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Surgical resection remains the mainstay treatment for solid cancers, especially for localized disease. However, the postoperative immunosuppression provides a window for cancer cell proliferation and awakening dormant cancer cells, leading to rapid recurrences or metastases. This immunosuppressive status after surgery is associated with the severity of surgical trauma since immunosuppression induced by minimally invasive surgery is less than that of an extensive open surgery. The systemic response to tissue damages caused by surgical operations and the subsequent wound healing induced a cascade alteration in cellular immunity. After surgery, patients have a high level of circulating damage‐associated molecular patterns (DAMPs), triggering a local and systemic inflammation. The inflammatory metrics in the immediate postoperative period was associated with the prognosis of cancer patients. Neutrophils provide the first response to surgical trauma, and the production of neutrophil extracellular traps (NETs) promotes cancer progression. Activated macrophage during wound healing presents a tumor‐associated phenotype that cancers can exploit for their survival advantage. In addition, the amplification and activation of myeloid‐derived suppressor cells (MDSCs), regulatory T cells (Tregs) or the elevated programmed death ligand‐1 and vascular endothelial growth factor expression under surgical trauma, exacerbate the immunosuppression and favor of the formation of the premetastatic niche. Therapeutic strategies to reduce the cellular immunity impairment after surgery include anti‐DAMPs, anti‐postoperative inflammation or inflammatory/pyroptosis signal, combined immunotherapy with surgery, antiangiogenesis and targeted therapies for neutrophils, macrophages, MDSCs, and Tregs. Further, the application of enhanced recovery after surgery also has a feasible outcome for postoperative immunity restoration. Overall, current therapies to improve the cellular immunity under the special condition after surgery are relatively lacking. Further understanding the underlying mechanisms of surgical trauma‐related immunity dysfunction, phenotyping the immunosuppressive cells, and developing the related therapeutic intervention should be explored.

BackgroundCervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.MethodsA novel residual learning method based on convolutional neural network (RL-ARCNN) is proposed to reduce metal artifacts in cervical CT images. For MAR, a dataset is generated by simulating various metal artifacts in the first step, which will be applied to train the CNN. This dataset includes artifact-insert, artifact-free, and artifact-residual images. Numerous image patches are extracted from the dataset for training on deep residual learning artifact reduction based on CNN (RL-ARCNN). Afterwards, the trained model can be used for MAR on cervical CT images.ResultsThe proposed method provides a good MAR result with a PSNR of 38.09 on the test set of simulated artifact images. The PSNR of residual learning (38.09) is higher than that of ordinary learning (37.79) which shows that CNN-based residual images achieve favorable artifact reduction. Moreover, for a 512 × 512 image, the average removal artifact time is less than 1 s.ConclusionsThe RL-ARCNN indicates that residual learning of CNN remarkably reduces metal artifacts and improves critical structure visualization and confidence of radiation oncologists in target delineation. Metal artifacts are eliminated efficiently free of sinogram data and complicated post-processing procedure.

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Immunotherapies like the adoptive transfer of gene-engineered T cells and immune checkpoint inhibitors are novel therapeutic modalities for advanced cancers. However, some patients are refractory or resistant to these therapies, and the mechanisms underlying tumor immune resistance have not been fully elucidated. Immunosuppressive cells such as myeloid-derived suppressive cells, tumor-associated macrophages, tumor-associated neutrophils, regulatory T cells (Tregs), and tumor-associated dendritic cells are critical factors correlated with immune resistance. In addition, cytokines and factors secreted by tumor cells or these immunosuppressive cells also mediate the tumor progression and immune escape of cancers. Thus, targeting these immunosuppressive cells and the related signals is the promising therapy to improve the efficacy of immunotherapies and reverse the immune resistance. However, even with certain success in preclinical studies or in some specific types of cancer, large perspectives are unknown for these immunosuppressive cells, and the related therapies have undesirable outcomes for clinical patients. In this review, we comprehensively summarized the phenotype, function, and potential therapeutic targets of these immunosuppressive cells in the tumor microenvironment.