Spinal chordomas are rare primary osseous tumors that arise from the remnants of the notochord. They are commonly considered slow-growing, locally invasive neoplasms with little tendency to metastasize, but the high recurrent rate of spinal chordomas may seriously affect the survival rate and quality of life of patients. The aim of the study is to describe the epidemiological data and determine the prognostic factors for decreased survival in patients with primary spinal chordoma. The Surveillance, Epidemiology, and End Results (SEER) Registry database, a US population-based cancer registry database, was used to identify all patients diagnosed with primary spinal chordoma from 1973 to 2014. We utilized Kaplan–Meier method and Cox proportional hazards regression analysis to evaluate the association between patients overall survival and relevant characteristics, including age, gender, race, disease stage, treatment methods, primary tumor site, marital status, and urban county background. In the data set between 1973 and 2014, a total of 808 patients were identified with primary spinal chordoma. The overall rate of distant metastatic cases in our cohort was only 7.7%. Spinal chordoma was more common occurred in men (62.6%) than women (37.3%). Majority of neoplasms were found in the White (87.9%), while the incidence of the Black is relatively infrequent (3.3%). Three hundred fifty-seven spinal chordomas (44.2%) were located in the vertebral column, while 451 patients’ tumor (55.8%) was located in the sacrum or pelvis. Age ≥ 60 years (HR = 2.72; 95%CI, 1.71 to 2.89), distant metastasis (HR = 2.16; 95%CI, 1.54 to 3.02), and non-surgical therapy (HR = 2.14; 95%CI, 1.72 to 2.69) were independent risk factors for survival reduction in analysis. Survival did not significantly differ as a factor of tumor site (vertebrae vs sacrum/pelvis) for primary spinal chordoma (HR = 0.93, P = 0.16). Race (P = 0.52), gender (P = 0.11), marital status (P = 0.94), and urban background (P = 0.72) were not main factors which affected overall survival rate. There was no significant difference in overall survival rate between chordomas located in the sacrum and vertebral column. Spinal chordoma patients with an elderly age (age ≥ 60), performing non-surgical therapy, and distant metastasis were associated with worse overall survival. Performing surgery was an effective and reliable treatment method for patients with spinal chordoma, and public health efforts should pay more attention to the elderly patients with spinal chordoma prior to distant metastasis.

Osteosarcoma is the most common malignant osteosarcoma, and most developing countries face great challenges in the diagnosis due to the lack of medical resources. Magnetic resonance imaging (MRI) has always been an important tool for the detection of osteosarcoma, but it is a time-consuming and labor-intensive task for doctors to manually identify MRI images. It is highly subjective and prone to misdiagnosis. Existing computer-aided diagnosis methods of osteosarcoma MRI images focus only on accuracy, ignoring the lack of computing resources in developing countries. In addition, the large amount of redundant and noisy data generated during imaging should also be considered. To alleviate the inefficiency of osteosarcoma diagnosis faced by developing countries, this paper proposed an artificial intelligence multiprocessing scheme for pre-screening, noise reduction, and segmentation of osteosarcoma MRI images. For pre-screening, we propose the Slide Block Filter to remove useless images. Next, we introduced a fast non-local means algorithm using integral images to denoise noisy images. We then segmented the filtered and denoised MRI images using a U-shaped network (ETUNet) embedded with a transformer layer, which enhances the functionality and robustness of the traditional U-shaped architecture. Finally, we further optimized the segmented tumor boundaries using conditional random fields. This paper conducted experiments on more than 70,000 MRI images of osteosarcoma from three hospitals in China. The experimental results show that our proposed methods have good results and better performance in pre-screening, noise reduction, and segmentation.

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model’s segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.

Osteosarcoma is one of the most common primary malignancies of bone in the pediatric and adolescent populations. The morphology and size of osteosarcoma MRI images often show great variability and randomness with different patients. In developing countries, with large populations and lack of medical resources, it is difficult to effectively address the difficulties of early diagnosis of osteosarcoma with limited physician manpower alone. In addition, with the proposal of precision medicine, existing MRI image segmentation models for osteosarcoma face the challenges of insufficient segmentation accuracy and high resource consumption. Inspired by transformer’s self-attention mechanism, this paper proposes a lightweight osteosarcoma image segmentation architecture, UATransNet, by adding a multilevel guided self-aware attention module (MGAM) to the encoder-decoder architecture of U-Net. We successively perform dataset classification optimization and remove MRI image irrelevant background. Then, UATransNet is designed with transformer self-attention component (TSAC) and global context aggregation component (GCAC) at the bottom of the encoder-decoder architecture to perform integration of local features and global dependencies and aggregation of contexts to learned features. In addition, we apply dense residual learning to the convolution module and combined with multiscale jump connections, to improve the feature extraction capability. In this paper, we experimentally evaluate more than 80,000 osteosarcoma MRI images and show that our UATransNet yields more accurate segmentation performance. The IOU and DSC values of osteosarcoma are 0.922 ± 0.03 and 0.921 ± 0.04, respectively, and provide intuitive and accurate efficient decision information support for physicians.

Osteosarcoma is a malignant bone tumor commonly found in adolescents or children, with high incidence and poor prognosis. Magnetic resonance imaging (MRI), which is the more common diagnostic method for osteosarcoma, has a very large number of output images with sparse valid data and may not be easily observed due to brightness and contrast problems, which in turn makes manual diagnosis of osteosarcoma MRI images difficult and increases the rate of misdiagnosis. Current image segmentation models for osteosarcoma mostly focus on convolution, whose segmentation performance is limited due to the neglect of global features. In this paper, we propose an intelligent assisted diagnosis system for osteosarcoma, which can reduce the burden of doctors in diagnosing osteosarcoma from three aspects. First, we construct a classification-image enhancement module consisting of resnet18 and DeepUPE to remove redundant images and improve image clarity, which can facilitate doctors' observation. Then, we experimentally compare the performance of serial, parallel, and hybrid fusion transformer and convolution, and propose a Double U-shaped visual transformer with convolution (DUconViT) for automatic segmentation of osteosarcoma to assist doctors' diagnosis. This experiment utilizes more than 80,000 osteosarcoma MRI images from three hospitals in China. The results show that DUconViT can better segment osteosarcoma with DSC 2.6% and 1.8% higher than Unet and Unet++, respectively. Finally, we propose the pixel point quantification method to calculate the area of osteosarcoma, which provides more reference basis for doctors' diagnosis. Code: https://github.com/lingziqiang/DUconViT