Immune system can recognize self vs transformed self. That is why cancer immunotherapy achieves notable benefits in a wide variety of cancers. Recently, several papers reported that immune checkpoint blockade therapy led to upregulation of IFNγ and in turn clearance of tumor cells. In this review, we conducted an extensive literature search of recent 5‐year studies about the roles of IFNγ signaling in both tumor immune surveillance and immune evasion. In addition to well‐known functions, IFNγ signaling also induces tumor ischemia and homeostasis program, resulting in tumor clearance and tumor escape, respectively. The yin and the yang of IFNγ signaling are summarized. Thus, this review helps us to comprehensively understand the roles of IFNγ in tumor immunity, which contributes to better design and management of clinical immunotherapy approaches.

Because of its proven efficacy, intravesical Bacillus Calmette-Guérin (BCG) immunotherapy is an important treatment for nonmuscle invasive bladder cancer at high risk of recurrence or progression. However, approximately 8% of patients have to stop BCG instillation as a result of its complications. Complications induced by BCG therapy can have a variety of clinical manifestations. These adverse reactions may occur in conjunction with BCG instillation or may not develop until months or years after BCG cessation. An essential step in the management complications arising from BCG is early establishment of diagnosis, particularly for distant, disseminated, and obscure infections. Therefore we reviewed the literature on the potential complications after intravesical BCG immunotherapy for bladder cancer and provide an overview on the incidence, diagnosis, and treatment modality of genitourinary and systemic BCG-induced complications.

Chemotherapy has an essential role not only in advanced solid tumor therapy intervention but also in society’s health at large. Chemoresistance, however, seriously restricts the efficiency and sensitivity of chemotherapeutic agents, representing a significant threat to patients’ quality of life and life expectancy. How to reverse chemoresistance, improve efficacy sensitization response, and reduce adverse side effects need to be tackled urgently. Recently, studies on the effect of ultrasonic microbubble cavitation on enhanced tissue permeability and retention (EPR) have attracted the attention of researchers. Compared with the traditional targeted drug delivery regimen, the microbubble cavitation effect, which can be used to enhance the EPR effect, has the advantages of less trauma, low cost, and good sensitization effect, and has significant application prospects. This article reviews the research progress of ultrasound-mediated microbubble cavitation in the treatment of solid tumors and discusses its mechanism of action to provide new ideas for better treatment strategies.

Semi-supervised learning is becoming an effective solution in medical image segmentation because annotations are costly and tedious to acquire. Methods based on the teacher-student model use consistency regularization and uncertainty estimation and have shown good potential in dealing with limited annotated data. Nevertheless, the existing teacher-student model is seriously limited by the exponential moving average algorithm, which leads to the optimization trap. Moreover, the classic uncertainty estimation method calculates the global uncertainty for images but does not consider local region-level uncertainty, which is unsuitable for medical images with blurry regions. In this paper, the Voxel Stability and Reliability Constraint (VSRC) model is proposed to address these issues. Specifically, the Voxel Stability Constraint (VSC) strategy is introduced to optimize parameters and exchange effective knowledge between two independent initialized models, which can break through the performance bottleneck and avoid model collapse. Moreover, a new uncertainty estimation strategy, the Voxel Reliability Constraint (VRC), is proposed for use in our semi-supervised model to consider the uncertainty at the local region level. We further extend our model to auxiliary tasks and propose a task-level consistency regularization with uncertainty estimation. Extensive experiments on two 3D medical image datasets demonstrate that our method outperforms other state-of-the-art semi-supervised medical image segmentation methods under limited supervision. The source code and pre-trained models of this method are available at https://github.com/zyvcks/JBHI-VSRC .

Biochemical recurrence (BCR) occurs in up to 27% of patients after radical prostatectomy (RP) and often compromises oncologic survival. To determine whether imaging signatures on clinical prostate magnetic resonance imaging (MRI) could noninvasively characterize biochemical recurrence and optimize treatment. We retrospectively enrolled 485 patients underwent RP from 2010 to 2017 in three institutions. Quantitative and interpretable features were extracted from T2 delineated tumors. Deep learning-based survival analysis was then applied to develop the deep-radiomic signature (DRS-BCR). The model’s performance was further evaluated, in comparison with conventional clinical models. The model achieved C-index of 0.802 in both primary and validating cohorts, outweighed the CAPRA-S score (0.677), NCCN model (0.586) and Gleason grade group systems (0.583). With application analysis, DRS-BCR model can significantly reduce false-positive predictions, so that nearly one-third of patients could benefit from the model by avoiding overtreatments. The deep learning-based survival analysis assisted quantitative image features from MRI performed well in prediction for BCR and has significant potential in optimizing systemic neoadjuvant or adjuvant therapies for prostate cancer patients.