The ACR Mammography Positioning Improvement Collaborative: A Multicenter Improvement Program Within a Learning Network Framework.

Purpose/objective: To share the experience and results of the first cohort of the ACR Mammography Positioning Improvement Collaborative, in which participating sites aimed to increase the mean percentage of screening mammograms meeting the established positioning criteria to 85% or greater and show at least modest evidence of improvement at each site by the end of the improvement program.

Methods: The sites comprising the first cohort of the collaborative were selected on the basis of strength of local leadership support, intra-organizational relationships, access to data and analytic support, and experience with quality improvement initiatives. During the improvement program, participating sites organized their teams, developed goals, gathered data, evaluated their current state, identified key drivers and root causes of their problems, and developed and tested interventions.

Standardizing imaging findings representation: harnessing Common Data Elements semantics and Fast Healthcare Interoperability Resources structures.

Objectives: Designing a framework representing radiology results in a standards-based data structure using joint Radiological Society of North America/American College of Radiology Common Data Elements (CDEs) as the semantic labels on standard structures. This allows radiologist-created report data to integrate with artificial intelligence-generated results for use throughout downstream systems.

Materials And Methods: We developed a framework modeling radiology findings as Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) observations using CDE set/element identifiers as standardized semantic labels.

Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge.

The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research.

Validation of Patient-Reported Outcomes in Patients With Nonmetastatic Breast Cancer Receiving Comprehensive Nodal Irradiation in the RadComp Trial.

Purpose: Our purpose was to evaluate the measurement properties of patient-reported outcome (PRO) measures used in the ongoing RadComp pragmatic randomized clinical trial (PRCT).

Methods And Materials: The deidentified and blinded data set included 774 English-speaking female participants who completed their 6-month posttreatment assessment. Eleven PRO measures were evaluated, including the Trial Outcome Index from the Functional Assessment of Cancer Therapy-Breast (FACT-B), Satisfaction with Breast Cosmetic Outcomes, the BREAST-Q, and selected Patient-Reported Outcomes Measurement Information System (PROMIS) measures.

Improving Prostate MR Image Quality in Practice-Initial Results From the ACR Prostate MR Image Quality Improvement Collaborative.

Objective: Variability in prostate MRI quality is an increasingly recognized problem that negatively affects patient care. This report aims to describe the results and key learnings of the first cohort of the ACR Learning Network Prostate MR Image Quality Improvement Collaborative.

Methods: Teams from five organizations in the United States were trained on a structured improvement method.

Bone Density Screening Rates Among Medicare Beneficiaries: An Analysis with a focus on Asian Americans.

Purpose: To report osteoporosis screening utilization rates among Asian American (AsA) populations in the USA.

Methods: We retrospectively assessed the use of dual-energy X-ray absorptiometry (DXA) screening using the Medicare 5% Research Identifiable Files. Using Current Procedural Terminology (CPT) codes indicative of a DXA scan, we identified patients recommended for DXA screening according to the ACR-SPR-SSR Practice Parameters (females ≥ 65 years, males ≥ 70 years).

A Claims-Based Method for Identification and Characterization of Practicing Interventional Radiologists.

Purpose: To propose a research method for identifying "practicing interventional radiologists" using 2 national claims data sets.

Materials And Methods: The 2015-2019 100% Medicare Part B data and 2015-2019 private insurance claims from Optum's Clinformatics Data Mart (CDM) database were used to rank-order radiologists' interventional radiology (IR)-related work as a percentage of total billed work relative value units (RVUs). Characteristics were analyzed at various threshold percentages.

Prospective Evaluation of the Cost of Performing Breast Imaging Examinations Using a Time-Driven Activity-Based Costing Method: A Single-Center Study.

Objective: Measuring the cost of performing breast imaging is difficult in healthcare systems. The purpose of our study was to evaluate this cost using time-driven activity-based costing (TDABC) and to evaluate cost drivers for different exams.

Methods: An IRB-approved, single-center prospective study was performed on 80 female patients presenting for breast screening, diagnostic or biopsy exams from July 2020 to April 2021.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis.

External Validation of a Digital Pathology-based Multimodal Artificial Intelligence Architecture in the NRG/RTOG 9902 Phase 3 Trial.

Background: Accurate risk stratification is critical to guide management decisions in localized prostate cancer (PCa). Previously, we had developed and validated a multimodal artificial intelligence (MMAI) model generated from digital histopathology and clinical features. Here, we externally validate this model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA.

Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

Radiologists' Out-of-Network Billing Trends, 2007 to 2021.

Purpose: Given the financial hardships of surprise billing for patients, the aim of this study was to assess the degree to which radiologists effectively participate in commercial insurance networks by examining the trend in the share of radiologists' imaging claims that are out of network (OON).

Methods: A retrospective study over a 15-year period (2007-2021) was conducted using claims from Optum's deidentified Clinformatics Data Mart Database to assess the share of radiologists' imaging claims that are OON. Radiologists' annual OON rate was assessed overall as well as for claims associated with inpatient stays and emergency department (ED) visits.

Adjuvant platinum versus capecitabine for residual, invasive, triple-negative breast cancer: Patient-reported outcomes in ECOG-ACRIN EA1131.

Background: Patient-reported outcomes (PROs) are a better tool for evaluating the experiences of patients who have symptomatic, treatment-associated adverse events (AEs) compared with clinician-rated AEs. The authors present PROs assessing health-related quality of life (HRQoL) and treatment-related neurotoxicity for adjuvant capecitabine versus platinum on the Eastern Cooperative Oncology Group-American College of Radiology Imaging Network (ECOG-ACRIN) EA1131 trial (ClinicalTrials.gov identifier NCT02445391).