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Query: UMLS:C0027627 (metastases)
 103,950 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Palliative care is seeing cancer patients earlier in the disease trajectory with a multitude of chronic issues. Chronic non-malignant pain (CNMP) in cancer patients is under-studied. In this prospective study, we examined the prevalence and management of CNMP in cancer patients seen at our supportive care clinic for consultation. We systematically characterized each pain type with the Brief Pain Inventory (BPI) and documented current treatments. The attending physician made the pain diagnoses according to the International Association for the Study of Pain (IASP) task force classification. Among 200 patients (mean age 60 years, 69% metastatic disease, 1-year survival of 77%), the median number of pain diagnosis was 2 (IQR 1-2); 67 (34%, 95% CI 28-41%) had a diagnosis of CNMP; 133 (67%) had cancer-related pain; and 52 (26%) had treatment-related pain. In total, 12/31 (39%) patients with only CNMP and 21/36 (58%) patients with CNMP and other pain diagnoses were on opioids. There was a total of 94 CNMP diagnoses among 67 patients, including 37 (39%) osteoarthritis and 20 (21%) lower back pain; 30 (32%) were treated with opioids. In summary, CNMP was common in the timely palliative care setting and many patients were on opioids. Our findings highlight the need to develop clinical guidelines for CNMP in cancer patients to standardize its management.
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PMID:Chronic Non-Malignant Pain in Patients with Cancer Seen at a Timely Outpatient Palliative Care Clinic. 3195 20

Artificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.
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PMID:Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence. 3199 51

It is important to review the physics of current MRI developments in nontraumatic spinal imaging and their specific applications for assessing the bone marrow. Techniques include chemical shift imaging and its use in differentiating aggressive from benign lesions and in confirming the presence of diffuse red marrow conversion, which may mimic diffuse marrow infiltration in metastatic disease. The principles of dynamic contrast MRI and its uses in multiple myeloma and discriminating between postoperative change/scarring versus recurrence in soft-tissue tumors warrant discussion. The basic physics of diffusion-weighted imaging (DWI) in bone marrow pathologies are distinguished from the principles of DWI as applied to solid organs, and DWI is used in the staging of multiple myeloma and in differentiating between benign versus malignant compressive vertebral fractures. The orthopaedic surgeon should be knowledgeable about whole-body MRI principles and its uses in staging multiple myeloma and sarcoma. Knowledge about PET-MRI principles and its limitations as well as its potential use in assessing the subchondral bone plate and bony remodeling is also important. This technique may play a role in the future for predicting progression to osteoarthritis.
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PMID:Advanced MRI Techniques for Assessing Marrow Abnormalities of the Spine. 3201 56

A 72-year-old woman with a prior history of stage IIIa lung adenocarcinoma was seen with complaints of knee pain, swelling, and difficulty sleeping at night for 1 month. Although mimicking osteoarthritis, patellofemoral syndrome, and iliotibial band syndrome, radiographs showed a lytic lesion suspicious for metastatic disease. The right tibial lesion was excised, saphenous neurolysis was performed, and radiation treatment and four cycles of chemotherapy were administered. This case shows the importance of early imaging in patients with knee pain of prolonged duration seen in the primary care setting.
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PMID:Importance of imaging in knee pain. 3206 86

Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementation. This article explores the impact of AI through the entire imaging cycle of musculoskeletal radiology, from the placement of the requisition to the generation of the report, with an added Canadian perspective. Noninterpretive tasks which may be assisted by AI include the ordering of appropriate imaging tests, automatic exam protocoling, optimized scheduling, shorter magnetic resonance imaging acquisition time, computed tomography imaging with reduced artifact and radiation dose, and new methods of generation and utilization of radiology reports. Applications of AI for image interpretation consist of the determination of bone age, body composition measurements, screening for osteoporosis, identification of fractures, evaluation of segmental spine pathology, detection and temporal monitoring of osseous metastases, diagnosis of primary bone and soft tissue tumors, and grading of osteoarthritis.
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PMID:Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report. 3280 57


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