Evaluating research ethics committees in Vietnam and Laos: Results of a validated self-assessment tool.

BACKGROUND: There is an increase in human subject research in developing countries and conducting them in an ethical manner depends on the research ethics oversight in these countries. The purpose of this study is to evaluate the operational, financial, and educational characteristics of research ethics committees (RECs) at institutions in Vietnam and Laos. METHODS: A validated self-assessment tool designed to assess nine major characteristics of RECs was translated into Vietnamese and Laotian. The translated surveys were delivered to and completed by representatives from RECs at institutions in Vietnam and Laos. The surveys were collected, translated back into English, and scored. The data was analyzed to identify potential areas of strength and areas for improvement. RESULTS: The mean survey score for the 19 RECs surveyed was 165.3 out of a maximum of 200 points with a standard deviation of 22.9. Committees scored the highest in the review of specific protocol items (95.6%), submission arrangements and materials (89.5%), and the policies referring to review procedures (85.6%) domains. RECs scored the lowest in the resources domain (65.5%), with only 26.3% of committees having an annual budget. Nearly all RECs have standard operating procedures (94.7%) and policies for disclosing conflicts of interest (89.5%). Most committees use prior ethics training as a criterion to select REC chairs (78.9%) and members (73.7%), with the majority of committees requiring a training course in ethics (76.5%). 68.4% of committees have continuing education in ethics for members and only 42.1% of committees have a budget for member training. CONCLUSION: This study demonstrated that RECs in Vietnam and Laos have strong foundational review processes for research protocols. Important areas of improvement include improved institutional oversight, financial and administrative resources, and the continued ethics education for current committee members.

Intratumoural immunotherapy plus focal thermal ablation for localized prostate cancer.

Major advances have been made in the use of immunotherapy for the treatment of solid tumours, including the use of intratumourally injected immunotherapy instead of systemically delivered immunotherapy. The success of immunotherapy in prostate cancer treatment has been limited to specific populations with advanced disease, which is thought to be a result of prostate cancer being an immunologically 'cold' cancer. Accordingly, combining intratumoural immunotherapy with other treatments that would increase the immunological heat of prostate cancer is of interest. Thermal ablation therapy is currently one of the main strategies used for the treatment of localized prostate cancer and it causes immunological activation against prostate tissue. The use of intratumoural immunotherapy as an adjunct to thermal ablation offers the potential to elicit a systemic and lasting adaptive immune response to cancer-specific antigens, leading to a synergistic effect of combination therapy. The combination of thermal ablation and immunotherapy is currently in the early stages of investigation for the treatment of multiple solid tumour types, and the potential for this combination therapy to also offer benefit to prostate cancer patients is exciting.

Antigen-loaded Monocyte Administration and Flt3 Ligand Augment the Antitumor Efficacy of Immune Checkpoint Blockade in a Murine Melanoma Model.

Undifferentiated monocytes can be loaded with tumor antigens (Ag) and administered intravenously to induce antitumor cytotoxic T lymphocyte (CTL) responses. This vaccination strategy exploits an endogenous Ag cross-presentation pathway, where Ag-loaded monocytes (monocyte vaccines) transfer their Ag to resident splenic dendritic cells (DC), which then stimulate robust CD8 + CTL responses. In this study, we investigated whether monocyte vaccination in combination with CDX-301, a DC-expanding cytokine Fms-like tyrosine kinase 3 ligand (Flt3L), could improve the antitumor efficacy of anti-programmed cell death (anti-PD-1) immune checkpoint blockade. We found that Flt3L expanded splenic DC over 40-fold in vivo and doubled the number of circulating Ag-specific T cells when administered before monocyte vaccination in C57BL/6 mice. In addition, OVA-monocyte vaccination combined with either anti-PD-1, anti-programmed cell death ligand 1 (anti-PD-L1), or anti-cytotoxic T lymphocyte antigen-4 (anti-CTLA-4) suppressed subcutaneous B16/F10-OVA tumor growth to a greater extent than checkpoint blockade alone. When administered together, OVA-monocyte vaccination improved the antitumor efficacy of Flt3L and anti-PD-1 in terms of circulating Ag-specific CD8 + T cell frequency and inhibition of subcutaneous B16/F10-OVA tumor growth. To our knowledge, this is the first demonstration that a cancer vaccine strategy and Flt3L can improve the antitumor efficacy of anti-PD-1. The findings presented here warrant further study of how monocyte vaccines can improve Flt3L and immune checkpoint blockade as they enter clinical trials.

CD8+ T cells maintain killing of MHC-I-negative tumor cells through the NKG2D-NKG2DL axis.

The accepted paradigm for both cellular and anti-tumor immunity relies upon tumor cell killing by CD8+ T cells recognizing cognate antigens presented in the context of target cell major histocompatibility complex (MHC) class I (MHC-I) molecules. Likewise, a classically described mechanism of tumor immune escape is tumor MHC-I downregulation. Here, we report that CD8+ T cells maintain the capacity to kill tumor cells that are entirely devoid of MHC-I expression. This capacity proves to be dependent instead on interactions between T cell natural killer group 2D (NKG2D) and tumor NKG2D ligands (NKG2DLs), the latter of which are highly expressed on MHC-loss variants. Necessarily, tumor cell killing in these instances is antigen independent, although prior T cell antigen-specific activation is required and can be furnished by myeloid cells or even neighboring MHC-replete tumor cells. In this manner, adaptive priming can beget innate killing. These mechanisms are active in vivo in mice as well as in vitro in human tumor systems and are obviated by NKG2D knockout or blockade. These studies challenge the long-advanced notion that downregulation of MHC-I is a viable means of tumor immune escape and instead identify the NKG2D-NKG2DL axis as a therapeutic target for enhancing T cell-dependent anti-tumor immunity against MHC-loss variants.

Career Research Performance Among Radiology Early Career Grant Recipients Compared With National Institutes of Health K Award Recipients.

OBJECTIVE: To compare academic and demographic metrics among recipients of three major early career radiology, interventional radiology, and radiation oncology grants to National Institutes of Health (NIH) K awardees at the time the grants were awarded and then over the course of their careers. METHODS: Radiologists who received the RSNA Research Scholar Grant, General Electric Radiology Research Academic Fellowship (GERRAF), American Roentgen Ray Society (ARRS) Scholar Award, or NIH Career Development (K) Award before January 1, 2015, were included. Research metrics at the time of grant award (eg, publications) and subsequent scholarly productivity (eg, academic rank, h-index, NIH funding) were recorded until April 2020. Wilcoxon ranked-sum, χ2, logistic regression, and standard least-square regression were used for data analysis. RESULTS: There were 279 recipients: 48 K Award, 115 RSNA Research Scholar Grant, 36 ARRS, and 80 GERRAF. At the time of grant awarding, GERRAF recipients were less likely to have an MD-PhD degree (odds ratio [OR]: 0.36; P = .002) and were more likely to be women (OR: 1.55; P = .042) than K Award recipients. Similarly, recipients of the ARRS (OR: 2.87; P = .010) and GERRAF (OR: 3.19; P = .002) were more likely to have a master's degree. Academic rank, leadership positions, and R01 funding were significant predictors of h-index and total publications over time. Academic rank and the GERRAF were significant predictors of subsequent NIH funding duration but there were no significant predictors of NIH funding amount. CONCLUSIONS: Early career radiology awards, specifically the GERRAF, provide support for female and non-PhD investigators and result in comparable academic performance metrics to NIH K Award recipients.

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.

BACKGROUND: There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottleneck for developing multi-disease classifiers is a lack of manually annotated data. The purpose of this work was to develop high throughput multi-label annotators for body CT reports that can be applied across a variety of abnormalities, organs, and disease states thereby mitigating the need for human annotation. METHODS: We used a dictionary approach to develop rule-based algorithms (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with four diseases per system based on their prevalence in our dataset. To expand the algorithms beyond pre-defined keywords, attention-guided recurrent neural networks (RNN) were trained using the RBA-extracted labels to classify reports as being positive for one or more diseases or normal for each organ system. Alternative effects on disease classification performance were evaluated using random initialization or pre-trained embedding as well as different sizes of training datasets. The RBA was tested on a subset of 2158 manually labeled reports and performance was reported as accuracy and F-score. The RNN was tested against a test set of 48,758 reports labeled by RBA and performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method. RESULTS: Manual validation of the RBA confirmed 91-99% accuracy across the 15 different labels. Our models extracted disease labels from 261,229 radiology reports of 112,501 unique subjects. Pre-trained models outperformed random initialization across all diseases. As the training dataset size was reduced, performance was robust except for a few diseases with a relatively small number of cases. Pre-trained classification AUCs reached > 0.95 for all four disease outcomes and normality across all three organ systems. CONCLUSIONS: Our label-extracting pipeline was able to encompass a variety of cases and diseases in body CT reports by generalizing beyond strict rules with exceptional accuracy. The method described can be easily adapted to enable automated labeling of hospital-scale medical data sets for training image-based disease classifiers.

Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning.

PURPOSE: To design multidisease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. MATERIALS AND METHODS: This retrospective study included a total of 12 092 patients (mean age, 57 years ± 18 [standard deviation]; 6172 women) for model development and testing. Rule-based algorithms were used to extract 19 225 disease labels from 13 667 body CT scans performed between 2012 and 2017. Using a three-dimensional DenseVNet, three organ systems were segmented: lungs and pleura, liver and gallbladder, and kidneys and ureters. For each organ system, a three-dimensional convolutional neural network classified each as no apparent disease or for the presence of four common diseases, for a total of 15 different labels across all three models. Testing was performed on a subset of 2158 CT volumes relative to 2875 manually derived reference labels from 2133 patients (mean age, 58 years ± 18; 1079 women). Performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method. RESULTS: Manual validation of the extracted labels confirmed 91%-99% accuracy across the 15 different labels. AUCs for lungs and pleura labels were as follows: atelectasis, 0.77 (95% CI: 0.74, 0.81); nodule, 0.65 (95% CI: 0.61, 0.69); emphysema, 0.89 (95% CI: 0.86, 0.92); effusion, 0.97 (95% CI: 0.96, 0.98); and no apparent disease, 0.89 (95% CI: 0.87, 0.91). AUCs for liver and gallbladder were as follows: hepatobiliary calcification, 0.62 (95% CI: 0.56, 0.67); lesion, 0.73 (95% CI: 0.69, 0.77); dilation, 0.87 (95% CI: 0.84, 0.90); fatty, 0.89 (95% CI: 0.86, 0.92); and no apparent disease, 0.82 (95% CI: 0.78, 0.85). AUCs for kidneys and ureters were as follows: stone, 0.83 (95% CI: 0.79, 0.87); atrophy, 0.92 (95% CI: 0.89, 0.94); lesion, 0.68 (95% CI: 0.64, 0.72); cyst, 0.70 (95% CI: 0.66, 0.73); and no apparent disease, 0.79 (95% CI: 0.75, 0.83). CONCLUSION: Weakly supervised deep learning models were able to classify diverse diseases in multiple organ systems from CT scans.Keywords: CT, Diagnosis/Classification/Application Domain, Semisupervised Learning, Whole-Body Imaging© RSNA, 2022.

Monocytes as a Cellular Vaccine Platform to Induce Antitumor Immunity.

We recently developed a monocyte-based cellular vaccine platform for cancer treatment. In contrast to the traditional utilization of monocytes as precursors to generate dendritic cells (DC) for vaccination purposes, we find that freshly isolated monocytes with no differentiation process can be loaded with tumor antigens (Ag) and trigger robust antitumor cytotoxic T lymphocyte (CTL) responses. In this chapter, we describe methods to prepare, administer, and evaluate murine Ly-6Chi monocyte-based cellular vaccines for their therapeutic efficacy. This includes procedures for isolation, purity determination, Ag loading, administration of bone marrow (BM)-derived monocytes, as well as methods to determine vaccine efficacy through the examination of Ag-specific CD8+ T cell expansion and antitumor responses in murine melanoma models. As a vaccine platform, undifferentiated monocytes can be easily adapted to different tumor models with a multitude of target antigens. The method described here seeks to facilitate preclinical research of monocyte-based vaccination as a strategy for cancer immunotherapy.