MRI of Soft-Tissue Tumors: What to Include in the Report.

MRI serves as a critical step in the workup, local staging, and treatment planning of extremity soft-tissue masses. For the radiologist to meaningfully contribute to the management of soft-tissue masses, they need to provide a detailed list of descriptors of the lesion outlined in an organized report. While it is occasionally possible to use MRI to provide a diagnosis for patients with a mass, it is more often used to help with determining the differential diagnosis and planning of biopsies, surgery, radiation treatment, and chemotherapy (when provided). Each descriptor on the list outlined in this article is specifically aimed to assist in one or more facets of the overall approach to soft-tissue masses. This applies to all masses, but in particular sarcomas. Those descriptors are useful to help narrow the differential diagnosis and ensure concordance with a pathologic diagnosis and its accompanying grade assignment of soft-tissue sarcomas. These include a lesion's borders and shape, signal characteristics, and contrast enhancement pattern; the presence of peritumoral edema and peritumoral enhancement; and the presence of lymph nodes. The items most helpful in assisting surgical planning include a lesion's anatomic location, site of origin, size, location relative to a landmark, relationship to adjacent structures, and vascularity including feeding and draining vessels. The authors provide some background information on soft-tissue sarcomas, including their diagnosis and treatment, for the general radiologist and as a refresher for radiologists who are more experienced in tumor imaging. ©RSNA, 2024 See the invited commentary by Murphey in this issue.

MR Imaging of Benign Soft Tissue Tumors: Highlights for the Practicing Radiologist.

The overwhelming majority of soft tissue masses encountered on routine imaging are incidental and benign. When incidental, the radiologist is usually limited to routine MR imaging sequences, often without contrast. In these situations, there are typical imaging features pointing to a single diagnosis or limited differential diagnosis. Although these imaging features can be helpful, many lesions are nonspecific and may require contrast administration, evaluation with other imaging modalities, follow-up imaging, or biopsy for diagnosis. This article will provide an overview of the most commonly encountered benign soft tissue masses along with some of their characteristic MR imaging features.

Society of Skeletal Radiology- white paper. Guidelines for the diagnostic management of incidental solitary bone lesions on CT and MRI in adults: bone reporting and data system (Bone-RADS).

The purpose of this article is to present algorithms for the diagnostic management of solitary bone lesions incidentally encountered on computed tomography (CT) and magnetic resonance (MRI) in adults. Based on review of the current literature and expert opinion, the Practice Guidelines and Technical Standards Committee of the Society of Skeletal Radiology (SSR) proposes a bone reporting and data system (Bone-RADS) for incidentally encountered solitary bone lesions on CT and MRI with four possible diagnostic management recommendations (Bone-RADS1, leave alone; Bone-RADS2, perform different imaging modality; Bone-RADS3, perform follow-up imaging; Bone-RADS4, biopsy and/or oncologic referral). Two algorithms for CT based on lesion density (lucent or sclerotic/mixed) and two for MRI allow the user to arrive at a specific Bone-RADS management recommendation. Representative cases are provided to illustrate the usability of the algorithms.

Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports.

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90-8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.

Capsular thinning on magnetic resonance arthrography is associated with intra-operative hip joint laxity in women.

Hip microinstability is a recognized cause of hip pain in young patients. Intra-operative evaluation is used to confirm the diagnosis, but limited data exist associating magnetic resonance arthrography (MRA) findings with hip microinstability. To determine if a difference exists in the thickness of the anterior joint capsule and/or the width of the anterior joint recess on MRA in hip arthroscopy patients with and without an intra-operative diagnosis of hip laxity. Sixty-two hip arthroscopy patients were included in the study. Two musculoskeletal radiologists blinded to surgical results reviewed the MRAs for two previously described findings: (i) anterior joint capsule thinning; (ii) widening of the anterior joint recess distal to the zona orbicularis. Operative reports were reviewed for the diagnosis of joint laxity. In all patients with and without intra-operative laxity, there were no significant differences with either MRA measurement. However, twenty-six of 27 patients with intra-operative laxity were women compared with 11 of 35 patients without laxity (P < 0.001). In subgroup analysis of women, the intra-operative laxity group had a higher rate of capsular thinning compared with the non-laxity group (85% versus 45%; P = 0.01). A 82% of women with capsular thinning also had intra-operative laxity, compared with 40% without capsular thinning (P = 0.01). There were no differences regarding the width of the anterior joint recess. In this study, there was an association between capsular thinning and intra-operative laxity in female patients. Measuring anterior capsule thickness on a pre-operative MRA may be useful for the diagnosis of hip microinstability.

The Cliff Sign: A New Radiographic Sign of Hip Instability.

BACKGROUND: The preoperative diagnosis of hip microinstability is challenging. Although physical examination maneuvers and magnetic resonance imaging findings associated with microinstability have been described, there are limited reports of radiographic features. In patients with microinstability, we observed a high incidence of a steep drop-off on the lateral edge of the femoral head, which we have named the "cliff sign." PURPOSE: (1) To determine the relationship of the cliff sign and associated measurements with intraoperative microinstability and (2) to determine the interobserver reliability of these measurements. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: A total of 115 consecutive patients who underwent hip arthroscopy were identified. Patients with prior hip surgery, Legg-Calve-Perthes disease, fractures, pigmented villonodular synovitis, or synovial chondromatosis were excluded, resulting in the inclusion of 96 patients in the study. A perfect circle around the femoral head was created on anteroposterior pelvis radiographs. If the lateral femoral head did not completely fill the perfect circle, it was considered a positive cliff sign. Five additional measurements relating to the cliff sign were calculated. The diagnosis of microinstability was made intraoperatively by the (1) amount of traction required to distract the hip, (2) lack of hip reduction after initial traction release following joint venting, or (3) intraoperative findings consistent with hip microinstability. Continuous variables were analyzed through use of unpaired t tests and discrete variables with Fisher exact tests. Interobserver reliability (n = 3) was determined for each measurement. RESULTS: Overall, 89% (39/44) of patients with microinstability had a cliff sign, compared with 27% of patients (14/52) without instability (P < .0001). Conversely, 74% of patients with a cliff sign had microinstability, while only 12% of patients without a cliff sign had instability (P < .0001). In women younger than 32 years with a cliff sign, 100% (20/20) were diagnosed with instability. No differences were found in any of the 5 additional measurements. Excellent interobserver reliability was found for the presence of a cliff sign and the cliff angle measurement. CONCLUSION: We have identified a radiographic finding, the cliff sign, that is associated with the intraoperative diagnosis of hip microinstability and has excellent interobserver reliability. Results showed that 100% of young women with a cliff sign had intraoperative microinstability. The cliff sign may be useful in the preoperative diagnosis of hip microinstability.

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.

BACKGROUND: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation. METHODS AND FINDINGS: Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts. CONCLUSIONS: Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

MRI of the hip for the evaluation of femoroacetabular impingement; past, present, and future.

The concept of femoroacetabular impingement (FAI) has, in a relatively short time, come to the forefront of orthopedic imaging. In just a few short years MRI findings that were in the past ascribed to degenerative change, normal variation, or other pathologies must now be described and included in radiology reports, as they have been shown, or are suspected to be related to, FAI. Crucial questions have come up in this time, including: what is the relationship of bony morphology to subsequent cartilage and labral damage, and most importantly, how is this morphology related to the development of osteoarthritis? In this review, we attempt to place a historical perspective on the controversy, provide guidelines for interpretation of MRI examinations of patients with suspected FAI, and offer a glimpse into the future of MRI of this complex condition.

Hip-femoral acetabular impingement.

Magnetic resonance imaging (MRI) has become a valuable technology for the diagnosis and treatment of femoroacetabular impingement (FAI). This article reviews the basic pathophysiology of FAI, as well as the techniques and indications for MRI and magnetic resonance arthrography. Normal MRI anatomy of the hip and pathologic MRI anatomy associated with FAI are also discussed. Several case examples are presented demonstrating the diagnosis and treatment of FAI.

Foot and ankle injuries in sport: imaging correlation with arthroscopic and surgical findings.

Foot and ankle injuries are common in sport. Although many available imaging techniques can be useful in identifying and classifying injuries, magnetic resonance imaging (MRI) provides high levels of sensitivity and specificity for articular and soft-tissue injuries. Arthroscopic and minimally invasive treatment techniques for foot and ankle injuries are rapidly evolving, minimizing morbidity and improving postoperative rehabilitation and return to play. Correlation between MRI and surgical findings can aid in both accessing and treating pathologic processes and structures.

Magnetic resonance arthrography.

Magnetic resonance arthrography is widely used throughout the world for joint imaging. It extends the capabilities of conventional MR imaging because contrast solution distends the joint capsule, outlines intraarticular structures, and extends into soft tissue tears and defects. MR arthrography exploits the natural advantages gained from a joint effusion and can be performed on any joint.

Magnetic resonance imaging in the evaluation of sports injuries of the foot and ankle: a pictorial essay.

Magnetic resonance imaging is playing an increasingly important role in evaluation of the injured athlete's foot and ankle. Magnetic resonance imaging allows accurate detection of bony abnormalities, such as stress fractures, and soft-tissue abnormalities, including ligament tears, tendon tears, and tendinopathy. The interpreter of magnetic resonance images should systematically review the images, noting normal structures and accounting for changes in soft-tissue and bony signal.

Magnetic resonance imaging of sports injuries of the elbow.

Many abnormalities seen in the elbow result from trauma, often from sports such as baseball and tennis. Elbow problems are frequently related to the medial tension-lateral compression phenomenon, where repeated valgus stress produces flexor-pronator strain, ulnar collateral ligament sprain, ulnar traction spurring, and ulnar neuropathy. Lateral compression causes osteochondral lesions of the capitellum and radial head, degenerative arthritis, and loose bodies. Other elbow abnormalities seen on magnetic resonance imaging include radial collateral ligament injuries, biceps and triceps tendon injuries, other nerve entrapment syndromes, loose bodies, osseous and soft-tissue trauma, arthritis, and masses, including bursae.