Sequential oxygen evolution and decoupled water splitting via electrochemical redox reaction of nickel hydroxides.

Alkaline water electrolysis is a promising low-cost strategy for clean and sustainable hydrogen production but is largely limited by the sluggish anodic oxygen evolution reaction and the challenges in maintaining adequate separation between H2 and O2. Here, we reveal an anodic-cathodic sequential oxygen evolution process via electrochemical oxidation and subsequent reduction of Ni hydroxides, enabling much lower overpotentials than conventional anodic oxygen evolution. By using (isotope-labeled) differential electrochemical mass spectrometry and in situ Raman spectroscopy combined with density functional theory calculations, we evidence that the sequential oxygen evolution originates from the electrochemical oxidation of Ni hydroxides to NiOO- active species while undergoing a different, reductive step of NiOO- for the final release of O2 due to weakened Ni-O covalency. Based on this sequential process, we propose and demonstrate a hybrid water electrolysis and energy storage device, which enables time-decoupled hydrogen and oxygen evolution and electrochemical energy storage in the Ni hydroxides.

Treating primary membranous nephropathy with extremely high titer of anti-phospholipase A2 receptor antibodies: A case of failed treatment with very high-dose rituximab.

Rituximab (RTX) is the anti-CD20 monoclonal antibody that has been used as the first-line therapy for primary membranous nephropathy (PMN) in recent years. However, the optimal dosing regimen and timing of RTX, or combination with other immunosuppressants, especially in patients with extremely high titers (> 1,000 RU/mL) of anti-PLA2R antibody (aPLA2R), are unclear at present. This report describes the case of a 70-year-old PMN patient with extremely high aPLA2R titer who failed to respond to very high doses of RTX. We also discuss the possible reasons for treatment failure.

[Soil Cadmium Prediction and Health Risk Assessment of an Oasis on the Eastern Edge of the Tarim Basin Based on Feature Optimization and Machine Learning].

Soil heavy metal pollution poses a serious threat to food security, human health, and soil ecosystems. Based on 644 soil samples collected from a typical oasis located at the eastern margin of the Tarim Basin, a series of models, namely, multiple linear regression （LR）, neural network （BP）, random forest （RF）, support vector machine （SVM）, and radial basis function （RBF）, were built to predict the soil heavy metal content. The optimal prediction result was obtained and utilized to analyze the spatial distribution features of heavy metal contamination and relevant health risks. The outcomes demonstrated that： ① The average Cd content in the study area was 0.14 mg·kg-1, which was 1.17 times the soil background value of Xinjiang, making it the primary factor of soil heavy metal contamination in the area. Additionally, the carcinogenicity risk coefficients of Cd for both adults and children were less than 10-4, indicating that there were no significant long-term health risks for humans in the area. ② The estimation accuracies of the five inversion models were compared, and the validation set of the RF model had an R2 value of 0.763 7, which was the highest among the five models. Additionally, the RMSE, MAE, and MBE of the RF model were the smallest among the five models. Therefore, the predicted values of the RF model were most consistent with the measured values of the soil Cd content. The predicted map of soil Cd distribution derived from the RF model coincided best with the interpolation map. ③ The RF model outperformed the other four models in predicting health risks associated with the soil Cd element for both adults and children, resulting in better prediction results. Comparatively, the predicted values of the LR model in the validation set varied greatly, leading to unreliable results. It was demonstrated that the RF was the best model for predicting soil Cd content and evaluating health risks in the study area, considering its superior generalization capability and anti-overfitting ability.

A dual-radiomics model for overall survival prediction in early-stage NSCLC patient using pre-treatment CT images.

Introduction: Radiation therapy (RT) is one of the primary treatment options for early-stage non-small cell lung cancer (ES-NSCLC). Therefore, accurately predicting the overall survival (OS) rate following radiotherapy is crucial for implementing personalized treatment strategies. This work aims to develop a dual-radiomics (DR) model to (1) predict 3-year OS in ES-NSCLC patients receiving RT using pre-treatment CT images, and (2) provide explanations between feature importanceand model prediction performance. Methods: The publicly available TCIA Lung1 dataset with 132 ES-NSCLC patients received RT were studied: 89/43 patients in the under/over 3-year OS group. For each patient, two types of radiomic features were examined: 56 handcrafted radiomic features (HRFs) extracted within gross tumor volume, and 512 image deep features (IDFs) extracted using a pre-trained U-Net encoder. They were combined as inputs to an explainable boosting machine (EBM) model for OS prediction. The EBM's mean absolute scores for HRFs and IDFs were used as feature importance explanations. To evaluate identified feature importance, the DR model was compared with EBM using either (1) key or (2) non-key feature type only. Comparison studies with other models, including supporting vector machine (SVM) and random forest (RF), were also included. The performance was evaluated by the area under the receiver operating characteristic curve (AUCROC), accuracy, sensitivity, and specificity with a 100-fold Monte Carlo cross-validation. Results: The DR model showed highestperformance in predicting 3-year OS (AUCROC=0.81 ± 0.04), and EBM scores suggested that IDFs showed significantly greater importance (normalized mean score=0.0019) than HRFs (score=0.0008). The comparison studies showed that EBM with key feature type (IDFs-only demonstrated comparable AUCROC results (0.81 ± 0.04), while EBM with non-key feature type (HRFs-only) showed limited AUCROC (0.64 ± 0.10). The results suggested that feature importance score identified by EBM is highly correlated with OS prediction performance. Both SVM and RF models were unable to explain key feature type while showing limited overall AUCROC=0.66 ± 0.07 and 0.77 ± 0.06, respectively. Accuracy, sensitivity, and specificity showed a similar trend. Discussion: In conclusion, a DR model was successfully developed to predict ES-NSCLC OS based on pre-treatment CT images. The results suggested that the feature importance from DR model is highly correlated to the model prediction power.

Identification of KIFC1 as a putative vulnerability in lung cancers with centrosome amplification.

Centrosome amplification (CA), an abnormal increase in the number of centrosomes in the cell, is a recurrent phenomenon in lung and other malignancies. Although CA promotes tumor development and progression by inducing genomic instability (GIN), it also induces mitotic stress that jeopardizes cellular integrity. CA leads to the formation of multipolar mitotic spindles that can cause lethal chromosome segregation errors. To sustain the benefits of CA by mitigating its consequences, malignant cells are dependent on adaptive mechanisms that represent therapeutic vulnerabilities. We aimed to discover genetic dependencies associated with CA in lung cancer. Combining a CRISPR/Cas9 functional genomics screen with tumor genomic analyses, we identified the motor protein KIFC1, also known as HSET, as a putative vulnerability specifically in lung adenocarcinoma (LUAD) with CA. KIFC1 expression was positively correlated with CA in LUAD and associated with worse patient outcomes, smoking history, and indicators of GIN. KIFC1 loss-of-function sensitized LUAD cells with high basal KIFC1 expression to potentiation of CA, which was associated with a diminished ability to cluster extra centrosomes into pseudo-bipolar mitotic spindles. Our work suggests that KIFC1 inhibition represents a novel approach for potentiating GIN to lethal levels in LUAD with CA by forcing cells to divide with multipolar spindles, rationalizing further studies to investigate its therapeutic potential.

Formation of marine oil snow by soot particles generated from burning of oils.

This study aims to investigate the interactions between marine oil snow (MOS) formation and soot particles derived from two distinct oils: condensate and heavy oil. Experimental findings demonstrate that the properties of oil droplets and soot particles play a key role in MOS formation. Peak MOS formation is observed within the initial days for condensate, while for heavy oil, peak formation occurs at a later stage. Furthermore, the addition of oils and soot particles influences the final concentrations of polycyclic aromatic hydrocarbons (PAHs) in MOS. Remarkably, the ranking order of PAHs with different rings in various MOS samples remains consistent: 4- > 3- > 5- > 2- > 6-ring. Specific diagnostic ratios such as Phe/Ant, Ant/(Ant + Phe), BaA/(Chr + BaA), and LMW/HMW effectively differentiate petrogenic and pyrogenic sources of PAHs in MOS. And stable ratios like Flu/(Pyr + Flu), InP/(InP + BghiP), and BaF/BkF are identified for source analysis of soot MOS.

Smart Bandage Based on a ZIF-8 Triboelectric Nanogenerator for In Situ Real-Time Monitoring of Drug Concentration.

For chronic wounds, frequent replacement of bandages not only increases the likelihood of secondary damage and the risk of cross infection but also wastes medication. Therefore, in situ real-time monitoring of the concentrations of residual drugs in bandages is crucial. Here, we propose a novel strategy that combines a triboelectric nanogenerator (TENG) with medical bandages to develop a smart bandage based on zeolite imidazolate framework TENG. During the process of wound healing, the electrical output of TENG changes with the continuous release of drugs. Based on the correlation between the electrical signal of TENG and drug concentration, the concentration of residual drugs in the bandage can be monitored in real-time in situ, guiding medical staff to replace the bandage at the most appropriate time. The smart bandage based on TENG provides a new strategy for in situ real-time monitoring of drug concentration and also provides an ideal and feasible solution for the field of biomedical drug sensing.

Spiral-Pipe Gas Anaerobic Digester.

The reduction of carbon dioxide to methane using hydrogen is an important process in biogas production. However, designing gas anaerobic digesters (GADs) based on this reaction presents several challenges. In this study, we developed an innovative spiral-pipe gas anaerobic digester (SGAD) to increase the displacement distance between the bubbles, thus prolonging the gas retention time and facilitating the reduction of CO2 to CH4 via H2. The process was successfully demonstrated by using a CO2/H2 ratio of 1:3 and a gas-feeding rate of 3.9 L Lr -1 d-1. During the experiment, more than 98% of the CO2 and 96% of the H2 were consumed, resulting in biogas containing ca. 86-96% CH4. Additionally, we applied our proposed evaluation methodology for assessing GAD performance to evaluate the performance of the SGAD. This methodology serves as a reference for evaluating and designing GAD systems. The innovative design of the SGAD and the corresponding evaluation methodology offer new insights into the design of reactors.

A review of the pollution signatures of polycyclic aromatic hydrocarbons in the sediments of the East China Sea.

Marine sediments serve as crucial reservoirs for polycyclic aromatic hydrocarbons (PAHs), and their PAH signatures offer valuable historical pollution records. This article provides a comprehensive review of the pollution status of 16 priority PAHs in more than 1000 sediments from the East China Sea (ECS). It focuses on the PAH sources, spatiotemporal distributions, driving factors, and ecological risks, with information derived from peer-reviewed papers published between 2003 and 2023. The results revealed that vehicular emissions, mixed combustion sources of coal, biomass, and coke, as well as petrogenic sources, were the primary contributors to PAH pollution in the ECS sediments, accounting for 50%, 34%, and 16%, respectively. Human activities, hydrodynamic mechanisms, and environmental variables such as particle size and organic matter, collectively influenced the distribution of PAHs. Additionally, the population size and economic development played a key role in the temporal distribution of PAHs in the ECS sediments. The ecotoxicity assessment of PAHs in sediments indicated a low risk level. These outcomes are expected to provide environmentalists with detailed and up-to-date insights into sedimentary PAHs in the ECS, helping to develop suitable monitoring plans and strategies for promoting better management of ECS environment.

Assessment of medical information on irritable bowel syndrome information in Wikipedia and Baidu Encyclopedia: comparative study.

Background: Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder (FGID) with heterogeneous clinical presentations. There are no clear testing parameters for its diagnosis, and the complex pathophysiology of IBS and the limited time that doctors have to spend with patients makes it difficult to adequately educate patients in the outpatient setting. An increased awareness of IBS means that patients are more likely to self-diagnose and self-manage IBS based on their own symptoms. These factors may make patients more likely to turn to Internet resources. Wikipedia is the most popular online encyclopedia among English-speaking users, with numerous validations. However, in Mandarin-speaking regions, the Baidu Encyclopedia is most commonly used. There have been no studies on the reliability, readability, and objectivity of IBS information on the two sites. This is an urgent issue as these platforms are accessed by approximately 1.45 billion people. Objective: We compared the IBS content on Wikipedia (in English) and Baidu Baike (in Chinese), two online encyclopedias, in terms of reliability, readability, and objectivity. Methods: The Baidu Encyclopedia (in Chinese) and Wikipedia (in English) were evaluated based on the Rome IV IBS definitions and diagnoses. All possible synonyms and derivatives for IBS and IBS-related FGIDs were screened and identified. Two gastroenterology experts evaluated the scores of articles for both sites using the DISCERN instrument, the Journal of the American Medical Association scoring system (JAMA), and the Global Quality Score (GQS). Results: Wikipedia scored higher overall with DISCERN (p < .0001), JAMA (p < .0001) and GQS (p < .05) than the Baidu Encyclopedia. Specifically, Wikipedia scored higher in DISCERN Section 1 (p < .0001), DISCERN Section 2 (p < .01), DISCERN Section 3 (p < .001), and the General DISCERN score (p < .0001) than the Baidu Encyclopedia. Both sites had low DISCERN Section 2 scores (p = .18). Wikipedia also had a larger percentage of high quality scores in total DISCERN, DISCERN Section 1, and DISCERN Section 3 (p < .0001, P < .0001, P < .0004, respectively, based on the above 3 (60%) rule). Conclusions: Wikipedia provides more reliable, higher quality, and more objective IBS-related health information than the Baidu Encyclopedia. However, there should be improvements in the information quality for both sites. Medical professionals and institutions should collaborate with these online platforms to offer better health information for IBS.

Clinical study on the treatment of Qingfei Paidu Granules combined with nondrug therapy for asymptomatic patients with novel coronavirus.

BACKGROUND: The COVID-19 pandemic, ongoing for over 2 years with evolving viral strains, including the highly infectious Omicron variant, underscores the pivotal role of Traditional Chinese Medicine (TCM) in pandemic intervention. Qingfei Paidu Granules (QFPG) are incorporated into the national TCM diagnosis and treatment protocol. This study aims to assess the clinical effectiveness of QFPG combined with nonpharmacological interventions in asymptomatic novel coronavirus infection. MATERIALS AND METHODS: Using a full-group randomized controlled trial, asymptomatic individuals from 3 wards of Fangcang Hospital were randomly assigned to 3 groups, each comprising 150 cases: F1, the nonpharmacological treatment group, receiving only Five Elements Music Therapy and Gongfa Therapy; F2, the comprehensive treatment group, receiving QFPG treatment combined with Five Elements Music Therapy and Gongfa Therapy; and F3, the pharmacological treatment group, receiving only QFPG treatment. The treatment duration for each group was 6 days. Clinical efficacy and safety among different treatment groups were observed, including the conversion time of COVID-19 nucleic acid detection, duration of hospitalization, conversion rate from mild to moderate cases, and occurrence of adverse events. RESULTS: All 450 participants were included and completed the study. The conversion rates on the first day of treatment were 10.7%, 30%, and 11.3% for the F1, F2, and F3 groups, respectively. Compared to F1, both F2 and F3 showed shortened first conversion time and time to double negative results, with first conversion times of 1.92 days for F2 and 2.29 days for F3. Additionally, the time in F2 was shorter compared to F3. Hospitalization duration was shortened in both F2 and F3 compared to F1, with no statistically significant difference between F2 and F3. The conversion rate from mild to moderate cases was lower in both F2 and F3 compared to F1, but the difference between F2 and F3 was not significant. CONCLUSION: Combining QFPG with traditional Chinese nonpharmacological interventions effectively shortened the conversion time of COVID-19 nucleic acid detection and reduced the conversion rate from asymptomatic infection to mild/moderate cases. This treatment approach is worth promoting in the management of asymptomatic patients during pandemics. TRIAL REGISTRATION: ClinicalTrials.gov, ChiCTR2200059007.

Lysophosphatidic Acid Receptor 1 (LPA1) Antagonists as Potential Migrastatics for Triple Negative Breast Cancer.

Metastasis is responsible for about 90 % of cancer deaths. Anti-metastatic drugs, termed as migrastatics, offer a distinctive therapeutic approach to address cancer migration and invasion. However, therapeutic exploitation of metastasis-specific targets remains limited, and the effective prevention and suppression of metastatic cancer continue to be elusive. Lysophosphatidic acid receptor 1 (LPA1) is activated by an endogenous lipid molecule LPA, leading to a diverse array of cellular activities. Previous studies have shown that the LPA/LPA1 axis supports the progression of metastasis for many types of cancer. In this study, we report the synthesis and biological evaluation of fluorine-containing triazole derivatives as potent LPA1 antagonists, offering potential as migrastatic drugs for triple negative breast cancer (TNBC). In particular, compound 12 f, the most potent and highly selective in this series with an IC50 value of 16.0 nM in the cAMP assay and 18.4 nM in the calcium mobilization assay, inhibited cell survival, migration, and invasion in the TNBC cell line. Interestingly, the compound did not induce apoptosis in TNBC cells and demonstrated no cytotoxic effects. These results highlight the potential of LPA1 as a migrastatic target. Consequently, the LPA1 antagonists developed in this study hold promise as potential migrastatic candidates for TNBC.

Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS). METHODS AND MATERIALS: A total of 114 brain metastases (BMs) from 82 patients were evaluated, including 26 BMs that developed biopsy-confirmed local failure post-SRS. The SRS spatial dose distribution (Dmap) of each BM was registered to the planning contrast-enhanced T1 (T1-CE) magnetic resonance imaging (MRI). Axial slices of the Dmap, T1-CE, and planning target volume (PTV) segmentation (PTVseg) intersecting the BM center were extracted within a fixed field of view determined by the 60% isodose volume in Dmap. A spherical projection was implemented to transform planar image content onto a spherical surface using multiple projection centers, and the resultant T1-CE/Dmap/PTVseg projections were stacked as a 3-channel variable. Four Visual Geometry Group (VGG-19) deep encoders were used in an ensemble design, with each submodel using a different spherical projection formula as input for BM outcome prediction. In each submodel, clinical features after positional encoding were fused with VGG-19 deep features to generate logit results. The ensemble's outcome was synthesized from the 4 submodel results via logistic regression. In total, 10 model versions with random validation sample assignments were trained to study model robustness. Performance was compared with (1) a single VGG-19 encoder, (2) an ensemble with a T1-CE MRI as the sole image input after projections, and (3) an ensemble with the same image input design without clinical feature inclusion. RESULTS: The ensemble model achieved an excellent area under the receiver operating characteristic curve (AUCROC: 0.89 ± 0.02) with high sensitivity (0.82 ± 0.05), specificity (0.84 ± 0.11), and accuracy (0.84 ± 0.08) results. This outperformed the MRI-only VGG-19 encoder (sensitivity: 0.35 ± 0.01, AUCROC: 0.64 ± 0.08), the MRI-only deep ensemble (sensitivity: 0.60 ± 0.09, AUCROC: 0.68 ± 0.06), and the 3-channel ensemble without clinical feature fusion (sensitivity: 0.78 ± 0.08, AUCROC: 0.84 ± 0.03). CONCLUSIONS: Facilitated by the spherical image projection method, a deep ensemble model incorporating Dmap and clinical variables demonstrated excellent performance in predicting BM post-SRS local failure. Our novel approach could improve other radiation therapy outcome models and warrants further evaluation.

Enhancing biomass and lipid accumulation by a novel microalga for unsterilized piggery biogas slurry remediation.

The cost and efficiency of an algal-BS treatment system are determined by the specific microalgal species and BS pretreatment method. This study examines the growth of a novel algae Chlorella sp. YSD-2 and the removal of nutrients from the BS using different pretreatment methods, including dilution ratio and sterilization. The highest biomass production (1.84 g L-1) was achieved in the 1:2 unsterilized biogas slurry, which was 2.03 times higher than that in the sterilized group, as well as higher lipid productivity (17.29 mg L-1 d-1). Nevertheless, the sterilized biogas slurry at a 1:1 dilution ratio exhibited the most notable nutrient-removal efficiency, with COD at 71.97%, TP at 91.32%, and TN at 88.80%. Additionally, the analysis of 16S rRNA sequencing revealed a significant alteration in the indigenous bacterial composition of the biogas slurry by microalgal treatment, with Proteobacteria and Cyanobacteria emerging as the predominant phyla, and unidentified_Cyanobacteria as the primary genus. These findings suggest that Chlorella sp. YSD-2 exhibits favorable tolerance and nutrient-removal capabilities in unsterilized, high-strength biogas slurry, along with high productivity of biomass and lipids. Consequently, these results offer a theoretical foundation for the development of an efficient and economically viable treatment method for algal-BS.

A rare waterborne outbreak of Bacillus paranthracis in Shandong province, China, 2020: epidemiologic survey, genomic insights, and virulence characteristics.

Bacillus paranthracis, a Gram-positive conditional pathogen of Bacillus cereus group species, is capable of causing foodborne and waterborne illnesses, leading to intestinal diseases in humans characterized by diarrhoea and vomiting. However, documented cases of B. paranthracis infection outbreaks are rare in the world, and the genomic background of outbreak strains is seldom characterized. This study retrospectively analyzed strains obtained from an outbreak in schools, as well as from water systems in peri-urban areas, China, in 2020. In total, 28 B. cereus group isolates were retrieved, comprising 6 from stool samples and 22 from water samples. Epidemiological and phylogenetic investigations indicated that the B. paranthracis isolate from drinking water as the causative agent of the outbreak. The genomic comparison revealed a high degree of consistency among 8 outbreak-related strains in terms of antimicrobial resistance gene profiles, virulence gene profiles, genomic content, and multilocus sequence typing (MLST). The strains related to the outbreak show highly similar genomic ring diagrams and close phylogenetic relationships. Additionally, this study shed light on the pathogenic potential and complexity of B. cereus group through its diversity in virulence genes and mice infection model. The findings highlight the usefulness of B. paranthracis genomes in understanding genetic diversity within specific environments and in tracing the source of pathogens during outbreak situations, thereby enabling targeted infection control interventions.

Effect of Thermo- and pH-Sensitive Block Copolymer Structure and Composition on the Synthesis and Stabilization of Gold Nanoparticles.

Homopolymers of poly[N-(2-(diethylamino)ethyl) acrylamide] exhibit the ability to adsorb onto the surface of preformed or growing gold nanoparticles. The resulting hybrid materials possess a pH and thermo-sensitive nature. Consequently, their optical properties can be modulated by manipulating either the temperature or the pH. Moreover, introducing monomers based on poly(N-isopropyl acrylamide) into block or random statistical polymers enables further modulation of the thermosensitive properties. These copolymers, employed for the in-situ synthesis and/or stabilization of gold nanoparticles, lead to hybrid materials whose properties and/or particle size depend on the polymer composition and microstructure: statistical polymers emerge as superior stabilizing agents compared to their block counterparts at a constant composition.

[Microtubule-associated tumor suppressor 1 inhibits hemin-induced apoptosis of vascular endothelial cells via hemeoxygenase 1].

This study aimed to investigate the effects of microtubule associated tumor suppressor 1 (MTUS1) on hemeoxygenase 1 (HMOX1) expression and hemin-induced apoptosis of vascular endothelial cells and its regulatory mechanism. RNA sequencing, RT-qPCR and Western blot were used to assess altered genes of hemin binding proteins, the expression of cAMP response element-binding protein (CREB) and nuclear respiratory factor 2 (NRF2), hemin-induced HMOX1 expression in MTUS1 knockdown human umbilical vein endothelial cells (HUVEC), and the effect of overexpression of CREB and NRF2 on HMOX1 expression in MTUS1 knockdown 293T cells. The effect of MTUS1 or HMOX1 knockdown on hemin-induced apoptosis in HUVEC, and the overexpression of NRF2 on hemin-induced apoptosis in MTUS1 knockdown 293T cells were assayed with CCK8 and Western blot. The results showed that MTUS1 was knocked down significantly in HUVEC by siRNA (P < 0.01), accompanied by decreased HMOX1 expression (P < 0.01). The increased HMOX1 expression induced by hemin was also inhibited by MTUS1 knockdown (P < 0.01). And the apoptosis of HUVEC induced by hemin was amplified by MTUS1 or HMOX1 knockdown (P < 0.01). Moreover the expression of CREB and NRF2 were both inhibited by MTUS1 knockdown in HUVEC (P < 0.01). The decreased HMOX1 regulated by MTUS1 knockdown could be rescued partly by overexpression of NRF2 (P < 0.01), however, not by overexpression of CREB. And the MTUS1 knockdown mediated decreased 293T cells viability induced by hemin could be partly rescued by NRF2 overexpression (P < 0.01). These results suggest that MTUS1 can inhibit hemin-induced apoptosis of HUVEC, and the mechanism maybe related to MTUS1/NRF2/HMOX1 pathway.

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma.

Purpose: We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. Approach: A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (N=69; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment 18fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response. The primary tumor volume was manually segmented by a radiation oncologist on PET/CT images acquired two weeks into treatment (20 Gy). From this, 54 radiomic texture features were extracted. Two image discretization techniques-fixed bin number (FBN) and fixed bin size (FBS)-were considered to evaluate systematic changes in the bin number ({32, 64, 128, 256} gray levels) and bin size ({0.10, 0.15, 0.22, 0.25} bin-widths). For each discretization-specific radiomic feature space, an LASSO-regularized logistic regression model was independently trained to predict residual and/or recurrent disease. The model training was based on Monte Carlo cross-validation with a 20% testing hold-out, 50 permutations, and minor-class up-sampling to account for imbalanced outcomes data. Performance differences among the discretization-specific models were quantified via receiver operating characteristic curve analysis. A final parameter-optimized logistic regression model was developed by incorporating different settings parameterizations into the same model. Results: FBN outperformed FBS in predicting residual and/or recurrent disease. The four FBN models achieved AUC values of 0.63, 0.61, 0.65, and 0.62 for 32, 64, 128, and 256 gray levels, respectively. By contrast, the average AUC of the four FBS models was 0.53. The parameter-optimized model, comprising features joint entropy (FBN = 64) and information measure correlation 1 (FBN = 128), achieved an AUC of 0.70. Kaplan-Meier analyses identified these features to be associated with disease-free survival (p=0.0158 and p=0.0180, respectively; log-rank test). Conclusions: Our findings suggest that the prognostic value of individual radiomic features may depend on feature-specific discretization parameter settings.