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ObjectiveTo explore the efficacy of c in the multiline treatment of late-stage lung adenocarcinoma with Her-2 overexpression and epidermal growth factor receptor (EGFR) mutations.MethodsWe summarize the diagnosis and treatment of a female patient with EGFR 21L858R mutation combined with Her-2 overexpression in advanced lung adenocarcinoma, and analyze the effect of c in her treatment process.ResultsThe patient was diagnosed with lung adenocarcinoma 8 years ago. After first-line treatment, the lung lesions enlarged. Following second-line treatment 5 years ago, intracranial metastasis occurred. After third-line treatment 3 years ago, intracranial and lung lesions enlarged. New lesions in the lungs, liver, and spleen appeared after fourth-line treatment 32 months ago. Lung progression occurred after fifth-line treatment 29 months ago. Liver and lung progression occurred after sixth-line treatment 22 months ago. Lung progression occurred after seventh-line treatment 19 months ago. The patient underwent eighth-line treatment with disitamab vedotin (RC48) + lung radiotherapy + liver intervention 13 months ago. Currently, the patient’s condition is stable, with a good quality of life, and the efficacy assessment is stable disease (SD). Conclusion: Her-2 overexpression can occur in late-stage EGFR-mutant lung adenocarcinoma after multiline treatment. RC48 can achieve sustained remission in these patients.

Gastrointestinal stromal tumours (GISTs) are the most common mesenchymal tumours, arising mainly from the interstitial cells of Cajal (ICCs) of the gastrointestinal tract. As radiotherapy and chemotherapy are generally ineffective for GISTs, the current primary treatment is surgical resection. However, surgical resection is not choice for most patients. Therefore, new therapeutic strategies are urgently needed. Targeted therapy, represented by tyrosine kinase inhibitors (TKIs), and immunotherapy, represented by immune checkpoint inhibitor therapies and chimeric antigen receptor T-cell immunotherapy (CAR-T), offer new therapeutic options in GISTs and have shown promising treatment responses. In this review, we summarize the molecular classification and immune microenvironment of GISTs and discuss the corresponding targeted therapy and immunotherapy options. This updated knowledge may provide more options for future therapeutic strategies and applications in GISTs.

IntroductionTriple-negative breast cancer (TNBC) is linked to a poorer outlook, heightened aggressiveness relative to other breast cancer variants, and limited treatment choices. The absence of conventional treatment methods makes TNBC patients susceptible to metastasis. The objective of this research was to assess the clinical and pathological traits of TNBC patients, predict the influence of risk elements on their outlook, and create a prediction model to assist doctors in treating TNBC patients and enhancing their prognosis.MethodsWe included 23,394 individuals with complete baseline clinical data and survival information who were diagnosed with primary TNBC between 2010 and 2015 based on the SEER database. External validation utilised a group from The Affiliated Lihuili Hospital of Ningbo University. Independent risk factors linked to TNBC prognosis were identified through univariate, multivariate, and least absolute shrinkage and selection operator regression methods. These characteristics were chosen as parameters to develop 3- and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) nomogram models. Model accuracy was assessed using calibration curves, consistency indices (C-indices), receiver operating characteristic curves (ROCs), and decision curve analyses (DCAs). Finally, TNBC patients were divided into groups of high, medium, and low risk, employing the nomogram model for conducting a Kaplan-Meier survival analysis.ResultsIn the training cohort, variables such as age at diagnosis, marital status, grade, T stage, N stage, M stage, surgery, radiation, and chemotherapy were linked to OS and BCSS. For the nomogram, the C-indices stood at 0.762, 0.747, and 0.764 in forecasting OS across the training, internal validation, and external validation groups, respectively. Additionally, the C-index values for the training, internal validation, and external validation groups in BCSS prediction stood at 0.793, 0.755, and 0.811, in that order. The findings revealed that the calibration of our nomogram model was successful, and the time-variant ROC curves highlighted its effectiveness in clinical settings. Ultimately, the clinical DCA showcased the prospective clinical advantages of the suggested model. Furthermore, the online version was simple to use, and nomogram classification may enhance the differentiation of TNBC prognosis and distinguish risk groups more accurately.ConclusionThese nomograms are precise tools for assessing risk in patients with TNBC and forecasting survival. They can help doctors identify prognostic markers and create more effective treatment plans for patients with TNBC, providing more accurate assessments of their 3- and 5-year OS and BCSS.

BackgroundAcute promyelocytic leukemia (APL) is rarely caused by the PLZF::RARα fusion gene. While APL patients with PLZF::RARα fusion commonly exhibit diverse hematologic symptoms, the presentation of myeloid sarcoma (MS) as an initial manifestation is infrequent.Case presentationA 61-year-old patient was referred to our hospital with 6-month history of low back pain and difficulty walking. Before this admission, spine magnetic resonance imaging (MRI) conducted at another hospital revealed multiple abnormal signals in the left iliac bone and vertebral bodies spanning the thoracic (T11-T12), lumbar (L1-L4), and sacral (S1/S3) regions. This led to a provisional diagnosis of bone tumors with an unknown cause. On admission, complete blood count (CBC) test and peripheral blood smear revealed a slightly increased counts of monocytes. Immunohistochemical staining of both spinal and bone marrow (BM) biopsy revealed positive expression for CD117, myeloperoxidase (MPO), and lysozyme. BM aspirate showed a significant elevation in the percentage of promyelocytes (21%), which were morphologically characterized by round nuclei and hypergranular cytoplasm. Multiparameter flow cytometry of BM aspirate revealed that blasts were positive for CD13, CD33, CD117, and MPO. Through the integrated application of chromosome analysis, fluorescence in situ hybridization (FISH), reverse transcriptase polymerase chain reaction (RT-PCR), and Sanger sequencing, it was determined that the patient possessed a normal karyotype and a rare cryptic PLZF::RARα fusion gene, confirming the diagnosis of APL.ConclusionIn the present study, we report the clinical features and outcome of a rare APL patient characterized by a cryptic PLZF::RARα fusion and spinal myeloid sarcoma (MS) as the initial presenting symptom. Our study not only offers valuable insights into the heterogeneity of APL clinical manifestations but also emphasizes the crucial need to promptly consider the potential link between APL and MS for ensuring a timely diagnosis and personalized treatments.

In the field of medical image segmentation, achieving fast and accurate semantic segmentation of tumor cell nuclei and skin lesions is of significant importance. However, the considerable variations in skin lesion forms and cell types pose challenges to attaining high network accuracy and robustness. Additionally, as network depth increases, the growing parameter size and computational complexity make practical implementation difficult. To address these issues, this paper proposes MD-UNet, a fast cell nucleus segmentation network that integrates Tokenized Multi-Layer Perceptron modules, attention mechanisms, and Inception structures. Firstly, tokenized MLP modules are employed to label and project convolutional features, reducing computational complexity. Secondly, the paper introduces Depthwise Attention blocks and Multi-layer Feature Extraction modules. The Depthwise Attention blocks eliminate irrelevant and noisy responses from coarse-scale extracted information, serving as alternatives to skip connections in the UNet architecture. The Multi-layer Feature Extraction modules capture a wider range of high-level and low-level semantic features during decoding and facilitate feature fusion. The proposed MD-UNet approach is evaluated on two datasets: the International Skin Imaging Collaboration (ISIC2018) dataset and the PanNuke dataset. The experimental results demonstrate that MD-UNet achieves the best performance on both datasets.

PurposeThe purpose of our meta-analysis and systematic review was to evaluate and compare the diagnostic effectiveness of [18F]FET PET and [18F]FDOPA PET in detecting glioma recurrence.MethodsSensitivities and specificities were assessed using the DerSimonian and Laird methodology, and subsequently transformed using the Freeman-Tukey double inverse sine transformation. Confidence intervals were computed employing the Jackson method, while heterogeneity within and between groups was evaluated through the Cochrane Q and I² statistics. If substantial heterogeneity among the studies was observed (P < 0.10 or I² > 50%), we conducted meta-regression and sensitivity analyses. Publication bias was assessed through the test of a funnel plot and the application of Egger’s test. For all statistical tests, except for assessing heterogeneity (P < 0.10), statistical significance was determined when the two-tailed P value fell below 0.05.ResultsInitially, 579 publications were identified, and ultimately, 22 studies, involving 1514 patients(1226 patients for [18F]FET PET and 288 patients for [18F]FDOPA PET), were included in the analysis. The sensitivity and specificity of [18F]FET PET were 0.84 (95% CI, 0.75-0.90) and 0.86 (95% CI, 0.80-0.91), respectively, while for [18F]FDOPA PET, the values were 0.95 (95% CI, 0.86-1.00) for sensitivity and 0.90 (95% CI, 0.77-0.98) for specificity. A statistically significant difference in sensitivity existed between these two radiotracers (P=0.04), while no significant difference was observed in specificity (P=0.58).ConclusionIt seems that [18F]FDOPA PET demonstrates superior sensitivity and similar specificity to [18F] FET PET. Nevertheless, it’s crucial to emphasize that [18F]FDOPA PET results were obtained from studies with limited sample sizes. Further larger prospective studies, especially head-to-head comparisons, are needed in this issue.Systematic Review Registrationidentifier CRD42023463476

BackgroundThe early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.MethodsA systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.ResultsThis systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.ConclusionAlthough the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.

BackgroundIt has been reported that hepatitis B virus (HBV) double mutations (A1762T, G1764A) are an aetiological factor of hepatocellular carcinoma (HCC). However, it is unclear who is prone to develop HCC, among those infected with the mutant. Exploring HBV quasispecies, which are strongly influenced by host immune pressure, may provide more information about the association of viral factors and HCC.Materials and methodsNine HCC cases and 10 controls were selected from the Long An cohort. Serum samples were collected in 2004 and 2019 from subjects with HBV double mutations and the complete genome of HBV was amplified and sequenced using next-generation sequencing (NGS).ResultsThe Shannon entropy values increased from 2004 to 2019 in most cases and controls. There was no significant difference in mean intrahost quasispecies genetic distances between cases and controls. The change in the values of mean intrahost quasispecies genetic distances of the controls between 2004 and 2019 was significantly higher than that of the cases (P

PurposeTo develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC).MethodsThis retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves.ResultsThe area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580–0.757] in the training cohort and 0.62 (95% CI, 0.467–0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727–0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662–0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p < 0.05).ConclusionsThe 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC.

PurposeThis study aimed to investigate the value of quantified extracellular volume fraction (fECV) derived from dual-energy CT (DECT) for predicting the survival outcomes of patients with hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE).Materials and methodsA total of 63 patients with HCC who underwent DECT before treatment were retrospectively included. Virtual monochromatic images (VMI) (70 keV) and iodine density images (IDI) during the equilibrium phase (EP) were generated. The tumor VMI-fECV and IDI-fECV were measured and calculated on the whole tumor (Whole) and maximum enhancement of the tumor (Maximum), respectively. Univariate and multivariate Cox models were used to evaluate the effects of clinical and imaging predictors on overall survival (OS) and progression-free survival (PFS).ResultsThe correlation between tumor VMI-fECV and IDI-fECV was strong (both p< 0.001). The Bland–Altman plot between VMI-fECV and IDI-fECV showed a bias of 5.16% for the Whole and 6.89% for the Maximum modalities, respectively. Increasing tumor VMI-fECV and IDI-fECV were positively related to the effects on OS and PFS (both p< 0.05). The tumor IDI-fECV-Maximum was the only congruent independent predictor in patients with HCC after TACE in the multivariate analysis on OS (p = 0.000) and PFS (p = 0.028). Patients with higher IDI-fECV-Maximum values had better survival rates above the optimal cutoff values, which were 35.42% for OS and 29.37% for PFS.ConclusionThe quantified fECV determined by the equilibrium-phase contrast-enhanced DECT can potentially predict the survival outcomes of patients with HCC following TACE treatment.

ObjectiveTo evaluate the predictive value of tumor regression grade assessed by MRI (mr-TRG) after neoadjuvant chemoradiotherapy (neo-CRT) for postoperative pathological TRG (pTRG) and prognosis in patients with locally advanced rectal adenocarcinoma (LARC).Materials and methodsThis was a retrospective study from a single center experience. The patients who were diagnosed with LARC and received neo-CRT in our department between January 2016 and July 2021 were enrolled. The agreement between mrTRG and pTRG was assessed with the weighted κ test. Overall survival (OS), progress-free survival (PFS), local recurrence-free survival (LRFS), and distant metastasis-free survival (DMFS) were calculated by Kaplan-Meier analysis and log-rank test.ResultsFrom January 2016 to July 2021, 121 LARC patients received neo-CRT in our department. Among them, 54 patients had complete clinical data, including MRI of pre- and post-neo-CRT, postoperative tumor samples, and follow-up. The median follow-up time was 34.6 months (range: 4.4-70.6 months). The estimated 3-year OS, PFS, LRFS and DMFS were 78.5%, 70.7%, 89.0%, and 75.2%, respectively. The median time from the completion of neo-CRT to preoperative MRI and surgery was 7.1 weeks and 9.7 weeks, respectively. Out of 54 patients, 5 patients achieved mrTRG1 (9.3%), 37 achieved mrTRG2 (68.5%), 8 achieved mrTRG3 (14.8%), 4 achieved mrTRG4 (7.4%), and no patient achieved mrTRG5 after neo-CRT. Regarding pTRG, 12 patients achieved pTRG0 (22.2%), 10 achieved pTRG1 (18.5%), 26 achieved pTRG2 (48.1%), and 6 achieved pTRG3 (11.1%). The agreement between three-tier mrTRG (mrTRG1 vs. mrTRG2-3 vs. mrTRG4-5) and pTRG (pTRG0 vs. pTRG1-2 vs. pTRG3) was fair (weighted kappa=0.287). In a dichotomous classification, the agreement between mrTRG(mrTRG1 vs. mrTRG2-5)and pTRG(pTRG0 vs. pTRG1-3) also resulted in fair agreement (weighted kappa=0.391). The sensitivity, specificity, positive, and negative predictive values of favorable mrTRG (mrTRG 1-2) for pathological complete response (PCR) were 75.0%, 21.4%, 21.4%, and 75.0%, respectively. In univariate analysis, favorable mrTRG (mrTRG1-2) and downstaging N were significantly associated with better OS, while favorable mrTRG (mrTRG1-2), downstaging T, and downstaging N were significantly associated with superior PFS (p

BackgroundIn recent years, AI has been applied to disease diagnosis in many medical and engineering researches. We aimed to explore the diagnostic performance of the models based on different imaging modalities for ovarian cancer.MethodsPubMed, EMBASE, Web of Science, and Wanfang Database were searched. The search scope was all published Chinese and English literatures about AI diagnosis of benign and malignant ovarian tumors. The literature was screened and data extracted according to inclusion and exclusion criteria. Quadas-2 was used to evaluate the quality of the included literature, STATA 17.0. was used for statistical analysis, and forest plots and funnel plots were drawn to visualize the study results.ResultsA total of 11 studies were included, 3 of them were modeled based on ultrasound, 6 based on MRI, and 2 based on CT. The pooled AUROCs of studies based on ultrasound, MRI and CT were 0.94 (95% CI 0.88-1.00), 0.82 (95% CI 0.71-0.93) and 0.82 (95% Cl 0.78-0.86), respectively. The values of I2 were 99.92%, 99.91% and 92.64% based on ultrasound, MRI and CT. Funnel plot suggested no publication bias.ConclusionThe models based on ultrasound have the best performance in diagnostic of ovarian cancer.

BackgroundGliomas are the most common primary tumors of the central nervous system and portend a poor prognosis. The efficacy of emerging and promising immunotherapies varies significantly among individuals. Distinction and transformation of cold and hot tumors may improve the antitumor efficacy of immunotherapy.Methods and ResultsIn this study, we constructed a necroptosis-related lncRNA module based on public databases. The association of this module with survival was assessed using the Cox regression, Kaplan-Meier survival analysis, and nomogram, external validation was also conducted in another public database. Furthermore, we performed gene set enrichment analysis (GSEA), immune checkpoint and tumor microenvironment analysis, and in vitro qRT-PCR validation. Finally, we clustered all samples into 2 clusters based on the expression of model lncRNAs and identified cluster 1 as cold tumors with fewer infiltrating T cells.ConclusionsIdentifying cold and hot tumors by necroptosis-related lncRNAs can help available immunotherapeutic strategies to achieve efficacy in the precise treatment of individuals. Prior treatment failure can be overcome by targeting necroptosis-related lncRNAs.

ObjectivesDNA mismatch repair deficiency (dMMR) status has served as a positive predictive biomarker for immunotherapy and long-term prognosis in gastric cancer (GC). The aim of the present study was to develop a computed tomography (CT)-based nomogram for preoperatively predicting mismatch repair (MMR) status in GC.MethodsData from a total of 159 GC patients between January 2020 and July 2021 with dMMR GC (n=53) and MMR-proficient (pMMR) GC (n=106) confirmed by postoperative immunohistochemistry (IHC) staining were retrospectively analyzed. All patients underwent abdominal contrast-enhanced CT. Significant clinical and CT imaging features associated with dMMR GC were extracted through univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and internal validation of the cohort data were performed.ResultsThe nomogram contained four potential predictors of dMMR GC, including gender (odds ratio [OR] 9.83, 95% confidence interval [CI] 3.78-28.20, P < 0.001), age (OR 3.32, 95% CI 1.36-8.50, P = 0.010), tumor size (OR 5.66, 95% CI 2.12-16.27, P < 0.001) and normalized tumor enhancement ratio (NTER) (OR 0.15, 95% CI 0.06-0.38, P < 0.001). Using an optimal cutoff value of 6.6 points, the nomogram provided an area under the curve (AUC) of 0.895 and an accuracy of 82.39% in predicting dMMR GC. The calibration curve demonstrated a strong consistency between the predicted risk and observed dMMR GC. The DCA justified the relatively good performance of the nomogram model.ConclusionThe CT-based nomogram holds promise as a noninvasive, concise and accurate tool to predict MMR status in GC patients, which can assist in clinical decision-making.

PurposeStudies have reported that breast cancer (BC) patients’ circulating tumor cells (CTCs) have varying results for their diagnostic role. Thus, we conducted a meta-analysis to systematically assess the accuracy of CTCs in the diagnosis of BC.MethodsA meta-analysis was conducted to evaluate the overall accuracy of CTC detection. A pooled analysis of sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic advantage ratio (DOR) was used to measure diagnostic accuracy. In addition, the area under the summary receiver operating characteristic curve (AUC) was used to discriminate BC from non-BC. An analysis of the threshold effect was calculated using the Spearman correlation coefficient. We calculated the Q and I2 statistics to determine whether the studies were heterogeneous. Sensitivity analysis was performed by removing studies one by one. Publication bias was assessed by Deeks’ funnel plot asymmetry test.ResultsStudies from the PubMed, Cochrane Library, Embase, Web of Science, Wanfang, Vip, and CNKI databases were collected for diagnosing BC from January 2000 to April March 2023. Finally, 8 publications were retrieved in total containing 2014 cases involved in the study. Based on a random-effects model, it was found that the pooled SEN was 0.69 (0.55 - 0.80), SPE was 0.93 (0.60 - 0.99), PLR was 9.5 (1.4 - 65.9), NLR was 0.33 (0.23 - 0.48), DOR was 29 (4 - 205) and the AUC of the summary receiver operating characteristic (SROC) curve was 0.81 (0.77 - 0.84). Some heterogeneity was found in the article, but there was no threshold effect to account for it (P = 0.27). Deek’s funnel plot asymmetry test indicated that no publication bias was observed in this meta-analysis (P = 0.52).ConclusionThe results of this meta-analysis confirmed that CTCs were an important component of noninvasive methods of confirming BC with SEN of 0.69 (0.55 - 0.80), SPE of 0.93 (0.60 - 0.99) and AUC of 0.81 (0.77 - 0.84).

BackgroundGastric cancer with synchronous distant metastases indicates a dismal prognosis. The success in survival improvement mainly relies on our ability to predict the potential benefit of a therapy. Our objective is to develop an artificial intelligence annotated clinical-pathologic risk model to predict its outcomes.MethodsIn participants (n=47553) with gastric cancer of the surveillance, epidemiology, and end results program, we selected patients with distant metastases at first diagnosis, complete clinical-pathologic data and follow-up information. Patients were randomly divided into the training and test cohort at 7:3 ratio. 93 patients with advanced gastric cancer from six other cancer centers were collected as the external validation cohort. Multivariable analysis was used to identify the prognosis-related clinical-pathologic features. Then a survival prediction model was established and validated. Importantly, we provided explanations to the prediction with artificial intelligence SHAP (Shapley additive explanations) method. We also provide novel insights into treatment options.ResultsData from a total 2549 patients were included in model development and internal test (median age, 61 years [range, 53-69 years]; 1725 [67.7%] male). Data from an additional 93 patients were collected as the external validation cohort (median age, 59 years [range, 48-66 years]; 51 [54.8%] male). The clinical-pathologic model achieved a consistently high accuracy for predicting prognosis in the training (C-index: 0.705 [range, 0.690-0.720]), test (C-index: 0.737 [range, 0.717-0.757]), and external validation (C-index: 0.694 [range, 0.562-0.826]) cohorts. Shapley values indicated that undergoing surgery, chemotherapy, young, absence of lung metastases and well differentiated were the top 5 contributors to the high likelihood of survival. A combination of surgery and chemotherapy had the greatest benefit. However, aggressive treatment did not equate to a survival benefit. SHAP dependence plots demonstrated insightful nonlinear interactive associations among predictors in survival benefit prediction. For example, patients who were elderly, or poor differentiated, or presence of lung or bone metastases had a worse prognosis if they undergo surgery or chemotherapy, while patients with metastases to liver alone seemed to gain benefit from surgery and chemotherapy.ConclusionIn this large multicenter cohort study, we developed an artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer. It could be used to discuss treatment options.

ObjectivesTo investigate whether combining radiomics extracted from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) with an artificial neural network enables differentiation of MR BI-RADS 4 breast lesions and thereby avoids false-positive biopsies.MethodsThis retrospective study consecutively included patients with MR BI-RADS 4 lesions. The ultrafast imaging was performed using Differential sub-sampling with cartesian ordering (DISCO) technique and the tenth and fifteenth postcontrast DISCO images (DISCO-10 and DISCO-15) were selected for further analysis. An experienced radiologist used freely available software (FAE) to perform radiomics extraction. After principal component analysis (PCA), a multilayer perceptron artificial neural network (ANN) to distinguish between malignant and benign lesions was developed and tested using a random allocation approach. ROC analysis was performed to evaluate the diagnostic performance.Results173 patients (mean age 43.1 years, range 18–69 years) with 182 lesions (95 benign, 87 malignant) were included. Three types of independent principal components were obtained from the radiomics based on DISCO-10, DISCO-15, and their combination, respectively. In the testing dataset, ANN models showed excellent diagnostic performance with AUC values of 0.915-0.956. Applying the high-sensitivity cutoffs identified in the training dataset demonstrated the potential to reduce the number of unnecessary biopsies by 63.33%-83.33% at the price of one false-negative diagnosis within the testing dataset.ConclusionsThe ultrafast DCE-MRI radiomics-based machine learning model could classify MR BI-RADS category 4 lesions into benign or malignant, highlighting its potential for future application as a new tool for clinical diagnosis.

BackgroundAlthough the overall global incidence of gastric cancer has been declining, the number of new cases in people under the age of 50 is increasing, which is related to metastasis, late pathological stages, and poor prognosis. There is a scarcity of large-scale studies to evaluate and predict distant metastasis in patients with early-onset gastric cancer.MethodsFrom January 2010 to December 2019, data on early-onset GC patients undergoing surgery were gathered from the Surveillance, Epidemiology, and End Results (SEER) database. We investigated the independent risk factors for distant metastasis in patients with early-onset gastric cancer. Based on these risk factors, we developed a nomogram to predict distant metastasis. The model underwent internal validation on the test set and external validation on 205 patients from the First Affiliated Hospital of Sun Yat-sen University and the seventh Affiliated Hospital of Sun Yat-sen University. The novel nomogram model was then evaluated using the receiver operating characteristic (ROC) curve, calibration, the area under the curve (AUC), and decision curve analysis (DCA). The training set nomogram score was used to classify the different risk clusters of distant metastasis.ResultsOur study enrolled 2217 patients after establishing the inclusion and exclusion criteria, with 1873 having no distant metastasis and 344 having distant metastasis. The tumor size, total lymph nodes, whether or not receiving radiotherapy and chemotherapy, T stage, and N stage were significant predictors of advanced distant metastasis (p < 0.05). The AUC of the ROC analysis demonstrated our model’s high accuracy. Simultaneously, the prediction model shows high stability and clinical practicability in the calibration curve and DCA analysis.ConclusionsWe developed an innovative nomogram containing clinical and pathological characteristics to predict distant metastasis in patients younger than 50 years old with gastric cancer. The tool can alert clinicians about distant metastasis and help them develop more effective clinical treatment plans.

Cervical cancer (CC) remains one of the leading causes of cancer-related deaths worldwide. However, cervical cancer is preceded by the pre-malignant cervical intraepithelial neoplasia (CIN) that can last for up to 20 years before becoming malignant. Therefore, early screening is the key to prevent the progression of cervical lesions into invasive cervical cancer and decrease the incidence. The genes, down-regulated and hypermethylated in cancers, may provide potential drug targets for cervical cancer. In our current study, using the datasets from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases, we found that endothelin 3 (EDN3) was downregulated and hypermethylated in cervical squamous cell carcinoma (CSCC). The further analysis in GSE63514 (n=128) dataset and in our samples (n=221) found that the expression of EDN3 was decreased with the degree of cervical lesions. Pyrosequencing was performed to evaluate 4 CpG sites of the EDN3 promoter region in our samples (n=469). The data indicated that the methylation level of EDN3 was increased with the degree of cervical lesions. EDN3 silencing mediated by methylation can be blocked by 5-Azacytidine (5-Aza), a DNA methyltransferase 1 (DNMT1) inhibitor, treatment in cervical cancer cell lines. Ethynyldeoxyuridine (EdU) assay, would-healing assay, clone formation assay and transwell assay were conducted to investigate the biological function of EDN3 in cervical cancer cell lines. The results of these experiments confirmed that overexpression of EDN3 could inhibit the proliferation, clone formation, migration and invasion of cervical cancer cells. EDN3 may provide potential biomarker and therapeutic target for CSCC.

BackgroundEwing sarcoma (ES) represents a rare, aggressive bone and soft-tissue cancer. Unlike breast, liver, pancreatic, and prostate cancers, Ewing sarcoma has had no representative tumor marker until now. The use of procalcitonin (PCT) as a tumor marker is also rarely reported. PCT is a clinically recognized and widely used inflammatory marker in recent years. In rare cases, PCT may also be falsely positive due to non-infectious factors. In the few previously reported papers regarding the correlation between tumors and PCT, we learned that abnormalities of PCT level can also be impacted by individual cancers.Case presentationHere, we first reported a case of Ewing sarcoma with markedly elevated PCT without infection and carried out some literature review. The patient was a middle-aged man with extraskeletal Ewing sarcoma whose lesion was located in the distal abdominal ileum. He had a sudden and unprovoked onset of high fever during chemotherapy before surgery. After multiple examinations, the patient’s blood routine, C-reactive protein, blood culture, and CT examination showed no signs of infection, and even the culture from the end of the central venous catheter showed no pathogen growth. Only PCT increased dramatically to more than 200 ng/ml. PCT remained at this level for several months until a single abdominal lumpectomy was performed before it dropped to near-normal levels.ConclusionIn our report, PCT is significantly elevated in Ewing sarcoma in the absence of infection. Not only that, but we particularly highlighted the precipitous drop in PCT following tumor resection.

BackgroundHepatocellular carcinoma (HCC) is the major cause of malignancy-related deaths worldwide, and its incidence is likely to increase in the future as life expectancy increases. Therefore, the management of elderly patients with HCC has become a global issue. Aim of this study was to assess whether elderly patients with small HCC could obtain survival benefit from cryoablation (CRYO) in a real-world.Materials and methodsFrom July 2007 to June 2013, 185 patients with small HCC who underwent curative-intent percutaneous CRYO. All patients were divided into three groups according to age distribution. Overall survival (OS) and tumor-free survival (TFS) were compared between among of groups before and after the 1:1 propensity score matching, respectively. Univariate and multivariate Cox analyses were performed to determine the potential relationships between variables and prognostic outcomes.ResultsOne hundred and eighty-five patients (144 men, 41 women) received CRYO for small HCC, including 59 patients with age 65 years. The three age groups showed significant differences in the terms of underlying chronic liver disease and the number of patients with minor postoperative complications. After propensity score matching, the younger and elderly groups showed significant differences in mean OS (P=0.008) and tumor progression (P=0.050). However, no significant differences were shown in mean progression-free survival (PFS) (P=0.303). The Cox multivariate analysis showed that the Child-Pugh grade (HR=3.1, P

ObjectiveMinimally invasive surgical resection of lumbar dumbbell-shaped tumors is rarely reported. We retrospectively collected clinical data of lumbar dumbbell-shaped tumors treated with the spinal microtubular technique to evaluate the feasibility, complications and efficacy of the surgical methods.MethodsFrom September 2013 to August 2021, clinical data of patients with lumbar dumbbell-shaped tumors that underwent paravertebral approach and micro-tubular tumorectomy (PAMT) were collected; neurological function was assessed using the pain visual analog scale (VAS) and the Japanese Orthopaedic Association (JOA) score.ResultsA total of 46 patients that underwent PAMT were included in this study. In all patients, total resection of the tumor was performed at one stage (100%). The median follow-up period was 27.5 months (P25, P75: 16.5- 57 months). Symptoms such as pain or lower extremity weakness were significantly relieved in 46 patients. The postoperative VAS score and JOA score were significantly higher compared with preoperative scores (p

Bladder cancer (BC) is a malignant disease with high rates of recurrence and mortality. It is mainly classified as non-muscle-invasive BC and muscle-invasive BC (MIBC). Often, MIBC is chemoresistant, which, according to cancer stem cells (CSCs) theory, is linked to the presence of bladder cancer stem cells (BCSCs). Sex-determining region Y- (SRY) Box transcription factor 2 (SOX2), which is a molecular marker of BCSCs, is aberrantly over-expressed in chemoresistant BC cell lines. It is one of the standalone prognostic factors for BC, and it has an inherently significant function in the emergence and progression of the disease. This review first summarizes the role of SRY-related high-mobility group protein Box (SOX) family genes in BC, focusing on the SOX2 and its significance in BC. Second, it discusses the mechanisms relevant to the regulation of SOX2. Finally, it summarizes the signaling pathways related to SOX2 in BC, suggests current issues to be addressed, and proposes potential directions for future research to provide new insights for the treatment of BC.

A unique lactate dehydrogenase (LDH) isoenzyme designated as lactate dehydrogenase C4 (LDH-C4) is found in mammalian mature testis and spermatozoa. Thus far, LDH-C4 has been well studied with regard to its gene and amino acid sequences, structure, biological properties, and peptide synthesis. Accumulating evidence has shown that LDH-C4 is closely related to spermatic energy metabolism and plays a critical role in sperm motility, capacitation, and fertilization. Defects in the catalytic activity of LDH-C4 are key to pathophysiological abnormalities underlying infertility. LDH-C4 was originally thought to be present only in mature testis and spermatozoa; however, recent studies have implicated LDH-C4 as a cancer-testis antigen (CTA), owing to its aberrant transcription in a broad spectrum of human neoplasms. This review highlights the recent findings on LDH-C4 with particular emphasis on its role in male infertility and tumors.

BackgroundThe family with sequence similarity 72 member A (FAM72A) protein has been identified as an effector of multiple pathological processes in many cancers. The value of FAM72A in HCC remains largely unknown.MethodsData from TCGA-LIHC, ICGC-LIRI-JP, IMvigor210, cBioPortal, GeneMANIA, and TIMER were processed and visualized to explore the association between FAM72A and the prognosis, stemness phenotype, mutational burden, immune cell infiltration, and drug sensitivity in HCC patients. Potential pathways were also revealed. Furthermore, we experimentally verified the results in vivo and in vitro using immunohistochemistry, western blotting, and CCK-8 assays.ResultsFirst, FAM72A mRNA expression was significantly upregulated in HCC. High FAM72A expression was independently associated with a poor prognosis. Experimental validation confirmed that FAM72A was remarkably overexpressed in HCC patients and mice. Moreover, FAM72A knockdown suppressed HCC cell proliferation. In addition, the frequency of TP53 mutations was significantly higher in the high FAM72A expression group. Subsequently, the enrichment analysis revealed that FAM72A was closely related to immune processes and mTOR pathways. Silencing FAM72A increased the expression levels of mTOR in HCC cell lines. The FAM72A-mTOR pathway was strongly associated with a poor prognosis for patients with HCC. Patients with high FAM72A expression levels might be more resistant to sorafenib. Furthermore, the expression of FAM72A and mTOR was significantly associated with the abundance of some tumor-infiltrating immune cells, especially CD4+ T cells. Finally, patients with high levels of FAM72A and mTOR were more sensitive to immunotherapy.ConclusionsFAM72A, a member of the FAM72 family, might be a prognostic and immunotherapeutic target for HCC patients.

A more accurate prognosis is important for clinical treatment of lung adenocarcinoma. However, due to the limitation of sample and technical bias, most prognostic signatures lacked reproducibility, and few were applied to clinical practice. In addition, understanding the molecular driving mechanism is indispensable for developing more promising therapies for lung adenocarcinoma. Here, we built an unbiased prognostic significance model to perform an integrative analysis, including differentially expressed genes and clinical data with lung adenocarcinoma patients from TCGA. Multivariable Cox proportional hazards model with the Lasso penalty and 10-fold cross-validate were used to identify the best gene signature. We generated a 17-gene signature for prognostic risk prediction based on the overall survival time of lung adenocarcinoma patients. To further test the model’s predictive ability, we have applied an independent GEO database to verify the predictive ability of prognostic signature. The model can more objectively describe several biological processes related to tumors and reveal important molecular mechanisms in tumor development by GO and KEGG analysis. Furthermore, differential expression analysis by GSEA revealed that tumor microenvironments such as ER stress, exosome, and immune microenvironment were enriched. Using single-cell RNA sequence technology, we found that risk score was positively correlated with lung adenocarcinoma marker genes and copy number variation but negatively correlated with lung epithelial marker genes. High-risk cell populations with the model had stronger cancer stemness and tumor-related pathway activation. As we expected, the risk score was in accordance with the malignancy of each cluster from tumor progression. In conclusion, the risking model established in this study is more reliable than others in evaluating the prognosis of LUAD patients.

Mesenchymal stem cell is a kind of pluripotent cells with the ability of self-renewal and multi-directional differentiation, which exist in bone marrow, umbilical cord blood, umbilical cord tissue, placenta tissue, adipose tissue and so on. Extracellular vesicles are membranous lipid vesicles secreted by a variety of cells and widely present in body fluids, which contain proteins, mRNA, microRNA and other substances, and are an important medium of intercellular communication. At present, more and more evidence shows that mesenchymal stem cell-derived extracellular vesicles play an important role in the development of lung cancer. Regulating the levels of proteins, RNAs and other substances in MSC-EVs and then transplanting them into patients may be a new way to alleviate the development of lung cancer. We mainly introduce the role of extracellular vesicles derived from human umbilical cord mesenchymal stem cells, bone marrow mesenchymal stem cells and adipose mesenchymal stem cells in lung cancer, to provide new alternatives for the treatment of lung cancer.

Recent studies have shown that haploidentical hematopoietic stem cell transplantation supported by third-party cord blood (haplo-cord-HSCT) results in rapid hematopoietic recovery, low incidences of graft-versus-host disease (GVHD), and relapse of hematologic malignancies. However, few reports on haploidentical peripheral blood stem cell transplantation supported by third-party cord blood (haplo-cord-PBSCT) have been published. To evaluate the outcomes of patients who underwent haplo-cord-PBSCT or human leukocyte antigen (HLA)-matched sibling donor peripheral blood stem cell transplantation (MSD-PBSCT), we retrospectively reviewed the clinical data of patients with hematologic malignancies who underwent haplo-cord-PBSCT (n = 93) or MSD-PBSCT (n = 72) in our hospital from March 2017 to December 2020. In the haplo-cord-PBSCT and MSD-PBSCT groups, the median time for neutrophil and platelet engraftment was 13 vs. 12 days (p = 0.07) and 16 vs. 13 days (p = 0.06), respectively. The 30-day cumulative incidences of neutrophil engraftment were 100.0% and 98.6% (p = 0.12). The 100-day cumulative incidences of platelet engraftment were 96.8% and 98.6% (p = 0.01). The 100-day cumulative incidences of grade II–IV and grade III–IV acute GVHD were 29.1% vs. 23.6% (p = 0.42) and 9.7% vs. 4.2% (p = 0.18). The cumulative incidences of total and moderate/severe chronic GVHD at 1 year were 26.5% vs. 17.4% and 8.1% vs. 4.5%, respectively, and at 3 years were 34.7% vs. 34.3% (p = 0.60) and 13.6% vs. 10.6% (p = 0.49), respectively. The cumulative incidences of relapse at 1 year were 9.3% and 7.2% and at 3 years were 17.0% and 17.0% (p = 0.98). Non-relapse mortality (NRM) at 1 year was 14.6% and 8.6% and at 3 years was 17.4% and 8.6% (p = 0.13) in two groups. The probabilities of overall survival (OS), disease-free survival (DFS), and GVHD-free/relapse-free survival (GRFS) at 1 year were 81.7% vs. 88.6%, 76.1% vs. 84.2%, and 71.7% vs. 79.7%, respectively, and at 3 years were 78.7% vs. 79.0%, 65.6% vs. 74.4%, and 55.5% vs. 63.6%, respectively, in the corresponding group, p > 0.05. In conclusion, for patients with acute myeloid leukemia/myelodysplastic syndrome (AML/MDS) and acute lymphoid leukemia (ALL), haplo-cord-PBSCT results in similar outcomes compared with MSD-PBSCT, and it may be a valid alternative transplantation method.

Background and ObjectivesPancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI’s use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues.MethodsWe examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles.ResultsOverall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied.ConclusionsAI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.

ObjectiveTo explore the effects of the expression level of miR-520-5p/PPP5C in pancreatic cancer cells and exosomes on cell viability, angiogenesis, autophagy, which involved in the mechanism of gemcitabine resistance in pancreatic cancer.MethodsAPSC-1 cell line was treated with gemcitabine, after which its exosomes were extracted for NTA assay. Subsequently, the drug resistance of APSC-1 cells was assayed using CCK8, as well as the activity of HUVEC cells treated with exosomes from each group of APSC-1 cells after drug resistance treatment as well as overexpression treatment. Five groups of HUVEC cells treated with exosomes were subjected to in vitro tubule formation assay. levels of PPP5C in each group of ASPC-1 cells and their exosomes, levels of overexpressed PPP5C, and related exosomal proteins were examined by WB. mRNA expression levels of PPP5C and levels of miR-520a were examined by qPCR The relationship between miR-520a-5p and PPP5C was investigated. After that, the autophagy of PPP5C was detected. Finally, it was analyzed by TCGA database for survival prognosis analysis.ResultsAPSC-1 cells had an IC50 value of 227.1 μM for gemcitabine, elevated PPP5C expression, drug resistance, and enhanced HUVEC cell activity; exosomes CD9, CD63, and CD81 were significantly expressed in all groups; meanwhile, enhanced PPP5C expression not only promoted in vitro tubule formation but also increased autophagy levels; meanwhile, its relationship with miR-520-5p and There was a targeted inhibitory relationship between its level and miR-520-5p and PPP5C, and its elevated level also led to a decrease in the survival level of patients over 3-5 years.ConclusionPPP5C has a prognostic role in pancreatic cancer by promoting the value-added and invasion of pancreatic cancer cells, and a targeted inhibitory relationship between miR-520-5p and PPP5C was found.

Gastric cancer is a complex multifactorial and multistage process that involves a large number of tumor-related gene structural changes and abnormal expression. Therefore, knowing the related genes of gastric cancer can further understand the pathogenesis of gastric cancer and provide guidance for the development of targeted drugs. Traditional methods to discover gastric cancer-related genes based on biological experiments are time-consuming and expensive. In recent years, a large number of computational methods have been developed to identify gastric cancer-related genes. In addition, a large number of experiments show that establishing a biological network to identify disease-related genes has higher accuracy than ordinary methods. However, most of the current computing methods focus on the processing of homogeneous networks, and do not have the ability to encode heterogeneous networks. In this paper, we built a heterogeneous network using a disease similarity network and a gene interaction network. We implemented the graph transformer network (GTN) to encode this heterogeneous network. Meanwhile, the deep belief network (DBN) was applied to reduce the dimension of features. We call this method “DBN-GTN”, and it performed best among four traditional methods and five similar methods.

ObjectiveAs a cancer-testis antigen (CTA), human lactate dehydrogenase C4 (LDH-C4) enzyme protein encoded by the LDHC gene has been reported to be involved in the occurrence and development of various malignancies, while its expression and clinical significance in lung adenocarcinoma (LUAD) remain unclear. This study aims to investigate the expression of LDH-C4 in LUAD and its diagnostic and prognostic value.MethodsThe mRNA and protein levels of LDH-C4 in LUAD and adjacent normal tissues were analyzed based on the UALCAN database, and the prognostic significance was assessed using the LOGpc database. The LDHC mRNA level in serum and serum secretion of LUAD patients was determined by quantitative real-time PCR (qRT-PCR). Based on the high-throughput LUAD tissue chip combined with immunohistochemistry (IHC), the protein level of LDH-C4 in LUAD tissues was measured, and its correlation with clinicopathological features and prognosis was analyzed.ResultsLDHC expression was upregulated in LUAD, which was related to the clinical stage and poor prognosis of patients. The positive rates of LDHC mRNA expression in serum and exosome of LUAD patients were 78.3% and 66.7%, respectively. The area under the curve (AUC) of serum and exosomal LDHC in the diagnosis of LUAD was 0.8121 and 0.8925, respectively. The expression of LDHC in serum and serum-derived exosomes from LUAD patients was negatively correlated with medical treatment and positively correlated with the recurrence of LUAD. The positive expression rate of LDH-C4 in LUAD tissues was 96.7% (89/92), which was significantly higher than that in adjacent normal tissues 22.6% (19/84) (p < 0.001). The median overall survival (OS) time of patients with a high expression of LDH-C4 was significantly shorter than that of patients with low expression (34 months versus 62 months) (p = 0.016). Further relative risk analysis exhibited that the expression of LDH-C4 was an independent prognostic factor of OS in patients with LUAD.ConclusionsLDHC/LDH-C4 expression was upregulated in LUAD, and LDH-C4 could be used as a molecular indicator of the prognosis of LUAD. Serum and serum-derived exosomes of LDHC can be used as an important biomarker for the diagnosis, efficacy evaluation, and recurrence monitoring of LUAD.

Patients diagnosed with more than one cancer generally develop the individual tumors sequentially. There are a few cases of co-occurring multiple myeloma and lung cancer reported in the literature. Here, we report two cases of co-occurring multiple myeloma and lung cancer in patients who presented with the chief complaint of pain. The diagnoses of multiple myeloma and lung cancer were supported by hematologic and biochemical investigations, as well as bone marrow and lung histopathologic examination. We provided suitable interventions for both two patients. The patients are still currently undergoing treatment and followed up closely. We first performed a bioinformatic analysis to determine commonly shared genes and pathways in the two types of cancer types. Fortunately, we identified the hub gene mitochondrial trans-2-enoyl-CoA reductase (MECR), which was overexpressed in both tumors. Survival analysis correlated higher MECR expression with poorer overall survival. Signaling pathway analysis suggested possible transduction pathways implicated in the co-occurrence of both tumors. The clinical cases combined with bioinformatic analysis may provide insight for the pathogenesis of synchronous tumors.

PurposeThe study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively.Materials and MethodsFrom February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time. Venous phase images of contrast-enhanced CT were used for radiomics analysis. The intraclass correlation efficient (ICC), the correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics features and built radiomics signature. Logistic regression analyses were performed to establish a clinical model, a radiomics model, and a combined model. The performance of the predictive models was measured by area under the receiver operating characteristic curve (AUC), and pairwise ROC comparisons between models were tested using the Delong method. Finally, the model with the best performance was presented as a nomogram, and its calibration and clinical usefulness were assessed.ResultsFinally, 15 radiomics features were selected to build a radiomics signature, and three models were developed through logistic regression. In the training cohort, the combined model showed a higher predictive capability (AUC = 0.950) than the radiomics model and the clinical model (AUC: radiomics = 0.914, clinical = 0.756). However, in the validation cohort, the AUC of the radiomics model (AUC = 0.885) was significantly higher than the other two models (AUC: combined = 0.791, clinical = 0.567). After comprehensive consideration, the radiomics model was chosen to develop the nomogram. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had a good consistency and clinical utility.ConclusionWe developed a CT-based radiomics model with good performance to noninvasively predict PNI of pCCA preoperatively.

ObjectivesTo develop and validate a radiomics model based on multimodal MRI combining clinical information for preoperative distinguishing concurrent endometrial carcinoma (CEC) from atypical endometrial hyperplasia (AEH).Materials and MethodsA total of 122 patients (78 AEH and 44 CEC) who underwent preoperative MRI were enrolled in this retrospective study. Radiomics features were extracted based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. After feature reduction by minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithm, single-modal and multimodal radiomics signatures, clinical model, and radiomics-clinical model were constructed using logistic regression. Receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis were used to assess the models.ResultsThe combined radiomics signature of T2WI, DWI, and ADC maps showed better discrimination ability than either alone. The radiomics-clinical model consisting of multimodal radiomics features, endometrial thickness >11mm, and nulliparity status achieved the highest area under the ROC curve (AUC) of 0.932 (95% confidential interval [CI]: 0.880-0.984), bootstrap corrected AUC of 0.922 in the training set, and AUC of 0.942 (95% CI: 0.852-1.000) in the validation set. Subgroup analysis further revealed that this model performed well for patients with preoperative endometrial biopsy consistent and inconsistent with postoperative pathologic data (consistent group, F1-score = 0.865; inconsistent group, F1-score = 0.900).ConclusionsThe radiomics model, which incorporates multimodal MRI and clinical information, might be used to preoperatively differentiate CEC from AEH, especially for patients with under- or over-estimated preoperative endometrial biopsy.

ObjectiveTo develop and evaluate the performance of a magnetic resonance imaging (MRI)-based radiomics nomogram for prediction of response of patients with muscle-invasive bladder cancer (MIBC) to neoadjuvant chemotherapy (NAC).MethodsA total of 70 patients with clinical T2-4aN0M0 MIBC were enrolled in this retrospective study. For each patient, 1316 radiomics features were extracted from T2-weighted images (T2WI), diffusion-weighted images (DWI), and apparent diffusion coefficient (ADC) maps. The variance threshold algorithm and the Student’s t-test or the Mann–Whitney U test were applied to select optimal features. Multivariate logistic regression analysis was used to eliminate irrelevant features, and the retained features were incorporated into the final single-modality radiomics model. Combined radiomic models were generated by combining single-modality radiomics models. A radiomics nomogram, incorporating radiomics signatures and independent clinical risk factors, was developed to determine whether the performance of the model in predicting tumor response to NAC could be further improved.ResultsBased on pathological T stage post-surgery, 36 (51%) patients were classified as good responders (GR) and 34 (49%) patients as non-good responders (non-GR). In addition, 3 single-modality radiomics models and 4 combined radiomics models were established. Among all radiomics models, the combined radiomics model based on T2WI_Score, DWI_Score, and ADC_Score yielded the highest area under the receiver operating characteristics curve (AUC) (0.967, 95% confidence interval (CI): 0.930–0.995). A radiomics nomogram, integrating the clinical T stage and 3 single-modality radiomics models, yielded a higher AUC (0.973, 95%CI: 0.934–0.998) than other combined radiomics models.ConclusionThe proposed MRI-based radiomics nomogram has the potential to be used as a non-invasive tool for the quantitatively prediction of tumor response to NAC in patients with MIBC.

PET/CT with 18F-2-fluoro-2-deoxyglucose (18F-FDG) has been proposed as a promising modality for diagnosing and monitoring treatment response and evaluating prognosis for patients with non-small cell lung cancer (NSCLC). The status of epidermal growth factor receptor (EGFR) mutation is a critical signal for the treatment strategies of patients with NSCLC. Higher response rates and prolonged progression-free survival could be obtained in patients with NSCLC harboring EGFR mutations treated with tyrosine kinase inhibitors (TKIs) when compared with traditional cytotoxic chemotherapy. However, patients with EGFR mutation treated with TKIs inevitably develop drug resistance, so predicting the duration of resistance is of great importance for selecting individual treatment strategies. Several semiquantitative metabolic parameters, e.g., maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), measured by PET/CT to reflect 18F-FDG metabolic activity, have been demonstrated to be powerful in predicting the status of EGFR mutation, monitoring treatment response of TKIs, and assessing the outcome of patients with NSCLC. In this review, we summarize the biological and clinical correlations between EGFR mutation status and 18F-FDG metabolic activity in NSCLC. The metabolic activity of 18F-FDG, as an extrinsic manifestation of NSCLC, could reflect the mutation status of intrinsic factor EGFR. Both of them play a critical role in guiding the implementation of treatment modalities and evaluating therapy efficacy and outcome for patients with NSCLC.

Ferroptosis is a recently discovered category of programmed cell death. It is much different from other types of cell death such as apoptosis, necrosis and autophagy. The main pathological feature of ferroptosis is the accumulation of iron-dependent lipid peroxidation. The typical changes in the morphological features of ferroptosis include cell volume shrinkage and increased mitochondrial membrane area. The mechanisms of ferroptosis may be mainly related to lipid peroxidation accumulation, imbalance in amino acid antioxidant system, and disturbance of iron metabolism. Besides, hypoxia-inducible factor (HIF), nuclear factor-E2-related factor 2 (Nrf2), and p53 pathway have been demonstrated to be involved in ferroptosis. At present, the molecular mechanisms of ferroptosis pathway are still unmapped. In this review, an outlook has been put forward about the crucial role of apurinic/apyrimidinic endodeoxyribonuclease 1 (APEX1) in the regulation of ferroptosis. APEX1 plays an important role in the regulation of intracellular redox balance and can be used as a potential inhibitor of ferroptotic cell death. Bioinformatics analysis indicated that the mRNA level of APEX1 is decreased in cases of ferroptosis triggered by erastin. Besides, it was found that there was a significant correlation between APEX1 and genes in the ferroptosis pathway. We have discussed the possibility to employ APEX1 inducers or inhibitors in the regulation of ferroptosis as a new strategy for the treatment of various human diseases.

Ferroptosis is caused by accumulation of iron-dependent lipid peroxidation, which is characterized by reduction in cell volume and increase in mitochondrial membrane density. Studies have shown that ferroptosis contributes to the development and progression of numerous major diseases, including hepatocellular carcinoma (HCC). As a unique biomedical resource, Traditional Chinese Medicine (TCM) has been widely used in the treatment of HCC. In this present study, Scutellaria barbata was used to treat HCC cells in vitro, and the results revealed that S. barbata suppressed HCC cell growth through inducing ferroptosis. Next, the exploration of the molecular mechanism on how S. barbata induced ferroptosis in HCC cells suggested that S. barbata may induce ferroptosis by promoting iron perioxidation and lipid ROS metabolism. Finally, S. barbata also inhibited HCC tumorigenicity in vivo by inducing ferroptosis of HCC cells. These results provided theoretical basis for explaining the mechanism of TCM treatment for HCC and offered therapeutic opportunities for HCC patients.

ObjectiveTo investigate the MRI features and clinical significance of hepatic epithelioid hemangioendothelioma (HEHE).MethodsClinical records and MRI findings were retrospectively evaluated in nine HEHE patients from May 2010 to January 2020.ResultThere were 121 lesions in nine patients with a predominantly peripheral distribution. Five lesions (4.13%) in two patients (22.22%) had evidence of capsular retraction, and three patients had lung metastasis (33.33%). Dynamic contrast-enhanced MRI showed progressive enhancement, mainly in two ways: ring enhancement with hypovascularity in four patients (44.44%) and ring enhancement with hypervascularity in five patients (55.56%). Imaging demonstrated a multilayer ring appearance, which was typically observed on T2-weighted imaging (T2WI). The most common appearance consisted of two layers of varying signal, with some images displaying up to four layers. There were significant differences in the size of lesions between different layers of multilayer ring appearance (p < 0.001). All lesions exhibited a two-layer appearance on diffusion-weighted imaging (DWI), with hyperintensity at the periphery and a slightly high signal at the center (except for those with a single layer on T2WI). The “vascular penetration sign” was observed in most lesions, and the blood vessels of 112 lesions (92.56%) were portal vein branches, and five (4.13%) were hepatic vein branches. Pulmonary metastasis was found in three patients with the “vascular penetration sign” of hepatic vein branches.ConclusionThe multilayer ring appearance on T2WI, the “vascular penetration sign”, and the two enhancement patterns may be of great significance in the diagnosis and treatment of HEHE. The “vascular penetration sign” of hepatic vein branches may indicate extrahepatic metastasis.

Despite N6-methyladenosine (m6A) is functionally important in various biological processes, its role in the underlying regulatory mechanism in TNBC are lacking. In this study, we investigate the pathological role and the underlying mechanism of the m6A methylated RNA level and its major methyltransferase METTL3 in the TNBC progression. We found that the m6A methylated RNA was dramatically decreased in TNBC tissues and cell lines. Functionally, we demonstrated that METTL3 inhibits the proliferation, migration, and invasion ability of TNBC cells. Moreover, we found METTL3 is repressed by miR-34c-3p in TNBC cells. On the mechanism, we found that circMETTL3 could act as a sponge for miR-34c-3p and inhibits cell proliferation, invasion, tumor growth and metastasis by up-regulating the expression of miR-34c-3p target gene METTL3. In conclusion, our study demonstrates the functional importance and regulatory mechanism of METTL3 in suppressing the tumor growth of TNBC.

ObjectiveTo establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).Materials and MethodsFifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.ResultsFollowing multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.ConclusionThis study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

ObjectivesTo investigate the diagnostic performance of the Kaiser score and apparent diffusion coefficient (ADC) to differentiate Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions at dynamic contrast-enhanced (DCE) MRI.MethodsThis was a single-institution retrospective study of patients who underwent breast MRI from March 2020 to June 2021. All image data were acquired with a 3-T MRI system. Kaiser score of each lesion was assigned by an experienced breast radiologist. Kaiser score+ was determined by combining ADC and Kaiser score. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of Kaiser score+, Kaiser score, and ADC. The area under the curve (AUC) values were calculated and compared by using the Delong test. The differences in sensitivity and specificity between different indicators were determined by the McNemar test.ResultsThe study involved 243 women (mean age, 43.1 years; age range, 18–67 years) with 268 MR BI-RADS 4 lesions. Overall diagnostic performance for Kaiser score (AUC, 0.902) was significantly higher than for ADC (AUC, 0.81; p = 0.004). There were no significant differences in AUCs between Kaiser score and Kaiser score+ (p = 0.134). The Kaiser score was superior to ADC in avoiding unnecessary biopsies (p < 0.001). Compared with the Kaiser score alone, the specificity of Kaiser score+ increased by 7.82%, however, at the price of a lower sensitivity.ConclusionFor MR BI-RADS category 4 breast lesions, the Kaiser score was superior to ADC mapping regarding the potential to avoid unnecessary biopsies. However, the combination of both indicators did not significantly contribute to breast cancer diagnosis of this subgroup.

PurposeGlioma incidence in the US seems to have stabilized over the past 20 years. It’s also not clear whether changes in glioblastoma incidence are associated with glioma mortality trends. Our study investigated trends in glioma incidence and mortality according to tumor characteristics.MethodsThis study obtained data from the Surveillance, Epidemiology, and End Results-9 (SEER-9) registries to calculate glioma incidence and mortality trends. Annual percent changes (APC) and 95% CIs were calculated using the Joinpoint program.Results62,159 patients (34,996 males and 55,424 whites) were diagnosed with glioma during 1975-2018, and 31,922 deaths occurred from 1995-2018. Glioblastoma (32,893 cases) and non-glioblastoma astrocytoma (17,406 cases) were the most common histologic types. During the study period, the incidence of glioma first experienced a significant increase (APC=1.8%, [95% CI, 1.3% to 2.3%]) from 1975 to 1987, and then experienced a slight decrease (APC=-0.4%, [95% CI, -0.5% to -0.3%]) from 1987 to 2018, while the APC was 0.8% for glioblastoma, -2.0% for non-glioblastoma astrocytoma, 1.1% for oligodendroglial tumors, 0.7% for ependymoma and -0.3% for glioma NOS during the study period. Glioblastoma incidence increased for all tumor size and tumor extension except for distant. From 1995 to 2018, glioma mortality declined 0.4% per year (95% CI: -0.6% to -0.2%) but only increased in patients older than 80 years [APC=1.0%, (95% CI, 0.4% to 1.6%)].ConclusionSignificant decline in glioma incidence (1987-2018) and mortality (1995-2018) were observed. Epidemiological changes in non-glioblastoma astrocytoma contributed the most to overall trends in glioma incidence and mortality. These findings can improve understanding of risk factors and guide the focus of glioma therapy.

ObjectiveProstate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4–10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4–10 ng/ml was explored.MethodsA total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4–10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.ResultsSeveral differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or 1, and Student’s t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4–10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).ConclusionThe findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4–10 ng/ml.

ObjectiveTo confirm the diagnostic performance of computed tomography (CT)-based texture analysis (CTTA) and magnetic resonance imaging (MRI)-based texture analysis for grading cartilaginous tumors in long bones and to compare these findings to radiological features.Materials and MethodsTwenty-nine patients with enchondromas, 20 with low-grade chondrosarcomas and 16 with high-grade chondrosarcomas were included retrospectively. Clinical and radiological information and 9 histogram features extracted from CT, T1WI, and T2WI were evaluated. Binary logistic regression analysis was performed to determine predictive factors for grading cartilaginous tumors and to establish diagnostic models. Another 26 patients were included to validate each model. Receiver operating characteristic (ROC) curves were generated, and accuracy rate, sensitivity, specificity and positive/negative predictive values (PPV/NPV) were calculated.ResultsOn imaging, endosteal scalloping, cortical destruction and calcification shape were predictive for grading cartilaginous tumors. For texture analysis, variance, mean, perc.01%, perc.10%, perc.99% and kurtosis were extracted after multivariate analysis. To differentiate benign cartilaginous tumors from low-grade chondrosarcomas, the imaging features model reached the highest accuracy rate (83.7%) and AUC (0.841), with a sensitivity of 75% and specificity of 93.1%. The CTTA feature model best distinguished low-grade and high-grade chondrosarcomas, with accuracies of 71.9%, and 80% in the training and validation groups, respectively; T1-TA and T2-TA could not distinguish them well. We found that the imaging feature model best differentiated benign and malignant cartilaginous tumors, with an accuracy rate of 89.2%, followed by the T1-TA feature model (80.4%).ConclusionsThe imaging feature model and CTTA- or MRI-based texture analysis have the potential to differentiate cartilaginous tumors in long bones by grade. MRI-based texture analysis failed to grade chondrosarcomas.

BackgroundLung squamous cell carcinoma (LUSC) generally correlates with poor clinical prognoses due to the lack of available prognostic biomarkers. This study is designed to identify a potential biomarker significant for the prognosis and treatment of LUSC, so as to provide a scientific basis for clinical treatment decisions.MethodsGenomic changes in LUSC samples before and after radiation were firstly discussed to identify E2 factor (E2F) pathway of prognostic significance. A series of bioinformatics analyses and statistical methods were combined to construct a robust E2F-related prognostic gene signature. Furthermore, a decision tree and a nomogram were established according to the gene signature and multiple clinicopathological characteristics to improve risk stratification and quantify risk assessment for individual patients.ResultsIn our investigated cohorts, the E2F-related gene signature we identified was capable of predicting clinical outcomes and therapeutic responses in LUSC patients, besides, discriminative to identify high-risk patients. Survival analysis suggested that the gene signature was independently prognostic for adverse overall survival of LUSC patients. The decision tree identified the strong discriminative performance of the gene signature in risk stractification for overall survival while the nomogram demonstrated a high accuracy.ConclusionThe E2F-related gene signature may help distinguish high-risk patients so as to formulate personalized treatment strategy in LUSC patients.

BackgroundTao Hong Si Wu Decoction (THSWD) is a well-known traditional Chinese medicine used clinically alone or combined with drugs to treat breast cancer. However, there has been no study to date on the underlying mechanisms of its therapeutic effects.ObjectivesTo explore the potential mechanism of THSWD for the treatment of breast cancer using network pharmacology and experimental research.MethodsThe active ingredients of THSWD were screened according to Lipinski’s rule of five based on the 107 ingredients of THSWD identified by UPLC-Q-TOF-MSE. The targets of THSWD and breast cancer from multiple databases were collected, and a Compound-Target-Pathway network based on protein-protein interaction (PPI) was constructed. Gene ontology (GO) analysis and KEGG pathway analysis were performed via the DAVID server. Molecular docking studies verified the selected key ingredients and key targets. The results of network pharmacology were verified by in vitro experiments. Including the effects of THSWD drug-containing rat serum (THSWD serum) on cell proliferation, and on the targets HRAS, MAPK1, AKT1, GRB2, and MAPK14 were assayed by RT-qPCR and Western blot assays.ResultsIn total, 27 active ingredients including 8 core components, were obtained from 107 ingredients and 218 THSWD target genes for the treatment of breast cancer were identified. THSWD is active in the treatment of breast cancer by targeting Ras, FoxO, PI3K-Akt and other signaling pathways. MCF-7 and MDA-MB-231 cell proliferation was inhibited by THSWD serum in a time and concentration dependent manner. THSWD could regulated the RNA and protein expression of core targets HRAS, MAPK1, AKT1, GRB2, and MAPK14 for treatment of breast cancer.ConclusionThe results of network pharmacology study showed that THSWD is active against breast cancer by intervening with multiple targets and pathways. Luteolin, kaempferol, senkyunolide E, and other 8 compounds may be the core active ingredients of THSWD in the treatment of breast cancer. THSWD treatment of breast cancer may be related to targeting Ras, FoxO, PI3K-Akt, and other signal pathways associated with the core targets HRAS, MAPK1, AKT1, GRB2, and MAPK14.

Acute leukemia (AL) is a highly heterogeneous hematologic malignancy, and although great progress has been made in the treatment of AL with allogeneic hematopoietic stem cell transplantation (Allo-HSCT) and new targeted drugs, problems such as infection and GVHD in AL treatment are still serious. How to reduce the incidence of AL, improve its prognosis and reduce the side effects of treatment is a crucial issue. The gut microbiota plays an important role in regulating disease progression, pathogen colonization, and immune responses. This article reviews recent advances in the gut microbiota and AL pathogenesis, infection, treatment and its role in allo-HSCT.