Predicting Regional Recurrence and Prognosis in Stereotactic Body Radiation Therapy-Treated Clinical Stage I Non-small Cell Lung Cancer Using a Radiomics Model Constructed With Surgical Data.

PURPOSE: Risk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). This study aimed to develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients. METHODS AND MATERIALS: Patients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A preoperative computed tomography-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the 3 models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from 2 academic medical centers. RESULTS: A total of 769 patients were included. Eight computed tomography features were identified in the radiomics model, achieving areas under the curves of 0.85 (95% CI, 0.81-0.89) and 0.83 (95% CI, 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI, 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (P < .001) and shorter regional recurrence-free survival (P = .02), progression-free survival (P = .004) and overall survival (P = .006) than those in the low-risk group. CONCLUSIONS: The radiomics model based on pathologically confirmed data effectively identified patients with OLNM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.

Exogenous and Endogenous Dual-Activated Nanoladder for Precise Imaging of Mitochondrial Ferroptosis-Related Inhibition miRNA with Tumor Cell Specificity.

Developing precise tumor cell-specific mitochondrial ferroptosis-related inhibition miRNA imaging methods holds enormous potential for anticancer drug screening and cancer treatment. Nevertheless, traditional amplification methods still tolerated the limited tumor specificity because of the "off-tumor" signal leakage resulting from their "always-active" sensing mode. To overcome this limitation, we herein developed a dual (exogenous 808 nm NIR light and endogenous APE1) activated nanoladder for precise imaging of mitochondrial ferroptosis-related miRNA with tumor cell specificity and improved imaging resolution. Exogenous NIR light-activation can regulate the ferroptosis-related inhibition miRNA imaging signals within mitochondria, and endogenous enzyme-activation can confine signals to tumor cells. Based on this dual activation design, off-tumor signals were greatly reduced and tumor-to-background contrast was enhanced with an improved tumor/normal discrimination ratio, realizing tumor cell-specific precise imaging of mitochondrial ferroptosis-related inhibition miRNA.

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.

A modified diffusion-weighted magnetic resonance imaging-based model from the radiologist's perspective: improved performance in determining the surgical resectability of advanced high-grade serous ovarian cancer.

BACKGROUND: Complete resection of all visible lesions during primary debulking surgery is associated with the most favorable prognosis in patients with advanced high-grade serous ovarian cancer. An accurate preoperative assessment of resectability is pivotal for tailored management. OBJECTIVE: This study aimed to assess the potential value of a modified model that integrates the original 8 radiologic criteria of the Memorial Sloan Kettering Cancer Center model with imaging features of the subcapsular or diaphragm and mesenteric lesions depicted on diffusion-weighted magnetic resonance imaging and growth patterns of all lesions for predicting the resectability of advanced high-grade serous ovarian cancer. STUDY DESIGN: This study included 184 patients with high-grade serous ovarian cancer who underwent preoperative diffusion-weighted magnetic resonance imaging between December 2018 and May 2023 at 2 medical centers. The patient cohort was divided into 3 subsets, namely a study cohort (n=100), an internal validation cohort (n=46), and an external validation cohort (n=38). Preoperative radiologic evaluations were independently conducted by 2 radiologists using both the Memorial Sloan Kettering Cancer Center model and the modified diffusion-weighted magnetic resonance imaging-based model. The morphologic characteristics of the ovarian tumors depicted on magnetic resonance imaging were assessed as either mass-like or infiltrative, and transcriptomic analysis of the primary tumor samples was performed. Univariate and multivariate statistical analyses were performed. RESULTS: In the study cohort, both the scores derived using the Memorial Sloan Kettering Cancer Center (intraclass correlation coefficients of 0.980 and 0.959, respectively; both P<.001) and modified diffusion-weighted magnetic resonance imaging-based models (intraclass correlation coefficients of 0.962 and 0.940, respectively; both P<.001) demonstrated excellent intra- and interobserver agreement. The Memorial Sloan Kettering Cancer Center model (odds ratio, 1.825; 95% confidence interval, 1.390-2.395; P<.001) and the modified diffusion-weighted magnetic resonance imaging-based model (odds ratio, 1.776; 95% confidence interval, 1.410-2.238; P<.001) independently predicted surgical resectability. The modified diffusion-weighted magnetic resonance imaging-based model demonstrated improved predictive performance with an area under the curve of 0.867 in the study cohort and 0.806 and 0.913 in the internal and external validation cohorts, respectively. Using the modified diffusion-weighted magnetic resonance imaging-based model, patients with scores of 0 to 2, 3 to 4, 5 to 6, 7 to 10, and ≥11 achieved complete tumor debulking rates of 90.3%, 66.7%, 53.3%, 11.8%, and 0%, respectively. Most patients with incomplete tumor debulking had infiltrative tumors, and both the Memorial Sloan Kettering Cancer Center and the modified diffusion-weighted magnetic resonance imaging-based models yielded higher scores. The molecular differences between the 2 morphologic subtypes were identified. CONCLUSION: When compared with the Memorial Sloan Kettering Cancer Center model, the modified diffusion-weighted magnetic resonance imaging-based model demonstrated enhanced accuracy in the preoperative prediction of resectability for advanced high-grade serous ovarian cancer. Patients with scores of 0 to 6 were eligible for primary debulking surgery.

Ecological Risk Assessment of Organochlorine Pesticides and Polychlorinated Biphenyls in Coastal Sediments in China.

Although the ecological risk of emerging contaminants is currently a research hotspot in China and abroad, few studies have investigated the ecological risk of pesticide pollutants in Chinese coastal sediments. In this study, nine pesticide pollutants included in the "List of New Key Pollutants for Control (2023 Edition)" issued by the Chinese government were used as the research objects, and the environmental exposure of pesticide pollutants in China's coastal sediments was analyzed. The baseline sediment quality criteria were deduced using the balanced distribution method, and a multi-level ecological risk assessment of pesticides in sediment was performed. The results showed that the nine pesticide pollutants were widespread in Chinese coastal sediments, with concentrations ranging from 0.01 ng·g-1 to 330 ng·g-1. The risk quotient assessment showed that endosulfan and DDT posed medium environmental risks to the Chinese coastal sediment environment, and PCBs posed medium risks in some bays of the East China Sea. The semi-probabilistic, optimized semi-probability evaluation and joint probability curve (JPC) assessments all show that endosulfan and DDT pose a certain degree of risk to the environment.

Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study.

Objective: To develop and validate a multiparametric MRI-based radiomics model for prediction of microsatellite instability (MSI) status in patients with endometrial cancer (EC). Methods: A total of 225 patients from Center I including 158 in the training cohort and 67 in the internal testing cohort, and 132 patients from Center II were included as an external validation cohort. All the patients were pathologically confirmed EC who underwent pelvic MRI before treatment. The MSI status was confirmed by immunohistochemistry (IHC) staining. A total of 4245 features were extracted from T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI) and apparent diffusion coefficient (ADC) maps for each patient. Four feature selection steps were used, and then five machine learning models, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF), were built for MSI status prediction in the training cohort. Receiver operating characteristics (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of these models. Results: The SVM model showed the best performance with an AUC of 0.905 (95%CI, 0.848-0.961) in the training cohort, and was subsequently validated in the internal testing cohort and external validation cohort, with the corresponding AUCs of 0.875 (95%CI, 0.762-0.988) and 0.862 (95%CI, 0.781-0.942), respectively. The DCA curve demonstrated favorable clinical utility. Conclusion: We developed and validated a multiparametric MRI-based radiomics model with gratifying performance in predicting MSI status, and could potentially be used to facilitate the decision-making on clinical treatment options in patients with EC.

Effect of Lignin Structure Characteristics on the Performance of Lignin Based Phenol Formaldehyde Adhesives.

As the second most abundant biopolymer, lignin remains underutilized in various industrial applications. Various forms of lignin generated from different methods affect its physical and chemical properties to a certain extent. To promote the broader commercial utilization of currently available industrial lignins, lignin sulfonate (SL), kraft lignin (KL), and organosolv lignin (OL) are utilized to partially replace phenol in the synthesis of phenol formaldehyde (PF) adhesives. The impact of lignin production process on the effectiveness of lignin-based phenolic (LPF) adhesives is examined based on the structural analysis of the selected industrial lignin. The results show that OL has more phenolic hydroxyl groups, lower molecular weight, and greater number of reactive sites than the other two types of lignins. The maximum replacement rate of phenol by OL reaches 70% w/w, resulting in organosolv lignin phenolic (OLPF) adhesives with a viscosity of 960 mPa·s, a minimal free formaldehyde content of 0.157%, and a shear strength of 1.84 MPa. It exhibits better performance compared with the other two types of lignin-based adhesives and meets the requirements of national standards.

Prognostic significance and survival benefits of postoperative adjuvant chemotherapy in patients with stage IA lung adenocarcinoma with non-predominant micropapillary components.

BACKGROUND: The prognostic significance of adjuvant chemotherapy (ACT) for patients with stage IA micropapillary non-predominant (MPNP) lung adenocarcinoma (LUAD) remains unknown. This study aimed to investigate the effects of postoperative ACT in patients with stage IA MPNP-LUAD. METHODS: A total of 149 patients with pathological stage IA MPNP-LUAD who underwent surgery at our center were retrospectively analyzed. Propensity score matching (PSM) analysis was conducted to reduce potential selection bias. Kaplan-Meier analyses were used to assess the impact of ACT on recurrence-free survival (RFS), overall survival (OS), and disease-specific survival (DSS). Subgroup analyses were performed for the survival outcomes based on the percentage of micropapillary components. Cox proportional hazards regression analyses were applied to identify risk factors associated with survival. RESULTS: The receipt or non-receipt of postoperative ACT had no significant effect on RFS, OS, and DSS among all enrolled patients with stage IA MPNP-LUAD (P > 0.05). For patients with a micropapillary component > 5%, the 5-year rates of RFS, OS, and DSS were significantly higher in the ACT group compared to the observation group, both before and after PSM (P < 0.05). However, the differences between the two groups were not significant for patients with a micropapillary component ≤ 5% (P > 0.05). The resection range (HR = 0.071; 95% CI: 0.020-0.251; P < 0.001), tumor size (HR = 2.929; 95% CI: 1.171-7.330; P = 0.022), and ACT (HR = 0.122; 95% CI: 0.037-0.403; P = 0.001) were identified as independent prognostic factors for RFS through Cox regression analysis. CONCLUSION: Patients with stage IA MPNP-LUAD who have a micropapillary component greater than 5% might benefit from postoperative ACT, while those with a micropapillary component ≤ 5% did not appear to derive the same benefit from postoperative ACT.

Baseline symptom-related white matter tracts predict individualized treatment response to 12-week antipsychotic monotherapies in first-episode schizophrenia.

There is significant heterogeneity in individual responses to antipsychotic drugs, but there is no reliable predictor of antipsychotics response in first-episode psychosis. This study aimed to investigate whether psychotic symptom-related alterations in fractional anisotropy (FA) and mean diffusivity (MD) of white matter (WM) at the early stage of the disorder may aid in the individualized prediction of drug response. Sixty-eight first-episode patients underwent baseline structural MRI scans and were subsequently randomized to receive a single atypical antipsychotic throughout the first 12 weeks. Clinical symptoms were evaluated using the eight "core symptoms" selected from the Positive and Negative Syndrome Scale (PANSS-8). Follow-up assessments were conducted at the 4th, 8th, and 12th weeks by trained psychiatrists. LASSO regression model and cross-validation were conducted to examine the performance of baseline symptom-related alterations FA and MD of WM in the prediction of individualized treatment outcome. Fifty patients completed both clinical follow-up assessments by the 8th and 12th weeks. 30 patients were classified as responders, and 20 patients were classified as nonresponders. At baseline, the altered diffusion properties of fiber tracts in the anterior thalamic radiation, corticospinal tract, callosum forceps minor, longitudinal fasciculi (ILF), inferior frontal-occipital fasciculi (IFOF) and superior longitudinal fasciculus (SLF) were related to the severity of symptoms. These abnormal fiber tracts, especially the ILF, IFOF, and SLF, significantly predicted the response to antipsychotic treatment at the individual level (AUC = 0.828, P < 0.001). These findings demonstrate that early microstructural WM changes contribute to the pathophysiology of psychosis and may serve as meaningful individualized predictors of response to antipsychotics.

Utility of enteral nutrition via percutaneous transhepatic cholangiography drainage catheterization in late-stage malignant obstructive jaundice.

Objective: The purpose of this study was to explore the clinical benefits of establishing an enteral nutrition (EN) pathway via percutaneous transhepatic cholangiography drainage (PTCD) catheterization in patients with late-stage malignant obstructive jaundice (MOJ).Methods: We selected 30 patients diagnosed as having late-stage MOJ with malnutrition. A dual-lumen biliary-enteral nutrition tube was placed via PTCD along with a biliary stent implantation. Postoperative EN was provided, and we observed the time taken for tube placement, its success rate, complications, and therapeutic efficacy.Results: Tube placement was successful in all 30 patients with an average procedural time of 5.7 ± 1.4 min with no tube placement complications. Compared to preoperative measures, there was a significant improvement in postoperative jaundice reduction and nutritional indicators one month after the procedure (p < 0.05). Post-placement complications included tube perileakage in 5 cases, entero-biliary reflux in 4 cases, tube blockage in 6 cases, tube displacement in 4 cases, accidental tube removal in 3 cases, and tube replacement due to degradation in 8 cases, with tube retention time ranging from 42 to 314 days, averaging 124.7 ± 37.5 days. All patients achieved the parameters for effective home-based enteral nutrition with a noticeable improvement in their quality of life.Conclusion: In this study, we found that the technique of establishing an EN pathway via PTCD catheterization was minimally invasive, safe, and effective; the tube was easy to maintain; and patient compliance was high. It is, thus, suitable for long-term tube retention in patients with late-stage MOJ.

Serological response and immune-related adverse events following COVID-19 vaccination in cancer patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis.

With the popularity of Coronavirus disease 2019 (COVID-19) vaccine and the development of vaccination strategies, the impact of COVID-19 vaccine on cancer patients receiving immune checkpoint inhibitors (ICIs) is still unclear. In the systematic review and meta-analysis of patients with ICIs, we assessed the serological response of cancer patients receiving COVID-19 vaccine, and explored the risk of immune related adverse events (irAEs). We searched PubMed, EMBASE and Cochrane Library as of 10 June 2023, and included cancer patients who received ICIs and COVID-19 vaccine. The systematic review and meta-analysis include cohort study, cross-sectional study and case report. The outcome included the serological response, Spike-specific T-cell response, irAEs and rare adverse events. When possible, the data were analysed by random effect analysis, and the statistical heterogeneity was assessed by Q-test and I2 statistics. We explored the sources of heterogeneity through L'Abbe plots, Galbraith radial plots, and sensitivity analysis. The publication bias was evaluated by Egger's, Begg's linear regression test and funnel plot, and the impact of publication bias was further analysed by trim and fill method. 27 studies were eligible (19 cohort studies, 1 cross-sectional study and 7 case reports), involving 8331 patients (with 4724 receiving ICIs). Most studies used mRNA vaccine (BNT162b2 or mRNA-1273). Compared with cancer patients receiving chemotherapy, cancer patients receiving ICIs were significantly more likely to have seroconversion (RR = 1.05, 95%CI 1.01-1.10, P = 0.02). There were no statistically significant differences in seroconversion rates when comparing cancer patients receiving ICIs with controls without cancer (RR = 0.95, 95% CI 0.89-1.01, P = 0.09) or with cancer patients receiving targeted therapy (RR = 1.05, 95% CI 0.79-1.39, P = 0.75). The incidence of irAEs in patients receiving ICIs before and after COVID-19 vaccination was (21.96%, 95%CI 16.66%-28.94%) and (14.88%, 95%CI 8.65%-25.57%), respectively. The most common irAEs were endocrine abnormalities, skin disorders, etc. The certainty of evidence was low in cancer patients with ICIs, compared with those receiving chemotherapy, and very low versus controls without cancer. Cancer patients treated with ICIs seem to be able to receive COVID-19 vaccine safely without increasing the incidence of irAEs.

Compound Kushen injection combined with transarterial chemoembolization for hepatocellular carcinoma: An evidence map and overview of systematic reviews.

ETHNOPHARMACOLOGICAL RELEVANCE: For the treatment of hepatocellular carcinoma (HCC), compound Kushen injection (CKi) is commonly used in combination with transarterial chemoembolization (TACE). AIMS OF THE STUDY: Our objective was to evaluate the reporting quality, methodological quality, risk of bias, and certainty of evidence for CKi combined with TACE for the treatment of patients with HCC by conducting systematic reviews (SRs). The purpose of this study was to improve the clinical application of CKis, strengthen clinical decision-making regarding CKis, and inform future research. MATERIALS AND METHODS: We used eight databases to systematically search SRs of CKi combined with TACE for HCC through February 21, 2023. The quality of reporting of SRs was evaluated using the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, methodological quality using A MeaSurement Tool to Assess systematic Reviews 2, risk of bias using the Risk of Bias in Systematic Review, and certainty of evidence using the Grading of Recommendations Assessment. Finally, the assessment results were visualized by the evidence mapping method. This overview has been registered on PROSPERO with the registration title "Compound Kushen injection for hepatocellular carcinoma: An overview of systematic reviews" and registration number CRD42022369120. RESULTS: A total of 12 SRs meeting the inclusion criteria were included. In terms of reporting quality, 42% of SRs reported relatively complete reports and 58% had certain deficiencies. The methodological quality of all SRs was " critically low". The risk of bias was evaluated as low in 33% of SRs and high in 67% of SRs. The results of the evidence synthesis showed that, in the "moderate" level of evidence, CKi combined with TACE resulted in a 12.7%-21.5% benefit for one-year survival rate, 11.7%-17.2% benefit for objective response rate (ORR), 20.5%-27.1% benefit for quality of life, 22.2% benefit for nausea and vomiting, and 24.7%-27.4% benefit for leukopenia in HCC patients. CONCLUSION: In conclusion, CKi combined with TACE improved survival, ORR and quality of life in patients with HCC, and reduced adverse events. The results should be interpreted with caution due to the low methodological quality of the included SRs. The clinical efficacy of CKis must be confirmed in a large number of randomized controlled trials.

Habitat Radiomics Based on MRI for Predicting Platinum Resistance in Patients with High-Grade Serous Ovarian Carcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of MRI-based habitat radiomics for predicting response of platinum-based chemotherapy in patients with high-grade serous ovarian carcinoma (HGSOC), and compared to conventional radiomics and deep learning models. MATERIALS AND METHODS: A retrospective study was conducted on HGSOC patients from three hospitals. K-means algorithm was used to perform clustering on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (CE-T1WI), and apparent diffusion coefficient (ADC) maps. After feature extraction and selection, the radiomics model, habitat model, and deep learning model were constructed respectively to identify platinum-resistant and platinum-sensitive patients. A nomogram was developed by integrating the optimal model and clinical independent predictors. The model performance and benefit was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: A total of 394 eligible patients were incorporated. Three habitats were clustered, a significant difference in habitat 2 (weak enhancement, high ADC values, and moderate T2WI signal) was found between the platinum-resistant and platinum-sensitive groups (P < 0.05). Compared to the radiomics model (0.640) and deep learning model (0.603), the habitat model had a higher AUC (0.710). The nomogram, combining habitat signatures with a clinical independent predictor (neoadjuvant chemotherapy), yielded a highest AUC (0.721) among four models, with positive NRI and IDI. CONCLUSION: MRI-based habitat radiomics had the potential to predict response of platinum-based chemotherapy in patients with HGSOC. The nomogram combining with habitat signature had a best performance and good model gains for identifying platinum-resistant patients.

Components of the tumor immune microenvironment based on m-IHC correlate with prognosis and subtype of triple-negative breast cancer.

BACKGROUND AND AIM: The spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple-negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m-IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m-IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system. METHODS: The clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD-L1, and Pan-CK markers were stained by m-IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes. RESULTS: The components of the TIME score were established by m-IHC and demonstrated positive prognostic value for TNBC (p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD-L1+ cell% in the SA, CD3 + cell% in the TC, and PD-L1+ /CD8+ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes. CONCLUSION: We demonstrated that the m-IHC-based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.

Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer.

OBJECTIVES: Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC. METHODS: The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects. RESULTS: Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828-0.942) and 0.810 (95%CI: 0.653-0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2. CONCLUSIONS: The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.

Comprehensive analysis of resistance mechanisms to EGFR-TKIs and establishment and validation of prognostic model.

PURPOSE: Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the first-line therapy for patients with lung adenocarcinoma (LUAD) harboring activating EGFR mutations. However, the emergence of drug resistance to EGFR-TKIs remains a critical obstacle for successful treatment and is associated with poor patient outcomes. The overarching objective of this study is to apply bioinformatics tools to gain insights into the mechanisms underlying resistance to EGFR-TKIs and develop a robust predictive model. METHODS: The genes associated with gefitinib resistance in the LUAD cell Gene Expression Omnibus (GEO) database were identified using gene chip expression data. Functional enrichment analysis, gene set enrichment analysis (GSEA), and immune infiltration analysis were performed to comprehensively explore the mechanism of gefitinib resistance. Furthermore, a GRRG_score was constructed by integrating genes related to LUAD prognosis from The Cancer Genome Atlas (TCGA) database with the screened Gefitinib Resistant Related differentially expressed genes (GRRDEGs) using the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses. Furthermore, we conducted an in-depth analysis of the tumor microenvironment (TME) features and their association with immune infiltration between different GRRG_score groups. A prognostic model for LUAD was developed based on the GRRG_score and validated. The HPA database was used to validate protein expression. The CTR-DB database was utilized to validate the results of drug therapy prediction based on the relevant genes. RESULTS: A total of 110 differentially expression genes were identified. Pathway enrichment analysis of DEGs showed that the differentially expressed genes were mainly enriched in Mucin type O-glycan biosynthesis, Cytokine-cytokine receptor interaction, Sphingolipid metabolism. Gene set enrichment analysis showed that biological processes strongly correlated with gefitinib resistance were cell proliferation and immune-related pathways, EPITHELIAL_MESENCHYMAL_TRANSITION, APICAL_SURFACE, and APICAL_JUNCTION were highly expressed in the drug-resistant group; KRAS_SIGNALING_DN, HYPOXIA, and HEDGEHOG_SIGNALING were highly expressed in the drug-resistant group. The GRRG_score was constructed based on the expression levels of 13 genes, including HSPA2, ATP8B3, SPOCK1, EIF6, NUP62CL, BCAR3, PCSK9, NT5E, FLNC, KRT8, FSCN1, ANGPTL4, and ID1. We further screened and validated two key genes, namely, NUP62CL and KRT8, which exhibited predictive value for both prognosis and drug resistance. CONCLUSIONS: Our study identified several novel GRRDEGs and provided insight into the underlying mechanisms of gefitinib resistance in LUAD. Our results have implications for developing more effective treatment strategies and prognostic models for LUAD patients.

Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images.

We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin-stained pathological sections of the primary tumor. In total, 1524 hematoxylin and eosin-stained whole slide images (WSIs) of primary cervical tumors from 564 patients were used in this retrospective, proof-of-concept study. Primary tumor sections (1161 WSIs) were obtained from 405 patients who underwent radical cervical cancer surgery at the Fudan University Shanghai Cancer Center (FUSCC) between 2008 and 2014; 165 and 240 patients were negative and positive for lymph node metastasis, respectively (including 166 with positive pelvic lymph nodes alone and 74 with positive pelvic and para-aortic lymph nodes). We constructed and trained a multi-instance deep convolutional neural network based on a multiscale attention mechanism, in which an internal independent test set (100 patients, 228 WSIs) from the FUSCC cohort and an external independent test set (159 patients, 363 WSIs) from the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program database were used to evaluate the predictive performance of the network. In predicting the occurrence of lymph node metastasis, our network achieved areas under the receiver operating characteristic curve of 0.87 in the cross-validation set, 0.84 in the internal independent test set of the FUSCC cohort, and 0.75 in the external test set of the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program. For patients with positive pelvic lymph node metastases, we retrained the network to predict whether they also had para-aortic lymph node metastases. Our network achieved areas under the receiver operating characteristic curve of 0.91 in the cross-validation set and 0.88 in the test set of the FUSCC cohort. Deep learning analysis based on pathological images of primary foci is very likely to provide new ideas for preoperatively assessing cervical cancer lymph node status; its true value must be validated with cervical biopsy specimens and large multicenter datasets.

Diffusion-weighted magnetic resonance imaging for the pre-operative evaluation of epithelial ovarian cancer patients.

OBJECTIVE: To investigate the value diffusion-weighted magnetic resonance imaging (DWI/MR) in the selection of ovarian cancer patients suitable for primary debulking surgery. METHODS: Patients with suspected ovarian cancer who underwent pre-operative DWI/MR were enrolled between April 2020 and March 2022. All participants received preoperative clinic-radiological assessment according to the Suidan criteria for R0 resection with a predictive score. Data for patients with primary debulking surgery were prospectively recorded. The diagnostic value was calculated with ROC curves, and the cut-off value for the predictive score was also explored. RESULTS: 80 patients with primary debulking surgery were included in the final analysis. The majority (97.5%) of patients were at advanced stage (III-IV), and 90.0% of patients had high-grade serous ovarian histology. 46 (57.5%) patients had no residual disease (R0), and 27 (33.8%) patients had optimal debulking surgery with zzmacroscopic disease less than or equal to 1 cm (R1). Patients with BRCA1 mutation had lower R0 resection rate, higher R1 resection rate compared with wild-type patients (42.9% vs 63.0%, 50.0% vs 29.6%, respectively). The median (range) predictive score was 4 (0-13), and the AUC for R0 resection was 0.742 (0.632-0.853). The R0 rates for patients with predictive score 0-2, 3-5, and ≥ 6 were 77.8%, 62.5% and 23.8%, respectively. CONCLUSION: DWI/MR was a sufficient technique for pre-operative evaluation of ovarian cancer. Patients with predictive score 0-5 were suitable for primary debulking surgery at our institution.

Oleogels and reverse emulsions stabilized by acetylated Kraft lignins.

Acetylated Kraft lignins were evaluated for their ability of structuring vegetable oils into oleogels. Microwave-assisted acetylation was used to adjust lignin's degree of substitution according to reaction temperature (130 to 160 °C), and its effect in improving the viscoelasticity of the oleogels, which was related to the hydroxyl group content. The results were compared with those obtained by Kraft lignins acetylated using conventional methods at room temperature. A higher microwave temperature resulted in gel-like oil dispersions with improved viscoelastic properties, and stronger shear-thinning character, along with enhanced long-term stability. Lignin nanoparticles structured castor oil by enhancing hydrogen bonding between the hydroxyl groups of the oil and the nanoparticles. The oil structuring capacity of the modified lignins enhanced the stability of water-in-oil Pickering emulsions that resulted from low-energy mixing.