Self-supervised tumor segmentation and prognosis prediction in osteosarcoma using multiparametric MRI and clinical characteristics.

BACKGROUND AND OBJECTIVE: Osteosarcoma has a high mortality among malignant bone tumors. MRI-based tumor segmentation and prognosis prediction are helpful to assist doctors in detecting osteosarcoma, evaluating the patient's status, and improving patient survival. Current intelligent diagnostic approaches focus on segmentation with single-parameter MRI, which ignores the nature of MRI resulting in poor performance, and lacks the connection with prognosis prediction. Besides, osteosarcoma is a rare disease, and their few labeled data may lead to model overfitting. METHODS: We propose a three-stage pipeline for segmentation and prognosis prediction of osteosarcoma to assist doctors in diagnosis. First, we propose the Multiparameter Fusion Contrast Learning (MPFCLR) algorithm to share pre-training weights for the segmentation model using unlabeled data. Then, we construct a multiparametric fusion network (MPFNet), which fuses the complementary features from multiparametric MRI (CE-T1WI, T2WI). It can automatically segment tumor and necrotic regions. Finally, a fusion nomogram is constructed by segmentation masks and clinical characteristics (volume, tumor spread) to predict the patient's prognostic status. RESULTS: Our experiments used data from 136 patients at the Second Xiangya Hospital in China. According to experiments, the MPFNet achieves 84.19 % mean DSC and 84.56 % mean F1-score in segmenting tumor and necrotic regions, surpassing existing models and single-parameter MRI input for osteosarcoma segmentation. Besides, MPFCLR improves the segmentation performance and convergence speed. In prognosis prediction, our fusion nomogram (C-index: 0.806, 95 %CI: 0.758-0.854) is better than radiomics (C-index: 0.753, 95 %CI: 0.685-0.841) and clinical (C-index: 0.794, 95 %CI: 0.735-0.854) nomograms in predictive performance. Compared to the comparison models, our model is closest to the prediction model based on physician annotations. Moreover, it can accurately distinguish the patients' prognostic status with good or poor. CONCLUSION: Our proposed solution can provide references for clinicians to detect osteosarcoma, evaluate patient status, and make personalized decisions. It can reduce delayed treatment or overtreatment and improve patient survival.

A Cascaded Multi-Stage Framework for Automatic Detection and Segmentation of Pulmonary Nodules in Developing Countries.

Lung cancer has the highest mortality rate among all malignancies. Non-micro pulmonary nodules are the primary manifestation of early-stage lung cancer. If patients can be detected with nodules in the early stage and receive timely treatment, their survival rate can be improved. Due to the large number of patients and limited medical resources, doctors take a longer time to make a diagnosis, which reduces efficiency and accuracy. Besides, there are no suitable approaches for developing countries. Therefore, we propose a 2.5D-based cascaded multi-stage framework for automatic detection and segmentation (DS-CMSF) of pulmonary nodules. The first three stages of the framework are used to discover lesions, and the latter stage is used to segment them. The first locating stage introduces the classical 2D-based Yolov5 model to locate the nodules roughly on axial slices. The second aggregation stage proposes a candidate nodule selection (CNS) algorithm to locate further and reduce redundant candidate nodules. The third classification stage uses a multi-size 3D-based fusion model to accommodate nodules of varying sizes and shapes for false-positive reducing. The last segmentation stage introduces multi-scale and attention modules into 3D-based UNet autoencoder to segment the nodular regions finely. Our proposed framework achieves 95.95% sensitivity and 89.50% CPM for nodules detection on the LUNA16 dataset, and 86.75% DSC for nodules segmentation on the LIDC-IDRI dataset. Moreover, our approach also achieves the accuracy-complexity trade-off, which can effectively realize the auxiliary diagnosis of pulmonary nodules in developing countries.

An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images.

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.

Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries.

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.

Two new dammarane-type saponins from leaves of Panax quinquefolium.

Two new saponins were isolated from the leaves of Panax quinquefolium and their structures were elucidated as 3ß, 6α, 20S-trihydroxy-12ß, 23R-epoxydammar-24-ene 6-O-[α-L-rhamnosyl(1 → 2)-ß-D-glucopyranosyl]-20-O-ß-D-glucopyranoside (1) and 3ß, 20S-dihydroxy-12ß, 23R-epoxydammar-24-ene 3-O-[ß-D-glucopyranosyl(1 → 2)-ß-D-glucopyranosyl]-20-O-ß-D-glucopyranoside (2) on the basis of physicochemical evidence.