Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.

Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively. A deep learning neural network, using a multimodal ensemble approach based on CT imaging data before immunotherapy, was trained in the training cohort to predict survival, and an internal validation cohort was constructed to select the optimal ensemble model. Data from another cohort were used for external validation. The area under the receiver operating characteristic curve was analyzed to evaluate performance in predicting survival. Detailed clinicopathological data and peripheral blood prior to immunotherapy were collected for each patient. Univariate and multivariable logistic regression analysis of imaging models and clinicopathological variables was also applied to identify the independent predictors of survival. A nomogram based on multivariable logistic regression was constructed.

A total of 79 GC patients in the training cohort and 97 patients in the external validation cohort were enrolled in this study. A multi-model ensemble approach was applied to train a model to predict the 1-year survival of GC patients. Compared to individual models, the ensemble model showed improvement in performance metrics in both the internal and external validation cohorts. There was a significant difference in overall survival (OS) among patients with different imaging models based on the optimum cutoff score of 0.5 (HR = 0.20, 95 % CI: 0.10-0.37, P < 0.001). Multivariate Cox regression analysis revealed that the imaging models, PD-L1 expression, and lung immune prognostic index were independent prognostic factors for OS. We combined these variables and built a nomogram. The calibration curves showed that the C-index of the nomogram was 0.85 and 0.78 in the training and validation cohorts.

The deep learning model in combination with several clinical factors showed predictive value for survival in patients with unresectable GC receiving immunotherapy.

This study aimed to assess the diagnostic accuracy of multidetector contrast-enhanced computerised tomography (MDCT) and to establish a correlation between radiological and histopathological staging in upfront resected localised gastric cancers (GC).

All consecutive patients of resectable, localised GC who underwent upfront elective resection between 2010 and 2022 were included. The initial clinical staging determined during multidisciplinary meetings was compared with the pathological stage obtained after surgery. Subsequently, a retrospective, blinded review was conducted to assign a revised clinical staging, and accuracy was correlated.

The analysis of 138 patients revealed varying accuracy of MDCT in determining the T stage (66.9% for T1/T2, 64.6% for T3, and 87.2% for T4) and N stage (60.8% for N0, 63.7% for N1, and 83.2% for N2). The accuracy for stage group ranged from 71 to 78.65%. There was weak agreement observed between the T, N, and overall stage on clinicopathological correlation. However, a blinded radiology review by oncoradiologists resulted in improved accuracy, particularly in T1/T2 disease, and also improved pathological stage correlation.

Although MDCT is a valuable initial staging tool for gastric cancer, we found weak agreement between the clinical and the pathological stages in upfront resected gastric cancers. By implementing an expert radiology review and standardising scanning and reporting protocols, we can significantly improve the accuracy and correlation of MDCT with pathology, even for T1/T2 disease. This may help in better selecting patients for upfront surgery versus perioperative chemotherapy, especially in resource-constrained settings.

Our objective is to develop and validate a deep learning radiomics nomogram (DLRN) based on preoperative ultrasound images and clinical features, for predicting the malignancy of thyroid nodules with indeterminate cytology (Bethesda III).

Between June 2017 and June 2022, we conducted a retrospective study on 194 patients with surgically confirmed indeterminate cytology (Bethesda III) in our hospital. The training and internal validation cohorts were comprised of 155 and 39 patients, in a 7:3 ratio. To facilitate external validation, we selected an additional 80 patients from each of the remaining two medical centers. Utilizing preoperative ultrasound data, we obtained imaging markers that encompass both deep learning and manually radiomic features. After feature selection, we developed a comprehensive diagnostic model to evaluate the predictive value for Bethesda III benign and malignant cases. The model's diagnostic accuracy, calibration, and clinical applicability were systematically assessed.

The results showed that the prediction model, which integrated 512 DTL features extracted from the pre-trained Resnet34 network, ultrasound radiomics, and clinical features, exhibited superior stability in distinguishing between benign and malignant indeterminate thyroid nodules (Bethesda Class III). In the validation set, the AUC was 0.92 (95% CI: 0.831-1.000), and the accuracy, sensitivity, specificity, precision, and recall were 0.897, 0.882, 0.909, 0.882, and 0.882, respectively.

The comprehensive multidimensional data model based on deep transfer learning, ultrasound radiomics features, and clinical characteristics can effectively distinguish the benign and malignant indeterminate thyroid nodules (Bethesda Class III), providing valuable guidance for treatment selection in patients with indeterminate thyroid nodules (Bethesda Class III).

The goal of this paper is to compare the effectiveness of three deep learning models (2D, 3D, and 2.5D), three radiomics models(INTRA, Peri2mm, and Fusion2mm), and a combined model in predicting the spread through air spaces (STAS) in non-small cell lung cancer (NSCLC) to identify the optimal model for clinical surgery planning.

We included 480 patients who underwent surgery at four centers between January 2019 and August 2024, dividing them into a training cohort, an internal test cohort, and an external validation cohort. We extracted deep learning features using the ResNet50 algorithm. Least absolute shrinkage selection operator(Lasso) and spearman rank correlation were utilized to choose features. Extreme Gradient Boosting (XGboost) was used to execute deep learning and radiomics. Then, a combination model was developed, integrating both sources of data.

The combined model showed outstanding performance, with an area under the receiver operating characteristic curve (AUC) of 0.927 (95% CI 0.870 - 0.984) in the test set and 0.867 (95% CI 0.819 - 0.915) in the validation set. This model significantly distinguished between high-risk and low-risk patients and demonstrated significant advantages in clinical application.

The combined model is adequate for preoperative prediction of STAS in patients with stage T1 NSCLC, outperforming the other six models in predicting STAS risk.

To evaluate the value of a magnetic resonance imaging (MRI)-based deep learning radiomic nomogram (DLRN) for distinguishing intracranial solitary fibrous tumors (ISFTs) from angiomatous meningioma (AMs) and predicting overall survival (OS) for ISFT patients.

In total, 1090 patients from Beijing Tiantan Hospital, Capital Medical University, and 131 from Lanzhou University Second Hospital were categorized as primary cohort (PC) and external validation cohort (EVC), respectively. An MRI-based DLRN was developed in PC to distinguish ISFTs from AMs. We validated the DLRN and compared it with a clinical model (CM) in EVC. In total, 149 ISFT patients were followed up. We carried out Cox regression analysis on DLRN score, clinical characteristics, and histological stratification. Besides, we evaluated the association between independent risk factors and OS in the follow-up patients using Kaplan-Meier curves.

The DLRN outperformed CM in distinguishing ISFTs from AMs (area under the curve [95% confidence interval (CI)]: 0.86 [0.84-0.88] for DLRN and 0.70 [0.67-0.72] for CM, p < 0.001) in EVC. Patients with high DLRN score [per 1 increase; hazard ratio (HR) 1.079, 95% CI: 1.009-1.147, p = 0.019] and subtotal resection (STR) [per 1 increase; HR 2.573, 95% CI: 1.337-4.932, p = 0.004] were associated with a shorter OS. A statistically significant difference in OS existed between the high and low DLRN score groups with a cutoff value of 12.19 (p < 0.001). There is also a difference in OS between total excision (GTR) and STR groups (p < 0.001).

The proposed DLRN outperforms the CM in distinguishing ISFTs from AMs and can predict OS for ISFT patients.

The proposed MRI-based deep learning radiomic nomogram outperforms the clinical model in distinguishing ISFTs from AMs and can predict OS of ISFT patients, which could guide the surgical strategy and predict prognosis for patients.

Distinguishing ISFTs from AMs based on conventional radiological signs is challenging. The DLRN outperformed the CM in our study. The DLRN can predict OS for ISFT patients.

Cancer of unknown primary (CUP), a set of histologically confirmed metastases that cannot be identified or traced back to its primary despite comprehensive investigations, accounts for 2-5% of all malignancies. CUP is the fourth leading cause of cancer-related deaths worldwide, with a median overall survival (OS) of 3-16 months. CUP has long been challenging to diagnose principally due to the occult properties of primary site. In the current era of molecular diagnostics, advancements in methodologies based on cytology, histology, gene expression profiling (GEP), and genomic and epigenomic analysis have greatly improved the diagnostic accuracy of CUP, surpassing 90%. Our center conducted the world's first phase III trial and demonstrated improved progression-free survival and favorable OS by GEP-guided site-specific treatment of CUP, setting the foundation of site-specific treatment in first-line management for CUP. In this review, we detailed the epidemiology, etiology, pathogenesis, as well as the histologic, genetic, and clinical characteristics of CUP. We also provided an overview of the advancements in the diagnostics and therapeutics of CUP over the past 50 years. Moving forward, we propose optimizing diagnostic modalities and exploring further-line treatment regimens as two focus areas for future studies on CUP.

Early symptoms of hepatocellular carcinoma (HCC) are not obvious, and more than 70% of which does not receive radical hepatectomy, when first diagnosed. In recent years, molecular-targeted drugs combined with immunotherapy and other therapeutic methods have provided new treatment options for middle and advanced HCC (aHCC). Predicting the effect of targeted combined immunotherapy has become a hot topic in current research.

To explore the relationship between nodule enhancement in hepatobiliary phase and the efficacy of combined targeted immunotherapy for aHCC.

Data from 56 patients with aHCC for magnetic resonance imaging with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid were retrospectively collected. Signal intensity of intrahepatic nodules was measured, and the hepatobiliary relative enhancement ratio (RER) was calculated. Progression-free survival (PFS) of patients with high and low reinforcement of HCC nodules was compared. The model was validated using receiver operating characteristic curves. Univariate and multivariate logistic regression and Kaplan-Meier analysis were performed to explore factors influencing the efficacy of targeted immunization and PFS.

Univariate and multivariate analyses revealed that the RER, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and prognostic nutritional index were significantly associated with the efficacy of tyrosine kinase inhibitors combined with immunotherapy (P < 0.05). The area under the curve of the RER for predicting the efficacy of tyrosine kinase inhibitors combined with anti-programmed death 1 antibody in patients with aHCC was 0.876 (95% confidence interval: 0.781-0.971, P < 0.05), the optimal cutoff value was 0.904, diagnostic sensitivity was 87.5%, and specificity was 79.2%. Kaplan-Meier analysis showed that neutrophil-to-lymphocyte ratio < 5, platelet-to-lymphocyte ratio < 300, prognostic nutritional index < 45, and RER < 0.9 significantly improved PFS.

AHCC nodules enhancement in the hepatobiliary stage was significantly correlated with PFS. Imaging information and immunological indicators had high predictive efficacy for targeted combined immunotherapy and were associated with PFS.

Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC's heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding of GC by integrating molecular and imaging data. In this review, we summarize the current landscape of ML-based multiomics integration for GC, highlighting its role in precision diagnosis, prognosis prediction, and biomarker discovery for achieving personalized medicine.

Carcinoembryonic antigen (CEA), systemic immune-inflammation index(SII), and prognostic nutritional index (PNI) are diagnostic markers for cancer, but their combined significance in gastric cancer (GC) with lymph node metastasis remains unclear. The aim of this study was to evaluate the association between these serum biomarkers and lymph node metastasis in patients with GC.

Records of patients with GC were reviewed retrospectively. Univariate and multivariate logistic regression were performed to examine the association between tumor markers, serum biomarkers and lymph node metastasis in GC. Based on the results of multivariate regression, a nomogram was developed and verified.

Of the 395 patients aged 68.5 ± 9.1 years, 192 (48.6%) were diagnosed with lymphatic node metastasis. After adjusting for confounding factors, CEA (Odd ratio (OR):2.21; 95%CI: 1.17-3.81) and SII (OR:1.02; 95%CI: 1.01-1.04) was identified as significant risk factors, while PNI (OR:0.90; 95%CI: 0.85~0.96) was a protective factor for lymph node metastasis. The established nomogram by incorporating CEA, SII, PNI, differentiation, and tumor diameter can effectively predict lymph node metastasis in GC.

CEA, SII, PNI, differentiation, and tumor diameter were significantly associated with lymph node metastasis in patients with GC, and the combination of CEA, SII, PNI, differentiation, and tumor diameter has a better diagnostic value than either index alone.

This study was undertaken to develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) for predicting recurrence in patients with hepatocellular carcinoma (HCC) following postoperative adjuvant transarterial chemoembolization (PA-TACE).

In this retrospective study, 149 HCC patients (81 for training, 36 for internal validation, 32 for external validation) treated with PA-TACE were included in two medical centers. Multiparametric radiomics features were extracted from three MRI sequences. Least absolute shrinkage and selection operator (LASSO)-COX regression was utilized to select radiomics features. Optimal clinical characteristics selected by multivariate Cox analysis were integrated with Rad-score to develop a recurrence-free survival (RFS) prediction model. The model performance was evaluated by time-dependent receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), and calibration curve.

Fifteen optimal radiomic features were selected and the median Rad-score value was 0.434. Multivariate Cox analysis indicated that neutrophil-to-lymphocyte ratio (NLR) (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.1-2.1, P = 0.022) and tumor size (HR = 1.28, 95% CI: 1.1-1.5, P = 0.001) were the independent predictors of RFS after PA-TACE. A combined model was established by integrating Rad-score, NLR, and tumor size in the training cohort (C-index 0.822; 95% CI 0.805-0.861), internal validation cohort (0.823; 95% CI 0.771-0.876) and external validation cohort (0.846; 95% CI 0.768-0.924). The calibration curve exhibited a satisfactory correspondence.

A multiparametric MRI-based radiomics model can predict RFS of HCC patients receiving PA-TACE and a nomogram can be served as an individualized tool for prognosis.

Gastric cancer in young people is a global health burden, although it is less common than in other age groups. The use of biomarkers is developing in the diagnosis, treatment selection and prognosis of gastric cancer in young patients. In this bibliometric analysis we aim to evaluate the progress of this knowledge, trend topic development and scientific teams and countries involvements in the topic of biomarkers role in gastric cancer in young patients.

The data were obtained from Scopus (536 publications) for the period 1993-2024, all relevant metadata were analyzed using RStudio and Biblioshiny package to perform global trends and hotspots analysis.

Publication trends show a constant increase in interest in gastric cancer biomarkers used in the diagnosis and prognosis of gastric cancer in young patients (7.71% per year). The leading countries were China, USA, and Japan, between which there is strong and sustained collaborations. International co-authorship is relatively low (19.4%). The most prolific research centers were Sungkyunkwan University, Sun Yat-sen University, and Fudan University. The most productive researchers were Zhang X., Wang Y., and Li Y. Keywords analysis showed an increase in mentions of topics related to diagnostics (biomarkers, immunohistochemistry), personalized medicine and prognosis.

Bibliometric analysis of more than three decades research articles on gastric cancer biomarkers in young patients showed a steady increase, with strong contributions from leading countries and institutions, highlighting the growing focus on diagnostics, personalized medicine, and prognosis.

To address heterogeneity in prostate cancer (PCa) pathological grading, we developed an interpretable multimodal fusion model integrating 18F prostate-specific membrane antigen (18F-PSMA)-targeted positron emission tomography/computed tomography (18F-PSMA-PET/CT) imaging features with clinical variables for predicting post-surgical ISUP grade (psISUP ≥ 4 vs. < 4).

This retrospective study analyzed 222 patients with PCa (2020-2024) undergoing 18F-PSMA PET/CT. We constructed a deep transfer learning framework incorporating radiomic features from PET/CT and clinical parameters. Model performance was validated against three established methods and preoperative biopsy Gleason scores. Additionally, SHapley Additive exPlanations (SHAP) values elucidated feature contributions, and a radiomic nomogram was developed for clinical translation.

The fusion model achieved superior discrimination in psISUP grading (test set area under the curve (AUC) = 0.850, 95% confidence interval [CI] 0.769-0.932; validation set AUC = 0.833, 95% CI 0.657-1.000), significantly outperforming preoperative Gleason scores. SHAP analysis identified PSMA uptake heterogeneity and PSA density as key predictive features. The nomogram demonstrated clinical interpretability through visualised risk stratification.

Our deep learning-based multimodal fusion model enables accurate preoperative prediction of aggressive PCa pathology (ISUP ≥ 4), potentially optimising surgical planning and personalised therapeutic strategies. The interpretable framework enhances clinical trustworthiness in artificial intelligence-assisted decision-making.

This study aims to evaluate the effectiveness of combining transvaginal ultrasound (US)-based radiomics and deep learning model for the accurate differentiation between benign and malignant ovarian tumors in large-scale studies.

A multicenter retrospective study collected grayscale and color US images of ovarian tumors. Patients were divided into training, internal, and external validation groups. Models including a convolutional neural networks (CNN), optimal radiomics, and a combined model were constructed and evaluated for predictive performance using area under curve (AUC), sensitivity, and specificity. The DeLong test compared model AUCs with O-RADS and expert assessments.

3193 images from 2078 patients were analyzed. The CNN achieved AUCs of 0.970 (internal) and 0.959 (external), respectively. Optimal radiomic model achieved AUCs of 0.949 (internal) and 0.954 (external), respectively. The combined CNN-radiomics model attained the highest AUC of 0.977 (internal) and 0.972 (external), respectively, outperforming individual models, O-RADS, and expert methods (p < 0.05).

The combined CNN-radiomics model using transvaginal US images provides more accurate and reliable ovarian tumor diagnosis, enhancing malignancy prediction and offering clinicians a more precise diagnostic tool.

Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. We aimed to develop a model with multimodal features (MMF) using artificial intelligence (AI) approaches to enhance the performance of HCC detection.

A total of 1092 participants were enrolled from 16 centers. These participants were allocated into the training, internal validation, and external validation cohorts. Peripheral blood specimens were collected prospectively and subjected to mass cytometry analysis. Clinical and radiological data were obtained from electrical medical records. Various AI methods were employed to identify pertinent features and construct single-modal models with optimal performance. The XGBoost algorithm was utilized to amalgamate these models, integrating multimodal information and facilitating the development of a fusion model. Model evaluation and interpretability were demonstrated using the SHapley Additive exPlanations method.

We constructed the electronic health record, BioScore, RadiomicScore, and DLScore models based on clinical, radiological, and peripheral immunological features, respectively. Subsequently, these single-modal models were amalgamated to develop an all-in-one MMF model. The MMF model exhibited enhanced performance compared to models comprising only single-modal features in detecting HCC. This superiority in performance was confirmed through the internal and external validation cohorts, yielding area under the curve (AUC) values of 0.985 and 0.915, respectively. Additionally, the MMF model improved the detection ability in subpopulations of HCCs that were negative for alpha-fetoprotein and those with small size, with AUC values of 0.974 and 0.996, respectively.

Integrating MMF improved the performance of the model for HCC detection.

With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.

To explore the feasibility of Deep learning radiomics nomograms (DLRN) in predicting IDH genotype.

A total of 402 glioma patients from two independent centers were retrospectively included, and the data from center I was randomly divided into a training cohort (n = 239) and an internal validation cohort (n = 103) on a 7:3 basis. Center II served as an independent external validation cohort (n = 60). We developed a DLRN for IDH classification of gliomas based on T2 images. This hybrid model integrates deep learning features, radiomics features, and clinical features most relevant to IDH genotypes and finally classifies them using multivariate logistic regression analysis. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate the performance of the model and applied the DLRN score to the survival analysis of some of the follow-up glioma patients.

The proposed model had an area under the curve (AUC) of 0.98 in an externally validated cohort, and DLRN scores were significantly associated with the overall survival of glioma patients.

Deep learning radiomics nomograms performed well in non-invasively predicting IDH mutation status in gliomas, assisting stratified management and targeted therapy for glioma patients.

Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers. In recent years, an increasing number of studies suggest that artificial intelligence (AI) technology can effectively address clinical tasks related to upper gastrointestinal cancers. These studies mainly focus on four aspects: screening, diagnosis, treatment, and prognosis. In this review, we focus on the application of AI technology in clinical tasks related to upper gastrointestinal cancers. Firstly, the basic application pipelines of radiomics and deep learning in medical image analysis were introduced. Furthermore, we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric cancers. Finally, the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized, and explorations were conducted on the selection of AI algorithms in various scenarios, the popularization of early screening, the clinical applications of AI, and large multimodal models.

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.

Objectives: The accurate prediction of lymph node metastasis (LNM) and lymphovascular invasion (LVI) is crucial for determining treatment strategies for early gastric cancer (EGC). This study aimed to develop and validate a deep learning-based clinical decision support system (CDSS) to predict LNM including LVI in EGC using real-world data. Methods: A deep learning-based CDSS was developed by integrating endoscopic images, demographic data, biopsy pathology, and CT findings from the data of 2927 patients with EGC across five institutions. We compared a transformer-based model to an image-only (basic convolutional neural network (CNN)) model and a multimodal classification (CNN with random forest) model. Internal testing was conducted on 449 patients from the five institutions, and external validation was performed on 766 patients from two other institutions. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), probability density function, and clinical utility curve. Results: In the training, internal, and external validation cohorts, LNM/LVI was observed in 379 (12.95%), 49 (10.91%), 15 (9.09%), and 41 (6.82%) patients, respectively. The transformer-based model achieved an AUC of 0.9083, sensitivity of 85.71%, and specificity of 90.75%, outperforming the CNN (AUC 0.5937) and CNN with random forest (AUC 0.7548). High sensitivity and specificity were maintained in internal and external validations. The transformer model distinguished 91.8% of patients with LNM in the internal validation dataset, and 94.0% and 89.1% in the two different external datasets. Conclusions: We propose a deep learning-based CDSS for predicting LNM/LVI in EGC by integrating real-world data, potentially guiding treatment strategies in clinical settings.

The necessity of computed tomography (CT) scan for detecting potential lymph node metastasis (LNM) in early esophageal squamous cell carcinoma (ESCC) before endoscopic and surgical treatments is under debate.

Patients with histologically proven ESCC limited to the mucosa or submucosa were examined retrospectively. Diagnostic performance of CT for detecting LNM was analyzed by comparing original CT reports with pathology reports. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

A total of 625 patients from three tertiary referral hospitals were included. The rate of pathologically confirmed LNM was 12.5%. Based on original CT reports, the sensitivity, specificity, accuracy, PPV, and NPV of CT to determine LNM in T1 ESCC were 41.0%, 83.2%, 77.9%, 25.8%, and 90.8% respectively. For mucosal cancers (T1a), these parameters were 50.0%, 81.7%, 80.9%, 6.8%, and 98.4%, respectively. For submucosal cancers (T1b), they were 40.0%, 85.0%, 75.0%, 43.0%, and 83.3%, respectively. Additionally, the diagnostic performance of CT for LNM was relatively better for ESCC in the lower esophagus. Pathologically, 69.2% of patients with LNM did not exhibit lymphovascular invasion (LVI), and the sensitivity of CT for recognizing LNM in these patients (33.3%) was lower than those with LVI (58.3%).

Computed tomography can detect nearly half of the LNM cases in early ESCC with high specificity. The performance of CT further improved in LNM cases with LVI. Therefore, we conclude that routine preoperative CT for the assessment of potential LNM risk in patients with early ESCC is necessary.

The diagnosis of lymph node metastasis (LNM) is essential for colorectal cancer (CRC) treatment. The primary method of identifying LNM is to perform frozen sections and pathologic analysis, but this method is labor-intensive and time-consuming. Therefore, combining intraoperative fluorescence imaging with deep learning (DL) methods can improve efficiency. The majority of recent studies only analyze uni-modal fluorescence imaging, which provides less semantic information. In this work, we mainly established a multi-modal fluorescence imaging feature fusion prediction (MFI-FFP) model combining white light, fluorescence, and pseudo-color imaging of lymph nodes for LNM prediction. Firstly, based on the properties of various modal imaging, distinct feature extraction networks are chosen for feature extraction, which could significantly enhance the complementarity of various modal information. Secondly, the multi-modal feature fusion (MFF) module, which combines global and local information, is designed to fuse the extracted features. Furthermore, a novel loss function is formulated to tackle the issue of imbalanced samples, challenges in differentiating samples, and enhancing sample variety. Lastly, the experiments show that the model has a higher area under the receiver operating characteristic (ROC) curve (AUC), accuracy (ACC), and F1 score than the uni-modal and bi-modal models and has a better performance compared to other efficient image classification networks. Our study demonstrates that the MFI-FFP model has the potential to help doctors predict LNM and shows its promise in medical image analysis.

To investigate the diagnostic performance of Node Reporting and Data System (Node-RADS) combined with computed tomography (CT) radiomics for assessing nonenlargement regional lymph nodes in gastric cancer (GC).

Preoperative CT images were retrospectively collected from 376 pathologically confirmed of gastric adenocarcinoma from January 2019 to December 2023, with 605 lymph nodes included for analysis. They were divided into training (n = 362) and validation (n = 243) sets. Radiomics features were extracted from venous-phase, and the radiomics score was obtained. Clinical information, CT parameters, and Node-RADS classification were collected. A combined model was built using machine-learning approach and tested in validation set using receiver operating characteristic curve analysis. Further validation was conducted in different subgroups of lymph node short-axis diameter (SD) range.

Node-RADS score, SD, maximum diameter of thickness of tumor, and radiomics were identified as the most predictive factors. The results demonstrated that the integrated model combining SD, maximum diameter of thickness of tumor, Node-RADS, and radiomics outperformed the model excluding radiomics, yielding an area under the receiver operating characteristic curve of 0.82 compared with 0.79, with a statistically significant difference ( P  < 0.001). Subgroup analysis based on different SDs of lymph nodes also revealed enhanced diagnostic accuracy when incorporating the radiomics score for the 4- to 7.9-mm subgroups, all P  < 0.05. However, for the 8- to 9.9-mm subgroup, the combination of the radiomics did not significantly improve the prediction, with an area under the receiver operating characteristic curve of 0.85 versus 0.85, P  = 0.877.

The integration of radiomics scores with Node-RADS assessments significantly enhances the accuracy of lymph node metastasis evaluation for GC. This combined model is particularly effective for lymph nodes with smaller standard deviations, yielding a marked improvement in diagnostic precision.

The findings of this study indicate that a composite model, which incorporates Node-RADS, radiomics features, and conventional parameters, may serve as an effective method for the assessment of nonenlarged lymph nodes in GC.

To determine whether intratumoral and peritumoral radiomics derived from dual-phase contrast-enhanced CT imaging could predict lymph node metastasis (LNM) in gastric cancer.

Patients with gastric cancer from January 2017 to January 2022 were retrospectively collected and were randomly divided into training cohort (n = 287) and test cohort (n = 121) with a ratio of 7: 3. Clinical features and traditional radiological features were analyzed to construct clinical model. Radiomics features based on intratumoral (ITV) and peritumoral volumetric (PTV) regions of the tumor were extracted and screened to construct radiomics models. Clinical-radiomics combined model was constructed by the most predictive radiomics features and clinical independent predictors. The correlation between LNM predicted by the best model and 2-year disease-free survival (DFS) was evaluated by the Kaplan-Meier analysis.

CT-LNM and CT-T stage were independent predictors of LNM. Compared with other radiomics models, ITV + PTV on atrial and venous phase (ITV + PTV-AP + VP) radiomics model presented moderate AUCs of 0.679 and 0.670 in the training cohort and validation cohort, respectively. Among the models, clinical-radiomics combined model achieved the highest AUC of 0.894 and 0.872 in the training and test cohorts, and 0.744 and 0.784 in the T1-2 and T3-4 subgroups, respectively. Clinical-radiomics combined model based LNM could stratify patients into high-risk and low-risk groups, and 2-year DFS of high-risk group was significantly lower than that of low-risk group (p < 0.001).

Clinical-radiomics combined model integrating CT-LNM, CT-T stage, and ITV-PTV-AP + VP radiomics features could predict LNM, and this combined model based LNM was associated with 2-year DFS.

To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high proliferation and low proliferation groups of PCNSL.

83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman's correlation analysis. The Kaplan-Meier method and log-rank test were used to compare the survival rates of the high proliferation and low proliferation groups. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. DeLong test was utilized to compare the differences of models.

Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966-0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895-0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953-0.980). The differences in PFS (p = 0.016) and OS (p = 0.014) between the low and high proliferation groups were statistically significant. The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving an AUC of 0.828.

rDWImean, rADCmean and rT1-CEmean were correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.

Upper gastrointestinal cancer (UGC) sometimes metastasizes to the splenic hilum lymph node (SHLN). However, surgical removal of SHLN is technically difficult, and the risk of postoperative complications is high. Although there are models that predict SHLN metastasis, they usually only provide point estimates of risk, and there is a lack of sufficient information. To address this issue, we aimed to develop a Bayesian logistic regression model called Bayes-SHLNM. The performance of the models was compared with that of the frequentist logistic regression (FLR) model as a benchmark, and the posterior probability distribution (PPD) was shown individually. The performance of Bayes-SHLNM was equivalent to that of the FLR model, and the PPD for each case was visualized as the uncertainty. These results indicate that the Bayes-SHLNM model has the potential to be used as a decision support system in clinical settings where uncertainty is high.

This systematic literature review examines the application of Artificial Intelligence (AI) in the diagnosis, treatment, and follow-up of metastatic gastrointestinal cancers.

The databases PubMed, Scopus, Embase (Ovid), and Google Scholar were searched for published articles in English from January 2010 to January 2022, focusing on AI models in metastatic gastrointestinal cancers.

forty-six studies were included in the final set of reviewed papers. The critical appraisal and data extraction followed the checklist for systematic reviews of prediction modeling studies. The risk of bias in the included papers was assessed using the prediction risk of bias assessment tool.

AI techniques, including machine learning and deep learning models, have shown promise in improving diagnostic accuracy, predicting treatment outcomes, and identifying prognostic biomarkers. Despite these advancements, challenges persist, such as reliance on retrospective data, variability in imaging protocols, small sample sizes, and data preprocessing and model interpretability issues. These challenges limit the generalizability, clinical application, and integration of AI models.

A predictive model was developed based on enhanced computed tomography (CT), laboratory test results, and pathological indicators to achieve the convenient and effective prediction of single lymph node metastasis (LNM) in gastric cancer.

Sixty-six consecutive patients (235 regional lymph nodes) with pathologically confirmed gastric cancer who underwent surgery at our hospital between December 2020 and November 2021 were retrospectively reviewed. They were randomly allocated to training (n = 38, number of lymph nodes = 119) and validation (n = 28, number of lymph nodes = 116) datasets. The clinical data, laboratory test results, enhanced CT characteristics, and pathological indicators from gastroscopy-guided needle biopsies were obtained. Multivariable logistic regression with generalised estimation equations (GEEs) was used to develop a predictive model for LNM in gastric cancer. The predictive performance of the model developed using the training and validation datasets was validated using receiver operating characteristic curves.

Lymph node enhancement pattern, Ki67 level, and lymph node long-axis diameter were independent predictors of LNM in gastric cancer (p < 0.01). The GEE-logistic model was associated with LNM (p = 0.001). The area under the curve and accuracy of the model, with 95% confidence intervals, were 0.944 (0.890-0.998) and 0.897 (0.813-0.952), respectively, in the training dataset and 0.836 (0.751-0.921) and 0.798 (0.699-0.876), respectively, in the validation dataset.

The predictive model constructed based on lymph node enhancement pattern, Ki67 level, and lymph node long-axis diameter exhibited good performance in predicting LNM in gastric cancer and should aid the lymph node staging of gastric cancer and clinical decision-making.

To develop a nomogram based on liver CT and clinical features to preoperatively predict lung metastasis (LM) secondary to hepatic alveolar echinococcosis (HAE).

A total of 291 consecutive HAE patients from Institution A undergoing preoperative abdominal contrast-enhanced CT and chest unenhanced CT were retrospectively reviewed, and were randomly divided into the training and internal validation sets at the 7:3 ratio. 82 consecutive patients from Institution B were enrolled as an external validation set. A nomogram was constructed based on the significant CT and clinical features of HAE from the training set selected by univariable and multivariable analyses to predict LM, and its predictive accuracy was assessed by area under the receiver operating characteristic curve (AUC) and Brier score. Decision-curve analysis was applied to evaluate the clinical effectiveness. This nomogram was verified in two independent validation sets.

Eosinophil (odds ratio [OR] = 9.60; 95 % confidence interval [CI]: 1.80-51.11), lesion size (OR = 1.02; 95 %CI: 1.01-1.04), and moderate-severe invasion of inferior vena cava (IVC) (OR = 5.57; 95 %CI: 1.82-17.10) were independently associated with LM (all P-values < 0.05). The nomogram based on the three independent predictors displayed AUCs of 0.875 (95 %CI, 0.824-0.927), 0.872 (95 %CI, 0.787-0.957) and 0.836 (95 %CI, 0.729-0.943), and Brier score of 0.105, 0.1 and 0.118 in the training, internal validation and external validation sets, respectively. Decision-curve analysis showed good clinical utility.

A nomogram based on eosinophil, lesion size and moderate-severe invasion of IVC showed good ability and accuracy for preoperative prediction of LM due to HAE.

Early recurrence in patients with locally advanced gastric cancer (LAGC) portends aggressive biological characteristics and a dismal prognosis. Predicting early recurrence may help determine treatment strategies for LAGC. The goal is to develop a deep learning model for early recurrence prediction (DLER) based on preoperative multiphase computed tomography (CT) images and to further explore the underlying biological basis of the proposed model.

In this retrospective study, 620 LAGC patients from January 2015 to March 2023 were included in three medical centers and The Cancer Image Archive (TCIA). The DLER model was developed using DenseNet169 and multiphase 2.5D CT images, and then crucial clinical factors of early recurrence were integrated into the multilayer perceptron (MLP) classifier model (DLER MLP ). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were applied to measure the performance of different models. The log-rank test was used to analyze survival outcomes. The genetic analysis was performed using RNA-sequencing data from TCIA.

Using the MLP classifier combined with clinical factors, DLER MLP showed higher performance than DLER and clinical models in predicting early recurrence in the internal validation set (AUC: 0.891 vs. 0.797, 0.752) and two external test sets: test set 1 (0.814 vs. 0.666, 0.808) and test set 2 (0.834 vs. 0.756, 0.766). Early recurrence-free survival, disease-free survival, and overall survival can be stratified using the DLER MLP (all P < 0.001). High DLER MLP score is associated with upregulated tumor proliferation pathways (WNT, MYC, and KRAS signaling) and immune cell infiltration in the tumor microenvironment.

The DLER MLP based on CT images was able to predict early recurrence of patients with LAGC and served as a useful tool for optimizing treatment strategies and monitoring.

An increasing number of nasopharyngeal carcinoma (NPC) patients benefit from immunotherapy with chemotherapy as an induction treatment. Currently, there isn't a reliable method to assess the efficacy of this regimen, which hinders informed decision-making for follow-up care.

To establish and evaluate a model for predicting the efficacy of programmed death-1 (PD-1) inhibitor combined with GP (gemcitabine and cisplatin) induction chemotherapy based on deep learning features (DLFs) and radiomic features.

Ninety-nine patients diagnosed with advanced NPC were enrolled and randomly divided into training set and test set in a 7:3 ratio. From MRI scans, DLFs and conventional radiomic characteristics were recovered. The random forest algorithm was employed to identify the most valuable features. A prediction model was then created using these radiomic characteristics and DLFs to determine the effectiveness of PD-1 inhibitor combined with GP chemotherapy. The model's performance was assessed using Receiver Operating Characteristic (ROC) curve analysis, area under the curve (AUC), accuracy (ACC), and negative predictive value (NPV).

Twenty-one prediction models were constructed. The Tf_Radiomics+Resnet101 model, which combines radiomic features and DLFs, demonstrated the best performance. The model's AUC, ACC, and NPV values in the training and test sets were 0.936 (95%CI: 0.827-1.0), 0.9, and 0.923, respectively.

The Tf_Radiomics+Resnet101 model, based on MRI and Resnet101 deep learning, shows a high ability to predict the clinically complete response (cCR) efficacy of PD-1 inhibitor combined with GP in advanced NPC. This model can significantly enhance the treatment management of patients with advanced NPC.

The expansion of function-preserving surgery became possible due to a more profound understanding of gastric cancer (GC), and T1N + or T2N + gastric cancer patients might be potential beneficiaries. However, ways to evaluate the possibility of function-preserving pylorus surgery are still unknown.

A total of 288 patients at Renji Hospital and 58 patients at Huadong Hospital, pathologically diagnosed with gastric cancer staging at T1 and T2 with tumors located in the upper two-thirds of the stomach, were retrospectively enrolled from March 2015 to October 2022. Tumor regions of interest (ROIs) were manually delineated on bi-phase CT images, and a nomogram was built and evaluated.

The radiomic features distributed differently between positive and negative pLNm groups. Two radiomic signatures (RS1 and RS2) and one clinical signature were constructed. The radiomic signatures exhibited good performance for discriminating pLNm status in the test set. The three signatures were then combined into an integrated nomogram (IN). The IN showed good discrimination of pLNm in the Renji cohort (AUC 0.918) and the Huadong cohort (AUC 0.649). The verification models showed high values.

For GC patients with T1 and T2 tumors located in the upper two-thirds of the stomach, a nomogram was successfully built for predicting pylorus lymph node metastasis, which would guide the surgical indication extension of conservative gastrectomies.

The methyltransferase gene family is known for its diverse biological functions and critical role in tumorigenesis. This study aimed to identify these family genes in common gastrointestinal (GI) cancers using comprehensive methodologies.

Gene identification involved analysis of scientific literature and insights from The Cancer Genome Atlas (TCGA) database. RNA sequencing (RNA-seq) data for colon, gastric, pancreatic, esophageal, and liver cancers were collected, processed, and normalized. Differential expression analysis was conducted using R software with the Limma package. Additionally, real-time PCR analysis was performed on 30 tumor and 30 normal tissue samples from patients with colon and gastric cancer. Pathway analysis was conducted via the EnrichR web tool, while survival analysis used Cox regression methods, and biomarker potential was assessed with the pROC package. Prognostic significance was evaluated by examining associations between gene expression, patient survival, and recurrence rates. The study also investigated diagnostic potential through receiver operating characteristic (ROC) analysis, and assessed how small molecules affect gene expression, with implications for drug resistance and sensitivity, analyzed via CCLE and GDSC datasets.

Findings revealed METTL5 overexpression in colon, liver, esophagus, and pancreas cancers, while METTL7A was underexpressed in gastric, esophagus, liver, and colon cancers. METTL7B expression varied, being higher in gastric and esophagus cancers but lower in liver and colon cancers. Enrichment analysis identified pathways related to these genes, and survival analysis associated altered METTL7A and METTL5 expressions with poor prognosis and higher recurrence rates.

These findings suggest that METTL genes could serve as predictive biomarkers in GI cancers, offering potential implications for patient prognosis and treatment response.

Gastric cancer (GC) remains a prevalent and preventable disease, yet accurate early diagnostic methods are lacking. Exosome non-coding RNAs (ncRNAs), a type of liquid biopsy, have emerged as promising diagnostic biomarkers for various tumours. This study aimed to identify a serum exosome ncRNA feature for enhancing GC diagnosis.

Serum exosomes from patients with GC (n=37) and healthy donors (n=20) were characterised using RNA sequencing, and potential biomarkers for GC were validated through quantitative reverse transcription PCR (qRT-PCR) in both serum exosomes and tissues. A combined diagnostic model was developed using LASSO-logistic regression based on a cohort of 518 GC patients and 460 healthy donors, and its diagnostic performance was evaluated via receiver operating characteristic curves.

RNA sequencing identified 182 candidate biomarkers for GC, of which 31 were validated as potential biomarkers by qRT-PCR. The combined diagnostic score (cd-score), derived from the expression levels of four long ncRNAs (RP11.443C10.1, CTD-2339L15.3, LINC00567 and DiGeorge syndrome critical region gene (DGCR9)), was found to surpass commonly used biomarkers, such as carcinoembryonic antigen, carbohydrate antigen 19-9 (CA19-9) and CA72-4, in distinguishing GC patients from healthy donors across training, testing and external validation cohorts, with AUC values of 0.959, 0.942 and 0.949, respectively. Additionally, the cd-score could effectively identify GC patients with negative gastrointestinal tumour biomarkers and those in early-stage. Furthermore, molecular biological assays revealed that knockdown of DGCR9 inhibited GC tumour growth.

Our proposed serum exosome ncRNA feature provides a promising liquid biopsy approach for enhancing the early diagnosis of GC.

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.