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Varese (Italia). Uno stralcio della prima opera classificata nella sezione Biografie della sesta edizione del premio internazionale Thrinakìa.

Summary: 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.

Abstract 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.

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.

ObjectiveThe 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 methodsThis 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.Results1303 (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.ConclusionCompared 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.

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.

Abstract 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.

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.

Summary: 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.

The interaction of heparin with antithrombin (AT) involves a specific sequence corresponding to the pentasaccharide GlcNAc/NS6S-GlcA-GlcNS3S6S-IdoA2S-GlcNS6S (AGA*IA). Recent studies have revealed that two AGA*IA-containing hexasaccharides, which differ in the sulfation degree of the iduronic acid unit, exhibit similar binding to AT, albeit with different affinities. However, the lack of experimental data concerning the molecular contacts between these ligands and the amino acids within the protein-binding site prevents a detailed description of the complexes. Differential epitope mapping (DEEP)-STD NMR, in combination with MD simulations, enables the experimental observation and comparison of two heparin pentasaccharides interacting with AT, revealing slightly different bound orientations and distinct affinities of both glycans for AT. We demonstrate the effectiveness of the differential solvent DEEP-STD NMR approach in determining the presence of polar residues in the recognition sites of glycosaminoglycan-binding proteins. [ABSTRACT FROM AUTHOR]

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.