Journal highlights

The following are highlights from the current issues of RSNA’s peer-reviewed journals.

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Radiology Logo

Intra-articular Corticosteroid Injections for the Treatment of Hip and Knee Osteoarthritisrelated Pain: Considerations and Controversies with a Focus on Imaging—Radiology Scientific Expert Panel

Current management of osteoarthritis (OA) is primarily focused on symptom control. Intra-articular corticosteroid (IACS) injections are often used for pain management of hip and knee OA in patients who have not responded to oral or topical analgesics.

Recent case series suggest that negative structural outcomes including accelerated OA progression, subchondral insufficiency fracture, complications of pre-existing osteonecrosis and rapid joint destruction (including bone loss) may be observed in patients who received IACS injections.

In a recent review in Radiology, Ali Guermazi, MD, PhD, Boston University School of Medicine, and colleagues reviewed the current understanding of pain in OA, summarized current international guidelines regarding indications for IACS injection and offered pre-interventional safety measures, including imaging.

There is no established recommendation or consensus regarding imaging, clinical, or laboratory markers before an IACS injection to screen for OA-related imaging abnormalities. Repeating radiographs before each subsequent IACS injection remains controversial, according to the review.

To determine the cause and natural history of OA, large prospective studies evaluating the risk of accelerated OA or joint destruction after IACS injections are needed.

“Performing pre-injection joint radiographs to identify patients with no or mild radiographic OA may be needed but that depends on the prevalence of these abnormalities and the likelihood of developing adverse outcomes,” the authors write.

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Rapid progressive osteoarthritis type 2 in a 38-year-old woman referred to radiology for intra-articular corticosteroid (IACS) injection. (a) Baseline anteroposterior radiograph of the left hip shows mild osteoarthritis with small definite osteophytes at the lateral acetabulum and femoral head (arrows) and no definite joint space narrowing. (b) Six months after the IACS injection a repeat anteroposterior radiograph of the left hip shows a complete collapse of the head of the femur with marked bone loss of the femoral head (arrow) and surface remodeling and flattening of the acetabulum (arrowheads). (c) Coronal fat-suppressed proton density-weighted MRI scan obtained on the same day demonstrates diffuse bone marrow edema of the femur and acetabulum (black and white *) and a large hip joint effusion (#) reflecting an ongoing process of marked synovial activation.

Guermazi, et al, Radiology 2020; 297:503–512 ©Radiology 2020

Imaging Cancer Logo

Intratumoral Metabolic Heterogeneity and Other Quantitative 18F-FDG PET/CT Parameters for Prognosis Prediction in Esophageal Cancer

Histologically, esophageal cancer is classified as either esophageal squamous cell carcinoma (ESCC) or esophageal adenocarcinoma (EAC).

While fluorine 18 (18F) fluorodeoxyglucose (FDG) PET/CT has been proven to be the most sensitive and specific for detecting distant metastasis and the most specific for detecting lymph node involvement of local-regional disease, there is increasing interest to assess the impact of intratumoral metabolic heterogeneity (IMH) using 18FFDG PET/CT imaging.

In a recent study in Radiology: Imaging Cancer, Akilan Gopal, BS, University of Texas Southwestern Medical Center, Dallas, and colleagues evaluated the impact of IMH and other quantitative parameters for predicting progression-free survival and overall survival in patients with esophageal cancer.

Researchers used an automated gradient-based segmentation method to assess the maximum standardized uptake value, mean standardized uptake value, metabolic tumor volume (MTV), and IMH index of the primary tumor in patients with biopsy-proven adenocarcinoma or squamous cell carcinoma of the esophagus with an initial staging 18FFDG PET/CT.

Overall survival and progression-free survival were calculated using multivariable Cox proportional hazards regression with the adjustment (as covariates) of age, sex, weight, stage, tumor type, tumor grade and treatment.

In patients with esophageal cancer, IMH and MTV of the primary tumor derived from pretreatment 18F-FDG PET/CT were the only quantitative parameters predictive of progression-free survival.

“18F-FDG PET/ CT quantitative parameters on initial staging scan can provide prognostic information, potentially leading to a more personalized approach for a patient’s treatment,” the authors conclude.

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Reassessing the Patterns of Response to Immunotherapy with PET: From Morphology to Metabolism

The emergence of immune checkpoint inhibitors (ICIs) has dramatically changed the natural history of several cancer types; consequently, the clinical importance and use of immunotherapies have skyrocketed.

An essential tool for cancer management, imaging has also been impacted by ICI therapy, including adaptations in the consolidated criteria for assessment of cancer response (e.g., Response Evaluation Criteria in Solid Tumors [RECIST]), as well as increased demands for identifying a new spectrum of immune-related adverse events (irAEs) with diagnostic imaging.

However, these techniques are mainly based on morphologic imaging, which has well known advantages and limitations. In this scenario, PET — particularly fluorodeoxyglucose (FDG) PET — can provide valuable discriminative power regarding oncologic response and can also aid in prognostication and early detection of irAEs.

In a RadioGraphics article, Larissa B. Costa, MD, Hospital Sírio-Libanês, São Paulo, Brazil, and colleagues reviewed the response assessment criteria for ICI therapy with imaging and addressed examples of irAEs from the perspective of FDG PET as a valuable imaging modality to improve current practice.

There is a strong relationship between FDG uptake and the number of viable cancer cells. A reduction in FDG uptake usually denotes treatment response, which precedes changes in tumor size. Therefore, PET/CT may be used for assessing response to immunotherapy early after initiating treatment to predict efficacy, at mid-treatment, and at the end of treatment. “When combined with consolidated morphologic criteria, PET can play an essential role in reliably assessing response to ICI therapy and adverse events during immunotherapy as one of the main treatments for cancer,” the authors write.

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Axial CT images of the four most common patterns of immunotherapy- induced pneumonitis. A, Cryptogenic organizing pneumonia (COP) involves multifocal regions of consolidation and ground-glass opacities (GGOs) with a peripheral and basilar distribution. B, Nonspecific interstitial pneumonia (NSIP) involves GGOs and reticular opacities with a subpleural distribution. C, Hypersensitivity pneumonitis involves multifocal GGOs with a centrilobular distribution and mosaic attenuation. D, Acute interstitial pneumonia/acute respiratory distress syndrome (AIP/ARDS) involves diffuse GGOs and traction bronchiectasis.

Costa et al, RadioGraphics 2021:41;1 ©RSNA 2021

AI Logo

Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle

Obtaining appropriately annotated data in sufficient quantities for effective deep learning (DL) is costly, tedious, time-consuming and often impractical.

However, all of radiology artificial intelligence is lacking an automated method to obtain and annotate radiologic data during normal workflow from every clinical study that is performed with no additional work by the radiologist.

A recent Radiology: Artificial Intelligencestudy sought proof of concept that an algorithm combining eye tracking and speech recognition can extract lesion location labels automatically for DL.

Joseph N. Stember, MD, Memorial Sloan-Kettering Cancer Center, New York City, and colleagues clinically interpreted 700 two-dimensional brain tumor MRI scans from the Brain Tumor Segmentation database. For each image, a single radiologist dictated a standard phrase describing the lesion into a microphone, simulating clinical interpretation.

Eye-tracking data were recorded simultaneously. Using speech recognition, gaze points corresponding to each lesion were obtained. Lesion locations were used to train a keypoint detection convolutional neural network to find new lesions. A network was trained to localize lesions for an independent test set of 85 images.

The statistical measure to evaluate the method was percent accuracy.

Eye tracking with speech recognition was 92% accurate in labeling lesion locations from the training dataset, thereby demonstrating that fully simulated interpretation can yield reliable tumor location labels, which were used to train the DL network. The detection network trained on these labels predicted lesion location of a separate testing set with 85% accuracy.

“The proposed algorithm has potential to yield high quantities of labeled image data ‘for free’ from standard-of-care clinical interpretations,” the authors write.

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