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.

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 email, Facebook and social media groups using REDCap.

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

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.

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.

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.

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.

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.

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.

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.

Management Impact of Ga-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 Ga-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.

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.

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

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. We identified 24 potential risk factors affecting 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).

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.

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.

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.

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.

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.

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

Purpose: To determine the utility of F-sodium fluoride positron emission tomography-computed tomography (F-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 F-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.

Reliability of CT-based texture features: Phantom study.

Objective: To determine the intra-, inter- and test-retest variability of CT-based texture analysis (CTTA) metrics.

Materials And Methods: In this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors.

Prediction of Time to Hormonal Treatment Failure in Metastatic Castration-Sensitive Prostate Cancer with F-FDG PET/CT.

The aim of this prospective investigation was to assess the association of F-FDG PET/CT with time to hormonal treatment failure (THTF) in men with metastatic castration-sensitive prostate cancer. 76 men with metastatic castration-sensitive prostate cancer recruited from 2005 to 2011 underwent F-FDG PET/CT and were followed prospectively for THTF, defined as treatment change to chemotherapy or death. Patients who had not switched to chemotherapy were censored at the last follow-up date (median of 36 mo; range, 12-108 mo).

Contrast-Enhanced Ultrasound Imaging of Breast Masses: Adjunct Tool to Decrease the Number of False-Positive Biopsy Results.

Objectives: This pilot study evaluated use of contrast-enhanced ultrasound (CEUS) to reduce the number of benign breast masses recommended for biopsy.

Methods: This prospective study included 131 consenting women, from October 2016 to June 2017, with American College of Radiology Breast Imaging Reporting and Data System category 4a, 4b, and 4c masses detected by mammography, conventional ultrasound (US), or both. Contrast-enhanced US examinations (using intravenous injection of perflutren lipid microspheres or sulfur hexafluoride lipid-type A microspheres) were performed before biopsy.

Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping.

Objective: The purpose of this study was to assess the accuracy of a panel of texture features extracted from clinical CT in differentiating benign from malignant solid enhancing lipid-poor renal masses.

Materials And Methods: In a retrospective case-control study of 174 patients with predominantly solid nonmacroscopic fat-containing enhancing renal masses, 129 cases of malignant renal cell carcinoma were found, including clear cell, papillary, and chromophobe subtypes. Benign renal masses-oncocytoma and lipid-poor angiomyolipoma-were found in 45 patients.

A Decision-Support Tool for Renal Mass Classification.

We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification.