot study of 8 patients (9 tumors) pre- and post-neoadjuvant chemotherapy. One of the methods of q-MRI analysis (the "constant-cutoff" technique) was able to predict responders versus non-responders based on percent necrosis and viable tumor volume calculations (p = 0.05), respectively. Our results suggest that q-MRI of whole tumor volume contrast enhancement may have a role in tumor response assessment, although further validation is needed.

Comparative prognostic implication of treatment response assessments in mCRPC: PERCIST 1.0, RECIST 1.1, and PSA response criteria.

Accurate appraisal of treatment response in metastatic castrate-resistant prostate cancer (mCRPC) is challenging in view of remarkable tumor heterogeneity and the available choices among many established and novel therapeutic approaches. The purpose of this single-center prospective study was to evaluate the comparative prognostic utility of PERCIST 1.0 in predicting overall survival (OS) in patients with mCRPC compared to RECIST 1.1 and prostate-specific antigen (PSA)-based treatment response assessments. Methods: Patients with mCRPC were prospectively enrolled if they were beginning systemic medical therapy or transitioning to new systemic therapy after not responding to a prior treatment. All patients underwent a baseline 18F-fluorodeoxyglucose (FDG) positron emission tomography/ computed tomography (PET/CT) prior to the initiation of treatment and again 4 months after the start of therapy. Patients' responses to treatment at 4 months compared to baseline were evaluated with RECIST 1.1, PERCIST 1.0 and PSA response criteria. The associations between patients' response categories and OS were evaluated. OS was defined as the duration in time between the date of baseline PET/CT to death from any cause. Patients with different response status were compared with logrank tests. Survival probabilities were calculated using the Kaplan-Meier method. Results: Patients with progressive disease by PSA response criteria at 4 months demonstrated significantly shorter OS (24-month OS probability: 18% ± 11%) compared to patients with stable disease, SD, (44% ± 19%, p=0.03) and complete response, CR, or partial response, PR, (53% ± 11%, p=0.03). RECIST 1.1 response criteria demonstrated a similar trend in OS, however no statistically significant differences were noted between patients with PD (25% ± 15%) compared to SD/non-CR, non-PD (54% ± 13%) and CR/PR (54% ± 14%) (p=0.13). PERCIST 1.0 criteria demonstrated significant differences in OS between responders, CMR/PMR (56% ± 12%), compared to SMD (38% ± 17%, p=0.03) and PMD (21% ± 10%, p=0.01). Patients with progressive disease by both PERICST 1.0 and PSA response criteria demonstrated significantly worse OS (24-month OS: 0%, 12-month OS: 31% ± 14%) compared to patients with progressive disease by either response criteria. Conclusion: PERCIST 1.0 may provide significant prognostic information for patients with mCRPC undergoing systemic chemotherapy, particularly when incorporated with PSA treatment response criteria.

Treatment Response Assessment of Skeletal Metastases in Prostate Cancer with 18F-NaF PET/CT.

PURPOSE: To determine the utility of 18F-sodium fluoride positron emission tomography-computed tomography (18F-NaF PET/CT) in the imaging assessment of therapy response in men with osseous-only metastatic prostate cancer. METHODS: In this Institutional Review Board-approved single institution retrospective investigation, we evaluated 21 18F-NaF PET/CT scans performed in 14 patients with osseous metastatic disease from prostate cancer and no evidence of locally recurrent or soft-tissue metastatic disease who received chemohormonal therapy. Imaging-based qualitative and semi-quantitative parameters were defined and compared with changes in serum PSA level. RESULTS: Qualitative and semi-quantitative image-based assessments demonstrated > 80% concordance with good correlation (SUVmax κ = 0.71, SUVavg κ = 0.62, SUVsum κ = 0.62). Moderate correlation (κ = 0.43) was found between SUVmax and PSA-based treatment response assessments. There was no statistically significant correlation between PSA-based disease progression and semi-quantitative parameters. Qualitative imaging assessment was moderately correlated (κ = 0.52) with PSA in distinguishing responders and non-responders. CONCLUSION: 18F-NaF PET/CT is complementary to biochemical monitoring in patients with bone-only metastases from prostate cancer which can be helpful in subsequent treatment management decisions.