Classification Of Bone Tumors Research Articles

An enhanced AlexNet-Based model for femoral bone tumor classification and diagnosis using magnetic resonance imaging

ObjectiveBone tumors, known for their infrequent occurrence and diverse imaging characteristics, require precise differentiation into benign and malignant categories. Existing diagnostic approaches heavily depend on the laborious and variable manual delineation of tumor regions. Deep learning methods, particularly convolutional neural networks (CNNs), have emerged as a promising solution to tackle these issues. This paper introduces an enhanced deep-learning model based on AlexNet to classify femoral bone tumors accurately. MethodsThis study involved 500 femoral tumor patients from July 2020 to January 2023, with 500 imaging cases (335 benign and 165 malignant). A CNN was employed for automated classification. The model framework encompassed training and testing stages, with 8 layers (5 Conv and 3 FC) and ReLU activation. Essential architectural modifications included Batch Normalization (BN) after the first and second convolutional filters. Comparative experiments with various existing methods were conducted to assess algorithm performance in tumor staging. Evaluation metrics encompassed accuracy, precision, sensitivity, specificity, F-measure, ROC curves, and AUC values. ResultsThe analysis of precision, sensitivity, specificity, and F1 score from the results demonstrates that the method introduced in this paper offers several advantages, including a low feature dimension and robust generalization (with an accuracy of 98.34 %, sensitivity of 97.26 %, specificity of 95.74 %, and an F1 score of 96.37). These findings underscore its exceptional overall detection capabilities. Notably, when comparing various algorithms, they generally exhibit similar classification performance. However, the algorithm presented in this paper stands out with a higher AUC value (AUC=0.848), signifying enhanced sensitivity and more robust specificity. ConclusionThis study presents an optimized AlexNet model for classifying femoral bone tumor images based on convolutional neural networks. This algorithm demonstrates higher accuracy, precision, sensitivity, specificity, and F1-score than other methods. Furthermore, the AUC value further confirms the outstanding performance of this algorithm in terms of sensitivity and specificity. This research makes a significant contribution to the field of medical image classification, offering an efficient automated classification solution, and holds the potential to advance the application of artificial intelligence in bone tumor classification.

EENet: Application of convolutional neural network‐based deep learning methods in bone tumor pathological diagnosis

AbstractBone tumors are one of the most common diseases in bone and soft tissue tumors, and accurate classification is crucial for developing effective treatment strategies. However, traditional pathological morphology diagnosis is subjective and uncertain, requiring highly specialized knowledge and experience. Therefore, how to efficiently and accurately classify bone tumor types based on pathological images is an urgent problem to be solved. In this study, a lightweight convolutional neural network (CNN) model called Efficient and Enhance Network (EENet) was proposed for the automatic recognition of bone tumor pathological images. The model introduced efficient channel attention (ECA) blocks to improve the efficiency of feature extraction and reduce computational complexity. A comprehensive dataset of bone pathological images, including five types of bone lesion tissues and normal bone tissue, was compiled for the research. Due to the low incidence of bone tumors and the difficulty in obtaining a large amount of data, the transfer learning method was chosen to overcome the problem of limited data volume. The experimental results from the fivefold cross‐validation demonstrate that the proposed model achieves 99.06% accuracy, 99.08% precision, 99.19% recall, 99.81% specificity, and 99.03% F1‐score in the bone tumor classification task, highlighting its potential as a clinical diagnostic tool.

Bone metastases from chondroblastoma: a rare pattern of metastatic disease in an adult.

Chondroblastoma is a rare benign tumor, typically presenting in the first two decades. Systemic metastases in chondroblastoma are extremely rare and it is the rarity of these metastases which lead the World Health Organisation to re-classify this lesion from "intermediate" to "benign" in its updated classification of bone tumors in 2020. We present an unusual case of a 55year-old male patient who presented with multiple FDG-avid bone lesions on a background of conventional chondroblastoma of the rib excised at another institution 11-years previously. Two of these lesions were also histologically-proven as conventional chondroblastoma at biopsy. This case highlights that, although rare, metastases can be seen in patients with chondroblastoma. To our knowledge, this is the only case with an unusual pattern of metastases limited to bone.

A fusion of VGG-16 and ViT models for improving bone tumor classification in computed tomography

Background and ObjectiveBone tumors present significant challenges in orthopedic medicine due to variations in clinical treatment approaches for different tumor types, which includes benign, malignant, and intermediate cases. Convolutional Neural Networks (CNNs) have emerged as prominent models for tumor classification. However, their limited perception ability hinders the acquisition of global structural information, potentially affecting classification accuracy. To address this limitation, we propose an optimized deep learning algorithm for precise classification of diverse bone tumors. Materials and MethodsOur dataset comprises 786 computed tomography (CT) images of bone tumors, featuring sections from two distinct bone species, namely the tibia and femur. Sourced from The Second Affiliated Hospital of Fujian Medical University, the dataset was meticulously preprocessed with noise reduction techniques. We introduce a novel fusion model, VGG16-ViT, leveraging the advantages of the VGG-16 network and the Vision Transformer (ViT) model. Specifically, we select 27 features from the third layer of VGG-16 and input them into the Vision Transformer encoder for comprehensive training. Furthermore, we evaluate the impact of secondary migration using CT images from Xiangya Hospital for validation. ResultsThe proposed fusion model demonstrates notable improvements in classification performance. It effectively reduces the training time while achieving an impressive classification accuracy rate of 97.6%, marking a significant enhancement of 8% in sensitivity and specificity optimization. Furthermore, the investigation into secondary migration's effects on experimental outcomes across the three models reveals its potential to enhance system performance. ConclusionOur novel VGG-16 and Vision Transformer joint network exhibits robust classification performance on bone tumor datasets. The integration of these models enables precise and efficient classification, accommodating the diverse characteristics of different bone tumor types. This advancement holds great significance for the early detection and prognosis of bone tumor patients in the future.

Aggressive Osteoblastoma of Temporal Bone Causing Facial Palsy in a 9-year-old Child: A Case Report Based on 2020 WHO Classification of Bone Tumors

AbstractAggressive osteoblastoma (AO) is an uncommon bone tumor that represents a borderline lesion between osteoblastoma and osteosarcoma. The vertebral column, the sacrum, the pelvis, and jaw/craniofacial bones are primarily affected. Aggressive osteoblastoma does not metastasize and is treated by surgical resection. The authors report a case of AO in a 9-year-old female patient presenting with 5th and 7th cranial nerve palsy. Prior pathological history included resection of an expansile nodule in the left temporal bone. Conventional radiological examination and computed tomography (CT) of the skull revealed an osteoblastic lesion arising in the petrous portion of the left temporal bone, measuring 5.2 cm in the largest dimension. The patient was subjected to partial surgical resection of the process. Microscopy revealed a primary neoplastic bone composed of numerous epithelioid round osteoblasts disposed in solid sheets and with mild atypia, large eosinophilic cytoplasm, and an eccentric, ovoid nucleus. The process exhibited loose stroma, low mitotic index, osteoid formation, and a few osteoclast-like multinucleated giant cells. The diagnosis of AO was thus established. After 5 months of clinical follow-up, the patient is asymptomatic, without evidence of tumoral growth on CT scans.

FemurTumorNet: Bone tumor classification in the proximal femur using DenseNet model based on radiographs

Background & purposeFor the best possible outcomes from therapy, proximal femur bone cancers must be accurately classified. This work creates an artificial intelligence (AI) model based on plain radiographs to categorize bone tumor in the proximal femur. Materials and methodsA tertiary referral center's standard anteroposterior hip radiographs were employed. A dataset 538 images of the femur, including malignant, benign, and tumor-free cases, was employed for training the AI model. There is a total of 214 images showing bone tumor. Pre-processing techniques were applied, and DenseNet model utilized for classification. The performance of the DenseNet model was compared to that of human doctors using cross-validation, further enhanced by incorporating Grad-CAM to visually indicate tumor locations. ResultsFor the three-label classification job, the suggested method boasts an excellent area under the receiver operating characteristic (AUROC) of 0.953. It scored much higher (0.853) than the diagnosis accuracy of the human experts in manual classification (0.794). The AI model outperformed the mean values of the clinicians in terms of sensitivity, specificity, accuracy, and F1 scores. ConclusionThe developed DenseNet model demonstrated remarkable accuracy in classifying bone tumors in the proximal femur using plain radiographs. This technology has the potential to reduce misdiagnosis, particularly among non-specialists in musculoskeletal oncology. The utilization of advanced deep learning models provides a promising approach for improved classification and enhanced clinical decision-making in bone tumor detection.

World health organization classification of bone tumors (fifth edition): What a radiologist needs to know?

Since 1967, the World Health Organization (WHO) classification of tumors is regarded as a reference standard and practical guide and provides a precious resource not only for the histopathologists and oncosurgeons but also for the radiologists involved in cancer care. Primary bone tumors are a rare and heterogeneous group of neoplasms that have a broad spectrum of morphological, biological, genetic, and radiological features. Appropriate imaging workup and accurate histopathological diagnosis are crucial for appropriate management and prognostication. The fourth edition of the WHO classification of tumors of soft tissue and bone was introduced in 2013. In the past 7 years, there have been considerable advances in the understanding of this large and diverse group of tumors. With technological advances and the introduction of new molecular and genetic data about some bone tumors, there has been reorganization in the classification and introduction of a few new entities. The new WHO classification of soft tissue and bone tumors introduced in 2020 (fifth edition) has made essential refinements in the classification and has also introduced many new entities. Newly identified genetic alterations and corresponding immunohistochemical markers are included in the new classification, and this has helped in the reclassification of the existing tumor entities. These novel genetic alterations not only help in prognostication but are a target for potential therapeutic options which can bring a paradigm shift in the chemotherapeutic regimen for these entities in the future. The sole basis for the classification of bone tumors is histopathological. Although radiologists are not expected to know about the exquisite pathological details of bone tumors, a broad knowledge of the recent updates, including the reclassification of a few entities or the introduction of some, is vital for narrowing the differentials in imaging. A multidisciplinary approach including an orthopedic oncologist, radiologist, pathologist, surgical, and medical oncologist is required for accurate diagnosis and management of primary bone tumors. We hereby present a simplified review for the radiologists comprising the relevant details of the updates in bone tumors along with a simplified diagnostic algorithm to characterize these lesions.

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

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. 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. 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). 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. • 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.

The 2020 World Health Organization classification of bone tumors: what radiologists should know.

Improved understanding of tumor biology through molecular alteration and genetic advances has resulted in a number of major changes in the 2020 World Health Organization's (WHO) classification of bone tumors. These changes include the reclassification of the existing tumors and the introduction of several new entities. A new chapter on undifferentiated small round cell sarcomas of bone and soft tissue was added to classify Ewing sarcoma and the family of Ewing-like sarcomas, which share similar histologies but different molecular and clinical behaviors. Knowledge of the current classification of bone tumors is essential to ensure the appropriate recognition of the inherent biological potential of individual osseous lesions for optimal treatment, follow-up, and overall outcome. This article reviews the major changes to the 2020 WHO's classification of primary bone tumors and the pertinent imaging of selected tumors to highlight these changes.

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study.

BackgroundAs a research hotspot, deep learning has been continuously combined with various research fields in medicine. Recently, there is a growing amount of deep learning-based researches in orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends.MethodsWe screened global publication on deep learning applications in orthopedics by accessing the Web of Science Core Collection. The articles and reviews were collected without language and time restrictions. Citespace was applied to conduct the bibliometric analysis of the publications.ResultsA total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopedics has great prospects for development based on the annual publications. The most prolific country is the USA, followed by China. University of California San Francisco, and Skeletal Radiology are the most prolific institution and journal, respectively. LeCun Y is the most frequently cited author, and Nature has the highest impact factor in the cited journals. The current hot keywords are convolutional neural network, classification, segmentation, diagnosis, image, fracture, and osteoarthritis. The burst keywords are risk factor, identification, localization, and surgery. The timeline viewer showed two recent research directions for bone tumors and osteoporosis.ConclusionPublications on deep learning applications in orthopedics have increased in recent years, with the USA being the most prolific. The current research mainly focused on classifying, diagnosing and risk predicting in osteoarthritis and fractures from medical images. Future research directions may put emphasis on reducing intraoperative risk, predicting the occurrence of postoperative complications, screening for osteoporosis, and identification and classification of bone tumors from conventional imaging.

Giant cell-containing tumors of bone and differential diagnoses

Osteoclastic giant cells represent acommon cellular component of lesions arising in bone. Highlighting this morphological finding, the current WHO classification of bone tumors defines adiagnostic group comprising aneurysmal bone cyst, giant cell tumor of bone and non-ossifying fibroma, which may display morphologic similarities while being distinct with regard to molecular and biological features. Starting with these tumors - putting afocus on lesions arising in bone - this article gives asurvey of other (chondrogenic and osteogenic) tumors that frequently contain osteoclastic giant cells, which may, particularly in small biopsies, enter differential diagnosis. Overlapping features with selected giant cell-containing soft tissue tumors, which may be of differential diagnostic relevance in daily routine, are discussed.

Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation.

PurposeEarly detection and classification of bone tumors in the proximal femur is crucial for their successful treatment. This study aimed to develop an artificial intelligence (AI) model to classify bone tumors in the proximal femur on plain radiographs.MethodsStandard anteroposterior hip radiographs were obtained from a single tertiary referral center. A total of 538 femoral images were set for the AI model training, including 94 with malignant, 120 with benign, and 324 without tumors. The image data were pre-processed to be optimized for training of the deep learning model. The state-of-the-art convolutional neural network (CNN) algorithms were applied to pre-processed images to perform three-label classification (benign, malignant, or no tumor) on each femur. The performance of the CNN model was verified using fivefold cross-validation and was compared against that of four human doctors.ResultsThe area under the receiver operating characteristic (AUROC) of the best performing CNN model for the three-label classification was 0.953 (95% confidence interval, 0.926–0.980). The diagnostic accuracy of the model (0.853) was significantly higher than that of the four doctors (0.794) (P = 0.001) and also that of each doctor individually (0.811, 0.796, 0.757, and 0.814, respectively) (P<0.05). The mean sensitivity, specificity, precision, and F1 score of the CNN models were 0.822, 0.912, 0.829, and 0.822, respectively, whereas the mean values of four doctors were 0.751, 0.889, 0.762, and 0.797, respectively.ConclusionsThe AI-based model demonstrated high performance in classifying the presence of bone tumors in the proximal femur on plain radiographs. Our findings suggest that AI-based technology can potentially reduce the misdiagnosis of doctors who are not specialists in musculoskeletal oncology.

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.

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors.

Objectives To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the classification of bone tumors. Materials and Methods In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience. SHapley Additive exPlanations (SHAP) was used to identify the most essential features for the classification of bone tumors. The diagnostic performance of the RF models was quantified using receiver operating characteristic (ROC) curves. Results The features extracted by the three radiologists had a satisfactory agreement and the minimum intraclass correlation coefficient (ICC) was 0.761 (CI: 0.686-0.824, P < .001). The binary and tertiary models were built to classify tumors as benign, malignant, or intermediate based on the imaging and clinical features from 627 and 796 patients. The AUC of the binary (19 variables) and tertiary (22 variables) models were 0.97 and 0.94, respectively. The accuracy of binary and tertiary models were 94.71% and 82.77%, respectively. In descending order, the most important features influencing classification in the binary model were margin, cortex involvement, and the pattern of bone destruction, and the most important features in the tertiary model were margin, high-density components, and cortex involvement. Conclusions This study developed interpretable models to classify bone tumors with great performance. These should allow radiographers to identify imaging features that are important for the classification of bone tumors in the clinical setting.

Primary extradural tumors of the spinal column: A comprehensive treatment guide for the spine surgeon based on the 5th Edition of the World Health Organization bone and soft-tissue tumor classification.

Background:In 2020, the World Health Organization (WHO) published the 5th version of the soft tissue and bone tumor classification. Based on this novel classification system, we reviewed the current knowledge on all tumor entities with spinal manifestations, their biologic behavior, and most importantly the appropriate treatment options as well as surgical approaches.Methods:All tumor entities were extracted from the WHO Soft-Tissue and Bone Tumor Classification (5th Edition). PubMed and Google Scholar were searched for the published cases of spinal tumor manifestations for each entity, and the following characteristics were extracted: Growth pattern, ability to metastasize, peak age, incidence, treatment, type of surgical resection indicated, recurrence rate, risk factors, 5-year survival rate, key molecular or genetic alterations, and possible associated tumor syndromes. Surgical treatment strategies as well as nonsurgical treatment recommendations are presented based on the biologic behavior of each lesion.Results:Out of 163 primary tumor entities of bone and soft tissue, 92 lesions have been reported along the spinal axis. Of these 92 entities, 54 have the potential to metastasize. The peak age ranges from conatal lesions to 72 years. For each tumor entity, we present recommended surgical treatment strategies based on the ability to locally destruct tissue, to grow, recur after resection, undergo malignant transformation as well as survival rates. In addition, potential systemic treatment recommendations for each tumor entity are outlined.Conclusion:Based on the 5th Edition of the WHO bone and soft tumor classification, we identified 92 out of 163 tumor entities, which potentially can have spinal manifestations. Exact preoperative tissue diagnosis and interdisciplinary case discussions are crucial. Surgical resection is indicated in a significant subset of patients and has to be tailored to the specific biologic behavior of the targeted tumor entity based on the considerations outlined in detail in this article.

Approach to Primary Vertebral Tumors in the Light of the 2020 Updated World Health Organization Classification of Bone Tumors.

To define a new approach for primary vertebral tumors by discussing them in the light of 2020 World Health Organization (WHO) classification of bone tumors. In this study, we have discussed primary vertebral tumors in light of the 2020 Updated WHO Classification of Soft Tissue and Bone Tumors. Chondroblastoma and chondromyxoid fibroma has been classified in the benign category, while synovial chondromatosis has been moved from the benign category to the intermediate category. In the updated classification, grade I chondrosarcoma has been classified in the malignant category with grade II?III chondrosarcomas. Minor amendments have been made for osteosarcoma subtypes. Neoplasms of undetermined origin, such as aneurysmal cysts, simple bone cysts, fibrosis dysplasia, and osteofibrous dysplasia, have been classified as neoplastic lesions. Chordomas have been classified into ?not otherwise stated,? poorly differentiated chordomas, and dedifferentiated chordomas. Ewing?s sarcomas have been classified in a separate section for undifferentiated, small, round cell sarcomas of the bone and soft tissue. In this section, three distinct subsets different from Ewing?s sarcoma have been discussed. CIC-rearranged sarcoma, BCOR-rearranged sarcoma, and round cell sarcomas with EWSR1 gene fusion with non-ETS family members. In this study, we have reviewed the new classifications and discussed their effect on decision making in spinal oncologic surgery.

Primary Vascular Tumors of Bone: A Monoinstitutional Morphologic and Molecular Analysis of 427 Cases With Emphasis on Epithelioid Variants.

Recent molecular discoveries have refined vascular bone tumor classification. To investigate the clinical relevance of these refinements, we reviewed all cases of primary vascular bone tumors treated at our Institute. On the basis of morphology, cases were assessed immunohistochemically and molecularly. A total of 427 cases of primary vascular tumor of bone with available follow-up and histologic material were retrieved and reclassified according to the most recent diagnostic criteria as follows: 289 hemangiomas, 38 epithelioid hemangiomas, 21 epithelioid hemangioendotheliomas, 2 retiform hemangioendotheliomas, 1 intraosseous papillary intralymphatic angioendothelioma, 24 pseudomyogenic hemangioendotheliomas, and 52 angiosarcomas (of these, 45 were epithelioid angiosarcomas and 7 spindle cell secondary angiosarcoma). Both epithelioid and classic hemangiomas behave as benign tumors with excellent prognosis. The distinction between cellular and conventional type of epithelioid hemangioma was not associated with a different clinical course. Conversely, epithelioid hemangioendothelioma exhibited a more aggressive clinical behavior than hemangioma, with higher rates of multifocality and distant spread. Immunohistochemical positivity for CAMTA1 or TFE3 did not have a prognostic implication. In epithelioid hemangioendothelioma, the presence of morphologic malignant features was associated with reduced disease-free (P=0.064) and overall survival (P=0.055). Pseudomyogenic hemangioendothelioma featured local aggressiveness in 5/24 patients exhibiting a clinical behavior closer to epithelioid hemangioma than epithelioid hemangioendothelioma. Last, 32/45 patients with epithelioid angiosarcoma died of disease with a median survival time of 10 months from diagnosis. In conclusion, the integration of morphologic, immunohistochemical, and molecular features allows a better stratification of primary vascular tumors of bone with significant prognostic and therapeutic implications.

Molecular pathology in the diagnosis of bone tumors: current concepts

The integrative evaluation of histology and corresponding imaging is essential for the classification of bone tumors. Until afew years ago, there were hardly any molecular markers that could be used for diagnostic purposes. However, exome- and genome-wide sequencing analyses have since uncovered anumber of tumor-specific aberrations that can be very helpful in ambiguous cases. In addition to characteristic gene mutations (e.g. H3F3A and H3F3B in giant-cell tumors and chondroblastomas), the detection of fusion transcripts (e.g. structural rearrangements in the AP‑1 transcription factors FOS and FOSB in osteoid osteomas and osteoblastomas) plays an increasing role. The article gives an overview of the current state of knowledge of the most important alterations in bone tumors.

Retrospective study of tumor and tumor like lesions of the bone-Tertiary center experience

Introduction: Bone tumors are important contributors for morbidity and mortality. Our hospital being, trauma and orthopedic institution, many orthopedic cases are referred to our center. We have done this study to determine the pattern of bone tumors, age and sex distribution, clinico pathological characteristics seen in our tertiary orthopedic Hospital. Materials and Methods: The study comprised of bone tumors diagnosed from January 2014 to December 2018 at Sanjay Gandhi Institute of Trauma & Orthopedics, Bangalore. Clinical and radiological details were collected from the data register. Microscopic findings were analyzed on Hematoxylin and eosin stained sections. Special stains and immunohistochemistry were done wherever essential. The bone tumors were classified based on WHO classification of bone tumors. Results: Of the 166 bone biopsies in a period of four years, 28 were malignant, 40 were benign and 14 were tumor like lesions. Most common age group was 11-20 years and were benign. Malignant tumors were common in 51-60 years age with male predominance and most of them were metastatic tumors. Conclusion: Diagnosis of bone tumors and treating them effectively reduces the morbidity and mortality as most of the tumors were benign and common in boys of younger age group. Correlation of clinico radiological features are essential for the conclusive histopathological diagnosis. This helps in planning the treatment and knowing the prognosis of the tumor. Keywords: Bone tumors, tumor like lesions, tertiary care hospital.

Regenerative Semi-Supervised Bidirectional W-Network-Based Knee Bone Tumor Classification on Radiographs Guided by Three-Region Bone Segmentation

The objective of this study is to develop and evaluate a new deep learning architecture, namely regenerative semi-supervised bidirectional W-network (RSS-BW), to predict the tumor state of the knee bone from radiographic images. First, we constructed an autoencoder model, called Bidirectional W-network (BW), for segmenting three-region (i.e., femur, tibia, and fibula) of knee bone. Using these regions as input data, RSS-BW architecture consisting of the autoencoding model for regenerating the bone structures, the backbone model for extracting features with pretrained ImageNet, and the predicting model for knee bone tumor classification are established. The developed scheme rapidly obtained segmentation results of the three-region of knee bone with a mean dice score of 98.06 ± 0.08%. Moreover, two types of classification are conducted: single-step and double-step. The single-step classifies the bone images into normal, benign, and malignant states. The mean values of accuracy, precision, recall and F-beta score obtained from the segmented images were 85.23±3.91%, 82.23±3.22%, 82.15±3.31%, and 82.21±3.25%, respectively. For the double-step of bone tumor classification, the images are classified first as normal versus abnormal. The second classification is conducted for abnormal images as benign versus malignant. The double-step classification shows an improvement of the mean accuracy by 1.7% compared to the single-step classification. In conclusion, the RSS-BW model presents higher accuracy than conventional models, indicating its potential clinical decision support for bone tumor classification.