Deep Learning to Differentiate Benign and Malignant Vertebral Fractures at Multidetector CT.

Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materials and Methods CT scans acquired in patients with benign or malignant vertebral fractures from June 2005 to December 2022 at two university hospitals were retrospectively identified based on a composite reference standard that included histopathologic and radiologic information. An internal test set was randomly selected, and an external test set was obtained from an additional hospital. Models used a three-dimensional U-Net encoder-classifier architecture and applied data augmentation during training. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with that of two residents and one fellowship-trained radiologist using the DeLong test. Results The training set included 381 patients (mean age, 69.9 years ± 11.4 [SD]; 193 male) with 1307 vertebrae (378 benign fractures, 447 malignant fractures, 482 malignant lesions). Internal and external test sets included 86 (mean age, 66.9 years ± 12; 45 male) and 65 (mean age, 68.8 years ± 12.5; 39 female) patients, respectively. The better-performing model of two training approaches achieved AUCs of 0.85 (95% CI: 0.77, 0.92) in the internal and 0.75 (95% CI: 0.64, 0.85) in the external test sets. Including an uncertainty category further improved performance to AUCs of 0.91 (95% CI: 0.83, 0.97) in the internal test set and 0.76 (95% CI: 0.64, 0.88) in the external test set. The AUC values of residents were lower than that of the best-performing model in the internal test set (AUC, 0.69 [95% CI: 0.59, 0.78] and 0.71 [95% CI: 0.61, 0.80]) and external test set (AUC, 0.70 [95% CI: 0.58, 0.80] and 0.71 [95% CI: 0.60, 0.82]), with significant differences only for the internal test set (P < .001). The AUCs of the fellowship-trained radiologist were similar to those of the best-performing model (internal test set, 0.86 [95% CI: 0.78, 0.93; P = .39]; external test set, 0.71 [95% CI: 0.60, 0.82; P = .46]). Conclusion Developed models showed a high discriminatory power to differentiate between benign and malignant vertebral fractures, surpassing or matching the performance of radiology residents and matching that of a fellowship-trained radiologist. © RSNA, 2024 See also the editorial by Booz and D'Angelo in this issue.

Muscle Reference Values From Thoracic and Abdominal CT for Sarcopenia Assessment: The Framingham Heart Study.

BACKGROUND: Loss of muscle mass is a known feature of sarcopenia and predicts poor clinical outcomes. Although muscle metrics can be derived from routine computed tomography (CT) images, sex-specific reference values at multiple vertebral levels over a wide age range are lacking. OBJECTIVE: The aim of this study was to provide reference values for skeletal muscle mass and attenuation on thoracic and abdominal CT scans in the community-based Framingham Heart Study cohort to aid in the identification of sarcopenia. MATERIALS AND METHODS: This secondary analysis of a prospective trial describes muscle metrics by age and sex for participants from the Framingham Heart Study without prior history of cancer who underwent at least 1 CT scan between 2002 and 2011. Using 2 previously validated machine learning algorithms followed by human quality assurance, skeletal muscle was analyzed on a single axial CT image per level at the 5th, 8th, 10th thoracic, and 3rd lumbar vertebral body (T5, T8, T10, L3). Cross-sectional muscle area (cm 2 ), mean skeletal muscle radioattenuation (SMRA, in Hounsfield units), skeletal muscle index (SMI, in cm 2 /m 2 ), and skeletal muscle gauge (SMRA·SMI) were calculated. Measurements were summarized by age group (<45, 45-54, 55-64, 65-74, ≥75 years), sex, and vertebral level. Models enabling the calculation of age-, sex-, and vertebral-level-specific reference values were created and embedded into an open access online Web application. RESULTS: The cohort consisted of 3804 participants (1917 [50.4%] males; mean age, 55.6 ± 11.8 years; range, 33-92 years) and 7162 CT scans. Muscle metrics qualitatively decreased with increasing age and female sex. CONCLUSIONS: This study established age- and sex-specific reference values for CT-based muscle metrics at thoracic and lumbar vertebral levels. These values may be used in future research investigating the role of muscle mass and attenuation in health and disease, and to identify sarcopenia.

Association of Sarcopenia With Toxicity-Related Discontinuation of Adjuvant Endocrine Therapy in Women With Early-Stage Hormone Receptor-Positive Breast Cancer.

PURPOSE: Sarcopenia, an age-related decline in muscle mass and physical function, is associated with increased toxicity and worse outcomes in women with breast cancer (BC). Sarcopenia may contribute to toxicity-related early discontinuation of adjuvant endocrine  therapy (aET) in women with hormone receptor-positive (HR+) BC but remains poorly characterized. METHODS AND MATERIALS: This multicenter, retrospective cohort study included consecutive women with stage 0-II HR+ BC who received breast conserving therapy (lumpectomy and radiation therapy) and aET from 2011 to 2017 with a 5-year follow-up. Skeletal muscle index (SMI, cm2/m2) was analyzed using a deep learning model on routine cross-sectional radiation simulation imaging; sarcopenia was dichotomized according to previously validated reports. The primary endpoint was toxicity-related aET discontinuation; logistic regression analysis evaluated associations between SMI/sarcopenia and aET discontinuation. Cox regression analysis evaluated associations with time to aET toxicity, ipsilateral breast tumor recurrence (IBTR), and disease-free survival (DFS). RESULTS: A total of 305 women (median follow-up, 89 months) were included with a median age of 67 years and early-stage BC (12% stage 0, 65% stage I). A total of 60 (20%) women experienced toxicity-related aET discontinuation. Sarcopenia was associated with toxicity-related early discontinuation of aET (odds ratio, 2.18; P = .036) and shorter time to aET toxicity (hazard ratio [HR], 1.62; P = .031). SMI or sarcopenia were not independently associated with IBTR or DFS; toxicity-related aET discontinuation was associated with worse IBTR (HR, 9.47; P = .002) and worse DFS (HR, 4.53; P = .001). CONCLUSIONS: Among women with early-stage HR+ BC who receive adjuvant radiation therapy and hormone therapy, sarcopenia is associated with toxicity-related early discontinuation of aET. Further studies should validate these findings in women who did not receive adjuvant radiation therapy. These high-risk patients may be candidates for aggressive symptom management and/or alternative treatment strategies to improve outcomes.

Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features.

PURPOSE: To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs). METHODS: A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs. RESULTS: Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs. CONCLUSION: Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.

Surgical Studies of Reconstructive Breast Surgery - An Overview of the Topics at the 2019 Annual Meeting of the Working Group for Reconstructive Surgery in Oncology-Gynecology.

This year's annual AWOgyn meeting focused on studies of reconstructive breast surgery. As the majority of breast reconstructions are implant-based, most studies also focused on implant-based reconstruction. Since 2011, the guidelines have recommended using interposed mesh materials as support. The basic idea behind every type of material is to provide coverage and stabilization for the implant by constructing an "internal bra" which will create the appropriate implant shape and maintain the position, stability and flexibility of the implant. The Working Group for Reconstructive Surgery in Oncology-Gynecology (AWOgyn) has undertaken to analyze different materials with regard to indications, success rates and side effects as documented in registers, clinical assessments and study protocols. This has increased application safety and is expected to improve it even further in future. Prospective studies are being carried out to investigate issues such as the optimal material, optimal implant site and best cosmetic results. The first results for porcine and human acellular matrices and for partially resorbable titanium-coated synthetic polypropylene meshes are now available. In 2019, the AWOgyn working group will be launching further studies to evaluate a perforated acellular dermal matrix (Fortiva ® ), a titanium-coated implant pocket (TiLOOP ® Bra Pocket) and a fully resorbable synthetic mesh (TIGR ® mesh).