Glioma is a type of severe brain tumor, and its accurate segmentation is useful in surgery planning and progression evaluation. Based on different biological properties, the glioma can be divided into three partially-overlapping regions of interest, including whole tumor (WT), tumor core (TC), and enhancing tumor (ET). Recently, UNet has identified its effectiveness in automatically segmenting brain tumor from multi-modal magnetic resonance (MR) images. In this work, instead of network architecture, we focus on making use of prior knowledge (brain parcellation), training and testing strategy (joint 3D+2D), ensemble and post-processing to improve the brain tumor segmentation performance. We explore the accuracy of three UNets with different inputs, and then ensemble the corresponding three outputs, followed by post-processing to achieve the final segmentation. Similar to most existing works, the first UNet uses 3D patches of multi-modal MR images as the input. The second UNet uses brain parcellation as an additional input. And the third UNet is inputted by 2D slices of multi-modal MR images, brain parcellation, and probability maps of WT, TC, and ET obtained from the second UNet. Then, we sequentially unify the WT segmentation from the third UNet and the fused TC and ET segmentation from the first and the second UNets as the complete tumor segmentation. Finally, we adopt a post-processing strategy by labeling small ET as non-enhancing tumor to correct some false-positive ET segmentation. On one publicly-available challenge validation dataset (BraTS2018), the proposed segmentation pipeline yielded average Dice scores of 91.03/86.44/80.58% and average 95% Hausdorff distances of 3.76/6.73/2.51 mm for WT/TC/ET, exhibiting superior segmentation performance over other state-of-the-art methods. We then evaluated the proposed method on the BraTS2020 training data through five-fold cross validation, with similar performance having also been observed. The proposed method was finally evaluated on 10 in-house data, the effectiveness of which has been established qualitatively by professional radiologists.
Obesity increases the risk of colorectal cancer (CRC) by 30%. The obese tumor microenvironment compromises antitumor immunity by eliciting exhausted T cells (Tex). Hypothesizing that Dahuang Fuzi Baijiang decoction (DFB) is a combined classical prescription from the “Synopsis of Prescriptions of the Golden Chamber”. We first determined that DFB regresses tumor growth in high‐fat diet–induced obese mice by expanding the TIM3− subset with intermediate expression of programmed cell death‐1 (PD‐1intTIM3−) and restricting the PD‐1hiTIM3+ subset. Transcription factor 1 (TCF1) is highly expressed in the PD‐1intTIM3− subset but is absent in PD‐1hiTIM3+ cells. We next confirmed that progenitor PD‐1intTCF+ cells robustly produce tumor necrosis factor‐α (TNFα) and interferon‐γ, whereas terminally differentiated PD‐1intTCF+ cells have defects in generating TNFα. With transgenic ob/ob mice, we found that DFB produces cooperative efficacy with anti‐PD‐1 (αPD‐1) by limiting the PD‐1hiTim3+ subset and amplifying the PD‐1intTCF+ population. Finally, we defined the recombinant chemokine C‐C‐motif receptor 2 (CCR2)+CD8+ subset as terminal Tex and identified that the differentiation from progenitor to terminal Tex is driven, at least in part, by the chemokine (C‐C motif) ligand 2 (CCL2)/CCR2 axis. The CCR2 inhibitor enhances the response to αPD‐1 by promoting the counts of progenitor Tex. Altogether, DFB dampens CCL2 and preserves progenitor Tex in the obese microenvironment to restrain CRC progression. These findings provide unambiguous evidence that the traditional Chinese formula DFB can prevent tumor progression by modulating adaptive immunity and establish a strong rationale for further clinical verification.
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