Establishing Relevant ADC-based Texture Analysis Metrics for Quantifying Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinomas

Purpose: The purpose of this study is to identify which texture analysis metrics calculated from apparent diffusion coefficient (ADC) maps from patients with head and neck squamous cell carcinomas (HNSCC) provide quantifiable measures of tumor physiology changes. We discerned which imaging metrics were relevant using baseline agreement and variations during early treatment. Methods: For selective patients with stages II-IV HNSCC, ADC maps were generated from two baselines, taken 1 week apart, and one early treatment scan, obtained during the 2nd week of curative-intent chemoradiation therapy. Regions of interest (ROI), consisting of primary and nodal disease were drawn onto resampled ADC maps. Four 3D texture matrices describing local and regional relationships between voxel intensities in the ROIs were generated. From these, 38 texture metrics and 7 histogram features were calculated for each patient, including the mean and median ADC. Agreement between the two baseline measures was estimated with the intra-class correlation coefficient (ICC). For each metric with an ICC≥0.80, the Wilcoxon signed-rank test was used to test if the difference between the mean of the baselines and the early treatment was non-zero. Results: Texture analysis was implemented on nine patients that had both baselines and early treatment images. Due to baseline agreement, only 9 of the 45 metrics had an ICC ≥0.80, including ADC mean and median. Six of these 9 metrics had a p-value \u3c 0.05. Only 1 of the 9 metrics remained of interest, after applying the Holm correction to the alpha levels: the run length non-uniformity metric (p = 0.004) in the Gray Level Run Length Matrix. Conclusion: The feasibility of texture analysis is dependent on the baseline agreement of each metric, which disqualifies many texture characteristics. However, metrics with high ICC have potential to provide additional quantitative information for the assessment of early treatment changes for HNSCC

MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma

PurposeTo identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS).Materials and methodsIn a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast in vivo MRI, three-dimensional (3D) multi-contrast high-resolution ex vivo MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.ResultsThree features correlated with ex vivo apparent diffusion coefficient (ADC), and no features correlated with in vivo ADC. Six features demonstrated significant linear relationships with ex vivo T2*, and fifteen features correlated significantly with in vivo T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.ConclusionRegistered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Evaluation of LMP-420: A Novel, Nontoxic Drug with Anti-Inflammatory Properties and Therapeutic Potential for CLL

<p>B-cell chronic lymphocytic leukemia (CLL) is the most common leukemia in the Western world. Although treatment of this disease has advanced considerably over the past decade, CLL remains incurable with current chemotherapeutics. In addition, available drug regimens for CLL are associated with frequent cytopenia-related complications, such as infection and fatigue. Thus, the major challenge in CLL treatment today is the need for alternative therapeutics with decreased toxicity and improved efficacy for disease refractory to currently available drugs.</p><p> </p><p>CLL is characterized by slow accumulation of malignant cells, which are supported in the microenvironment by cell-cell interactions and soluble cytokines such as tumor necrosis factor (TNF). We evaluated the effect of the small molecule TNF inhibitor LMP-420 on primary CLL cells. LMP-420 exhibited cytotoxic activity against these cells in the MTS assay, with similar potency to the front-line CLL drug fludarabine. LMP-420 induced time- and dose-dependent apoptosis in CLL cells, as demonstrated by annexin V staining, caspase activation, and DNA fragmentation. These changes were associated with decreased expression of the anti-apoptotic proteins Mcl-1, Bcl-xL, Bcl-2, and XIAP. CLL cells from patients with poor prognostic indicators exhibited LMP-420 sensitivity equal to that for cells from patients with favorable characteristics. In addition, LMP-420 potentiated the cytotoxic effect of fludarabine and inhibited in vitro proliferation of CLL cells. In contrast to other CLL therapeutics, LMP-420 exhibited minimal effects on normal peripheral blood mononuclear cell viability, mitogen-stimulated B- and T-cell proliferation, and hematopoietic colony formation. Our data suggest that LMP-420 may be a useful treatment for CLL with negligible hematologic toxicities. </p><p> </p><p>The effect profile of this compound in normal immune cells and the microarray studies in CLL cells indicate that the mechanism of action of LMP-420 likely involves modulation of the NF-kB pathway. Our initial studies demonstrate moderate but significant inhibitory activity against p65, a key member of the NF-kB transcription factor family. Research is ongoing to gain a better understanding of the specific cytotoxicity of LMP-420 for CLL cells and to elucidate other components of its mechanism of action. Regardless of the ultimate mechanistic findings with LMP-420, our studies support this molecule as a promising new CLL therapeutic that warrants further preclinical evaluation.</p>Dissertatio

stall Encodes an ADAMTS Metalloprotease and Interacts Genetically With Delta in Drosophila Ovarian Follicle Formation

Ovarian follicle formation in Drosophila melanogaster requires stall (stl) gene function, both within and outside the ovary, for follicle individualization, stalk cell intercalation, and oocyte localization. We have identified the stl transcript as CG3622 and confirmed the presence of three alternatively spliced isoforms, contrary to current genome annotation. Here we show that the gene is expressed in both ovarian and brain tissues, which is consistent with previous evidence of an ovary nonautonomous function. On the basis of amino acid sequence, stl encodes a metalloprotease similar to the “a disintegrin and metalloprotease with thrombospondin” (ADAMTS) family. Although stl mutant ovaries fail to maintain the branched structure of the fusome and periodically show improperly localized oocytes, stl mutants do not alter oocyte determination. Within the ovary, stl is expressed in pupal basal stalks and in adult somatic cells of the posterior germarium and the follicular poles. Genetically, stl exhibits a strong mutant interaction with Delta (Dl), and Dl mutant ovaries show altered stl expression patterns. Additionally, a previously described genetic interactor, daughterless, also modulates stl expression in the somatic ovary and may do so directly in its capacity as a basic helix-loop-helix (bHLH) transcription factor. We propose a complex model of long-range extraovarian signaling through secretion or extracellular domain shedding, together with local intraovarian protein modification, to explain the dual sites of Stl metalloprotease function in oogenesis

Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study

BackgroundArtificial intelligence (AI) and machine learning (ML) have resulted in significant enthusiasm for their promise in healthcare. Despite this, prospective randomized controlled trials and successful clinical implementation remain limited. One clinical application of ML is mitigation of the increased risk for acute care during outpatient cancer therapy. We previously reported the results of the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) study (NCT04277650), which was a prospective, randomized quality improvement study demonstrating that ML based on electronic health record (EHR) data can direct supplemental clinical evaluations and reduce the rate of acute care during cancer radiotherapy&nbsp;with and without chemotherapy. The objective of this study is to report the workflow and operational challenges encountered during ML implementation on the SHIELD-RT study.ResultsData extraction and manual review steps in the workflow represented significant time commitments for implementation of clinical ML on a prospective, randomized study. Barriers include limited data availability through the standard clinical workflow and commercial products, the need to aggregate data from multiple sources, and logistical challenges from altering the standard clinical workflow to deliver adaptive care.ConclusionsThe SHIELD-RT study was an early randomized controlled study which enabled assessment of barriers to clinical ML implementation, specifically those which leverage the EHR. These challenges build on a growing body of literature and may provide lessons for future healthcare ML adoption.Trial registrationNCT04277650. Registered 20 February 2020. Retrospectively registered quality improvement study

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

PurposeTo develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.MethodsBased on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively.ResultsThe proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (&lt;1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted.ConclusionThe developed biologically guided deep learning method achieved post-20-Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future