Proteomic Characterization of Undifferentiated Small Round Cell Sarcomas With EWSR1 and CIC::DUX4 Translocations Reveals Diverging Tumor Biology and Distinct Diagnostic Markers.

Undifferentiated small round cell sarcomas (USRS) of bone and soft tissue are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n = 42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n = 24) or EWSR1::ERG fusions (n = 4), sarcomas with an EWSR1 rearrangement (n = 2), CIC::DUX4 fusion (n = 8), as well as tumors classified as USRS with no genetic data available (n = 4). Proteins extracted from formalin-fixed, paraffin-embedded pretherapeutic biopsies were analyzed qualitatively and quantitatively using shotgun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in 2 cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas, and BACH2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study, and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expressions of these 3 markers in the 2 genetic groups were further validated in an independent cohort of n = 34 USRS. Finally, our proteomic results point toward diverging signaling pathways in the different USRS subgroups.

Diagnosis of joint invasion in patients with malignant bone tumors: value and reproducibility of direct and indirect signs on MR imaging.

OBJECTIVES: To evaluate the performance and reproducibility of MR imaging features in the diagnosis of joint invasion (JI) by malignant bone tumors. METHODS: MR images of patients with and without JI (n = 24 each), who underwent surgical resection at our institution, were read by three radiologists. Direct (intrasynovial tumor tissue (ITT), intraarticular destruction of cartilage/bone, invasion of capsular/ligamentous insertions) and indirect (tumor size, signal alterations of epiphyseal/transarticular bone (bone marrow replacement/edema-like), synovial contrast enhancement, joint effusion) signs of JI were assessed. Odds ratios, sensitivity, specificity, PPV, NPV, and reproducibilities (Cohen's and Fleiss' κ) were calculated for each feature. Moreover, the diagnostic performance of combinations of direct features was assessed. RESULTS: Forty-eight patients (28.7 ± 21.4 years, 26 men) were evaluated. All readers reliably assessed the presence of JI (sensitivity = 92-100 %; specificity = 88-100%, respectively). Best predictors for JI were direct visualization of ITT (OR = 186-229, p < 0.001) and destruction of intraarticular bone (69-324, p < 0.001). Direct visualization of ITT was also highly reliable in assessing JI (sensitivity, specificity, PPV, NPV = 92-100 %), with excellent reproducibility (κ = 0.83). Epiphyseal bone marrow replacement and synovial contrast enhancement were the most sensitive indirect signs, but lacked specificity (29-54%). By combining direct signs with high specificity, sensitivity was increased (96 %) and specificity (100 %) was maintained. CONCLUSION: JI by malignant bone tumors can reliably be assessed on preoperative MR images with high sensitivity, specificity, and reproducibility. Particularly direct visualization of ITT, destruction of intraarticular bone, and a combination of highly specific direct signs were valuable, while indirect signs were less predictive and specific. KEY POINTS: • Direct visualization of intrasynovial tumor was the single most sensitive and specific (92-100%) MR imaging sign of joint invasion. • Indirect signs of joint invasion, such as joint effusion or synovial enhancement, were less sensitive and specific compared to direct signs. • A combination of the most specific direct signs of joint invasion showed best results with perfect specificity and PPV (both 100%) and excellent sensitivity and NPV (both 96 %).

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. METHODS: In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. RESULTS: The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. CONCLUSIONS: An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. KEY POINTS: • The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020. Benign or malignant bone tumors were diagnosed in all patients by using the histopathologic findings as the reference standard. By using split-sample validation, 70% of the patients were assigned to the training set, 15% were assigned to the validation set, and 15% were assigned to the test set. The final performance was evaluated on an external test set by using geographic validation, with accuracy, sensitivity, specificity, and 95% CIs being used for classification, the intersection over union (IoU) being used for bounding box placements, and the Dice score being used for segmentations. Results Radiographs from 934 patients (mean age, 33 years ± 19 [standard deviation]; 419 women) were evaluated in the internal data set, which included 667 benign bone tumors and 267 malignant bone tumors. Six hundred fifty-four patients were in the training set, 140 were in the validation set, and 140 were in the test set. One hundred eleven patients were in the external test set. The multitask DL model achieved 80.2% (89 of 111; 95% CI: 72.8, 87.6) accuracy, 62.9% (22 of 35; 95% CI: 47, 79) sensitivity, and 88.2% (67 of 76; CI: 81, 96) specificity in the classification of bone tumors as malignant or benign. The model achieved an IoU of 0.52 ± 0.34 for bounding box placements and a mean Dice score of 0.60 ± 0.37 for segmentations. The model accuracy was higher than that of two radiologic residents (71.2% and 64.9%; P = .002 and P < .001, respectively) and was comparable with that of two musculoskeletal fellowship-trained radiologists (83.8% and 82.9%; P = .13 and P = .25, respectively) in classifying a tumor as malignant or benign. Conclusion The developed multitask deep learning model allowed for accurate and simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Carrino in this issue.

Soft tissue masses: distribution of entities and rate of malignancy in small lesions.

BACKGROUND: Small soft tissue masses are often falsely assumed to be benign and resected with failure to achieve tumor-free margins. Therefore, this study retrospectively investigated the distribution of histopathologic diagnosis to be encountered in small soft tissue tumors (≤ 5 cm) in a large series of a tertiary referral center. METHODS: Patients with a soft tissue mass (STM) with a maximum diameter of 5 cm presenting at our institution over a period of 10 years, who had undergone preoperative Magnetic resonance imaging and consequent biopsy or/and surgical resection, were included in this study. A final histopathological diagnosis was available in all cases. The maximum tumor diameter was determined on MR images by one radiologist. Moreover, tumor localization (head/neck, trunk, upper extremity, lower extremity, hand, foot) and depth (superficial / deep to fascia) were assessed. RESULTS: In total, histopathologic results and MR images of 1753 patients were reviewed. Eight hundred seventy patients (49.63%) showed a STM ≤ 5 cm and were therefore included in this study (46.79 +/- 18.08 years, 464 women). Mean maximum diameter of the assessed STMs was 2.88 cm. Of 870 analyzed lesions ≤ 5 cm, 170 (19.54%) were classified as superficial and 700 (80.46%) as deep. The malignancy rate of all lesions ≤ 5 cm was at 22.41% (superficial: 23.53% / deep: 22.14%). The malignancy rate dropped to 16.49% (20.79% / 15.32%) when assessing lesions ≤ 3 cm (p = 0.007) and to 15.0% (18.18% / 13.79%) when assessing lesions ≤ 2 cm (p = 0.006). Overall, lipoma was the most common benign lesion of superficial STMs (29.41%) and tenosynovial giant cell tumor was the most common benign lesion of deep STMs (23.29%). Undifferentiated pleomorphic sarcoma was the most common malignant diagnosis among both, superficial (5.29%) and deep (3.57%) STMs. CONCLUSIONS: The rate of malignancy decreased significantly with tumor size in both, superficial and deep STMs. The distribution of entities was different between superficial and deep STMs, yet there was no significant difference found in the malignancy rate.

COVID-19-related cancellation of elective orthopaedic surgery caused increased pain and psychosocial distress levels.

PURPOSE: Health care systems in most European countries were temporarily restructured to provide as much capacity as possible for the treatment of coronavirus disease 2019 (COVID-19) patients. Subsequently, all elective surgeries had to be cancelled and postponed for months. The aim of the present study was to assess the pretreatment health status before and after COVID-19-related cancellation and the psychosocial distress caused by the cancellation. METHODS: For this study, a questionnaire was developed collecting sociodemographic data and information on health status before and after the cancellation. To assess psychosocial distress, the validated depression module of the Patient Health Questionnaire (PHQ-9), was implemented. PHQ-9-Scores of 10 and above were considered to indicate moderate or severe depressive symptoms. In total, 119 patients whose elective orthopaedic surgery was postponed due to the COVID-19 pandemic were surveyed once at least 8 weeks after the cancellation. RESULTS: Seventy-seven patients (65%; 34 female, 43 male) completed the questionnaire and were included. The predominant procedures were total knee arthroplasty (TKA), hip arthroscopy and foot and ankle surgery. The mean pain level significantly increased from 5.5 ± 2.2 at the time of the initially scheduled surgery to 6.2 ± 2.5 at the time of the survey (p < 0.0001). The pain level before cancellation of the surgery was significantly higher in female patients (p = 0.029). An increased analgetic consumption was identified in 46% of all patients. A mean PHQ-9 score of 6.1 ± 4.9 was found after cancellation. PHQ-9 scores of 10 or above were found in 14% of patients, and 8% exhibited scores of 15 points or above. Significantly higher PHQ-9 scores were seen in female patients (p = 0.046). No significant differences in PHQ-9 scores were found among age groups, procedures or reasons for cancellation. CONCLUSION: Cancellation of elective orthopaedic surgery resulted in pain levels that were significantly higher than when the surgery was scheduled, leading to increased analgesic use. Additionally, significant psychosocial distress due to the cancellation was identified in some patients, particularly middle-aged women. Despite these results, confidence in the national health care system and in the treating orthopaedic surgeons was not affected. LEVEL OF EVIDENCE: Level III.

[Parosteal low-grade osteosarcoma with focal differentiation]. / Parossales Low-grade-Osteosarkom mit fokaler Dedifferenzierung.

This case study deals with the case of a 16-year-old male patient with a low-grade parosteal osteosarcoma of the distal femur with focal differentiation. Case history, disease course, and surgery as well as the pathological workup with final diagnosis are presented. Relevant radiologic and pathologic differential diagnoses und diagnostic pitfalls are explained in detail and discussed. Additionally, postoperative treatment options are illustrated.

[Amputations around the knee]. / Kniegelenknahe Amputationen.

An amputation around, through or below the knee joint constitutes a "huge" change in a patient's life. In Orthopaedics, amputations are most frequently performed in cases with musculoskeletal tumours or failed total knee arthroplasty. A multidisciplinary team approach (surgeon, anaesthetist, pain specialists, orthotist, psychologist etc.) and patient-specific treatment regime from the outset as well as a meticulous surgical technique are of the outmost importance. Nowadays, prosthetic legs can be fitted for nearly any amputation level. The functional outcome of amputations below the knee is usually superior to amputations above or through the knee joint. Postoperative stump conditioning is paramount and the final prosthetic leg should not be fitted earlier than 4-6 months postoperatively. Problems with wound healing, muscle contractures and phantom limb pain represent common complications which might adversely affect patient outcomes.

Differentiating atypical lipomatous tumors from lipomas with magnetic resonance imaging: a comparison with MDM2 gene amplification status.

BACKGROUND: To evaluate the diagnostic value of MR imaging for the differentiation of lipomas and atypical lipomatous tumors (ALT) in comparison with histology and MDM2 amplification status. METHODS: Patients with well-differentiated lipomatous tumors (n = 113), of which 66 were diagnosed as lipoma (mean age 53 years (range, 13-82); 47% women) and 47 as atypical lipomatous tumor (ALT; mean age 60 years (range, 28-88); 64% women), were included into this study using histology and MDM2 amplification status by fluorescence in situ hybridization (FISH) as standard of reference. Preoperative MR images were retrospectively assessed by two radiologists for the following imaging features: maximum tumor diameter (mm) as well as the affected compartment (intramuscular, intermuscular or subcutaneous), septa (absent, thin (< 2 mm) or thick septa (> 2 mm) with nodular components); contrast enhancing areas within the lipomatous tumor (< 1/3 of the tumor volume, > 1/3 of the tumor volume); RESULTS: Of the 47 patients with ALT, 40 (85.1%) presented thick septa (> 2 mm) and this finding significantly increased the likelihood of ALT (OR 6.24, 95% CI 3.36-11.59; P < 0.001). The likelihood of ALT was increased if the tumor exceeded a maximum diameter of 130.0 mm (OR 2.74, 95% CI 1.82-4.11, P < 0.001). The presence of contrast enhancement in lipomatous tumors significantly increased the likelihood of ALT (Odds ratio (OR) 2.95, 95% confidence interval (CI) 2.01-4.31; P < 0.001). Of the lipomas, 21.1% were located subcutaneously, 63.6% intramuscularly and 15.2% intermuscularly. On the other hand, none of the ALTs were located subcutaneously, the majority was located intermuscularly (87.3%) and a small number of ALTs was located intramuscularly (12.7%). CONCLUSIONS: Our results suggest that using specific morphological MR imaging characteristics (maximum tumor diameter, thick septa and contrast enhancement) and the information on the localization of the lipomatous tumor, a high sensitivity and substantial specificity can be achieved for the diagnosis of lipomas and ALTs.

Evaluation of MR-derived CT-like images and simulated radiographs compared to conventional radiography in patients with benign and malignant bone tumors.

OBJECTIVES: To evaluate the diagnostic value of MR-derived CT-like images and simulated radiographs compared with conventional radiographs in patients with benign and malignant bone tumors. METHODS: In 32 patients with a benign or malignant bone lesion (mean age 33.9 ± 18.5 years, 17 females), 3-T MR imaging was performed including a 3D T1-weighted gradient echo sequence as the basis for the CT-like images. From these, intensity-inverted MR image volumes were converted into 2D images via a forward projection to obtain simulated radiographs. Two radiologists assessed these images as well as conventional radiographs for the type of periosteal reaction, matrix mineralization and destruction pattern. Agreement between the modalities was calculated using Cohen's κ. RESULTS: The agreement between conventional radiographs and MR-derived CT-like images in combination with simulated radiographs was substantial (periosteal reaction, κ = 0.67; destruction pattern, κ = 0.75), and the sensitivity of both modalities for the final diagnosis of the lesion (aggressive vs. nonaggressive) was high (MR-derived CT-like images, 86.2% vs. conventional radiographs, 90.0%). Additional information on soft tissue extension (MR-derived CT-like images, 21.9% vs. conventional radiographs, 12.5%; p = 0.009) and lobulation (9.4% vs. 0%; p < 0.001) was significantly more often found on MR-derived CT-like images compared with conventional radiographs. CONCLUSIONS: The assessment of the destruction patterns, periosteal reaction and distinction between aggressive and nonaggressive tumors was feasible using MR-derived CT-like images and simulated radiographs and is comparable to that of conventional radiographs. Moreover, MR-derived CT-like images provided additional information on soft tissue extension and tumor architecture. KEY POINTS: • CT-like images and simulated radiographs can be generated from 3D MRI. • Evaluation of bone tumors is feasible with MR-derived images. • CT-like images and simulated radiographs provide additional information on bone tumors.

Prognostic factors and outcome of Liposarcoma patients: a retrospective evaluation over 15 years.

BACKGROUND: Soft tissue sarcomas are rare entities with over 50 histological subtypes. Liposarcoma (LS) is the most common neoplasm in this group; it is a complex neoplasm that is divided into different histological subtypes. Different therapy options, such as surgical resection, radiation, and chemotherapy, are available. Depending on the subtype, location, status of the resection margins and metastatic status, different therapy options are used. Therefore, the aim of this study was to determine the prognostic factors influencing the survival of patients affected by LS with consideration for the grading, histological subtype, state of the resection margin, size, location, metastases and local recurrence in a retrospective, single-centre analysis over 15 years. METHODS: We included 133 patients (male/female = 67/66) in this study. We recorded the histologic subtype, grade, TNM classification, localization, biopsy technique, tumour margins, number of operations, complications, radiation and dose, chemotherapy, survival, recrudescence, metastases and follow-up. Survivorship analysis was performed. RESULTS: We detected 56 (43%; 95%-CI 34.6-51.6%) atypical LS cases, 21 (16.2%; 95%-CI 9.8-22.5) dedifferentiated LS cases, 40 (30.8%; 95%-CI 22.8-38.7) myxoid LS cases and 12 (9.2%; 95%-CI 4.3-14.2) pleomorphic LS cases. G1 was the most common grade, which was followed by G3. Negative margins (R0) were detected in 67 cases (53.6%; 95%-CI 44.9-62.3) after surgical resection. Local recurrence was detected in 23.6% of cases. The presence of metastases and dedifferentiated LS subtype as well as negative margins, grade and tumour size are significant prognostic factors of the survival rates (p < 0.015). CONCLUSION: Grading, LS subtype, negative margins after surgery, metastases and tumour size are independently associated with disease-specific survival, and patients with local recurrence had lower survival rates. We hope our investigation may facilitate a further prospective study and clinical decision-making in LS.

C-Reactive Protein Pretreatment-Level Evaluation with Histopathological Correlation for Chondrosarcoma Prognosis Assessment-A 15-Year Retrospective Single-Center Study.

BACKGROUND: An aberrant cellular microenvironment characterized by pathological cells or inflammation represents an added risk factor across various cancer types. While the significance of chronic inflammation in the development of most diffuse tumors has been extensively studied, an exception to this analysis exists in the context of chondrosarcomas. Chondrosarcomas account for 20-30% of all bone sarcomas, with an estimated global incidence of 1 in 100,000. The average age at diagnosis is 50, and over 70% of patients are over 40. This retrospective study aimed to examine the role of C-reactive protein (CRP) as a prognostic factor in relation to the histopathological findings in chondrosarcoma. METHODS: In this retrospective study, 70 patients diagnosed with chondrosarcoma and treated between 2004 and 2019 were included. Preoperative CRP levels were measured in mg/dL, with non-pathological values defined as below 0.5 mg/dL. Disease-free survival time was calculated from the initial diagnosis to events such as local recurrence or metastasis. Follow-up status was categorized as death from disease, no evidence of disease, or alive with disease. Patients were excluded if they had insufficient laboratory values, missing follow-up information, or incomplete histopathological reports. RESULTS: The calculated risk estimation of a reduced follow-up time was 2.25 timed higher in the patients with a CRP level >0.5 mg/dL (HR 2.25 and 95% CI 1.13-4.45) and 3 times higher in patients with a tumor size > pT2 (HR 3 and 95% CI 1.59-5.92). We can easily confirm that risk factors for reduced prognosis lie in chondrosarcoma high grading, preoperative pathological CRP- level, and a size > 8 cm. CONCLUSIONS: A pretreatment CRP value greater than 0.5 mg/dL can be considered a sensitive prognostic and risk factor for distant metastasis for chondrosarcoma patients.

What Is the Impact of Multimodal Treatment in Patients with Leiomyosarcoma of Bone? A Multicenter Study of 35 Patients with an Ultra-Rare Tumor Entity.

Primary leiomyosarcoma of bone (LMSoB) is extremely rare, comprising only <0.7% of primary malignant bone tumors, and is therefore considered an ultra-rare tumor entity. There is currently no consensus as to whether therapeutic strategies should be based on the biological characteristics of soft tissue leiomyosarcoma or on primary tumor localization in the bone. The use of perioperative chemotherapy and its effectiveness in this rare tumor entity remains unclear. We aimed to evaluate the impact of different treatment approaches in a multicenter setting with a total of 35 patients included. The 5-year overall survival (OS) was 74%. Patients with localized disease undergoing surgery had a significantly higher 5-year OS compared to patients who did not undergo surgical treatment (82% vs. 0%, p = 0.0015). Axial tumor localization was associated with worse event-free survival (EFS) probability (p < 0.001) and OS (p = 0.0082). A high proportion of our patients developed secondary metastases. Furthermore, the perioperative chemotherapy protocols applied to our patients were not associated with an improved EFS or OS. Therefore, the benefit of perioperative chemotherapy in LMSoB needs to be further investigated, and the choice of agents still needs to be clarified.

Childhood Tumors around the Knee Revisited: Predilection Sites for Most Entities Confirmed.

Background: The diagnostic work-up of musculoskeletal tumors is a multifactorial process. During the early phase, differential diagnoses are made using basic radiological imaging. In this phase, part of the decision making is based on the patient's age, as well as the incidence and predilection sites of different entities. Unfortunately, this information is based on older and fragmented data. In this study, we retrospectively evaluated all soft-tissue and bone tumors around the knee in children treated at our tertiary center in the last 20 years, with the aim of verifying the data used today. Methods: In this retrospective study, the databank of our tertiary center was used to give an overview of treated tumors around the knee in children. Results: We were able to include 224 children with bone and soft-tissue tumors around the knee. The cohort consisted of 184 bone tumors, of which 144 were benign and 40 malignant. The 40 soft-tissue tumors comprised 30 benign and 10 malignant masses. The most common lesions were osteochondromas (88) in the bone and tenosynovial giant-cell tumors (12) in the soft tissue. Conclusions: With this original work, we were able to verify and supplement earlier studies, as well as deepen our insight into these very rare diseases.

Chondrosarcoma evaluation using hematein-based x-ray staining and high-resolution 3D micro-CT: a feasibility study.

BACKGROUND: Chondrosarcomas are rare malignant bone tumors diagnosed by analyzing radiological images and histology of tissue biopsies and evaluating features such as matrix calcification, cortical destruction, trabecular penetration, and tumor cell entrapment. METHODS: We retrospectively analyzed 16 cartilaginous tumor tissue samples from three patients (51-, 54-, and 70-year-old) diagnosed with a dedifferentiated chondrosarcoma at the femur, a moderately differentiated chondrosarcoma in the pelvis, and a predominantly moderately differentiated chondrosarcoma at the scapula, respectively. We combined a hematein-based x-ray staining with high-resolution three-dimensional (3D) microscopic x-ray computed tomography (micro-CT) for nondestructive 3D tumor assessment and tumor margin evaluation. RESULTS: We detected trabecular entrapment on 3D micro-CT images and followed bone destruction throughout the volume. In addition to staining cell nuclei, hematein-based staining also improved the visualization of the tumor matrix, allowing for the distinction between the tumor and the bone marrow cavity. The hematein-based staining did not interfere with further conventional histology. There was a 5.97 ± 7.17% difference between the relative tumor area measured using micro-CT and histopathology (p = 0.806) (Pearson correlation coefficient r = 0.92, p = 0.009). Signal intensity in the tumor matrix (4.85 ± 2.94) was significantly higher in the stained samples compared to the unstained counterparts (1.92 ± 0.11, p = 0.002). CONCLUSIONS: Using nondestructive 3D micro-CT, the simultaneous visualization of radiological and histopathological features is feasible. RELEVANCE STATEMENT: 3D micro-CT data supports modern radiological and histopathological investigations of human bone tumor specimens. It has the potential for being an integrative part of clinical preoperative diagnostics. KEY POINTS: • Matrix calcifications are a relevant diagnostic feature of bone tumors. • Micro-CT detects all clinically diagnostic relevant features of x-ray-stained chondrosarcoma. • Micro-CT has the potential to be an integrative part of clinical diagnostics.

Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas.

BACKGROUND: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). METHODS: A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. RESULTS: The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. CONCLUSION: The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning.

Outcome of patients with soft tissue sarcomas of the extremities and trunk treated by (neo)adjuvant intensity modulated radiation therapy with curative intent.

BACKGROUND: Soft tissue sarcomas (STS) are a relatively rare group of malignant tumors. Currently, there is very little published clinical data, especially in the context of curative multimodal therapy with image-guided, conformal, intensity-modulated radiotherapy. METHODS: Patients who received preoperative or postoperative intensity-modulated radiotherapy for STS of the extremities or trunk with curative intent were included in this single centre retrospective analysis. A Kaplan-Meier analysis was performed to evaluate survival endpoints. Multivariable proportional hazard models were used to investigate the association between survival endpoints and tumour-, patient-, and treatment-specific characteristics. RESULTS: 86 patients were included in the analysis. The most common histological subtypes were undifferentiated pleomorphic high-grade sarcoma (UPS) (27) and liposarcoma (22). More than two third of the patients received preoperative radiation therapy (72%). During the follow-up period, 39 patients (45%) suffered from some type of relapse, mainly remote (31%). The two-years overall survival rate was 88%. The median DFS was 48 months and the median DMFS was 51 months. Female gender (HR 0.460 (0.217; 0.973)) and histology of liposarcomas compared to UPS proved to be significantly more favorable in terms of DFS (HR 0.327 (0.126; 0.852)). CONCLUSION: Conformal, intensity-modulated radiotherapy is an effective treatment modality in the preoperative or postoperative management of STS. Especially for the prevention of distant metastases, the establishment of modern systemic therapies or multimodal therapy approaches is necessary.

Deep Learning-Based Detection of Bone Tumors around the Knee in X-rays of Children.

Even though tumors in children are rare, they cause the second most deaths under the age of 18 years. More often than in other age groups, underage patients suffer from malignancies of the bones, and these mostly occur in the area around the knee. One problem in the treatment is the early detection of bone tumors, especially on X-rays. The rarity and non-specific clinical symptoms further prolong the time to diagnosis. Nevertheless, an early diagnosis is crucial and can facilitate the treatment and therefore improve the prognosis of affected children. A new approach to evaluating X-ray images using artificial intelligence may facilitate the detection of suspicious lesions and, hence, accelerate the referral to a specialized center. We implemented a Vision Transformer model for image classification of healthy and pathological X-rays. To tackle the limited amount of data, we used a pretrained model and implemented extensive data augmentation. Discrete parameters were described by incidence and percentage ratio and continuous parameters by median, standard deviation and variance. For the evaluation of the model accuracy, sensitivity and specificity were computed. The two-entity classification of the healthy control group and the pathological group resulted in a cross-validated accuracy of 89.1%, a sensitivity of 82.2% and a specificity of 93.2% for test groups. Grad-CAMs were created to ensure the plausibility of the predictions. The proposed approach, using state-of-the-art deep learning methodology to detect bone tumors on knee X-rays of children has achieved very good results. With further improvement of the algorithm, enlargement of the dataset and removal of potential biases, this could become a useful additional tool, especially to support general practitioners for early, accurate and specific diagnosis of bone lesions in young patients.

A New Method for Assessing Patients' Obesity-Associated Infection Risk Using X-rays in Hip Arthroplasties.

Overweight patients have higher complication rates during and after surgical procedures. In total hip arthroplasty (THA), postoperative infection is a major complication. In this study, we show that the patient's body mass index (BMI) can be approximated by a newly developed grading system using preoperative X-rays. Furthermore, we show that a higher score and BMI result in a higher risk of infection. For this retrospective study, 635 patients undergoing THA or revision surgeries in 2018 and 2019 were included. The preoperatively acquired X-rays of the pelvis were analyzed using a four-stage grading system. The infection rate was compared to our score and the patients' BMI. The mean BMI (95% confidence) of all patients graded as grade 0 was 25.16 (24.83; 25.50) kg/m2, for grade 1, it was 30.31 (29.52; 31.09) kg/m2, for grade 2, it was 35.06 (33.59; 36.54) kg/m2, and it was 45.03 (39.65; 50.41) kg/m2 for grade 3. The risk of infection was 4% in patients with normal radiographs, rising from 7% in patients graded as 1 up to 18% in each of the highest categories. This study shows that we were able to create a semi-quantitative grading tool for the abdominal contour displayed on X-rays of the pelvis in order to estimate the patients' BMI and therefore the infection rate. A higher abdominal contour grade showed higher infection rates at follow-up.

Diagnostic Delay in Patients with Osteoid Osteoma.

PURPOSE: To assess diagnostic delay in patients with osteoid osteoma and to analyze influencing factors. MATERIALS AND METHODS: All patients treated for osteoid osteoma at our tertiary referral center between December 1997 and February 2021 were retrospectively identified (n = 302). The diagnosis was verified by an expert panel of radiologists and orthopedic surgeons. The exclusion criteria were post-interventional recurrence, missing data on symptom onset, and lack of pretherapeutic CT images. Clinical parameters were retrieved from the local clinical information system. CT and MR images were assessed by a senior specialist in musculoskeletal radiology. RESULTS: After all exclusions, we studied 162 patients (mean age: 24 ±â€Š11 years, 115 men). The average diagnostic delay was 419 ±â€Š485 days (median: 275 days; range: 21-4503 days). Gender, patient age, presence of nocturnal pain, positive aspirin test, extent of bone sclerosis, and location of the tumor within bone and relative to joints did not influence diagnostic delay (p > 0.05). It was, however, positively correlated with nidus size (r = 0.26; p < 0.001) and was shorter with affection of long tubular bones compared to all other sites (p = 0.04). If osteoid osteoma was included in the initial differential diagnoses, the diagnostic delay was also shorter (p = 0.007). CONCLUSION: The diagnostic delay in patients with osteoid osteoma is independent of demographics, clinical parameters, and most imaging parameters. A long average delay of more than one year suggests low awareness of the disease among physicians. Patients with unclear imaging findings should thus be referred to a specialized musculoskeletal center or an expert in the field should be consulted in a timely manner. KEY POINTS: · In this retrospective study of 162 patients treated for osteoid osteoma, the median diagnostic delay was 275 days (range: 21-4503 days).. · Gender, age, presence of nocturnal pain, positive aspirin test, extent of bone sclerosis, and location of the tumor did not influence the diagnostic delay (p > 0.05).. · Diagnostic delay was positively correlated with nidus size (r = 0.26; p < 0.001) and was shorter with affection of long tubular bones compared to all other sites (376 ±â€Š485 vs. 560 ±â€Š462 days; p = 0.04)..