PSTNet: Enhanced Polyp Segmentation With Multi-Scale Alignment and Frequency Domain Integration.

Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective diagnosis and management of colorectal cancer (CRC). However, current deep learning-based methods primarily rely on fusing RGB information across multiple scales, leading to limitations in accurately identifying polyps due to restricted RGB domain information and challenges in feature misalignment during multi-scale aggregation. To address these limitations, we propose the Polyp Segmentation Network with Shunted Transformer (PSTNet), a novel approach that integrates both RGB and frequency domain cues present in the images. PSTNet comprises three key modules: the Frequency Characterization Attention Module (FCAM) for extracting frequency cues and capturing polyp characteristics, the Feature Supplementary Alignment Module (FSAM) for aligning semantic information and reducing misalignment noise, and the Cross Perception localization Module (CPM) for synergizing frequency cues with high-level semantics to achieve efficient polyp segmentation. Extensive experiments on challenging datasets demonstrate PSTNet's significant improvement in polyp segmentation accuracy across various metrics, consistently outperforming state-of-the-art methods. The integration of frequency domain cues and the novel architectural design of PSTNet contribute to advancing computer-assisted polyp segmentation, facilitating more accurate diagnosis and management of CRC. Our source code is available for reference at https://github.com/clearxu/PSTNet.

Design, synthesis and antiproliferative evaluation of tetrahydro-ß-carboline histone deacetylase inhibitors bearing an aliphatic chain linker.

The use of histone deacetylase inhibitors (HDACis) is an effective approach for cancer treatment. In this work, a series of hydroxamic acid-based HDACis with a tetrahydro-ß-carboline core and aliphatic linker have been designed and synthesized. The optimal compound 13d potently inhibited HDAC1 and showed good antiproliferative activity against different tumor cell lines in vitro. Molecular docking of 13d was conducted to rationalize the high binding affinity for HDAC1. Therefore, this work provides a new structure design for HDAC inhibitors and also offers a promising treatment for solid tumors.

Investigating the association between inflammation mediated by mushroom consumption and mild cognitive impairment in Chinese older adults.

Background: Chronic inflammatory stimulation is a major risk factor for mild cognitive impairment. Mushroom consumption and inflammatory factors may play an important role in the pathogenesis of mild cognitive impairment. Additionally, consuming mushrooms can reduce the levels of inflammatory cytokines and preserve cognitive function. Therefore, this study aimed to investigate the relationship between mushroom consumption and serum inflammatory cytokines and mild cognitive impairment (MCI). Methods: Binary logistic regression was used to determine the relationship between mushroom consumption and MCI in 550 participants. Subsequently, mediation analysis was used to analyze the relationship between mushroom consumption, inflammatory factors, and the Montreal Cognitive assessment (MoCA) score in 248 participants. Results: Mushroom consumption was associated with MCI (odds ratio = 0.623, 95% confidence interval = 0.542-0.715, P < 0.001). The association between mushroom intake and MCI was mediated by interleukin-6 (IL-6) and hypersensitive C-reactive protein (hs-CRP), and the MoCA score was 12.76% and 47.59%, respectively. Conclusion: A high intake of mushrooms was associated with a low risk of MCI. Serum inflammatory factors including IL-6 and hs-CRP play a partial mediating role between mushroom intake and the MoCA score, and the underlying mechanism needs to be further explored.

The Idesia polycarpa genome provides insights into its evolution and oil biosynthesis.

The deciduous tree Idesia polycarpa can provide premium edible oil with high polyunsaturated fatty acid contents. Here, we generate its high-quality reference genome, which is ∼1.21 Gb, comprising 21 pseudochromosomes and 42,086 protein-coding genes. Phylogenetic and genomic synteny analyses show that it diverged with Populus trichocarpa about 16.28 million years ago. Notably, most fatty acid biosynthesis genes are not only increased in number in its genome but are also highly expressed in the fruits. Moreover, we identify, through genome-wide association analysis and RNA sequencing, the I. polycarpa SUGAR TRANSPORTER 5 (IpSTP5) gene as a positive regulator of high oil accumulation in the fruits. Silencing of IpSTP5 by virus-induced gene silencing causes a significant reduction of oil content in the fruits, suggesting it has the potential to be used as a molecular marker to breed the high-oil-content cultivars. Our results collectively lay the foundation for breeding the elite cultivars of I. polycarpa.

Multi-omics dataset of bovine mammary epithelial cells stimulated by ten different essential amino acids.

Application of high-throughput sequencing and screening help to detect the transcriptional and metabolic discrepancies in organs provided with various levels of nutrients. The influences of individual essential amino acid (EAA) administration on transcriptomic and metabolomic profilings of bovine mammary epithelial cells (BMECs) were systematically investigated. A RNA sequencing and liquid chromatography-tandem mass spectrometry generated a comprehensive comparison of transcriptomics, non-targeted metabolomics and targeted amino acids profilings of BMECs with individual EAA stimulation by turn. The sequencing data and raw LC-MS/MS data of samples were presented in the databases of Gene Expression Omnibus, MetaboLights and Figshare for efficient reuse, including exploring the divergences in metabolisms between different EAAs and screening valuable genes and metabolites regulating casein synthesis.

Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images.

Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication - a necessary step for precise medicine.

Nox2 inhibition reduces trophoblast ferroptosis in preeclampsia via the STAT3/GPX4 pathway.

AIMS: Ferroptosis, a novel mode of cell death characterized by lipid peroxidation and oxidative stress, plays an important role in the pathogenesis of preeclampsia (PE). The aim of this study is to determine the role of Nox2 in the ferroptosis of trophoblast cells, along with the underlying mechanisms. METHODS: The mRNA and protein levels of Nox2, STAT3, and GPX4 in placental tissues and trophoblast cells were respectively detected by qRT-PCR and western blot analysis. CCK8, transwell invasion and tube formation assays were used to evaluate the function of trophoblast cells. Ferroptosis was evaluated using flow cytometry and the lipid peroxidation assay. Glycolysis and mitochondrial respiration were investigated by detecting the extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) using Seahorse extracellular flux technology. The t-test or one-way ANOVA was used for statistical analysis. KEY FINDINGS: Nox2 was up-regulated while STAT3 and GPX4 were down-regulated in PE placental tissues. Nox2 knockdown inhibited ferroptosis in trophoblast cells, which was shown by enhanced proliferation and invasion, decreased ROS and lipid peroxide levels, and reduced glycolysis and mitochondrial dysfunction. Nox2 negatively correlated with MVD in PE placentas, and Nox2 knockdown restored ferroptosis-inhibited tube formation. Nox2 could interact with STAT3. Inhibiting Nox2 restored ferroptosis-induced alterations in the mRNA and protein levels of STAT3 and GPX4. SIGNIFICANCE: Nox2 may trigger ferroptosis through the STAT3/GPX4 pathway, subsequently leading to regulation of mitochondrial respiration, transition of glycolysis, and inhibition of placental angiogenesis. Therefore, targeted inhibition of Nox2 is expected to become a new therapeutic target for PE.

EGCG oxidation-derived polymers induce apoptosis in digestive tract cancer cells via regulating the renin-angiotensin system.

Green tea polyphenol (-)-Epigallocatechin-3-gallate (EGCG) has been well studied for its biological activities in the prevention of chronic diseases. However, the biological activities of EGCG oxidation-derived polymers remain unclear. Previously, we found that these polymers accumulated in intraperitoneal tissues after intraperitoneal injection and gained an advantage over native EGCG in increasing insulin sensitivity via regulating the renin-angiotensin system (RAS) in type 2 diabetic mice. The present study determined the pro-apoptosis activities and anticancer mechanisms of the EGCG oxidation-derived polymer preparation (the >10 kDa EGCG polymers) in digestive tract cancer cells. Upon incubation of the >10 kDa EGCG polymers with CaCo2 colon cancer cells, these polymers coated the cell surface and regulated multiple components of the RAS in favor of cancer inhibition, including the downregulation of angiotensin-converting enzyme (ACE), angiotensin-II (AngII) and AngII receptor type 1 (AT1R) in the pro-tumor axis, as well as the upregulation of angiotensin-converting enzyme 2 (ACE2) and angiotensin1-7 (Ang(1-7)) in the anti-tumor axis. The treatment also markedly increased angiotensinogen (AGT), which is the precursor of the angiotensin peptides. The regulation of these RAS components occurred prior to apoptosis. Similar pro-apoptotic mechanisms of the >10 kDa EGCG polymers, were also observed in TCA8113 oral cancer cells. The >10 kDa EGCG polymers exhibited compromised activities in scavenging or initiating reactive oxygen species compared to EGCG, but gained a higher reactivity toward sulfhydryl groups, including protein cysteine thiols. We propose that the polymers bind onto the cell surface and regulate multiple RAS components by reacting with the sulfhydryl groups on the ectodomains of transmembrane proteins.

14-3-3-η interacts with BCL-2 to protect human endothelial progenitor cells from ox-LDL-triggered damage.

Oxidized low-density lipoprotein (ox-LDL) causes dysfunction of endothelial progenitor cells (EPCs), and we recently reported that 14-3-3-η can attenuate the damage triggered by ox-LDL in EPCs. However, the molecular mechanisms by which 14-3-3-η protects EPCs from the damage caused by ox-LDL are not fully understood. In this study, we observed that the expression of 14-3-3-η and BCL-2 were downregulated in ox-LDL-treated EPCs. Overexpression of 14-3-3-η in ox-LDL-treated EPC significantly increased BCL-2 level, while knockdown of BCL-2 reduced 14-3-3-η expression and mitigated the protective effect of 14-3-3-η on EPCs. In addition, we discovered that 14-3-3-η colocalizes and interacts with BCL-2 in EPCs. Taken together, these data suggest that 14-3-3-η protects EPCs from ox-LDL-induced damage by its interaction with BCL-2.

Should All Pancreatic Cystic Lesions with Worrisome or High-Risk Features Be Resected? A Clinical and Radiological Machine Learning Model May Help to Answer.

RATIONALE AND OBJECTIVES: According to current guidelines, pancreatic cystic lesions (PCLs) with worrisome or high-risk features may have overtreatment. The purpose of this study was to build a clinical and radiological based machine-learning (ML) model to identify malignant PCLs for surgery among preoperative PCLs with worrisome or high-risk features. MATERIALS AND METHODS: Clinical and radiological details of 317 pathologically confirmed PCLs with worrisome or high-risk features were retrospectively analyzed and applied to ML models including Support Vector Machine, Logistic Regression (LR), Decision Tree, Bernoulli NB, Gaussian NB, K Nearest Neighbors and Linear Discriminant Analysis. The diagnostic ability for malignancy of the optimal model with the highest diagnostic AUC in the cross-validation procedure was further evaluated in internal (n = 77) and external (n = 50) testing cohorts, and was compared to two published guidelines in internal mucinous cyst cohort. RESULTS: Ten clinical and radiological feature-based LR model was the optimal model with the highest AUC (0.951) in the cross-validation procedure. In the internal testing cohort, LR model reached an AUC, accuracy, sensitivity, and specificity of 0.927, 0.909, 0.914, and 0.905; in the external testing cohort, LR model reached 0.948, 0.900, 0.963, and 0.826. When compared to the European guidelines and the ACG guidelines, LR model demonstrated significantly better accuracy and specificity in identifying malignancy, while maintaining the same high sensitivity. CONCLUSION: Clinical- and radiological-based LR model can accurately identify malignant PCLs in patients with worrisome or high-risk features, possessing diagnostic performance better than the European guidelines as well as ACG guidelines.

Correlation between AI-based CT organ features and normal lung dose in adjuvant radiotherapy following breast-conserving surgery: a multicenter prospective study.

BACKGROUND: Radiation pneumonitis (RP) is one of the common side effects after adjuvant radiotherapy in breast cancer. Irradiation dose to normal lung was related to RP. We aimed to propose an organ features based on deep learning (DL) model and to evaluate the correlation between normal lung dose and organ features. METHODS: Patients with pathology-confirmed invasive breast cancer treated with adjuvant radiotherapy following breast-conserving surgery in four centers were included. From 2019 to 2020, a total of 230 patients from four nationwide centers in China were screened, of whom 208 were enrolled for DL modeling, and 22 patients from another three centers formed the external testing cohort. The subset of the internal testing cohort (n = 42) formed the internal correlation testing cohort for correlation analysis. The outline of the ipsilateral breast was marked with a lead wire before the scanning. Then, a DL model based on the High-Resolution Net was developed to detect the lead wire marker in each slice of the CT images automatically, and an in-house model was applied to segment the ipsilateral lung region. The mean and standard deviation of the distance error, the average precision, and average recall were used to measure the performance of the lead wire marker detection model. Based on these DL model results, we proposed an organ feature, and the Pearson correlation coefficient was calculated between the proposed organ feature and ipsilateral lung volume receiving 20 Gray (Gy) or more (V20). RESULTS: For the lead wire marker detection model, the mean and standard deviation of the distance error, AP (5 mm) and AR (5 mm) reached 3.415 ± 4.529, 0.860, 0.883, and 4.189 ± 8.390, 0.848, 0.830 in the internal testing cohort and external testing cohort, respectively. The proposed organ feature calculated from the detected marker correlated with ipsilateral lung V20 (Pearson correlation coefficient, 0.542 with p < 0.001 in the internal correlation testing cohort and 0.554 with p = 0.008 in the external testing cohort). CONCLUSIONS: The proposed artificial Intelligence-based CT organ feature was correlated with normal lung dose in adjuvant radiotherapy following breast-conserving surgery in patients with invasive breast cancer. TRIAL REGISTRATION: NCT05609058 (08/11/2022).

Chlorogenic acid as an indispensible partner of caffeic acid in coffee via selective regulation of prooxidative actions of caffeic acid.

Chlorogenic acid (CGA) and caffeic acid (CA) are two major phenolic acids in coffee. Though the International Agency for Research on Cancer has classified CA as a Group2B carcinogen, coffee consumption seems generally safe within the usual levels of intake and is more likely to benefit health than to harm it. We thus speculated that CGA may effectively suppress the carcinogenic potential of CA. In a molar ratio achievable in vivo, this study shows that CGA can inhibit (i) copper reduction caused by CA, (ii) CA oxidation caused by copper, (iii) the formation of hydroxyl radicals by CA and copper, and (iv) DNA damage induced by CA, quercetin or (-)-epigallocatechin-3-gallate in the presence of copper. CA tends to undergo autoxidation to produce hydrogen peroxide and quinone, which further reacts with proteins to form quinoproteins. This autoxidation at a tolerable level normally induces beneficial adaptive responses. This study shows that CGA is less efficient than CA in producing hydrogen peroxide and quinoprotein; however, together they synergistically produce hydrogen peroxide and quinoprotein in vitro at a molar ratio achievable in vivo. In conclusion, CGA can selectively regulate the prooxidant activities of CA depending on whether copper is involved or not. CGA could be viewed as an indispensable partner of CA in coffee, given its dual role in suppressing the carcinogenic potential of CA and boosting CA autoxidation which is beneficial for disease prevention.

Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer.

OBJECTIVE: To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data. MATERIAL AND METHODS: We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model's output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated. RESULTS: The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762-0.964]) than the clinical model (AUC = 0.792 [0.630-0.953], p = 0.295) and the radiomic signature alone (AUC = 0.781 [0.636-0.926], p = 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (p = 0.023-0.041), while the specificities and accuracies were maintained (p = 0.074-1.000). CONCLUSION: Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.

Development and acceptability validation of a deep learning-based tool for whole-prostate segmentation on multiparametric MRI: a multicenter study.

Background: Accurate whole prostate segmentation on magnetic resonance imaging (MRI) is important in the management of prostatic diseases. In this multicenter study, we aimed to develop and evaluate a clinically applicable deep learning-based tool for automatic whole prostate segmentation on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). Methods: In this retrospective study, 3-dimensional (3D) U-Net-based models in the segmentation tool were trained with 223 patients who underwent prostate MRI and subsequent biopsy from 1 hospital and validated in 1 internal testing cohort (n=95) and 3 external testing cohorts: PROSTATEx Challenge for T2WI and DWI (n=141), Tongji Hospital (n=30), and Beijing Hospital for T2WI (n=29). Patients from the latter 2 centers were diagnosed with advanced prostate cancer. The DWI model was further fine-tuned to compensate for the scanner variety in external testing. A quantitative evaluation, including Dice similarity coefficients (DSCs), 95% Hausdorff distance (95HD), and average boundary distance (ABD), and a qualitative analysis were used to evaluate the clinical usefulness. Results: The segmentation tool showed good performance in the testing cohorts on T2WI (DSC: 0.922 for internal testing and 0.897-0.947 for external testing) and DWI (DSC: 0.914 for internal testing and 0.815 for external testing with fine-tuning). The fine-tuning process significantly improved the DWI model's performance in the external testing dataset (DSC: 0.275 vs. 0.815; P<0.01). Across all testing cohorts, the 95HD was <8 mm, and the ABD was <3 mm. The DSCs in the prostate midgland (T2WI: 0.949-0.976; DWI: 0.843-0.942) were significantly higher than those in the apex (T2WI: 0.833-0.926; DWI: 0.755-0.821) and base (T2WI: 0.851-0.922; DWI: 0.810-0.929) (all P values <0.01). The qualitative analysis showed that 98.6% of T2WI and 72.3% of DWI autosegmentation results in the external testing cohort were clinically acceptable. Conclusions: The 3D U-Net-based segmentation tool can automatically segment the prostate on T2WI with good and robust performance, especially in the prostate midgland. Segmentation on DWI was feasible, but fine-tuning might be needed for different scanners.

Preparation and anticancer actions of CuET-nanoparticles dispersed by bovine serum albumin.

Diethyldithiocarbamate-copper complex (CuET) shows promising anticancer effect; nonetheless, preclinical evaluations of CuET are hindered due to poor solubility. We prepared bovine serum albumin (BSA)-dispersed CuET nanoparticles (CuET-NPs) to overcome the shortcoming. Results from a cell-free redox system demonstrated that CuET-NPs reacted with glutathione, leading to form hydroxyl radical. Glutathione-mediated production of hydroxyl radicals may help explain why CuET selectively kills drug-resistant cancer cells with higher levels of glutathione. CuET-NPs dispersed by autoxidation products of green tea epigallocatechin gallate (EGCG) also reacted with glutathione; however, the autoxidation products eradicated hydroxyl radicals; consequently, such CuET-NPs exhibited largely compromised cytotoxicity, suggesting that hydroxyl radical is a crucial mediator of CuET anticancer activity. In cancer cells, BSA-dispersed CuET-NPs exhibited cytotoxic activities equivalent to CuET and induced protein poly-ubiquitination. Moreover, the reported powerful inhibition of CuET on colony formation and migration of cancer cells could be replicated by CuET-NPs. These similarities demonstrate BSA-dispersed CuET-NPs is identical to CuET. Thus, we advanced to pilot toxicological and pharmacological evaluations. CuET-NPs caused hematologic toxicities in mice and induced protein poly-ubiquitination and apoptosis of cancer cells inoculated in mice at a defined pharmacological dose. Given high interest in CuET and its poor solubility, BSA-dispersed CuET-NPs pave the way for preclinical evaluations.

FtMt reduces oxidative stress-induced trophoblast cell dysfunction via the HIF-1α/VEGF signaling pathway.

BACKGROUND: Preeclampsia (PE) is a complication of pregnancy that causes long-term adverse outcomes for the mother and fetus and may even lead to death. Oxidative stress caused by the imbalance of oxidants and antioxidants in the placenta has been considered as one of the key mechanisms of preeclampsia (together with inflammation, etc.), in which the placental mitochondria play an important role. The expression of hypoxia-inducible factor-1 (HIF-1α) and vascular endothelial growth factor (VEGF) is known to be increased in patients with PE. Mitochondrial ferritin (FtMt) is known to protect the mitochondria from oxidative stress, although its specific role in PE remains unclear. METHODS: We used qRT-PCR and western blotting to detect the expression levels of FtMt, HIF-1α, and VEGF in placental tissues from patients with PE. Human chorionic trophoblast cells were also administered with hypoxia treatment, followed by the detection of cell proliferation, invasion and angiogenic capacity by CCK8, Transwell, and endothelial cell angiogenesis assays; we also detected the expression of HIF-1α and VEGF in these cells. Finally, overexpression or inhibitory FtMt lentiviral vectors, along with negative control vectors, were constructed and transfected into hypoxia-treated human chorionic trophoblast cells; this was followed by analyses of cell function. RESULTS: The expression levels of FtMt, HIF-1α and VEGF in the PE group were higher than those in the control group (P < 0.05). Following hypoxia, there was an increase in the expression levels of HIF-1α and VEGF protein in trophoblast cells. There was also an increase in invasion ability and vascular formation ability along with a reduction in cell proliferation ability. These effects were reversed by transfecting cells with the knockout FtMt lentivirus vector. The differences were statistically significant. CONCLUSION: Analyses showed that FtMt plays a key role in the vascular regulation of PE trophoblast cells after hypoxia possibly acting via the HIF-1α/VEGF signaling pathway. These results provide us an enhanced understanding of the pathogenesis of PE and suggest that the HIF-1α/VEGF signaling pathway represents a new target for the treatment of PE.

Case report: Brachial plexopathy caused by malignant peripheral nerve sheath tumor and review of the literature.

Brachial plexopathy (BP) is easily misdiagnosed due to its complexity and varying clinical presentation. Malignant peripheral nerve sheath tumors (MPNST) can accumulate in the brachial plexus and share symptoms with BP, which may hinder the differential diagnosis between BP induced by radiation or metastases, and MPNST-derived BP, in patients with a history of breast cancer and radiation exposure. A 34-year-old Chinese female presented with MPNST. The tumor involved the brachial plexus. She had a history of breast cancer and radiotherapy. The first consideration was radiation- or breast cancer metastasis-derived BP. Clinical examination was performed. Finally, a diagnosis of MPNST of the brachial plexus was made, which guided an accurate treatment plan. This report highlights the importance of correctly diagnosing BP etiology for guiding precise treatment. BP caused by MPNST needs to be considered in clinical practice, and biopsy plays a central role in the differential diagnosis. Complete local surgical resection can prolong survival of patients with MPNST and improve treatment prognosis.

Preoperative CT features to predict risk stratification of non-muscle invasive bladder cancer.

PURPOSE: To investigate whether preoperative CT features can be used to predict risk stratification of non-muscle invasive bladder cancer (NMIBC). METHODS: The 168 patients with pathologically confirmed NMIBC who underwent preoperative CT urography were retrospectively analyzed and were divided into training (n = 117) and testing (n = 51) sets. According to the European Association of Urology Guidelines, patients were classified into low-risk (n = 50), medium-risk (n = 23), and high-risk (n = 95) groups. A random over-sample was performed to handle the offset caused by the unbalanced groups. We measured some CT features that may help stratify which for modeling were determined using an F-test-based feature selection with a tenfold cross-validation procedure, and the Gaussian Naive Bayes model was trained on the entire training set. In the testing set, the performance of the model was evaluated. RESULTS: The selected CT features were the maximum and the minimum diameter of the largest tumor, whether the largest tumor is located at the trigone, and tumor number. In the testing set, the model reached a macro- and micro- AUC of 0.783 and 0.745 with an accuracy of 0.529. As for the one-vs-rest problem, the model was most effective in identifying low-risk individuals, with an AUC, accuracy, sensitivity, and specificity of 0.870, 0.647, 1.000, and 0.438, respectively; the medium-risk group reached 0.814, 0.882, 0.250, and 0.936, respectively; the identification of the high-risk group was harder, going 0.665, 0.529, 0.250, and 0.870, respectively. CONCLUSION: It is feasible to predict the risk stratification of NMIBC using preoperative CT features.

A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer.

OBJECTIVES: To develop a pre-treatment CT-based predictive model to anticipate inoperable lung cancer patients' progression-free survival (PFS) to immunotherapy. METHODS: This single-center retrospective study developed and cross-validated a radiomic model in 185 patients and tested it in 48 patients. The binary endpoint is the durable clinical benefit (DCB, PFS ≥ 6 months) and non-DCB (NDCB, PFS < 6 months). Radiomic features were extracted from multiple intrapulmonary lesions and weighted by an attention-based multiple-instance learning model. Aggregated features were then selected through L2-regularized ridge regression. Five machine-learning classifiers were conducted to build predictive models using radiomic and clinical features alone and then together. Lastly, the predictive value of the model with the best performance was validated by Kaplan-Meier survival analysis. RESULTS: The predictive models based on the weighted radiomic approach showed superior performance across all classifiers (AUCs: 0.75-0.82) compared with the largest lesion approach (AUCs: 0.70-0.78) and the average sum approach (AUCs: 0.64-0.80). Among them, the logistic regression model yielded the most balanced performance (AUC = 0.87 [95%CI 0.84-0.89], 0.75 [0.68-0.82], 0.80 [0.68-0.92] in the training, validation, and test cohort respectively). The addition of five clinical characteristics significantly enhanced the performance of radiomic-only model (train: AUC 0.91 [0.89-0.93], p = .042; validation: AUC 0.86 [0.80-0.91], p = .011; test: AUC 0.86 [0.76-0.96], p = .026). Kaplan-Meier analysis of the radiomic-based predictive models showed a clear stratification between classifier-predicted DCB versus NDCB for PFS (HR = 2.40-2.95, p < 0.05). CONCLUSIONS: The adoption of weighted radiomic features from multiple intrapulmonary lesions has the potential to predict long-term PFS benefits for patients who are candidates for PD-1/PD-L1 immunotherapies. KEY POINTS: • Weighted radiomic-based model derived from multiple intrapulmonary lesions on pre-treatment CT images has the potential to predict durable clinical benefits of immunotherapy in lung cancer. • Early line immunotherapy is associated with longer progression-free survival in advanced lung cancer.

Expression of IRS2 in the female reproductive system during the estrous cycle in mice.

Insulin receptor substrate 2 (IRS2) participates in reproduction; however, the location and expression of IRS2 in the reproductive system of female mice is not clear. We used real-time quantitative polymerase chain reaction (RT-PCR), western blot and immunohistochemical staining to investigate the expression of IRS2 in the ovary, oviduct and uterus of female mice during the estrous cycle. We found that IRS2 was expressed in all reproductive organs of mouse and that the expression level changed with the estrous phases. The expression of IRS2 in reproductive organs was greatest during estrus.