Development and validation of an interpretable delta radiomics-based model for predicting invasive ground-glass nodules in lung adenocarcinoma: a retrospective cohort study.

Background: Radiomics models based on computed tomography (CT) can be used to differentiate invasive ground-glass nodules (GGNs) in lung adenocarcinoma to help determine the optimal timing of GGN resection, improve the accuracy of prognostic prediction, and reduce unnecessary surgeries. However, general radiomics does not fully utilize follow-up data and often lacks model interpretation. Therefore, this study aimed to build an interpretable model based on delta radiomics to predict GGN invasiveness. Methods: A retrospective analysis was conducted on a set of 303 GGNs that were surgically resected and confirmed as lung adenocarcinoma in Shanghai Chest Hospital between September 2017 and August 2022. Delta radiomics and general radiomics features were extracted from preoperative follow-up CT scans and combined with clinical features for modeling. The performance of the delta radiomics-clinical model was compared to that of the radiomics-clinical model. Additionally, Shapley additive explanations (SHAP) was employed to interpret and visualize the model. Results: Two models were constructed using a combination of 34 radiomic features and 10 delta radiomic features, along with 14 clinical features. The radiomics-clinical model and the delta radiomics-clinical model exhibited area under the curve (AUC) of 0.986 [95% confidence interval (CI): 0.977-0.995] and 0.974 (95% CI: 0.959-0.987) in the training set, respectively, and 0.949 (95% CI: 0.908-0.978) and 0.927 (95% CI: 0.879-0.966) in the test set, respectively. The DeLong test of the two models showed no statistical significance (P=0.10) in the test set. SHAP was used to output a summary plot for global interpretation, which showed that preoperative mass, three-dimensional (3D) length, mean diameter, volume, mean CT value, and delta radiomics feature original_firstorder_RootMeanSquared were the relatively more important features in the model. Waterfall plots for local interpretation showed how each feature contributed to the prediction output of a given GGN. Conclusions: The delta radiomics-based model proved to be a helpful tool for predicting the invasiveness of GGNs in lung adenocarcinoma. This approach offers a precise, noninvasive alternative in informing clinical decision-making. Additionally, SHAP provided insightful and user-friendly interpretations and visualizations of the model, enhancing its clinical applicability.

Global, regional, and national cancer burdens of respiratory and digestive tracts in 1990-2044: A cross-sectional and age-period-cohort forecast study.

BACKGROUND: Understanding the current status and future trends of cancer burdens by systems provides important information for specialists, policymakers, and specific risk populations. METHODS: The aim of this study was to compare the current and future cancer burdens of the gastrointestinal (GI) and respiratory tracts in terms of their magnitude and distribution. Data from a total of eight cancers of the digestive and respiratory tracts in the Global Burden of Disease (GBD) database were collected. The age-standardized incidence/death rates (ASIR/ASDRs), disability-adjusted life years (DALYs), and estimated annual percentage changes (EAPCs) were analyzed. Future trends were predicted with Bayesian age-period-cohort (BAPC) and NORDPRED models. RESULTS: In 2019, there was a significant increase in DALY for both digestive and respiratory tract cancers compared to 1990. Meanwhile, ASIR increased slightly and ASDR decreased notably. In 2019, the global cancer burdens of respiratory and digestive tracts were 38568363.53 and 66912328.72 in DALY, 34.28 and 55.32 in ASIR, and 656.82 and 808.22 in ASDR per 100,000 population with changes of +54.63% and +43.93%, +2.92% and +5.65%, and -17.39% and -26.83% compared to those in 1990, respectively. Significant cross-regional differences in the cancer burdens were observed among the regions. Compared to four representative chronic diseases, the burden of cancers showed less remission and greater global inequalities. The burdens of both digestive and respiratory tract cancers were higher in males than in females in terms of the ASIR, ASDR, and DALY. The incidence and mortality rates of respiratory tract cancers were up to 3-4 times higher in males than in females, whereas the difference between male and female rates of digestive tract cancers was relatively smaller. The main risk factor associated with all kinds of digestive and respiratory tract cancers is tobacco, leading to 18.5 in ASDR and 3.38×107 in DALY for respiratory tract cancers; 8.29 in ASDR and 1.60×107 in DALY for digestive tract cancers, in 2019. Additionally, alcohol use contributes to most digestive and respiratory tract cancers (1.23/1.03 in ASDR and 1.60×106/2.57×106 in DALY for respiratory tract cancers; 4.19/3.82 in ASDR and 4.49×106/8.06×106 in DALY for digestive tract cancers), except for stomach cancer and tracheal, bronchus, and lung cancer. The cancer burdens of respiratory and digestive tracts are likely to decrease substantially between 2020 and 2044. For most metrics, except for the ASIR and male-to-female ratios of ASDR and ASDALY in digestive tract cancers, the worldwide variances of burden metrics have been decreasing in the past decades and will possibly maintain stable trends in the future. CONCLUSIONS: The epidemiology of respiratory and GI tract cancers has common features and individual characteristics that are reflected in geography, age characteristics, and risk factors. Current epidemiological status, future trends, and the globalization of these disease burdens are important factors for making scientific planning of resources to minimize the cancer burden metrics and their cross-regional inequalities.

A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study.

OBJECTIVE: To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance. METHODS: The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS: The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %. CONCLUSION: The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.

Combination of Contrast-enhanced FlAIR and Contrast-enhanced T1WI: A Quick and Efficient Method in Detecting Brain Metastases of Lung Cancers.

BACKGROUND: Some patients with suspected brain metastases (BM) could not tolerate longer scanning examinations according to the standardized MRI protocol. OBJECTIVE: The purpose of this study was to evaluate the clinical value of contrast-enhanced fast fluid-attenuated inversion recovery (CE FLAIR) imaging in combination with contrast-enhanced T1 weighted imaging (CE T1WI) in detecting BM of lung cancer and explore a quick and effective MRI protocol. MATERIAL AND METHODS: In 201 patients with lung cancers and suspected BM, T1WI and FLAIR were performed before and after administration of gadopentetate dimeglumine. Two radiologists reviewed pre- and post-contrast images to determine the presence of abnormal contrast enhancement or signal intensity and decided whether it was metastatic or not on CE T1WI (Group 1) and CE FLAIR (Group 2). The number, locations and features of abnormal findings in two groups were recorded. Receiver Operating Characteristic (ROC) analyses were conducted in three groups: Group 1, 2 and 3(combination of CE FLAIR and CE T1WI). RESULTS: A total of 714 abnormal findings were revealed, of which 672 were considered as BM and 42 nonmetastatic. Superficial and small metastases(≤10mm) in parenchyma and ependyma, leptomeningeal and non-expansive skull metastases were typically better seen on CE FLAIR. The areas under ROC in the three groups were 0.720,0.887 and 0.973, respectively. Group 3 was significantly better in diagnostic efficiency of BMs than Group 1 (p<0.0001) or Group 2 (p=0.0006). CONCLUSION: The combination of CE T1WI and CE FLAIR promotes diagnostic performance and results in better observation and characterization of BM in patients with lung cancers. It provides a quick and efficient way of detecting BM.

A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke.

BACKGROUND: Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judgments. OBJECTIVE: We propose a method combining deep learning and radiomics to alleviate the problem of large inter-observer variance in ASPECTS faced by physicians and assist them to improve the accuracy and comprehensiveness of the ASPECTS. METHODS: Our study used a brain region segmentation method based on an improved encoding-decoding network. Through the deep convolutional neural network, 10 regions defined for ASPECTS will be obtained. Then, we used Pyradiomics to extract features associated with cerebral infarction and select those significantly associated with stroke to train machine learning classifiers to determine the presence of cerebral infarction in each scored brain region. RESULTS: The experimental results show that the Dice coefficient for brain region segmentation reaches 0.79. Three radioactive features are selected to identify cerebral infarction in brain regions, and the 5-fold cross-validation experiment proves that these 3 features are reliable. The classifier trained based on 3 features reaches prediction performance of AUC = 0.95. Moreover, the intraclass correlation coefficient of ASPECTS between those obtained by the automated ASPECTS method and physicians is 0.86 (95% confidence interval, 0.56-0.96). CONCLUSIONS: This study demonstrates advantages of using a deep learning network to replace the traditional template registration for brain region segmentation, which can determine the shape and location of each brain region more precisely. In addition, a new brain region classifier based on radiomics features has potential to assist physicians in clinical stroke detection and improve the consistency of ASPECTS.

Backlogs in formal interpretation of radiology examinations: a pilot global survey.

OBJECTIVE: Anecdotal reports from imaging facilities globally suggest growing radiology interpretation reporting delays. This pilot study's primary aim was to estimate the backlog of formal interpretation of imaging examinations. METHODS: An online survey was distributed to radiologists globally to gather practice-specific characteristics, imaging volumes, and reporting for 3 types of examinations (brain/head CT scans, chest CT scans, and chest radiographs) at 4 time points: 7, 30, 90 days, and 6 months. RESULTS: We received responses from 49 radiologists in 16 countries on six continents. Unreported examinations (backlog) were present in thirty of 44 (68%) facilities. Backlogs for brain/head CT, chest CT, and chest radiographs were present in, respectively, 48%, 50%, and 59% of facilities at 7 days and 20%, 23%, and 32% of facilities at 6 months. When present, the mean proportion of backlog (range) at 7 days was 17% (1 to 96) for brain/head CT, 18% (3 to 82) for chest CT, and 22% (1 to 99) for chest radiographs. CONCLUSIONS: Our findings from this pilot study show a widespread global backlog in reporting common imaging examinations, and further research is needed on the issue and contributing factors.

Non-linear modifications enhance prediction of pathological response to pre-operative PD-1 blockade in lung cancer: A longitudinal hybrid radiological model.

Major pathologic remission (MPR, residual tumor <10%) is a promising clinical endpoint for prognosis analysis in patients with lung cancer receiving pre-operative PD-1 blockade therapy. Most of the current biomarkers for predicting MPR such as PD-L1 and tumor mutation burden (TMB) need to be obtained invasively. They cannot overcome the spatiotemporal heterogeneity or provide dynamic monitoring solutions. Radiomics and artificial intelligence (AI) models provide a practical tool enabling non-invasive follow-up observation of tumor structural information through high-throughput data analysis. Currently, AI-based models mainly focus on the single baseline scan or pipeline, namely sole radiomics or deep learning (DL). This work merged the delta-radiomics based on the slope of classic radiomics indexes within a time interval and the features extracted by deep networks from the subtraction between the baseline and follow-up images. The subtracted images describing the tumor changes were based on the transformation generated by registration. Stepwise optimization of components was performed by repeating experiments among various combinations of DL networks, registration methods, feature selection algorithms, and classifiers. The optimized model could predict MPR with a cross-validation AUC of 0.91 and an external validation AUC of 0.85. A core set of 27 features (eight classic radiomics, 15 delta-radiomics, one classic DL features, and three delta-DL features) was identified. The changes in delta-radiomics indexes during the treatment were fitted with mathematic models. The fitting results revealed that over half of the features were of non-linear dynamics. Therefore, non-linear modifications were made on eight features by replacing the original features with non-linear fitting parameters, and the modified model achieved an improved power. The dynamic hybrid model serves as a novel and promising tool to predict the response of lesions to PD-1 blockade, which implies the importance of introducing the non-linear dynamic effects and DL approaches to the original delta-radiomics in the future.

Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study.

BACKGROUND: Reliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and surgical decision-making. We aimed to develop and validate a dual-delta deep-learning and radiomics model based on pretreatment computed tomography (CT) image series to predict the STAS in patients with lung cancer. METHOD: Six hundred seventy-four patients with pre-surgery CT follow-up scans (with a minimum interval of two weeks) and primary lung cancer diagnosed by surgery were retrospectively recruited from three Chinese hospitals. The training cohort and internal validation cohort, comprising 509 and 76 patients respectively, were selected from Shanghai Chest Hospital; the external validation cohorts comprised 36 and 53 patients from two other centers, respectively. Four imaging signatures (classic radiomics features and deep learning [DL] features, delta-radiomics and delta-DL features) reflecting the STAS status were constructed from the pretreatment CT images by comprehensive methods including handcrafting, 3D views extraction, image registration and subtraction. A stepwise optimized three-step procedure, including feature extraction (by DL and time-base radiomics slope), feature selection (by reproducibility check and 45 selection algorithms), and classification (32 classifiers considered), was applied for signature building and methodology optimization. The interpretability of the proposed model was further assessed with Grad-CAM for DL-features and feature ranking for radiomics features. RESULTS: The dual-delta model showed satisfactory discrimination between STAS and non-STAS and yielded the areas under the receiver operating curve (AUCs) of 0.94 (95% CI, 0.92-0.96), 0.84 (95% CI, 0.82-0.86), and 0.84 (95% CI, 0.83-0.85) in the internal and two external validation cohorts, respectively, with interpretable core feature sets and feature maps. CONCLUSION: The coupling of delta-DL model with delta-radiomics features enriches information such as anisotropy of tumor growth and heterogeneous changes within the tumor during the radiological follow-up, which could provide valuable information for STAS prediction in primary lung cancer.

Joint two-stage convolutional neural networks for intracranial aneurysms detection on 3D TOF-MRA.

Objective. This study aims to develop a three-dimensional convolutional neural network utilizing computer-aided diagnostic technology to facilitate the detection of intracranial aneurysms and automatically assess their location and extent, thereby enhancing the efficiency of radiologists, and streamlining clinical workflows.Approach. A retrospective study was conducted, proposing a joint segmentation and classification network (JSCD-Net) that employs 3D time-of-flight magnetic resonance angiography images for preliminary detection of aneurysms and the minimization of false positives. Specifically, the U-Net++ network was utilized for pre-detection of aneurysms. This was followed by the creation of a multi-path network, co-trained with U-Net++ to correct the results of the first stage to further reduce the rate of false positives. Model effectiveness and robustness were evaluated using sensitivity and false positive analyses on internal and external datasets. A cross-validated free-response receiver operating characteristic curve was also plotted.Main results. JSCD-Net demonstrated a sensitivity of 91.2% (31 of 34; 95% CI: 77.0, 97.0) with an average of 3.55 false positives per scan on the internal test set. For the external test set, it identified 97.2% (70 of 72; 95% CI: 90.4, 99.2) of aneurysms with an average of 2.7 false positives per scan.Significance. When compared with the existing studies, the proposed model shows high sensitivity in detecting intracranial aneurysms with a reasonable number of false positives per case. This result emphasizes the model's potential as a valuable tool in aiding clinical diagnoses.

Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma.

OBJECTIVE: To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT). METHODS: Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram. RESULTS: The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P = 0.029 OR = 4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR = 1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model. CONCLUSION: The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.

Serum total cholesterol levels associated with immediate memory performance in patients with chronic schizophrenia.

Cognitive impairments are common in patients with schizophrenia. Changes in total cholesterol (TC) may be involved in the development of schizophrenia and associated with cognitive function. This study aimed to investigate differences in serum TC level and cognitive function between schizophrenia patients and healthy controls and explore the relationship between serum TC level and cognitive function in patients with schizophrenia. A total of 105 schizophrenia patients and 105 healthy controls were recruited. Results showed that patients with schizophrenia had significantly lower scores on the overall RBANS scale and subscales (i.e., immediate memory, language, attention, and delayed memory) than those of healthy controls. Pearson's correlation analyses showed that in patients with schizophrenia, serum TC levels were positively associated with RBANS subscale scores of immediate memory and language. Furthermore, multivariate regression analyses showed that serum TC level was positively associated with the immediate memory index in patients with schizophrenia. However, no significant association was found between serum TC level and RBANS score in the healthy control group. Our results suggest that elevated serum TC level may be related to improved cognitive function in patients with schizophrenia, especially that of immediate memory.

Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study.

OBJECTIVES: This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features. METHODS: The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results. RESULTS: The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035-1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911-18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438-5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583-0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806-0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808-0.896) (sensitivity = 74.3%, specificity = 85.8%). CONCLUSION: The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC. KEY POINTS: • The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583-0.699), 0.851 (95% CI 0.806-0.896), and 0.852 (95% CI 0.808-0.896). • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.

Time-of-Flight Intracranial MRA at 3 T versus 5 T versus 7 T: Visualization of Distal Small Cerebral Arteries.

Background Three-dimensional (3D) time-of-flight (TOF) MR angiography (MRA) at 7 T has been reported to have high image quality for visualizing small perforating vessels. However, B1 inhomogeneity and more physiologic considerations limit its applications. Angiography at 5 T may provide another choice for intracranial vascular imaging. Purpose To evaluate the image quality and cerebrovascular visualization of 5-T 3D TOF MRA for visualizing intracranial small branch arteries. Materials and Methods Participants (healthy volunteers or participants with a history of ischemic stroke undergoing intracranial CT angiography or MRA for identifying steno-occlusive disease) were prospectively included from September 2021 to November 2021. Each participant underwent 3-T, 5-T, and 7-T 3D TOF MRA with use of customized MR protocols within 48 hours. Radiologist scoring from 0 (invisible) to 3 (excellent) and quantitative assessment were obtained to evaluate the image quality. The Friedman test was used for comparison of characteristics derived from 3 T, 5 T, and 7 T. Results A total of 12 participants (mean age ± SD, 38 years ± 9; nine men) were included. Visualizations of the distal arteries and small vessels at 5-T TOF MRA were significantly higher than those at 3 T (median score: 3.0 vs 2.0, all P < .001 for distal segments and lenticulostriate artery; median score: 2.0 vs 0, P < .001 for pontine artery). The total length of small vessel branches detected at 5 T was larger than that at 3 T (5.1 m ± 0.7 vs 1.9 m ± 0.4; P < .001). However, there was no evidence of a significant difference compared with 7 T in either the depiction of distal segments and small vessel branches (average median score, 2.5; all P > .05) or the quantitative measurements (total length, 5.6 m ± 0.5; P = .41). Conclusion Three-dimensional time-of-flight MR angiography at 5 T presented the capability to provide superior visualization of distal large arteries and small vessel branches (in terms of subjective and quantitative assessment) to 3 T and had image quality similar to 7 T. © RSNA, 2022 Online supplemental material is available for this article. An earlier incorrect version appeared online. This article was corrected on September 14, 2022.

Sublethal heat treatment enhances lactic acid uptake in macrophages via MCT1, leading to reduced paraspeckle formation and a subsequent decrease in macrophage pyroptosis.

Introduction: Heat ablation is one of the key modalities in treating liver cancer, yet the residual cancer tissues suffering sublethal heat treatment possess a potential for increased malignancy. This study conducts a comprehensive analysis of cellular dynamics, metabolic shifts, and macrophage polarization within the tumor microenvironment following sublethal heat treatment. Methods: We observed significant acidification in tumor cell supernatants, attributed to increased lactic acid production. The study focused on how this pH shift, crucial in tumor progression and resistance, influences macrophage polarization, especially towards the M2 phenotype known for tumor-promoting functions. We also examined the upregulation of MCT1 expression post sublethal heat treatment and its primary role in lactic acid transport. Results: Notably, the study found minimal disparity in MCT1 expression between hepatocellular carcinoma patients and healthy liver tissues, highlighting the complexity of cancer biology. The research further revealed an intricate relationship between lactic acid, MCT1, and the inhibition of macrophage pyroptosis, offering significant insights for therapeutic strategies targeting the tumor immune environment. Post sublethal heat treatment, a reduction in paraspeckle under lactic acid exposure was observed, indicating diverse cellular impacts. Additionally, PKM2 was identified as a key molecule in this context, with decreased levels after sublethal heat treatment in the presence of lactic acid. Discussion: Collectively, these findings illuminate the intertwined mechanisms of sublethal heat treatments, metabolic alterations, and immune modulation in the tumor milieu, providing a deeper understanding of the complex interplay in cancer biology and treatment.

Gastric duplication cyst with ectopic pancreas in a teenager successfully resected by endoscopic submucosal dissection.

BACKGROUND: Gastric duplication cyst associated with ectopic pancreas is rare and we aimed to alert clinician to this congenital anomaly. CASE PRESENTATION: A 15-year-old girl presented with intermittent vomiting. Gastroscopy showed a submucosal tumor with an approximate diameter of 40 mm in the anterior wall of the gastric antrum. The lesion had a central umbilication and was diagnosed preliminarily as gastric ectopic pancreas with pseudocyst formation on the basis of its appearance. However, computed tomographic scan showed a thick-walled cystic lesion with an enhanced outline of the cystic wall in the antrum of stomach, suggestive of duplication cyst. Serum amylase was normal. Endoscopic ultrasonography revealed a solid-cystic lesion; the solid portion were inhomogeneously mixed with echoes, and had indistinct border to muscularis propria; the cystic portion had echogenic internal mucosal layer and distinct border to muscularis propria. Endoscopic submucosal dissection (ESD) was suggested for the patient to relieve symptoms and diagnose the lesion definitely. The operation procedure was uneventful and the solid-cystic lesion was resected completely. Histopathologic examination revealed that the solid portion was ectopic pancreas, and the cystic portion was gastric duplication cyst. After resection, the patient discharged successfully and neither symptoms nor tumors recurred during the 9 months follow-up period. CONCLUSIONS: This is the first case of a solid-cystic lesion with central umbilication in the stomach diagnosed as gastric duplication cyst associated with ectopic pancreas. ESD could be an optional treatment to provide a definitive diagnosis.

A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis.

In the USA, there were about 1 |806 |590 new cancer cases in 2020, and 606 520 cancer deaths are expected to have occurred in 2021. Lung cancer has become the leading cause of death from cancer in both men and women (Siegel et al., 2020). Clinical studies show that the five-year survival rate of lung cancer patients after early diagnosis and treatment intervention can reach 80%, compared with that of patients having advanced lung cancer. Thus, the early diagnosis of lung cancer is a key factor to reduce mortality.

Determination of the geographical origin of Tetrastigma hemsleyanum Diels & Gilg using an electronic nose technique with multiple algorithms.

Tetrastigma hemsleyanum Diels & Gilg, an herbal medicinal plant, is planted widely in bamboo forests in southern China to promote economic benefits. Volatile compounds (VOCs) of T. hemsleyanum from different geographical regions are difficult to identify in field forests. In this study, VOCs from leaf samples of different geographical origins were analyzed using an electronic nose with 10 different sensors. Principal component analysis (PCA), partial least-squares regression (PLS), hierarchical cluster analysis (HCA), and radial basis function (RBF) neural networks were used to determine differences among different local samples. The results demonstrated that PCA achieved an accurate discrimination percentage of 91.31% for different samples and HCA separated the samples into different groups. The RBF neural network was successfully applied to predict samples with no specified localities. T. hemsleyanum samples from geographically close regions tended to group together, whereas those from distant geographical regions showed obvious differences. These results indicate that an electronic nose is an effective tool for detecting VOCs and discriminating the geographical origins of T. hemsleyanum. This study provides insights for further studies on the fast detection of VOCs from plants and effect of forests and plant herbal medicines on improving air quality.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification.

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm.

Objective: We aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance. Methods: The clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test. Results: The R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model. Conclusion: In this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.