Clinical recommendations for treatment of localized angiosarcoma: A consensus paper by the Italian Sarcoma Group.

Angiosarcoma (AS) represents a rare and aggressive vascular sarcoma, posing distinct challenges in clinical management compared to other sarcomas. While the current European Society of Medical Oncology (ESMO) clinical practice guidelines for sarcoma treatment are applicable to AS, its unique aggressiveness and diverse tumor presentations necessitate dedicated and detailed clinical recommendations, which are currently lacking. Notably, considerations regarding surgical extent, radiation therapy (RT), and neoadjuvant/adjuvant chemotherapy vary significantly in localized disease, depending on each different site of onset. Indeed, AS are one of the sarcoma types most sensitive to cytotoxic chemotherapy. Despite this, uncertainties persist regarding optimal management across different clinical presentations, highlighting the need for further investigation through clinical trials. The Italian Sarcoma Group (ISG) organized a consensus meeting on April 1st, 2023, in Castel San Pietro, Italy, bringing together Italian sarcoma experts from several disciplines and patient representatives from "Sofia nel Cuore Onlus" and the ISG patient advocacy working group. The objective was to develop specific clinical recommendations for managing localized AS within the existing framework of sarcoma clinical practice guidelines, accounting for potential practice variations among ISG institutions. The aim was to try to standardize and harmonize clinical practices, or at least highlight the open questions in the local management of the disease, to define the best evidence-based practice for the optimal approach of localized AS and generate the recommendations presented herein.

An algorithmic approach to the management of peripheral nerve tumours.

This article reviews the pathology and management of peripheral nerve tumours, including a framework for investigation and decision-making. Most tumours are benign, including schwannomas and neurofibromas, but malignant peripheral nerve sheath tumours can occur. The risk of malignant change is remote for schwannomas but higher for neurofibromas, particularly in neurofibromatosis type 1. Magnetic resonance imaging is useful for defining the relationship of a swelling with adjacent nerves but is not definitive for tissue diagnosis. Increasing size, pain and neurological deficit suggest malignant change and TruCut needle biopsy is indicated, although there is a risk of sampling error. Excision biopsy preserving nerve function may be carried out for benign tumours to relieve symptoms. Malignant tumours require a multidisciplinary approach. Complete surgical excision with clear margins is the only curative treatment and may be supplemented with radiotherapy and chemotherapy. However, prognosis remains poor, particularly for patients with neurofibromatosis.

X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones.

BACKGROUND: Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones. METHODS: This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test. FINDINGS: Five radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617). INTERPRETATION: X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones. FUNDING: AIRC Investigator Grant.

Diagnostic and prognostic role of synovial tissue analysis in juvenile idiopathic arthritis: a monocentric study.

OBJECTIVES: This study investigates the diagnostic role of synovial tissue analysis in children presenting with arthritis and assesses its prognostic significance to predict clinical outcome in juvenile idiopathic arthritis (JIA). METHODS: Synovial samples of paediatric patients undergoing synovial biopsy between 1995 and 2020 were analysed histologically and immunohistochemically. Relationships between histological/immunohistochemical parameters and clinical variables were assessed. RESULTS: Synovial biopsy was performed for diagnosis in 65 cases allowing to correctly classify 79% of patients.At histological analysis on 42 JIA samples, any difference in the number of synovial lining layers, subsynovial elementary lesions, fibrin deposit, Krenn Synovitis Score, inflammatory infiltrate score and pattern emerged between JIA subsets or on treatment exposure. Synovial tissue analysis predicted outcome: higher number of synovial layers predicted worse disease course (>4 flares during follow-up; 4.5 vs 3.0, p=0.035), even after adjusting for age at diagnosis and observation time (OR 2.2, p=0.007); subjects who had switched>2 biological disease-modifying antirheumatic drugs had higher prevalence of subsynovial elementary lesions (55.6% vs 10.3%, p=0.005) and fibrin deposits in synovial lining (60.0% vs 22.6%, p=0.049), even after adjustment for observation time and age at diagnosis (OR 8.1, p=0.047). At immunohistochemistry on 31 JIA samples, higher CD3 expression was described in polyarticular compared with oligoarticular subset (p=0.040). Patients with severe disease course had higher CD20+ rate (OR 7, p=0.023), regardless of JIA subset and treatment exposure. CONCLUSIONS: Synovial tissue analysis might support the clinicians in the diagnostic approach of paediatric patients presenting with arthritis and guide the clinical management in JIA.

Bizarre parosteal osteochondromatous proliferation: an educational review.

Bizarre parosteal osteochondromatous proliferation (BPOP) is a surface-based bone lesion belonging to the group of benign chondrogenic tumors. The aim of this review is to familiarize the readers with imaging features and differential diagnosis of BPOP, also addressing pathological presentation and treatment options. The peak of incidence of BPOP is in the third and fourth decades of life, although it can occur at any age. Hands are the most common location of BPOP (55%), followed by feet (15%) and long bones (25%). On imaging, BPOP appears as a well-marginated mass of heterotopic mineralization arising from the periosteal aspect of the bone. Typical features of BPOP are contiguity with the underlying bone and lack of cortico-medullary continuity, although cortical interruption and medullary involvement have been rarely reported. Histologically, BPOP is a benign bone surface lesion characterized by osteocartilaginous proliferation with disorganized admixture of cartilage with bizarre features, bone and spindle cells. Differential diagnosis includes both benign-such as florid reactive periostitis, osteochondroma, subungual exostosis, periosteal chondroma and myositis ossificans-and malignant lesions-such as periosteal chondrosarcoma and surface-based osteosarcoma. Treatment consists of surgical resection. Local recurrences are common and treated with re-excision.Critical relevance statement Bizarre parosteal osteochondromatous proliferation is a benign mineralized mass arising from the periosteal aspect of bone cortex. Multi-modality imaging characteristics, pathology features and differential diagnosis are here highlighted to familiarize the readers with this entity and offer optimal patient care.

MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities.

PURPOSE: To determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities. MATERIAL AND METHODS: This retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT). The external test cohort consisted of 36 patients from center 3 (n = 24 lipoma, n = 12 ALT). 3D segmentation was manually performed on T1- and T2-weighted MRI. After extraction and selection of radiomic features, three machine learning classifiers were trained and validated using nested fivefold cross-validation. The best-performing classifier according to previous analysis was evaluated and compared to an experienced musculoskeletal radiologist in the external test cohort. RESULTS: Eight features passed feature selection and were incorporated into the machine learning models. After training and validation (74% ROC-AUC), the best-performing classifier (Random Forest) showed 92% sensitivity and 33% specificity in the external test cohort with no statistical difference compared to the radiologist (p = 0.474). CONCLUSION: MRI radiomics-based machine learning may classify deep-seated lipoma and ALT of the extremities with high sensitivity and negative predictive value, thus potentially serving as a non-invasive screening tool to reduce unnecessary referral to tertiary tumor centers.

Significant association between FGFR1 mutation frequency and age in central giant cell granuloma.

Central giant cell granulomas (CGCG) are rare intraosseous osteolytic lesions of uncertain aetiology. Despite the benign nature of this neoplasia, the lesions can rapidly grow and become large, painful, invasive, and destructive. The identification of molecular drivers could help in the selection of targeted therapies for specific cases. TRPV4, KRAS and FGFR1 mutations have been associated with these lesions but no correlation between the mutations and patient features was observed so far. In this study, we analysed 17 CGCG cases of an Italian cohort and identified an interesting and significant (p=0.0021) correlation between FGFR1 mutations and age. In detail, FGFR1 mutations were observed frequently and exclusively in CGCG from young (<18 years old) patients (4/5 lesions, 80%). Furthermore, the combination between ours and previously published data confirmed a significant difference in the frequency of FGFR1 mutations in CGCG from patients younger than 18 years at the time of diagnosis (9/23 lesions, 39%) when compared to older patients (1/31 lesions, 0.03%; p=0.0011), thus corroborating our observation in a cohort of 54 patients. FGFR1 variants in young CGCG patients could favour fast lesion growth, implying that they seek medical attention earlier. Our observation might help prioritise candidates for FGFR1 testing, thus opening treatment options with FGFR inhibitors.

Secondary osteosarcoma: a challenge indeed.

BACKGROUND: The risk of survivors developing a secondary bone sarcoma after being treated for pediatric cancers is well established. The aim of this study was to examine the clinical characteristics and outcomes of patients with secondary osteosarcoma (SOS). METHODS: The study concerns survivors of childhood and adolescence primary neoplasms (PN) treated with chemotherapy, with or without radiotherapy and surgery, subsequently diagnosed with SOS. RESULTS: We identified 26 patients (13 females, 13 males) who developed SOS a median 7.3 years after being diagnosed with a PN (5/7 of these patients tested for Li-Fraumeni and found positive for the syndrome). The sample's median age was 8.0 and 15.0 years when their PN and SOS were diagnosed, respectively. To treat their PN, 24 out of 26 patients had been given radiotherapy, and 19 had received chemotherapy including doxorubicin. A considerable number of SOS occurred at unfavorable sites (nine hip bone, six skull). All but one patient received chemotherapy with tailored schedules, omitting doxorubicin in 19 cases. Eighteen of the 26 patients underwent surgery. The 5- and 10-year overall survival and probabilities after the diagnosis of SOS (95% confidence interval) were 50% (32.7-76.5%) and 38.9% (22.4-67.4%); 5- and 10-year progression-free survival was 47% (29.9-73.7%) and 35.2% (19.3-64.4%), respectively. CONCLUSIONS: The survival rates after SOS are lower than in patients with primary osteosarcoma, but not negligible. It is therefore mandatory to discuss the best choice of treatment for such patients at a referral center, in terms of their chances of cure and quality of life.

3D vs. 2D MRI radiomics in skeletal Ewing sarcoma: Feature reproducibility and preliminary machine learning analysis on neoadjuvant chemotherapy response prediction.

Objective: The extent of response to neoadjuvant chemotherapy predicts survival in Ewing sarcoma. This study focuses on MRI radiomics of skeletal Ewing sarcoma and aims to investigate feature reproducibility and machine learning prediction of response to neoadjuvant chemotherapy. Materials and methods: This retrospective study included thirty patients with biopsy-proven skeletal Ewing sarcoma, who were treated with neoadjuvant chemotherapy before surgery at two tertiary sarcoma centres. 7 patients were poor responders and 23 were good responders based on pathological assessment of the surgical specimen. On pre-treatment T1-weighted and T2-weighted MRI, 2D and 3D tumour segmentations were manually performed. Features were extracted from original and wavelet-transformed images. Feature reproducibility was assessed through small geometrical transformations of the regions of interest mimicking multiple manual delineations, and intraclass correlation coefficient >0.75 defined feature reproducibility. Feature selection also consisted of collinearity and significance analysis. After class balancing in the training cohort, three machine learning classifiers were trained and tested on unseen data using hold-out cross-validation. Results: 1303 (77%) 3D and 620 (65%) 2D radiomic features were reproducible. 4 3D and 4 2D features passed feature selection. Logistic regression built upon 3D features achieved the best performance with 85% accuracy (AUC=0.9) in predicting response to neoadjuvant chemotherapy. Conclusion: Compared to 2D approach, 3D MRI radiomics of Ewing sarcoma had superior reproducibility and higher accuracy in predicting response to neoadjuvant chemotherapy, particularly when using logistic regression classifier.

Rechallenge of denosumab in advanced giant cell tumor of the bone after atypical femur fracture: A case report and review of literature.

Giant cell tumor of the bone (GCTB) is a locally aggressive neoplasm where surgery is often curative. However, it can rarely give rise to distant metastases. Currently, the only available active therapeutic option for unresectable GCTB is denosumab, an anti-RANKL monoclonal antibody that dampens the aggressive osteolysis typically seen in this disease. For advanced/metastatic GCTB, denosumab should be continued lifelong, and although it is usually well tolerated, important questions may arise about the long-term safety of this drug. In fact, uncommon but severe toxicities can occur and eventually lead to denosumab discontinuation, such as atypical fracture of the femur (AFF). The optimal management of treatment-related AFF is a matter of debate, and to date, it is unknown whether reintroduction of denosumab at disease progression is a clinically feasible option, as no reports have been provided so far. Hereinafter, we present a case of a patient with metastatic GCTB who suffered from AFF after several years of denosumab; we describe the clinical features, orthopedic treatment, and oncological outcomes, finally providing the first evidence that denosumab rechallenge after AFF occurrence may be a safe and viable option at GCTB progression.

Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance.

PURPOSE: To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2-weighted magnetic resonance imaging (MRI). MATERIAL AND METHODS: This retrospective study included 101 patients with histology-proven spine bone tumor (22 benign; 38 primary malignant; 41 metastatic). All tumor volumes were manually segmented on morphologic T2-weighted sequences. The same region of interest (ROI) was used to perform radiomic analysis on ADC map. A total of 1702 radiomic features was considered. Feature stability was assessed through small geometrical transformations of the ROIs mimicking multiple manual delineations. Intraclass correlation coefficient (ICC) quantified feature stability. Feature selection consisted of stability-based (ICC > 0.75) and significance-based selections (ranking features by decreasing Mann-Whitney p-value). Class balancing was performed to oversample the minority (i.e., benign) class. Selected features were used to train and test a support vector machine (SVM) to discriminate benign from malignant spine tumors using tenfold cross-validation. RESULTS: A total of 76.4% radiomic features were stable. The quality metrics for the SVM were evaluated as a function of the number of selected features. The radiomic model with the best performance and the lowest number of features for classifying tumor types included 8 features. The metrics were 78% sensitivity, 68% specificity, 76% accuracy and AUC 0.78. CONCLUSION: SVM classifiers based on radiomic features extracted from T2- and diffusion-weighted imaging with ADC map are promising for classification of spine bone tumors. Radiomic features of spine bone tumors show good reproducibility rates.

Management of intrathoracic phosphaturic mesenchymal tumor by nonintubated uniportal video-assisted thoracic surgery in a fragile patient.

BACKGROUND: Phosphaturic mesenchymal tumors are rare neoplasms, frequently presenting with osteomalacia. These neoplasms usually grow at a slow rate and are associated with unspecific symptoms. CASE: In this study, we present the case of a 70-year-old woman who had been suffering from musculoskeletal pain, hypophosphatemia, and spontaneous fractures. Positron emission tomography with Gallium showed increase uptake in a subpleural lesion. CONCLUSION: The patient underwent surgical excision of the subpleural lesion with a non-intubated uniportal video-assisted thoracoscopic surgery approach.

MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones.

BACKGROUND: Atypical cartilaginous tumour (ACT) and grade II chondrosarcoma (CS2) of long bones are respectively managed with watchful waiting or curettage and wide resection. Preoperatively, imaging diagnosis can be challenging due to interobserver variability and biopsy suffers from sample errors. The aim of this study is to determine diagnostic performance of MRI radiomics-based machine learning in differentiating ACT from CS2 of long bones. METHODS: One-hundred-fifty-eight patients with surgically treated and histology-proven cartilaginous bone tumours were retrospectively included at two tertiary bone tumour centres. The training cohort consisted of 93 MRI scans from centre 1 (n=74 ACT; n=19 CS2). The external test cohort consisted of 65 MRI scans from centre 2 (n=45 ACT; n=20 CS2). Bidimensional segmentation was performed on T1-weighted MRI. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, a machine-learning classifier (Extra Trees Classifier) was tuned on the training cohort using 10-fold cross-validation and tested on the external test cohort. In centre 2, its performance was compared with an experienced musculoskeletal oncology radiologist using McNemar's test. FINDINGS: After tuning on the training cohort (AUC=0.88), the machine-learning classifier had 92% accuracy (60/65, AUC=0.94) in identifying the lesions in the external test cohort. Its accuracies in correctly classifying ACT and CS2 were 98% (44/45) and 80% (16/20), respectively. The radiologist had 98% accuracy (64/65) with no difference compared to the classifier (p=0.134). INTERPRETATION: Machine learning showed high accuracy in classifying ACT and CS2 of long bones based on MRI radiomic features. FUNDING: ESSR Young Researchers Grant.

Ki-67 immunoexpression and radiological assessment of necrosis improves accuracy of conventional and modified core biopsy systems in predicting the final grade assigned to adult-soft tissue sarcomas. An international collaborative study.

Based on the French Federation Nationale des Centers de Lutte Contre le Cancer (FNCLCC) grading system, this study assesses the accuracy of conventional and modified core biopsy (CB) systems in predicting the final grade (low vs high) assigned to the resected specimen. Substituting Ki-67 immunoexpression for mitotic count, and radiological for histological assessment of necrosis, we used two modified FNCLCC CB grading systems: (1) Ki-67 immunoexpression alone, and (2) Ki-67 plus radiological assessment of necrosis. We graded 199 soft tissue sarcomas (STS) from nine centers, and compared the results for the conventional (obtained from local histopathology reports) and modified CB systems with the final FNCLCC grading of the corresponding resected specimens. Due to insufficient sample quality or lack of available radiologic data, five cases were not evaluated for Ki67 or radiological assessment of necrosis. The conventional FNCLCC CB grading system accurately identified 109 of the 130 high-grade cases (83.8%). The CB grading matched the final FNCLCC grading (low vs high) in 175 (87.9%) of the 199 resected tumors; overestimating the final grade in three cases and underestimating in 21 cases. Modified system 1 (Ki-67) accurately identified 117 of the 130 high-grade cases (90.0%). The CB grading matched the final FNCLCC grading (low vs high) in 175 (89.7%) of the 195 evaluated cases; overestimating seven and underestimating 13 cases. Modified system 2 (Ki-67 plus radiological necrosis) accurately identified 120 of the 130 high-grade cases (92.3%). This last matched the final FNCLCC grading (low vs high) in 177 (91.2%) of the 194 evaluated cases; overestimating seven and underestimating 10 cases. Modified system 2 obtained highest area under ROC curves, although not statistically significant. Underestimated CB grades did not correlate with histological subtypes, although many of the discrepant cases were myxoid tumors (myxofibrosarcomas or myxoid liposarcomas), leiomyosarcomas or undifferentiated pleomorphic/spindle cell sarcomas. Using modified FNCLCC CB grading systems to replace conventional mitotic count and histologic assessment of necrosis may improve the distinction between low and high-grade STS on CB. Our study confirms that classifying grade 1 as low grade and grades 2 and 3 as high grade improves correlation between CB and final grade by up to 21%, irrespective of CB system used. A higher than expected Ki-67 score in a low-grade sarcoma diagnosed on CB should raise concern that a higher-grade component may not have been sampled. Furthermore, correlation of all clinicopathological and radiological findings at multidisciplinary meetings is essential to assess the histological grade on CB as accurately as possible.

CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas.

BACKGROUND: Clinical management ranges from surveillance or curettage to wide resection for atypical to higher-grade cartilaginous tumours, respectively. Our aim was to investigate the performance of computed tomography (CT) radiomics-based machine learning for classification of atypical cartilaginous tumours and higher-grade chondrosarcomas of long bones. METHODS: One-hundred-twenty patients with histology-proven lesions were retrospectively included. The training cohort consisted of 84 CT scans from centre 1 (n=55 G1 or atypical cartilaginous tumours; n=29 G2-G4 chondrosarcomas). The external test cohort consisted of the CT component of 36 positron emission tomography-CT scans from centre 2 (n=16 G1 or atypical cartilaginous tumours; n=20 G2-G4 chondrosarcomas). Bidimensional segmentation was performed on preoperative CT. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, the performance of a machine-learning classifier (LogitBoost) was assessed on the training cohort using 10-fold cross-validation and on the external test cohort. In centre 2, its performance was compared with preoperative biopsy and an experienced radiologist using McNemar's test. FINDINGS: The classifier had 81% (AUC=0.89) and 75% (AUC=0.78) accuracy in identifying the lesions in the training and external test cohorts, respectively. Specifically, its accuracy in classifying atypical cartilaginous tumours and higher-grade chondrosarcomas was 84% and 78% in the training cohort, and 81% and 70% in the external test cohort, respectively. Preoperative biopsy had 64% (AUC=0.66) accuracy (p=0.29). The radiologist had 81% accuracy (p=0.75). INTERPRETATION: Machine learning showed good accuracy in classifying atypical and higher-grade cartilaginous tumours of long bones based on preoperative CT radiomic features. FUNDING: ESSR Young Researchers Grant.

Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study.

PURPOSE: Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics and machine learning (ML) have proven useful even in absence of a standardized data mining pipeline. We aimed to assess ML diagnostic performance in spinal lesion differential diagnosis, employing radiomic data extracted by different software. METHODS: Patients undergoing MRI for a vertebral lesion were retrospectively analyzed (n = 146, 67 males, 79 females; mean age 63 ±â€¯16 years, range 8-89 years) and constituted the train (n = 100) and internal test cohorts (n = 46). Part of the latter had additional prior exams which constituted a multi-scanner, external test cohort (n = 35). Lesions were labeled as benign or malignant (2-label classification), and benign, primary malignant or metastases (3-label classification) for classification analyses. Features extracted via 3D Slicer heterogeneityCAD module (hCAD) and PyRadiomics were independently used to compare different combinations of feature selection methods and ML classifiers (n = 19). RESULTS: In total, 90 and 1548 features were extracted by hCAD and PyRadiomics, respectively. The best feature selection method-ML algorithm combination was selected by 10 iterations of 10-fold cross-validation in the training data. For the 2-label classification ML obtained 94% accuracy in the internal test cohort, using hCAD data, and 86% in the external one. For the 3-label classification, PyRadiomics data allowed for 80% and 69% accuracy in the internal and external test sets, respectively. CONCLUSIONS: MRI radiomics combined with ML may be useful in spinal lesion assessment. More robust pre-processing led to better consistency despite scanner and protocol heterogeneity.

MAP2K1-driven mixed Langerhans cell histiocytosis, Rosai-Dorfman-Destombes disease and Erdheim-Chester disease, clonally related to acute myeloid leukemia.

Mixed histiocytoses are a rare and recently recognized subset of histiocytic disorders that may involve the skin, characterized by the synchronous or metachronous development of lesions with Langerhans and/or non-Langerhans cell histiocytosis histopathological features. Around 10% of patients diagnosed with histiocytosis may develop a hematological malignancy, often with dramatic prognostic consequences. We hereby describe the exceptional case of a patient developing a MAP2K1-driven mixed histiocytosis with Langerhans cell histiocytosis, Rosai-Dorfman-Destombes disease, and Erdheim-Chester disease features and cutaneous involvement, progressing to a fatal and clonally-related acute myeloid leukemia. We reviewed the literature on similar cases and discussed the histopathological difficulties in their diagnosis and their clinical-pathological features.

Vertebroplasty shows no antitumoral effect on vertebral metastasis: a case-based study on anatomopathological examinations.

PURPOSE: Percutaneous vertebroplasty (VTP) is a well-known surgical technique used for pain management and vertebral consolidation in the treatment of osteolytic metastases of the spine. While this indication is proven and commonly accepted, an antitumoral effect of polymethylmethacrylate (PMMA) has been proposed but not yet demonstrated. The aim of our study is to evaluate the evidences of antitumoral effect on anatomopathological examination. We present a small series of pathology findings after VTP for spine metastases that support the lack of antitumoral effect of PMMA. METHODS: We have retrospectively analyzed three cases of patients treated for en bloc excision of recurrent spine metastases previously submitted elsewhere to VTP on the same levels. We discuss our results with the literature reporting of an antitumoral effect of VTP. RESULTS: In our series, after anatomopathological examination, a cement-induced tumor necrosis was never found. Conversely, a foreign-body reaction around the cement was found, inside vital tumor. These results are consistent with an immune reaction to a foreign body without evidences of an antitumoral effect of PMMA. CONCLUSION: The antitumoral effect of PMMA should not be taken into account as an indication for VTP in spinal metastases. It is important not to misuse VTP as a therapy aiming at tumor control. Other therapies such as radiotherapy, radiosurgery and open surgery are available for that purpose.

Expression profiling of microRNAs and isomiRs in conventional central chondrosarcoma.

Conventional central chondrosarcoma (CCC) is a malignant bone tumor that is characterized by the production of chondroid tissue. Since radiation therapy and chemotherapy have limited effects on CCC, treatment of most patients depends on surgical resection. This study aimed to identify the expression profiles of microRNAs (miRNAs) and isomiRs in CCC tissues to highlight their possible participation to the regulation of pathways critical for the formation and growth of this type of tumor. Our study analyzed miRNAs and isomiRs from Grade I (GI), Grade II (GII), and Grade III (GIII) histologically validated CCC tissue samples. While the different histological grades shared a similar expression profile for the top abundant miRNAs, we found several microRNAs and isomiRs showing a strong different modulation in GII + GIII vs GI grade samples and their involvement in tumor biology could be consistently hypothesized. We then in silico validated these differently expressed miRNAs in a larger chondrosarcoma public dataset and confirmed the expression trend for 17 out of 34 miRNAs. Our results clearly suggests that the contribution of miRNA deregulation, and their targeted pathways, to the progression of CCC could be relevant and strongly indicates that when studying miRNA deregulation in tumors, not only the canonical miRNAs, but the whole set of corresponding isomiRs should be taken in account. Improving understanding of the precise roles of miRNAs and isomiRs over the course of central chondrosarcoma progression could help identifying possible targets for precision medicine therapeutic intervention.

When primary hyperparathyroidism comes as good news.

SUMMARY: Brown tumors are osteoclastic, benign lesions characterized by fibrotic stroma, intense vascularization and multinucleated giant cells. They are the terminal expression of the bone remodelling process occurring in advanced hyperparathyroidism. Nowadays, due to earlier diagnosis, primary hyperparathyroidism keeps few of the classical manifestations and brown tumors are definitely unexpected. Thus, it may happen that they are misdiagnosed as primary or metastatic bone cancer. Besides bone imaging, endocrine evaluation including measurement of serum parathyroid hormone and calcium (Ca) levels supports the pathologist to address the diagnosis. Herein, a case of multiple large brown tumors misdiagnosed as a non-treatable osteosarcoma is described, with special regards to diagnostic work-up. After selective parathyroidectomy, treatment with denosumab was initiated and a regular follow-up was established. The central role of multidisciplinary approach involving pathologist, endocrinologist and oncologist in the diagnostic and therapeutic work-up is reported. In our opinion, the discussion of this case would be functional especially for clinicians and pathologists not used to the differential diagnosis in uncommon bone disorders. LEARNING POINTS: Brown tumors develop during the remodelling process of bone in advanced and long-lasting primary or secondary hyperparathyroidism. Although rare, they should be considered during the challenging diagnostic work-up of giant cell lesions. Coexistence of high parathyroid hormone levels and hypercalcemia in primary hyperparathyroidism is crucial for the diagnosis. A detailed imaging study includes bone X-ray, bone scintiscan and total body CT; to rule out bone malignancy, evaluation of bone lesion biopsy should include immunostaining for neoplastic markers as H3G34W and Ki67 index. If primary hyperparathyroidism is confirmed, selective parathyroidectomy is the first-line treatment. In advanced bone disease, treatment with denosumab should be considered, ensuring a strict control of Ca levels.