Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model.

OBJECTIVES: Automatic bone lesions detection and classifications present a critical challenge and are essential to support radiologists in making an accurate diagnosis of bone lesions. In this paper, we aimed to develop a novel deep learning model called You Only Look Once (YOLO) to handle detecting and classifying bone lesions on full-field radiographs with limited manual intervention. METHODS: In this retrospective study, we used 1085 bone tumor radiographs and 345 normal bone radiographs from two centers between January 2009 and December 2020 to train and test our YOLO deep learning (DL) model. The trained model detected bone lesions and then classified these radiographs into normal, benign, intermediate, or malignant types. The intersection over union (IoU) was used to assess the model's performance in the detection task. Confusion matrices and Cohen's kappa scores were used for evaluating classification performance. Two radiologists compared diagnostic performance with the trained model using the external validation set. RESULTS: In the detection task, the model achieved accuracies of 86.36% and 85.37% in the internal and external validation sets, respectively. In the DL model, radiologist 1 and radiologist 2 achieved Cohen's kappa scores of 0.8187, 0.7927, and 0.9077 for four-way classification in the external validation set, respectively. The YOLO DL model illustrated a significantly higher accuracy for intermediate bone tumor classification than radiologist 1 (95.73% vs 88.08%, p = 0.004). CONCLUSIONS: The developed YOLO DL model could be used to assist radiologists at all stages of bone lesion detection and classification in full-field bone radiographs. KEY POINTS: • YOLO DL model can automatically detect bone neoplasms from full-field radiographs in one shot and then simultaneously classify radiographs into normal, benign, intermediate, or malignant. • The dataset used in this retrospective study includes normal bone radiographs. • YOLO can detect even some challenging cases with small volumes.

Shoulder MRI-based radiomics for diagnosis and severity staging assessment of surgically treated supraspinatus tendon tears.

OBJECTIVE: To develop and validate MRI-based radiomics models capable of evaluating supraspinatus tendon tears within the shoulder joints by using arthroscopy as the reference standard. METHODS: A total of 432 patients (332 in the training set and 100 in the external validation set) with intact supraspinatus tendon (n = 202) and supraspinatus tendon tear (n = 230, 130 full-thickness tears and 100 partial-thickness tears) were enrolled. Radiomics features were extracted from fat-saturated T2-weighted coronal images. Two radiomics signature models for detecting supraspinatus tendon abnormalities (tear or not), and stage lesion severity (full- or partial-thickness tear) and radiomics scores (Rad-score), were constructed and calculated using multivariate logistic regression analysis. The diagnostic performance of the two models was validated using ROC curves on the training and validation datasets. RESULTS: For the radiomics model of no tears or tears, thirteen features from MR images were used to build the radiomics signature with an AUC value of 0.98 in the training set, 0.97 in the internal validation set, and 0.98 in the external validation set. For the radiomics model of full- or partial-thickness tears, thirteen features from MR images were used to build the radiomics signature with an AUC value of 0.79 in the training set, 0.69 in the internal validation set, and 0.77 in the external validation set. CONCLUSION: The proposed radiomics models in this study can accurately rule out supraspinatus tendon tears and are capable of assessing the severity staging of tears with moderate accuracy based on shoulder MR images. KEY POINTS: • The radiomics model of no tears or tears achieved a high overall accuracy of 93.6%, sensitivity of 91.6%, and specificity of 95.2% for supraspinatus tendon tears. • The radiomics model of full- or partial-thickness tears displayed moderate performance with an accuracy of 76.4%, a sensitivity of 79.2%, and a specificity of 74.3% for supraspinatus tendon tears severity staging.

Distal Femoral Non-Epiphyseal Cortical Chondroblastoma Confirmed with H3F3B p. Lys36Met Mutation.

Background: Chondroblastoma is a primary bone tumor typically arising from the intramedullary space of the epiphysis or epimetaphysis. A non-epiphyseal chondroblastoma is uncommon. Case report: An 11-year-old girl presented with an eccentric cortical osteolytic lesion in the distal femur metaphysis. The typical morphology, diffuse H3.3 K36M immunohistochemical expression and H3F3B point mutation (c. 110A > T) unequivocally supported the diagnosis of chondroblastoma. Discussion: We described a non-epiphyseal cortical-based chondroblastoma involving the distal femur harboring the typical H3F3B mutation. Non-epiphyseal chondroblastoma may harbor the H3F3B mutation.

Differential diagnosis of renal oncocytoma and chromophobe renal cell carcinoma using CT features: a central scar-matched retrospective study.

BACKGROUND: Renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) have a common cellular origin and different clinical management and prognosis. PURPOSE: To explore the utility of computed tomography (CT) in the differentiation of RO and chRCC. MATERIAL AND METHODS: Twenty-five patients with RO and 73 patients with chRCC presenting with the central scar were included retrospectively. Two experienced radiologists independently reviewed the CT imaging features, including location, tumor size, relative density ratio, segmental enhancement inversion (SEI), necrosis, and perirenal fascia thickening, among others. Interclass correlation coefficient (ICC, for continuous variables) or Kappa coefficient test (for categorical variables) was used to determine intra-observer and inter-observer bias between the two radiologists. RESULTS: The inter- and intra-reader reproducibility of the other CT imaging parameters were nearly perfect (>0.81) except for the measurements of fat (0.662). RO differed from chRCC in the cortical or medullary side (P = 0.005), relative density ratio (P = 0.020), SEI (P < 0.001), and necrosis (P = 0.045). The logistic regression model showed that location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were highly predictive of RO. The combined indicators from logistic regression model were used for ROC analysis. The area under the ROC curve was 0.923 (P < 0.001). The sensitivity and specificity of the four factors combined for diagnosing RO were 88% and 86.3%, respectively. The correlation coefficient between necrosis and tumor size in all tumors including both of RO and chRCC was 0.584, indicating a positive correlation (P < 0.001). CONCLUSION: The CT imaging features of location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were valuable indicators in distinguishing RO from chRCC.

MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland.

OBJECTIVES: Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT. METHODS: A total of 115 patients (80 in training set and 35 in external validation set) with BPGT (n = 60) or MPGT (n = 55) were enrolled. Radiomics features were extracted from T1-weighted and fat-saturated T2-weighted images. A radiomics signature model and a radiomics score (Rad-score) were constructed and calculated. A clinical-factors model was built based on demographics and MRI findings. A radiomics nomogram model combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The diagnostic performance of the three models was evaluated and validated using ROC curves on the training and validation datasets. RESULTS: Seventeen features from MR images were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature had an AUC value of 0.952 in the training set and 0.938 in the validation set. Decision curve analysis showed that the nomogram outperformed the clinical-factors model in terms of clinical usefulness. CONCLUSIONS: The above-described radiomics nomogram performed well for differentiating BPGT from MPGT, and may help in the clinical decision-making process. KEY POINTS: • Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.

Radiologic findings that aid in the reduction of misdiagnoses of Langerhans cell histiocytosis of the bone: a retrospective study.

BACKGROUND: This study aimed to identify the characteristic radiological signs for the diagnosis of Langerhans cell histiocytosis (LCH) of the bone. METHODS: We retrospectively studied 82 cases of LCH with bone lesions confirmed by pathology. Clinical and radiological features of the patients were analyzed. RESULTS: A total of 64 and 18 patients had single and multiple bone lesions, respectively. With regard to LCH with single bone lesions, 37.5% (24/64) of lesions were located in the skull and presented as bone destruction with or without soft tissue mass. The correct diagnosis rate of these lesions was 60.0% (9/15) in children and adolescents, but was only 22.2% (2/9) in adults. A total of 26.5% (17/64) of the solitary lesions were found in the spine. Of these, 88.2% (15/17) were located in the vertebral body and appeared to have different degrees of collapse, and 66.7% (10/15) of these lesions were correctly diagnosed. Of the unifocal lesions, 21.8% (14/64) were located in other flat and irregular bones and manifested as osteolysis. Only 21.4% (3/14) of these cases were correctly diagnosed. In total, 14.1% (9/64) of the isolated bone LCH lesions were located in the long bones. Of these, 77.8% (7/9) were located in the diaphysis and presented as central bone destruction with or without fusiform periosteal reaction and extensive peripheral edema, of which 42.9% (3/7) were correctly diagnosed before surgery or biopsy. With regard to LCH with multiple bony destructive lesions, 71.4% (10/14) of cases in children and adolescents were correctly diagnosed; however, all four cases among adults were misdiagnosed. CONCLUSION: In all age groups, isolated diaphyseal destruction of the long bone with fusiform periosteal reaction and extensive peripheral edema, vertebra plana of the spine, and bevelled edge of skull defects accompanied by soft tissue masses strongly suggest LCH diagnosis. Moreover, the multiple bone osteolytic destruction in children and adolescents strongly suggests LCH diagnosis. Familiarity with these typical radiological signs of LCH is necessary to decrease misdiagnoses.

CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV).

In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 novel coronavirus (2019-nCoV) were reviewed, with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease, as manifested by increasing extent and density of lung opacities.

An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma.

The objective of this study was to predict Ki-67 proliferation index of meningioma by using a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features. A total of 318 cases were enrolled in the study. The clinical, radiomics, and DTL features were selected to construct models. The calculation of radiomics and DTL score was completed by using selected features and correlation coefficient. The deep transfer learning radiomics (DTLR) nomogram was constructed by selected clinical features, radiomics score, and DTL score. The area under the receiver operator characteristic curve (AUC) was calculated. The models were compared by Delong test of AUCs and decision curve analysis (DCA). The features of sex, size, and peritumoral edema were selected to construct clinical model. Seven radiomics features and 15 DTL features were selected. The AUCs of clinical, radiomics, DTL model, and DTLR nomogram were 0.746, 0.75, 0.717, and 0.779 respectively. DTLR nomogram had the highest AUC of 0.779 (95% CI 0.6643-0.8943) with an accuracy rate of 0.734, a sensitivity value of 0.719, and a specificity value of 0.75 in test set. There was no significant difference in AUCs among four models in Delong test. The DTLR nomogram had a larger net benefit than other models across all the threshold probability. The DTLR nomogram had a satisfactory performance in Ki-67 prediction and could be a new evaluation method of meningioma which would be useful in the clinical decision-making.

Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study.

Objectives: The objective of this study was to compare the predictive performance of 2D and 3D radiomics features in meningioma grade based on enhanced T1 WI images. Methods: There were 170 high grade meningioma and 170 low grade meningioma were selected randomly. The 2D and 3D features were extracted from 2D and 3D ROI of each meningioma. The Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select the valuable features. The 2D and 3D predictive models were constructed by naive Bayes (NB), gradient boosting decision tree (GBDT), and support vector machine (SVM). The ROC curve was drawn and AUC was calculated. The 2D and 3D models were compared by Delong test of AUCs and decision curve analysis (DCA) curve. Results: There were 1143 features extracted from each ROI. Six and seven features were selected. The AUC of 2D and 3D model in NB, GBDT, and SVM was 0.773 and 0.771, 0.722 and 0.717, 0.733 and 0.743. There was no significant difference in two AUCs (p=0.960, 0.913, 0.830) between 2D and 3D model. The 2D features had a better performance than 3D features in NB models and the 3D features had a better performance than 2D features in GBDT models. The 2D features and 3D features had an equal performance in SVM models. Conclusions: The 2D and 3D features had a comparable performance in predicting meningioma grade. Considering the issue of time and labor, 2D features could be selected for radiomics study in meningioma. Key points: There was a comparable performance between 2D and 3D features in meningioma grade prediction. The 2D features was a proper selection in meningioma radiomics study because of its time and labor saving.

Difference of mean Hounsfield units (dHU) between follow-up and initial noncontrast CT scan predicts 90-day poor outcome in spontaneous supratentorial acute intracerebral hemorrhage with deep convolutional neural networks.

OBJECTIVES: This study aimed to investigate the usefulness of a new non-contrast CT scan (NCCT) sign called the dHU, which represented the difference in mean Hounsfield unit values between follow-up and the initial NCCT for predicting 90-day poor functional outcomes in acute supratentorial spontaneous intracerebral hemorrhage(sICH) using deep convolutional neural networks. METHODS: A total of 377 consecutive patients with sICH from center 1 and 91 patients from center 2 (external validation set) were included. A receiver operating characteristic (ROC) analysis was performed to determine the critical value of dHU for predicting poor outcome at 90 days. Modified Rankin score (mRS) >3 or >2 was defined as the primary and secondary poor outcome, respectively. Two multivariate models were developed to test whether dHU was an independent predictor of the two unfavorable functional outcomes. RESULTS: The ROC analysis showed that a dHU >2.5 was a critical value to predict the poor outcomes (mRS >3) in sICH. The sensitivity, specificity, and accuracy of dHU >2.5 for poor outcome prediction were 37.5%, 86.0%, and 70.6%, respectively. In multivariate models developed after adjusting for all elements of the ICH score and hematoma expansion, dHU >2.5 was an independent predictor of both primary and secondary poor outcomes (OR = 2.61, 95% CI [1.32,5.13], P = 0.006; OR = 2.63, 95% CI [1.36,5.10], P = 0.004, respectively). After adjustment for all possible significant predictors (p < 0.05) by univariate analysis, dHU >2.5 had a positive association with primary and secondary poor outcomes (OR = 3.25, 95% CI [1.52,6.98], P = 0.002; OR = 3.42, 95% CI [1.64,7.15], P = 0.001). CONCLUSIONS: The dHU of hematoma based on serial CT scans is independently associated with poor outcomes after acute sICH, which may help predict clinical evolution and guide therapy for sICH patients.

Radiomics for predicting revised hematoma expansion with the inclusion of intraventricular hemorrhage growth in patients with supratentorial spontaneous intraparenchymal hematomas.

BACKGROUND: Previous radiomics analyses of hematoma expansion have been based on the traditional definition, which only focused on changes in intraparenchymal volume. However, the ability of radiomics-related models to predict revised hematoma expansion (RHE) with the inclusion of intraventricular hemorrhage expansion remains unclear. To develop and validate a noncontrast computed tomography (NCCT)-based clinical- semantic-radiomics nomogram to identify supratentorial spontaneous intracerebral hemorrhage (sICH) patients with RHE on admission. METHODS: In this double-center retrospective study, data from 376 patients with sICH (training set: n=299; test set: n=77; external validation cohort: n=91) were reviewed. A radiomics model, a clinical-semantic model, and a combined model were then constructed based on the logistic regression machine learning approach. Radiomics features were extracted and selected by least absolute shrinkage and selection operator (LASSO) with 5-fold cross validation. Furthermore, the classical BRAIN scoring system was also constructed to predict RHE. Discriminative performance of the models was evaluated on the training and test set with area under the curve (AUC) and decision curve analysis (DCA). RESULTS: The addition of radiomics to clinical-semantic factors significantly improved the prediction performance of RHE compared with the clinical-semantic model alone in the training (AUC, 0.94 vs. 0.81, P<0.05) and test (AUC, 0.84 vs. 0.71, P<0.05) sets, with similar results in the validation set (AUC, 0.83 vs. 0.69, P<0.05). Moreover, the discrimination efficacy of the BRAIN score was significantly lower than the other 3 models (AUC of 0.71 in the training set, P<0.05). CONCLUSIONS: The clinical-semantic-radiomics combined model had the greatest potential for discriminating RHE, and significantly outperformed the classical BRAIN scoring system.

Extraosseous spinal epidural plasmocytoma associated with multiple myeloma: Two case reports.

BACKGROUND: Multiple myeloma is a malignant hematological disease characterized by proliferation of monoclonal plasma cells mainly in the bone marrow. Extraosseous epidural plasmacytoma associated with myeloma arises from lymphoid tissue in the epidural space without focal vertebral involvement, and is rare. CASE SUMMARY: A 52-year-old woman was diagnosed with kappa subtype nonsecretory multiple myeloma and presented with bilateral arm weakness 11 mo after completing multiple courses of chemotherapy. Spinal magnetic resonance imaging (MRI) showed a posterior C7-T3 epidural mass with spinal cord compression. After five courses of chemotherapy, follow-up MRI showed resolution of cord compression. A 54-year-old man presented with paraplegia 15 mo after a diagnosis of IgD kappa subtype multiple myeloma and completing multiple courses of chemotherapy. He underwent Th11 and L1 laminectomies for tumor resection because MRI showed an epidural mass causing cord compression. His-topathologic examination was consistent with IgD multiple myeloma. The patients have currently survived for 33 mo and 19 mo, respectively. CONCLUSION: Isolated extraosseous epidural plasmacytoma associated with multiple myeloma without bony involvement is difficult to diagnose by imaging. Definitive diagnosis requires pathological and immunohistochemical examination.

Chitin-hydroxyapatite-collagen composite scaffolds for bone regeneration.

Bone defect is usually difficult to recover quickly, and bone scaffold transplantation is considered to be an effective method. Biomaterials have a wide range of application prospects in bone tissue repair, and the two key problems are the selection of materials and cells. The object of this study was to discuss the structural characteristics of bone scaffold materials and their effects on bone repair in vivo. The chitin-hydroxyapatite (HAP)-collagen composite scaffolds (CHCS) was prepared with epichlorohydrin (ECH) as crosslinking agent. The structure was characterized and the compressive strength, porosity, water absorbency and stability were investigated. The biocompatibility and osteogenic differentiation of CHCS in vitro were detected, and the effect of defect repair in vivo was evaluated. The results suggested that HAP not only enhanced the compressive strength of CHCS, but also promoted the formation of calcium nodules due to its bone conductivity. Histological staining showed that collagen promoted collagen deposition and new bone formation. X-ray images also indicated that CHCS transplantation accelerated bone repair. Therefore, CHCs has immense potential in bone regeneration.

Computed tomography and magnetic resonance imaging features of cervical chordoma.

Computed tomography (CT) and magnetic resonance imaging (MRI) scans of 11 patients with histologically proven cervical chordoma were retrospectively evaluated. Imaging features assessed included location, morphology, association with adjacent structures, vertebral destruction, status of cortical bone, periosteal reaction, attenuation and calcification by CT, and signal intensity and enhancement pattern by MRI. Of 7 cases with CT, 6 exhibited lytic-sclerotic bone destruction. A total of 5 cases exhibited pressure erosion of outer cortex, 3 of which had spiculated periosteal reaction. Calcification was observed in 3 cases. All cases were heterogeneous and hypodense. MRI T2-weighted images (n=10) revealed heterogeneous hyperintense (n=5), intermediate (n=2) and intermediate-hyperintense signal intensity (n=3). Hypointense septa between lobules (n=5) and stripes (n=3) were observed on T2-weighted images. Post-contrast magnetic resonance images (n=6) demonstrated marked heterogeneous (n=3) and ring-like (n=3) enhancement. CT scanning is valuable in revealing the lytic-sclerotic bone destruction, pressure erosion of outer cortex and calcification. MRI is useful in demonstrating the results of soft tissue mass. The two examinations are necessary for differential diagnosis of patients with suspected cervical chordoma.

Magnetic Resonance Features and Characteristic Vascular Pattern of Alveolar Soft-Part Sarcoma.

OBJECTIVE: The aim of this study was to investigate the magnetic resonance (MR) features of alveolar soft-part sarcoma (ASPS). METHODS: We studied 12 patients with ASPS confirmed by pathology in this retrospective study. MR features were analyzed, especially for the location, morphology, signals, and related enhanced features of the tumor vessels. RESULTS: Flow voids were shown in the central part of the tumor on T2-weighted imaging (T2WI) in all patients; they were arrayed in a radiating mode gathered toward the center (8 cases), designated by us as vascular center-gathered syndrome (VCGS), or scattered like twigs (4 cases). The flow voids were accompanied by high signals in all patients, including tubular (6 cases) and platy (6 cases) signals. Slightly higher signals were shown in the peripheral part of the tumor in all patients. Flow voids in the peripheral part were shown in all patients, and the majority of the flow voids surrounded the tumor (8 cases). The vessels around the tumor in 9 patients showed high signals, and the majority of the vessels were located at the superior and inferior poles (8 cases). 6 patients underwent enhanced scanning, including moderate (5 cases) and significant enhancement (1 case). CONCLUSION: Low signals of radiating flow voids accompanied by high signals of slow blood flow or blood sinuses in the center part have high significance for the diagnosis of ASPS.