Glioma survival risk prediction is of great significance for the individualized treatment and assessment programs.Currently,most deep learning based survival prediction paradigms rely on invasive and expensive histopa...Glioma survival risk prediction is of great significance for the individualized treatment and assessment programs.Currently,most deep learning based survival prediction paradigms rely on invasive and expensive histopathology and genomics methods.However,magnetic resonance imaging(MRI)has emerged as a promising non-invasive alternative with significant prognostic potential.To leverage the benefits of MRI,we propose a segmentation-guided fully automated multimodal MRI-based survival network(SGS-Net),which can simultaneously perform glioma segmentation and survival risk prediction.Specifically,the task interrelation is addressed using a hybrid convolutional neural network-Transformer(CNN-Transformer)encoder to represent the shared high-level semantic features by co-training a decoder for glioma segmentation and a Cox model for survival prediction.Then,to ensure the effective representation of the high-level features,glioma segmentation as an auxiliary task is utilized to guide survival prediction by jointly optimizing the segmentation loss and the Cox partial log-likelihood loss.Furthermore,a pair-wise ranking loss is designed to allow the network to learn the survival difference between patients.To balance the multi-task losses,an uncertain weight manner is adopted to adaptively adjust the weights for preventing task bias.Finally,the proposed SGS-Net is assessed using a publicly available multi-institutional dataset.Experimental and visual results show that SGS-Net achieves promising segmentation performance and obtains a C-index of 81.07%for survival risk prediction,which outperforms several existing state-of-the-art methods and even histopathology-based methods.In addition,Kaplan-Meier survival analysis confirms that the prognosis risk generated by SGS-Net is consistent with the prior prognosis based on the grading or genotyping paradigms.展开更多
Integrase strand transfer inhibitors(INSTIs)have emerged as the first‐line choice for treating human immunodeficiency virus(HIV)infection due to their superior efficacy and safety.However,the impact of INSTIs on the ...Integrase strand transfer inhibitors(INSTIs)have emerged as the first‐line choice for treating human immunodeficiency virus(HIV)infection due to their superior efficacy and safety.However,the impact of INSTIs on the development of neuropsychiatric conditions in people living with HIV(PLWH)is not fully understood due to limited data.In this study,we conducted a cross‐sectional examination of PLWH receiving antiretroviral therapy,with a specific focus on HIV‐positive men who have sex with men(MSM)on INSTI‐based regimens(n=61)and efavirenz(EFV)‐based regimens(n=28).Participants underwent comprehensive neuropsychiatric evaluations and multimodal magnetic resonance imaging(MRI)scans,including T1‐weighted images and resting‐state functional MRI.Compared to the EFV group,the INSTI group exhibited primarily reduced gray matter volume(GMV)in the right superior parietal gyrus,higher regional homogeneity(ReHo)in the left postcentral gyrus,lower ReHo in the right orbital part of the inferior frontal gyrus,and increased voxel‐wise functional connectivity for the seed region in the left inferior temporal gyrus with clusters in the right cuneus.Furthermore,the analysis revealed a main effect of antiretroviral drugs on GMV changes,but no main effect of neuropsychiatric disorders or their interaction.The repeated analysis of participants who did not switch regimens confirmed the GMV changes in the INSTI group,validating the initial findings.Our study demonstrated gray matter atrophy and functional brain changes in PLWH on INSTI‐based regimens compared to those on EFV‐based regimens.These neuroimaging results provide valuable insights into the characteristics of brain network modifications in PLWH receiving INSTI‐based regimens。展开更多
基金supported in part by the National Key Research and Development Program of China(No.2021YFF1201200)National Natural Science Foundation of China(Nos.62302119,62172444,and 62476291)+3 种基金Science and Technology Innovation Program of Hunan Province(No.2022RC1031)Guizhou Provincial Basic Research Program(Natural Science)(No.QKHZK[2024]603)Guiyang City Science and Technology Plan Project(No.[2024]2-18)High Performance Computing Center of Central South University.
文摘Glioma survival risk prediction is of great significance for the individualized treatment and assessment programs.Currently,most deep learning based survival prediction paradigms rely on invasive and expensive histopathology and genomics methods.However,magnetic resonance imaging(MRI)has emerged as a promising non-invasive alternative with significant prognostic potential.To leverage the benefits of MRI,we propose a segmentation-guided fully automated multimodal MRI-based survival network(SGS-Net),which can simultaneously perform glioma segmentation and survival risk prediction.Specifically,the task interrelation is addressed using a hybrid convolutional neural network-Transformer(CNN-Transformer)encoder to represent the shared high-level semantic features by co-training a decoder for glioma segmentation and a Cox model for survival prediction.Then,to ensure the effective representation of the high-level features,glioma segmentation as an auxiliary task is utilized to guide survival prediction by jointly optimizing the segmentation loss and the Cox partial log-likelihood loss.Furthermore,a pair-wise ranking loss is designed to allow the network to learn the survival difference between patients.To balance the multi-task losses,an uncertain weight manner is adopted to adaptively adjust the weights for preventing task bias.Finally,the proposed SGS-Net is assessed using a publicly available multi-institutional dataset.Experimental and visual results show that SGS-Net achieves promising segmentation performance and obtains a C-index of 81.07%for survival risk prediction,which outperforms several existing state-of-the-art methods and even histopathology-based methods.In addition,Kaplan-Meier survival analysis confirms that the prognosis risk generated by SGS-Net is consistent with the prior prognosis based on the grading or genotyping paradigms.
基金supported by the National Natural Science Foundation of China(82072271,82241072,82072294)the National Key Research and Development Program of China(2021YFC2501402,2021YFC0122601)+8 种基金the Beijing Natural Science Foundation(7222095,7222091)the Peak Talent Program of Beijing Hospital Authority(DFL20191701)the Capital’s Funds for Health Improvement and Research(2022-1-1151)the Research and Translational Application of Clinical Characteristic Diagnostic and Treatment Techniques in Capital City(Z221100007422055)the Beijing Research Center for Respiratory Infectious Diseases(BJRID2024-001)the Beijing Hospitals Authority Innovation Studio of Young Staff Funding Support(2021037)the High-level Public Health Technical Personnel Construction Project(2022-1-007)the High-level Public Health Specialized Talents Project of Beijing Municipal Health commission(2022-02-20)the Beijing Key Laboratory for HIV/AIDS Research(BZ0089).
文摘Integrase strand transfer inhibitors(INSTIs)have emerged as the first‐line choice for treating human immunodeficiency virus(HIV)infection due to their superior efficacy and safety.However,the impact of INSTIs on the development of neuropsychiatric conditions in people living with HIV(PLWH)is not fully understood due to limited data.In this study,we conducted a cross‐sectional examination of PLWH receiving antiretroviral therapy,with a specific focus on HIV‐positive men who have sex with men(MSM)on INSTI‐based regimens(n=61)and efavirenz(EFV)‐based regimens(n=28).Participants underwent comprehensive neuropsychiatric evaluations and multimodal magnetic resonance imaging(MRI)scans,including T1‐weighted images and resting‐state functional MRI.Compared to the EFV group,the INSTI group exhibited primarily reduced gray matter volume(GMV)in the right superior parietal gyrus,higher regional homogeneity(ReHo)in the left postcentral gyrus,lower ReHo in the right orbital part of the inferior frontal gyrus,and increased voxel‐wise functional connectivity for the seed region in the left inferior temporal gyrus with clusters in the right cuneus.Furthermore,the analysis revealed a main effect of antiretroviral drugs on GMV changes,but no main effect of neuropsychiatric disorders or their interaction.The repeated analysis of participants who did not switch regimens confirmed the GMV changes in the INSTI group,validating the initial findings.Our study demonstrated gray matter atrophy and functional brain changes in PLWH on INSTI‐based regimens compared to those on EFV‐based regimens.These neuroimaging results provide valuable insights into the characteristics of brain network modifications in PLWH receiving INSTI‐based regimens。
