Rituximab with high-dose methotrexate is effective and cost-effective in newly diagnosed primary central nervous system lymphoma.

Induction chemotherapy based on high-dose methotrexate is considered as the standard approach for newly diagnosed primary central nervous system lymphomas (PCNSLs). However, the best combination chemotherapeutic regimen remains unclear. This study aimed to determine the efficacy and toxicities of rituximab with methotrexate (R-M regimen). Consecutive 37 Chinese patients receiving R-M regimen as induction chemotherapy were retrospectively identified from January 2015 to June 2020 from our center in eastern China. Fourteen patients receiving rituximab plus methotrexate with cytarabine (R-MA regimen) at the same period were identified as the positive control group. The response rates, survival, toxicities, length of hospital stay (LOS), and cost were compared. Compared with the R-MA regimen, the R-M regimen showed comparable response rate and survival outcomes, but had fewer grade 3-4 hematological toxicities, shorter LOS, lower mean total hospitalization cost and lower mean total antibiotic cost. Complete remission at the end of induction chemotherapy and ECOG > 3 were independent prognostic factors for overall survival. In conclusion, R-M regimen is an effective and cost-effective combination treatment for PCNSLs, which warrants further evaluation in randomized trials.

Bone age assessment based on deep convolution neural network incorporated with segmentation.

PURPOSE: Bone age assessment is not only an important means of assessing maturity of adolescents, but also plays an indispensable role in the fields of orthodontics, kinematics, pediatrics, forensic science, etc. Most studies, however, do not take into account the impact of background noise on the results of the assessment. In order to obtain accurate bone age, this paper presents an automatic assessment method, for bone age based on deep convolutional neural networks. METHOD: Our method was divided into two phases. In the image segmentation stage, the segmentation network U-Net was used to acquire the mask image which was then compared with the original image to obtain the hand bone portion after removing the background interference. For the classification phase, in order to further improve the evaluation performance, an attention mechanism was added on the basis of Visual Geometry Group Network (VGGNet). Attention mechanisms can help the model invest more resources in important areas of the hand bone. RESULT: The assessment model was tested on the RSNA2017 Pediatric Bone Age dataset. The results show that our adjusted model outperforms the VGGNet. The mean absolute error can reach 9.997 months, which outperforms other common methods for bone age assessment. CONCLUSION: We explored the establishment of an automated bone age assessment method based on deep learning. This method can efficiently eliminate the influence of background interference on bone age evaluation, improve the accuracy of bone age evaluation, provide important reference value for bone age determination, and can aid in the prevention of adolescent growth and development diseases.