Immunotherapies have led to substantial changes in cancer treatment and have been a persistently popular topic in cancer research because they tremendously improve the efficacy of treatment and survival of individuals with various cancer types. However, only a small proportion of patients are sensitive to immunotherapy, and specific biomarkers are urgently needed to separate responders from nonresponders. Mismatch repair pathways play a vital role in identifying and repairing mismatched bases during DNA replication and genetic recombination in normal and cancer cells. Defects in DNA mismatch repair proteins and subsequent microsatellite instability-high lead to the accumulation of mutation loads in cancer-related genes and the generation of neoantigens, which stimulate the anti-tumor immune response of the host. Mismatch repair deficiency/microsatellite instability-high represents a good prognosis in early colorectal cancer settings without adjuvant treatment and a poor prognosis in patients with metastasis. Several clinical trials have demonstrated that mismatch repair deficiency or microsatellite instability-high is significantly associated with long-term immunotherapy-related responses and better prognosis in colorectal and noncolorectal malignancies treated with immune checkpoint inhibitors. To date, the anti-programmed cell death-1 inhibitor pembrolizumab has been approved for mismatch repair deficiency/microsatellite instability-high refractory or metastatic solid tumors, and nivolumab has been approved for colorectal cancer patients with mismatch repair deficiency/microsatellite instability-high. This is the first time in the history of cancer therapy that the same biomarker has been used to guide immune therapy regardless of tumor type. This review summarizes the features of mismatch repair deficiency/microsatellite instability-high, its relationship with programmed death-ligand 1/programmed cell death-1, and the recent advances in predicting immunotherapy efficacy. Electronic supplementary material The online version of this article (10.1186/s13045-019-0738-1) contains supplementary material, which is available to authorized users.
ObjectivesTo evaluate the performance of a novel three-dimensional (3D) joint convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial hemorrhage (ICH) and its five subtypes (cerebral parenchymal, intraventricular, subdural, epidural, and subarachnoid) in non-contrast head CT.MethodsA total of 2836 subjects (ICH/normal, 1836/1000) from three institutions were included in this ethically approved retrospective study, with a total of 76,621 slices from non-contrast head CT scans. ICH and its five subtypes were annotated by three independent experienced radiologists, with majority voting as reference standard for both the subject level and the slice level. Ninety percent of data was used for training and validation, and the rest 10% for final evaluation. A joint CNN-RNN classification framework was proposed, with the flexibility to train when subject-level or slice-level labels are available. The predictions were compared with the interpretations from three junior radiology trainees and an additional senior radiologist.ResultsIt took our algorithm less than 30 s on average to process a 3D CT scan. For the two-type classification task (predicting bleeding or not), our algorithm achieved excellent values (≥ 0.98) across all reporting metrics on the subject level. For the five-type classification task (predicting five subtypes), our algorithm achieved > 0.8 AUC across all subtypes. The performance of our algorithm was generally superior to the average performance of the junior radiology trainees for both two-type and five-type classification tasks.ConclusionsThe proposed method was able to accurately detect ICH and its subtypes with fast speed, suggesting its potential for assisting radiologists and physicians in their clinical diagnosis workflow.Key Points • A 3D joint CNN-RNN deep learning framework was developed for ICH detection and subtype classification, which has the flexibility to train with either subject-level labels or slice-level labels. • This deep learning framework is fast and accurate at detecting ICH and its subtypes. • The performance of the automated algorithm was superior to the average performance of three junior radiology trainees in this work, suggesting its potential to reduce initial misinterpretations. Electronic supplementary materialThe online version of this article (10.1007/s00330-019-06163-2) contains supplementary material, which is available to authorized users.
Incision size contributed to postoperative corneal astigmatism. When incision size was reduced from 3.0 mm to 2.6 mm, SIA was reduced and refractive stabilization was faster. Reduction of incision size from 2.6 mm to 2.2 mm offered no greater reduction of SIA when using the C cartridge; however, the D cartridge (designed for 2.2-mm incisions) should be evaluated.
Chemoresistance is a severe outcome among patients with ovarian cancer that leads to a poor prognosis. MCL1 is an antiapoptotic member of the BCL-2 family that has been found to play an essential role in advancing chemoresistance and could be a promising target for the treatment of ovarian cancer. Here, we found that deubiquitinating enzyme 3 (DUB3) interacts with and deubiquitinates MCL1 in the cytoplasm of ovarian cancer cells, which protects MCL1 from degradation. Furthermore, we identified that O6-methylguanine-DNA methyltransferase (MGMT) is a key activator of DUB3 transcription, and that the MGMT inhibitor PaTrin-2 effectively suppresses ovarian cancer cells with elevated MGMT-DUB3-MCL1 expression both in vitro and in vivo. Most interestingly, we found that histone deacetylase inhibitors (HDACis) could significantly activate MGMT/DUB3 expression; the combined administration of HDACis and PaTrin-2 led to the ideal therapeutic effect. Altogether, our results revealed the essential role of the MGMT-DUB3-MCL1 axis in the chemoresistance of ovarian cancer and identified that a combined treatment with HDACis and PaTrin-2 is an effective method for overcoming chemoresistance in ovarian cancer.
Despite the fact that automatic target recognition (ATR) in Synthetic aperture radar (SAR) images has been extensively researched due to its practical use in both military and civil applications, it remains an unsolved problem. The major challenges of ATR in SAR stem from severe data scarcity and great variation of SAR images. Recent work started to adopt convolutional neural networks (CNNs), which, however, remain unable to handle the aforementioned challenges due to their high dependency on large quantities of data. In this paper, we propose a novel deep convolutional learning architecture, called Multi-Stream CNN (MS-CNN), for ATR in SAR by leveraging SAR images from multiple views. Specifically, we deploy a multi-input architecture that fuses information from multiple views of the same target in different aspects; therefore, the elaborated multi-view design of MS-CNN enables it to make full use of limited SAR image data to improve recognition performance. We design a Fourier feature fusion framework derived from kernel approximation based on random Fourier features which allows us to unravel the highly nonlinear relationship between images and classes. More importantly, MS-CNN is qualified with the desired characteristic of easy and quick manoeuvrability in real SAR ATR scenarios, because it only needs to acquire real-time GPS information from airborne SAR to calculate aspect differences used for constructing testing samples. The effectiveness and generalization ability of MS-CNN have been demonstrated by extensive experiments under both the Standard Operating Condition (SOC) and Extended Operating Condition (EOC) on the MSTAR dataset. Experimental results have shown that our proposed MS-CNN can achieve high recognition rates and outperform other state-of-the-art ATR methods.
Background The Liver Imaging Reporting and Data System (LI‐RADS) is widely adopted for noninvasive diagnosis of hepatocellular carcinoma (HCC). It's updated to version 2018 recently, with some major changes compared with v2017. However, the diagnostic performance of LI‐RADS v2018 and its difference with v2017 are yet to be validated. Purpose To compare the diagnostic performances of LI‐RADS on MR for diagnosing HCC between v2017 and v2018. Study Type Retrospective. Subjects In all, 181 patients with 217 hepatic observations (146 HCCs, 16 non‐HCC malignancies and 55 benign lesions) with liver MRI and pathological or follow‐up imaging diagnoses. Field Strength/Sequence 1.5 T or 3 T MRI. Dual‐echo T1WI, T2WI, diffusion‐weighted imaging, and a liver acquisition with volume acceleration. AssessmentImages were independently interpreted by three radiologists, and then in consensus for observations with different LR categories, according to LI‐RADS v2017 and v2018, separately. Statistical Tests Sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (+LR), and Youden index. Results When adopting LR‐5 as a predictor of HCC, the sensitivity (80.8% vs. 71.2%), NPV (69.6% vs. 60.7%), and accuracy (83.9% vs. 77.9%) were all increased for LI‐RADS v2018 compared with v2017, with a greater Youden index (0.709 vs. 0.627). However, the diagnostic performances of MRI for diagnosing HCC were not changed while adopting LR‐4/5 as a predictor. The threshold growths of 76% (19/25) observations in v2017 were revised to subthreshold growth in v2018, and 16 LR‐4 observations in v2017 were changed to LR‐5 based on v2018. Data Conclusion The diagnostic performance of LI‐RADS v2018 for diagnosing HCC is superior to v2017, with a greater sensitivity, NPV, and accuracy. The revisions in v2018 mainly affect the categorization when adopting LR‐5 as a predictor of HCC. Level of Evidence: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:746–755.
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