With the rapid growth of the use of computed tomography, advances in artificial intelligence enable opportunistic screening, the systematic extraction of clinically meaningful biomarkers from imaging scans performed for other indications. Modeling studies demonstrate that opportunistic screening can be highly cost effective by enabling early intervention and preventing complications such as osteoporotic fractures. Musculoskeletal radiologists are uniquely positioned to contribute to this paradigm shift because routine examinations frequently include vertebrae, skeletal muscle, adipose tissue, and vasculature, all structures that provide quantitative data on bone mineral density, sarcopenia, adiposity, and cardiovascular risk. However, widespread implementation faces challenges, such as the need for prospective outcomes data, normative reference standards, workflow integration, and clear pathways for clinical follow-up. This review examines the rationale, technical foundations, key applications, and challenges for opportunistic screening in musculoskeletal radiology.
Shear wave elastography (SWE) is an increasingly investigated ultrasound-based technique in musculoskeletal imaging that provides quantitative information on tissue stiffness and biomechanical properties. This narrative review aims to summarize the basic principles, technical considerations, current clinical applications, limitations, and future perspectives of SWE in musculoskeletal imaging. Unlike conventional grayscale and Doppler ultrasonography, which mainly assess morphology and vascularity, SWE may provide additional functional information in major musculoskeletal tissues, including tendons and ligaments, skeletal muscles, peripheral nerves, fibrocartilaginous structures, plantar fascia, and selected soft tissue lesions. Current evidence suggests potential roles for SWE in detecting early biomechanical alterations, assessing disease severity, differentiating symptomatic from asymptomatic tissues, and monitoring response to treatment or rehabilitation. However, musculoskeletal tissues are anisotropic, viscoelastic, and position-dependent; as a result, SWE measurements are influenced by acquisition-related factors, tissue biomechanics, positioning and loading conditions, region of interest (ROI) placement, tissue depth, and device-related variability. For this reason, SWE findings should not be interpreted as standalone diagnostic criteria but should be considered together with clinical findings, conventional ultrasonography, MRI, electrophysiology, histopathology, and patient-centered outcomes when appropriate. This review highlights the need for tissue-specific measurement protocols, standardized reporting, normative reference data, inter-vendor harmonization, and longitudinal validation against clinically meaningful outcomes before SWE can be more reliably integrated into routine musculoskeletal imaging and rehabilitation practice.
Depression and reduced muscle mass are common in patients with lung cancer, and both are associated with adverse clinical outcomes. However, data examining their interrelationship remain limited. This cross-sectional study included 100 patients with newly diagnosed lung cancer undergoing systemic therapy. Depressive symptoms were assessed using the Beck Depression Inventory (BDI). Skeletal muscle mass was evaluated using computed tomography (CT)-derived skeletal muscle index (SMI) at the L3 vertebral level. Clinical and demographic variables were analyzed for associations with depression. Clinically relevant depressive symptoms were identified in 71% of patients, and CT-defined low skeletal muscle mass (LSMM) was present in 67% of patients. Scores on BDI were significantly and inversely correlated with total muscle area (TMA; r = -0.418, p < 0.001) and skeletal muscle index (r = -0.358, p < 0.001). In age-adjusted multivariate analysis, low SMI (OR 3.05, 95% CI 1.15-8.09) and higher Charlson Comorbidity Index (CCI) values (OR 3.27, 95% CI 1.25-8.68) were independently associated with depression. Depressive symptoms are highly prevalent in lung cancer and are independently associated with CT-defined LSMM and a higher comorbidity burden. Routine assessment of SMI on staging CT scans may help identify patients at increased risk and support early multidisciplinary interventions.
To develop a fully automated 2D nnU-Net pipeline for multi-class skeletal muscle segmentation (psoas, paraspinal, and abdominal wall) at the third lumbar (L3) vertebral level, and to quantitatively evaluate its diagnostic performance and reliability compared to manual segmentation. A 2D nnU-Net was trained on 164 axial L3 CT slices from the multi-institutional AMOS22 dataset, spanning diverse abdominal pathologies and multivendor imaging. To assess generalizability under severe anatomical distortion, independent external validation was performed in 50 consecutive patients with advanced liver disease from a single institution (January-December 2025; mean age, 63 ± 15 years; 32 women, 18 men), of whom 88% had moderate-to-severe ascites. Model stability was examined by comparing a five-fold ensemble with the best-performing single-fold model. Intra-observer reliability of the manual reference standard was evaluated in a random subset of 30 cases. Inter-observer agreement was additionally assessed using an independent second reader. Performance metrics included the Dice Similarity Coefficient (DSC), Pearson correlation coefficient (r), and Bland-Altman analysis for cross-sectional areas and mean attenuation. The inference workflow was deployed via a custom Streamlit-based graphical user interface (GUI). In this anatomically complex external validation cohort, the 5-fold ensemble 2D nnU-Net achieved an overall mean DSC of 0.937 ± 0.043 (95% CI, 0.925-0.950), with 80% of cases achieving a mean DSC ≥ 0.90. While the mean DSC was statistically comparable to the best single-fold model (0.937, [95% CI, 0.921-0.952], p = 0.736), the ensemble strategy increased the minimum observed DSC (worst-case performance) from 0.720 to 0.822. Class-specific external validation performance for the 5-fold ensemble was highest for the paraspinal muscles (DSC: 0.960; 95% CI, 0.952-0.967), followed by the psoas muscles (DSC: 0.941; 95% CI, 0.927-0.956), and lowest for the anatomically complex abdominal wall muscles (DSC: 0.911; 95% CI, 0.893-0.929). Comparison between the ensemble model and manual segmentation yielded a Pearson correlation of r = 0.955 (p < 0.001) for total skeletal muscle area, with a mean bias of +7.17 cm2. Intra- and inter-observer agreements for the manual reference standard demonstrated correlation coefficients of r = 0.995 and 0.090 for total areas, respectively. The automated pipeline required 3-5 s per case for inference and quantitative reporting, compared to 3-5 min for manual segmentation. In patients with advanced liver disease and substantial anatomical distortion from ascites, an ensemble-based 2D nnU-Net provides high quantitative agreement with manual L3 skeletal muscle segmentation, while mitigating lower-bound (worst-case) errors relative to single-fold models. Integration with a dedicated GUI enables substantial time savings and supports scalable quantitative body composition measurement.
Artificial intelligence (AI) is increasingly reshaping musculoskeletal (MSK) imaging across the entire imaging pathway. This narrative review summarizes current AI applications in MSK radiology across four domains: acquisition and reconstruction, detection and triage, characterization and quantification, and prognosis and decision support. AI-based reconstruction has enabled faster MRI acquisitions, improved denoising and artifact reduction, and supported low-dose CT imaging while preserving diagnostic quality. Fracture detection and triage currently represent the most mature clinical applications, particularly in emergency settings. AI is also promoting a shift from qualitative interpretation to quantitative imaging phenotyping through automated assessment of body composition, cartilage, bone density, degenerative spine disease, skeletal maturity, and lesion heterogeneity. Emerging applications in prognostic modeling, implant evaluation, and multimodal risk stratification remain promising but less mature. Broader clinical implementation is still limited by restricted interpretability, dataset bias, insufficient prospective validation, regulatory complexity, and unresolved medico-legal issues. Overall, AI should be viewed as a tool to augment, not replace, radiological expertise.
99mTc-pyrophosphate (PYP) scintigraphy is widely used for the noninvasive evaluation of transthyretin cardiac amyloidosis. Although interpretation primarily focuses on myocardial uptake, confirmation of appropriate systemic radiotracer biodistribution is essential. We report a case in which an examination presumed to be 99mTc-PYP scintigraphy demonstrated free 99mTc-pertechnetate-like biodistribution. A 75-year-old woman with chronic kidney disease and conduction disturbance underwent 99mTc-PYP scintigraphy for suspected cardiac amyloidosis. The initial study, recorded as the administration of 740 MBq 99mTc-PYP, was imaged 3 h after injection. Planar imaging showed mild apparent activity over the cardiac region; however, SPECT/CT demonstrated no definite myocardial uptake. Instead, intense uptake was observed in the stomach and thyroid gland, with complete absence of skeletal activity. This distribution was inconsistent with correctly administered 99mTc-PYP and suggested free 99mTc-pertechnetate biodistribution, likely due to radiopharmaceutical preparation or administration error. A repeat 99mTc-PYP scan 1.5 months later showed expected skeletal uptake without gastric or thyroid activity and again demonstrated no myocardial uptake. The study was interpreted as negative for cardiac amyloidosis. Gastric and thyroid uptake with absent skeletal activity on presumed 99mTc-PYP scintigraphy should be considered nondiagnostic rather than negative.
Though subgroup performance reporting helps ensure the safety of artificial intelligence (AI) products, the extent of this reporting remains unclear. This scoping review identifies studies validating commercially available AI-based products and reports the trends in performance reporting across sex, age, and race/ethnicity demographic subgroups. Peer-reviewed validation studies of commercially available products published after 2010 were collected from the Health AI Register and PubMed on 29 November 2024. Study trends in the reporting of sex, age, and race/ethnicity were mapped with regression analysis. We apply the Wilson confidence interval equation to estimate which tuberculosis detection studies are underpowered for subgroup meta-analysis. Three hundred ninety-two of 545 studies validating 252 products reported subgroup demographic data for any of the three groups. Only 77 of these presented subgroup performance results. Skeletal (20/88) and lung (30/139) studies, and those utilizing chest (24/79) or bone (19/63) radiographs, most often presented subgroup performance data. We found no evidence that more recent studies (OR: 1.039 [95% CI: 0.959-1.127]) or company sponsorship (OR: 1.010 [95% CI: 0.492-1.920]) led to increased subgroup reporting. We show that 14/21 tuberculosis datasets may be underpowered for post-hoc subgroup meta-analysis. This scoping review quantifies how fragmented the commercial validation landscape is, showing that reporting for both the demographics and per-subgroup performance is inadequate for estimating subgroup bias. This systemic problem requires effort from all stakeholders, from researchers to regulatory agencies, encouraging thorough reporting and commercial product validation to support physician and patient trust in medical AI products. Question The number of studies validating the performance of each commercially available radiology AI product for minority subgroup bias is unclear. Findings The currently available commercial AI validation studies often neglect to describe demographic subgroup data, and fewer provide performance results per subgroup, prohibiting algorithmic bias meta-analysis. Clinical relevance Physician and patient trust in the medical AI already used clinically must be built on peer-reviewed literature and meta-analysis. The current literature is insufficient for determining the safety and performance of these products for demographic minorities.
Extraskeletal osteosarcoma (ESOS) is a rare high-grade malignant mesenchymal tumor that produces osteoid in soft tissue without skeletal involvement. Subcutaneous ESOS is exceptionally uncommon and may mimic benign soft tissue lesions. A 45-year-old Samoan man presented with a one-year history of a progressively enlarging, painless left gluteal mass. Contrast-enhanced CT showed a well-circumscribed subcutaneous lesion measuring 9.1 × 10.4 × 8.6 cm, with mild to moderate delayed enhancement and no osseous involvement. The tumor was surgically excised. Histopathology showed a spindle to pleomorphic high-grade sarcoma with osteoid produced by atypical tumor cells. Immunohistochemistry showed SATB2 positivity and negative staining for S100, SOX10, pan-cytokeratin, and ERG. The tumor involved the deep resection margin, consistent with R1 resection; the closest margin distance was not specified. The patient declined adjuvant therapy and was subsequently lost to follow-up. Subcutaneous ESOS is rare and diagnostically challenging. This case highlights the importance of imaging, histopathology, immunohistochemistry, and multidisciplinary management, particularly in resource-limited settings.
HACD1-related congenital myopathy is an ultra-rare congenital myopathy recently described in families from the Middle East or Asia. Clinical phenotype has been described, but little is known about the pattern of muscle MRI abnormalities. We describe four Brazilian patients from unrelated families including not only clinico-pathological, but also muscle MRI findings. All patients shared the known pattern of early onset of motor deficits combined with respiratory distress, later followed by remarkable improvement. Muscle biopsy revealed congenital fiber type disproportion. The same pathogenic biallelic HACD1 variant was found in all cases (c.373_375+2delGAGGT). Three out of the 4 patients underwent muscle MRI, which revealed symmetrical fatty infiltration predominantly affecting the pelvic girdle and the posterior compartment of lower limbs; anterior compartment of thighs and legs was essentially preserved. The current description expands the geographical landscape of the disease and refines its phenotypical characterization by presenting the pattern of muscle MRI abnormalities.
Analysis of dynamic phosphorus magnetic resonance spectroscopy (31P MRS) data is often hindered by variability in data quality. A quality control (QC) pipeline developed by Naëgel (2023) introduced six key parameters to ensure reliable 31P MRS results in large clinical datasets. This study tested the transferability of this QC scoring (QCS_REF) to two different research sites equipped with 3T and 7T MR systems and different ergometers. Twelve groups with the focus on frail and elderly subjects and patients with neurodegenerative diseases were included. The application of QCS_REF limits led to the improvement of the statistical power in some patient groups, but to the exclusion of substantial data for all our groups and experimental settings at both 3T and 7T. Only 28% of all recovery and exercise period data at 3T and 21% at 7T passed QCS_REF inclusion criteria. Therefore, two new sets of quality control criteria, QCS1 and QCS2, were proposed, reflecting achieved SNR of individual MR signalsand the patient phenotype included. We showed that the transferability of the QCS_REF did not depend on the magnetic field, the coil, or localization scheme. The new QCS did not significantly influence the mean recovery and exercise time constants of each group compared to QCS_REF. We verified that six proposed key parameters were adequate for an objective assessment of the quality of dynamic 31P MRS measurement at 3T as well as 7T. However, the patient group characteristics and experimental set-up significantly affect the ability to meet dynamic 31P MRS quality control thresholds, supporting the use of flexible QC criteria for robust data acquisition across diverse clinical populations.
Esophagectomy and pancreatectomy are invasive oncological surgeries with elevated mortality rates. Preoperative muscle mass deficit and myosteatosis, identifiable on L3 CT-scan, could be associated with poor postoperative outcomes in cancer patients. We aimed to determine their impact on short-term complications following esophageal or pancreatic cancer resection. We conducted a retrospective cohort study in two hospital centers from January 2018 to February 2023. Adult patients undergoing esophagectomy or pancreatectomy for cancer with a preoperative CT scan at L3 level were included. Muscle skeletal mass and quality were assessed using previously published thresholds. Poor postoperative short-term outcomes were defined as the occurrence of sepsis, septic shock, or death within 90 days postoperatively. Of 216 patients, 165 patients were eligible for muscle mass analysis and 143 for muscle quality assessment. Poor short outcome occurred in 55 patients (33.3%). Surprisingly, skeletal muscle depletion was inversely associated with poor outcomes in the multivariate logistic regression model (OR 0.38, 95% CI [0.15-0.97], p = 0.04). Myosteatosis was associated with a significantly 6-fold increased risk of poor short-term outcomes in univariate analysis (OR 6.04, 95% CI [2.35-15.55], p < 0.001), with a persistent trend in the multivariate logistic regression model (OR 3.51, 95% CI [0.99-12.39], p = 0.05). In the ICU subgroup, patients with preoperative skeletal muscle depletion and myosteatosis had higher 28-day mortality than those with preserved muscle mass and quality. Myosteatosis, rather than muscle mass deficit, showed a strong trend toward association with adverse postoperative short-term outcomes following esophagectomy and pancreatectomy. Defining population-specific thresholds for CT scan muscle assessment are necessary to improve the use of L3 scans for short-term outcome risk evaluation in oncologic surgery.
This review examines the role of marrow adipose tissue fatty acid composition in skeletal homeostasis, focusing on osteoporosis. To avoid conceptual confusion, we define evidence tiers: human studies assessing endogenous marrow lipid profiles; mechanistic studies applying exogenous fatty acids in vitro and in vivo; indirect MRI-based surrogates of marrow fat quantity; and direct ex vivo measurements of fatty acid composition via GC-MS, LC-MS, and lipidomics. We also clarify the distinction between marrow fat quantity and fatty acid quality. Human data reveal disease-, age-, and site-related alterations in marrow lipid saturation and unsaturation; however, findings vary by skeletal site, marrow compartment, fracture status, analytical platform, and study population. Experimental evidence demonstrates that saturated fatty acids (e.g., palmitic acid) induce lipotoxicity and osteoblast dysfunction, whereas unsaturated fatty acids (e.g., oleic acid and n-3 polyunsaturated fatty acids) exert protective effects via modulation of mesenchymal stem cell differentiation, osteoclastogenesis, ferroptosis, autophagy, and mitochondrial metabolism. Collectively, current evidence supports an association between marrow fatty acid biology and osteoporotic bone loss. Causal, diagnostic, and therapeutic implications remain preliminary. This review's main contribution is a fatty-acid-centered framework that integrates evidence tiers, molecular categories, and skeletal-site heterogeneity, guiding future research.
Sarcopenia is a known negative prognostic factor in oncology and is frequently observed in patients with pancreatic ductal adenocarcinoma (PDAC). Computed tomography (CT) enables longitudinal muscle assessment and may provide additional prognostic information. This study aims to assess their association with prognosis in pancreatic cancer and explore the diagnostic and prognostic value of CT radiomics. A retrospective single-center study included 62 patients with primary PDAC who underwent at least three abdominal CT scans: baseline (t0), 3 months (t1), 6 months (t2), and, in 35 patients, 12 months (t3). CT-based sarcopenia was assessed using the psoas muscle index (PMI) based on reference cutoffs and cohort-specific sex-specific quartiles. Skeletal muscles at the L3 level were semi-automatically segmented. Radiomic features of the psoas were extracted and analyzed using k-nearest neighbor, decision tree, and random forest models. Prognostic relevance was evaluated using logistic regression and feature selection via least absolute shrinkage and selection operator (LASSO) regression. Tumor progression was assessed radiologically according to RECIST 1.1 criteria. CT-based sarcopenia prevalence was 45.3% using reference-based PMI thresholds. PMI declined significantly from baseline to t1 and remained stable thereafter, with women exhibiting consistently lower values. Outcome analysis showed a higher proportion of disease progression at t1 in sarcopenic patients using reference cutoffs, whereas cohort-specific quartiles demonstrated no consistent differences. Random forest models predicted sarcopenia with up to 0.73 accuracy and receiver operating characteristics area under the curve (ROC-AUC) of 0.81. LASSO regression identified the psoas short axis and cross-sectional area as the most informative features. Logistic regression using baseline radiomic features predicted disease progression status at 12 months with 0.85 accuracy, weighted F1 0.841, and AUC 0.823. Interobserver agreement for psoas measurements was high (r = 0.86). Longitudinal CT-based assessment of PMI demonstrates progressive sarcopenia within the studied PDAC cohort, with sex-specific declines. Radiomic analysis of skeletal muscle provides complementary information and predictive insights, highlighting their potential to enhance the characterization of muscle status and its association with disease course in patients able to undergo repeated imaging.
This study aims to investigate the association between baseline CT-derived body composition parameters and progression-free survival (PFS) in patients with metastatic colorectal cancer (mCRC) receiving immunotherapy, and to develop a personalized clinico-radiological prognostic model. We retrospectively enrolled 107 mCRC patients treated with immune checkpoint inhibitors. Baseline abdominal CT images at the third lumbar vertebra were segmented to quantify visceral fat area (VFA), subcutaneous fat area (SFA), skeletal muscle area (SMA), and skeletal muscle radiodensity (SMD). Independent prognostic factors for PFS were identified via univariable and multivariable Cox regression analyses and further validated using the Least Absolute Shrinkage and Selection Operator (LASSO). Exploratory subgroup interaction analyses were conducted to evaluate prognostic consistency. Finally, a predictive nomogram was developed and performance was assessed via C-index, time-dependent receiver operating characteristic (ROC) curves, calibration plots with 1000 bootstrap resamples, and decision curve analysis (DCA). Multivariable and LASSO Cox regression analyses identified age, number of metastatic organs, baseline CA 19-9 level, VFA, and SMD as potential independent prognostic factors for PFS. Higher VFA and SMD were significantly associated with prolonged PFS. Notably, subgroup analyses revealed that the protective effect of high VFA was more evident in patients with baseline liver metastasis (P for interaction < 0.05). After internal validation with 1000 bootstrap resamples, the optimism-corrected C-index was 0.733 after optimism correction. The time-dependent area under the curve (AUC) for predicting 6-month PFS was 0.842 (95 % CI: 0.761-0.923). Calibration curves demonstrated good agreement between nomogram-predicted probabilities and actual observed survival outcomes. DCA suggested potential clinical net benefit of the developed nomogram. Baseline VFA and SMD are potential independent prognostic biomarkers for mCRC patients undergoing immunotherapy. The proposed clinico-radiological model may offer promising predictive accuracy for PFS, help personalized risk stratification and clinical decision-making.
This report aims to raise clinical awareness of a rare case of Maffucci syndrome in a 34-year-old female presenting with diffuse intraductal breast papillomatosis. It further explores a possible hypothesis-generating pathogenic association between Maffucci syndrome and a specific subtype of papillary breast neoplasia in the context of shared molecular pathways involved in disease pathogenesis. Maffucci syndrome is a rare, congenital, nonhereditary disorder characterized by enchondromas, hemangiomas, and skeletal deformities, typically presenting in early childhood. Since its initial description in 1881, fewer than 300 cases have been reported, with an estimated prevalence of < 1 in 27 million. Somatic mutations in IDH1 and IDH2 are key drivers of Maffucci syndrome and are also implicated in malignancies such as gliomas, chondrosarcomas, intrahepatic cholangiocarcinoma, and acute myeloid leukemia. Most relevant to this case, tall cell carcinoma with reversed polarity (TCCRP)-a rare subtype of papillary breast carcinoma-is characteristically associated with hotspot IDH2 R172 mutations, a molecular feature otherwise uncommon in both breast carcinomas and Maffucci syndrome. Only one documented case of an IDH1-mutated solid papillary carcinoma with reversed polarity (SPCRP) exists in the current literature. The patient is a 34-year-old female with Maffucci syndrome, diagnosed in childhood via clinical and radiographic evaluation, with a history of skeletal and vascular complications, including primary chondrosarcomas of the left scapula, right distal patella, and right proximal tibia. She initially presented in 2016 with left-sided hemorrhagic nipple discharge, prompting serial imaging and biopsies that identified recurrent intraductal papillary lesions, consistently benign on core needle biopsy and surgical excision. A right-sided lesion was excised in 2022, followed by two left-sided lesions resected in 2023 and 2024. Mammography in November 2024 revealed three nodular lesions in the left lateral breast, characterized as complex cystic masses, with core needle biopsy confirming benign intraductal papillomas without atypia. Although subsequent imaging demonstrated stability of these lesions on follow-up ultrasound in June 2025, the longitudinal course illustrates a recurrent pattern of intraductal papillary lesion development over time. This case raises the possibility of an association between Maffucci syndrome and recurrent papillary breast lesions. In the absence of molecular confirmation, this relationship remains speculative and should be regarded as hypothesis generating, underscoring the need for further investigation with genetic and immunohistochemical correlation rather than changes to established breast cancer screening guidelines.
Advanced epithelial ovarian cancer (EOC) is commonly diagnosed at late stages, and despite improvements with neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS), prognosis remains poor. Body composition has emerged as an important determinant of treatment outcomes. Low muscle mass (LMM), particularly in obese patients with low muscle mass (OwLMM), may combine impaired functional reserve with adverse metabolic effects. However, its role in NACT outcomes in EOC remains unclear. In this retrospective study, 52 overweight or obese women with FIGO stage III-IV EOC treated with platinum-taxane-based NACT and IDS between 2019 and 2022 were analyzed. Skeletal muscle mass was assessed on baseline computed tomography scans at the third lumbar vertebra (L3), and the lumbar skeletal muscle index (LSMI) was calculated. Patients were classified as LMM or high muscle mass (HMM) using the cohort median cut-off. Patients with both obesity and LMM were categorized as OwLMM. Treatment response was evaluated using RECIST v1.1 criteria. The median follow-up was 10.2 months (95% CI, 7.8-12.6). Median disease-free survival (DFS) from IDS was 19.3 months (95% CI, 8.5-30.1). OwLMM patients had lower objective response rates (46.7% vs. 62.2%, p = 0.02) and inferior disease control rates (80% vs. 100%, p = 0.03) compared with other groups. BMI or LMM alone showed no significant effect. Overall survival (OS) analysis was premature due to the short follow-up period. Low muscle mass, especially when combined with obesity, was associated with reduced NACT response and inferior disease control in advanced EOC. As sarcopenic obesity represents a key component of frailty in older adults, our findings support muscle mass assessment as a clinically relevant biomarker that may improve risk stratification and guide individualized therapy in geriatric oncology.
Congenital Generalized Lipodystrophy (CGL), usually caused by pathogenic variants in AGPAT2 (CGL1) and BSCL2 (CGL2), is characterized by near-total loss of subcutaneous adipose tissue, low leptin levels and severe metabolic and systemic comorbidities. Skeletal abnormalities including diffuse sclerosis, lytic-appearing bone lesions, and high bone mineral density have been recognized in CGL, but the prevalence and clinical and radiological features of these bone phenotypes remain ill-defined. The aim of this single-institution case series and systematic review was to evaluate bone manifestations and radiological findings associated with CGL1 and CGL2. Data sources were PubMed, Scopus, Embase, CINAHL Plus, Global Index Medicus, Web of Science: Core, National Institutes of Health medical records. Articles were screened utilizing a dual reviewer process in Covidence. Included publications reported primary bone and radiologic findings in patients with CGL1 or CGL2. Two reviewers extracted data using REDCap and assessed risk of bias. 43 articles were included in the review, presenting 214 cases of CGL (90 CGL1, 81 CGL2, and 43 genetics not reported). Data from NIH patients was extracted by retrospective chart review. The NIH cohort had 60 CGL patients (40 CGL1, 20 CGL2). Skeletal imaging included radiographs, MRI, CT, and NaF PET scans. In the literature and NIH cases, respectively, diffuse osteosclerosis was reported in 37% and 39%, lytic-appearing lesions in 64% and 53%, and high bone mineral density in 68% and 43%. Individuals with CGL1 and CGL2 present with distinct and heterogeneous bone phenotypes including lytic-appearing lesions primarily affecting long bones, diffuse osteosclerosis, and high bone mineral density. These bone manifestations are often overlooked despite high prevalence and clinical relevance. Potential mechanisms include increased differentiation of bone marrow mesenchymal cells into osteocytes and effects of increased insulin or decreased leptin signaling. Congenital Generalized Lipodystrophy (CGL) is a rare inherited disorder in which patients lack fat tissue. This causes extremely low levels of leptin, a hormone important for regulating metabolism, and can lead to serious health complications such as diabetes, metabolic dysfunction-associated steatotic liver disease, and heart problems. Two common forms of the disease are caused by changes in the AGPAT2 gene (CGL1) and the BSCL2 gene (CGL2). This study examined bone health in people with CGL by reviewing 37 published research articles and medical records from 60 patients evaluated at the National Institutes of Health (NIH) to better understand how the disease affects the skeleton. The study found that bone abnormalities are very common in both CGL1 and CGL2. Many patients had unusually dense or hardened bones, while others developed bone lesions that appeared like holes or areas of bone loss on imaging scans. Higher-than-normal bone density was also frequently observed. These bone changes often affected the long bones of the arms and legs. These bone conditions may result from abnormal bone cell development and/or from hormone imbalances linked to CGL, including low leptin and high insulin levels. Because these bone complications are common but often overlooked, the study highlights the importance of monitoring bone health and continuing research into the mechanisms behind bone abnormalities in people living with CGL.
Transarterial chemoembolization (TACE) is a standard treatment for patients with unresectable hepatocellular carcinoma (HCC), yet existing models provide limited individualized risk stratification. Automated CT-derived body composition analysis has emerged as an objective marker of patient physiological reserve, but its value in prognostication in TACE patients is insufficiently studied. Therefore, the aim of the study was to evaluate the prognostic value of a fully automated, open-source pipeline for CT-based body composition analysis in predicting overall survival (OS) in patients with HCC undergoing TACE. In this study, we used two independent cohorts of treatment-naive patients undergoing TACE: the WAW-TACE cohort (development; n = 230, OS: 28.6 months) and the HCC-TACE-Seg cohort (validation; n = 100, OS: 24.0 months). Skeletal muscle and fat metrics were extracted from pre-treatment CTs using a standardized deep learning pipeline and normalized by sex. Survival analyses were performed using Cox proportional hazards (CoxPH) models and random survival forests (RSF). Skeletal muscle density (SMD) at the L3 level was the strongest and independent predictor of OS across both cohorts (HR: development, 0.84; p = 0.029; validation, 0.79; p = 0.028). This association remained significant after adjustment for the best-performing clinical composite scores: mHAP-2 in the development (adjusted HR = 0.68; p = 0.049) and CLIP in the validation cohort (adjusted HR = 0.43; p = 0.003). In CoxPH, the addition of SMD metrics resulted in only modest improvements in discrimination (ΔC-index 0.011-0.037) that did not reach statistical significance. In contrast, RSF analysis demonstrated a statistically significant improvement in model discrimination when muscle-based variables were added to clinical features (ΔC-index = 0.023; p < 0.001). In both cohorts, SMD showed a reproducible independent prognostic association with overall survival. While adding SMD to traditional clinical models resulted in only modest, and in Cox-based analyses not statistically significant, improvements in discrimination, SMD provided complementary prognostic information. This suggests that the primary value of these automated CT-derived body composition metrics lies not in their performance as standalone predictors, but in their ability to provide an additional layer of objective biological data that may contribute to risk stratification in a complementary and exploratory manner within multivariable frameworks. Notably, in internally cross-validated RSF analyses, statistically significant increases in model discrimination were observed when muscle-based features were integrated into the model, highlighting their potential complementary value within machine learning frameworks.
To assess nomenclature variability and develop recommendations on standardized MRI reporting of meniscal findings. The Society of Skeletal Radiology identified standardized MRI reporting of knee menisci as an important topic for study and invited all members to serve on a panel to provide consensus recommendations. The Society empaneled 12 musculoskeletal radiologists and 2 orthopaedic surgeons. The panel reviewed published literature (PubMed, Scopus, and Embase) using predetermined criteria for inclusion (peer-reviewed, English-language, human studies) and exclusion (case reports, conference abstracts, book chapters, expert opinions, and commentaries). Literature analysis focused on nomenclature relevant to MRI reporting in two general domains: (I) meniscal anatomy and anatomic variants and (II) meniscal tears and associated findings. Substantial nomenclature variability was identified across both domains. For anatomy and variants, inconsistencies involved root zone definitions, vascular zone descriptors, perimeniscal stabilizer terminology (including eponyms), and diagnostic criteria for the discoid meniscus. For tears and associated findings, variability involved numerical grading systems, the terms "complete" and "incomplete," extrusion measurement methodology, and interpretive labels (e.g., "traumatic," "degenerative," "repairable," "stable"). Ten consensus recommendations for standardized MRI reporting were developed. Descriptive anatomic language-specifying what is seen and where-can be more reproducible and clinically actionable than reporting variable numerical grades and evolving classifications. Adoption of our ten pragmatic recommendations has the potential to reduce miscommunication, improve inter-institutional consistency, and support clinical research.