Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge, necessitating early detection, timely diagnosis, and prompt treatment to improve patient outcomes. Endoscopic examination plays a pivotal role in this regard. However, despite the availability of various endoscopic techniques, certain limitations can result in missed or misdiagnosed ESCCs. Currently, artificial intelligence (AI)-assisted endoscopic diagnosis has made significant strides in addressing these limitations and improving the diagnosis of ESCC and precancerous lesions. In this review, we provide an overview of the current state of AI applications for endoscopic diagnosis of ESCC and precancerous lesions in aspects including lesion characterization, margin delineation, invasion depth estimation, and microvascular subtype classification. Furthermore, we offer insights into the future direction of this field, highlighting potential advancements that can lead to more accurate diagnoses and ultimately better prognoses for patients.
Humans ; Artificial Intelligence ; Esophageal Squamous Cell Carcinoma/diagnosis* ; Esophageal Neoplasms/diagnosis* ; Precancerous Conditions/diagnosis*

The early diagnosis and precise treatment of esophageal squamous cell carcinoma (ESCC) hold significant clinical value in improving patient survival rate. Current diagnostic methods for early-stage ESCC primarily rely on invasive procedures and endoscopy, which can cause discomfort and financial burden for patients. Therefore, non-invasive biomarkers with high sensitivity and specificity present a more suitable alternative for early tumor diagnosis. Tumor associated autoantibodies (TAAb), identified as potential biomarkers, have considerable clinical implications for the early diagnosis, treatment monitoring, and prognosis assessment of ESCC. Here in we aim to summarize recent research on ESCC-related autoantibodies, including their background, types and development, analyze the potential of those autoantibodies in clinical diagnosis, treatment monitoring, and prognosis assessment, and also discuss the limitations of existing research and future directions. The goal is to provide a theoretical foundation for the early diagnosis and personalized treatment of ESCC.
Humans ; Autoantibodies/immunology* ; Esophageal Neoplasms/therapy* ; Esophageal Squamous Cell Carcinoma/immunology* ; Biomarkers, Tumor/immunology* ; Prognosis ; Carcinoma, Squamous Cell/diagnosis* ; Animals

OBJECTIVES: To develop a risk prediction model for the transformation of chronic atrophic gastritis to high-grade intraepithelial neoplasia (HGIN) based on traditional Chinese medicine (TCM) syndrome patterns. METHODS: Clinical data of 201 chronic atrophic gastritis patients who visited the Second People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine and Dong'erhuan Branch between January 2022 and March 2023 were retrospectively analyzed, including 32 patients with HGIN (HGIN group) and 169 patients with moderate and severe chronic atrophic gastritis (non-HGIN group). The information of demographic characteristics, dietary habits, lifestyle factors, social and psychosocial factors, family history of tumors, medical history and comorbidities, long-term medication, endoscopic findings, histopathological examination results, as well as TCM syndrome types were collected. Potential HGIN risk factors were screened using LASSO regression, and the significant risk factors for establishing an HGIN risk prediction model were identified using logistic regression analysis. The final model was visually presented using a nomogram, and its diagnostic performance was evaluated through receiver operating characteristic curve analysis. RESULTS: Spleen-stomach Qi deficiency was the most common TCM syndrome in both HGIN and non-HGIN groups. LASSO-logistic regression model analysis showed that heavy alcohol consumption (X1), syndrome of static blood in stomach collaterals (X2), low-grade intraepithelial neoplasia (X3), high-salt diet (X4), and age (X5) were independent risk factors related to the occurrence of HGIN, and the predictive model was ln［P/(1－P)］=2.159X1+2.230X2+1.664X3+2.070X4+0.122X5- 11.096. The model demonstrated good discriminative ability, calibration, and goodness-of-fit, with area under the curve values of 0.940 and 0.891 in the training and validation sets, respectively. CONCLUSIONS The TCM syndrome of static blood in stomach collaterals shows correlation with the transformation from chronic atrophic gastritis to HGIN. The HGIN prediction model based on TCM syndrome patterns developed in the study demonstrates potential value in clinical application.
Humans ; Gastritis, Atrophic/diagnosis* ; Medicine, Chinese Traditional ; Retrospective Studies ; Female ; Male ; Middle Aged ; Stomach Neoplasms/diagnosis* ; Adult ; Risk Factors ; Carcinoma in Situ/diagnosis* ; Aged ; Nomograms ; Chronic Disease ; Logistic Models

Small cell lung cancer (SCLC) is a highly malignant disease that has garnered significant attention in terms of treatment modalities and course management. Gaining an understanding of the clinical characteristics of SCLC, acquiring proficiency in screening, diagnosis, and treatment methods for this condition, as well as promptly addressing any adverse reactions to treatment are essential foundations for developing a scientific and rational pathological management plan for SCLC. By utilizing an intelligent whole course follow-up management platform, dynamic follow-up, timely warnings, and early interventions can enable high-quality whole life cycle management. This article aims to review the current treatment landscape of SCLC while exploring the challenges associated with implementing a comprehensive process-oriented management approach. The goal is to provide valuable insights for better managing SCLC patients and ultimately improving their quality of life and prognosis.
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Humans ; Small Cell Lung Carcinoma/diagnosis* ; Lung Neoplasms/diagnosis* ; Quality of Life ; Follow-Up Studies

BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic factors in some underdeveloped areas, some patients cannot undergo traditional genetic testing. The aim of this study is to establish a machine learning (ML) model using non-invasive peripheral blood markers to explore the biomarkers closely related to EGFR mutation status in NSCLC and evaluate their potential prognostic value. METHODS: 2642 lung cancer patients who visited Jiangsu Cancer Hospital from November 2016 to May 2023 were retrospectively enrolled and finally 175 NSCLC patients with complete follow-up data were included in the study. The ML model was constructed based on peripheral blood indicators and divided into training set and test set according to the ratio of 8:2. Unsupervised learning algorithms were used for clustering blood features and mutual information method for feature selection, and an ensemble learning algorithm based on Shapley value was designed to calculate the contribution of each feature to the model prediction result. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model. RESULTS: Through the feature extraction and contribution analysis of the predictive results of the interpretable ML model based on the Shapley value, the top ten indicators with the highest contribution were: pathological type, phosphorus, eosinophils, monocyte count, activated partial thromboplastin time, potassium, total bilirubin, sodium, eosinophil percentage, and total cholesterol. The area under the curve (AUC) of the model was 0.80. In addition, patients with hyponatremia and squamous cell carcinoma group had a poor prognosis (P<0.05). CONCLUSIONS The interpretable model constructed in this study provides a new approach for the prediction of EGFR mutation status in NSCLC patients, which provides a scientific basis for the diagnosis and treatment of patients who cannot undergo genetic testing.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Machine Learning ; Lung Neoplasms/diagnosis* ; Male ; Female ; Mutation ; Middle Aged ; ErbB Receptors/genetics* ; Prognosis ; Aged ; Retrospective Studies ; Adult ; Biomarkers, Tumor/genetics*

Accurate staging is the fundamental basis for the treatment and prognosis of non-small cell lung cancer (NSCLC), and whether the tumor involves the pleura or chest wall is a critical aspect in assessing the staging of peripheral lung cancer. Imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US) and positron emission tomography (PET) are widely used to determine pleural invasion in NSCLC. There has been an increasing number of studies evaluating whether NSCLC invades the pleura and the extent of such invasion. This article provides a review of the staging and the imaging diagnostic criteria of pleural invasion, aiming to offer references for peers in the precise diagnosis of pleural or chest wall invasion.
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Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Lung Neoplasms/diagnosis* ; Thoracic Wall/diagnostic imaging* ; Pleura/diagnostic imaging* ; Neoplasm Invasiveness ; Tomography, X-Ray Computed

Oligometastasis represents a transitional state between early localized disease and widespread metastasis, characterized by limited tumor burden and distinct tumor biological behavior. Due to the relatively restricted number of metastatic lesions and involved organs, aggressive systemic therapy combined with local consolidative therapy offers potential for cure. With rapid advancements in molecular targeted therapies and immunotherapy, comprehensive management of oligometastatic non-small cell lung cancer (NSCLC) has gained increasing attention. This review summarizes the definition of NSCLC oligometastasis, recent therapeutic progress, and existing challenges, aiming to provide insights for clinical diagnosis and treatment strategies.
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Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Lung Neoplasms/diagnosis* ; Neoplasm Metastasis

Objective:To development a deep learning（DL） model based on conventional MRI for automatic segmentation and differential diagnosis of nasopharyngeal carcinoma（NPC） and nasopharyngeal lymphoma（NPL）. Methods:The retrospective study included 142 patients with NPL and 292 patients with NPC who underwent conventional MRI at Renmin Hospital of Wuhan University from June 2012 to February 2023. MRI from 80 patients were manually segmented to train the segmentation model. The automatically segmented regions of interest（ROIs） formed four datasets: T1 weighted images（T1WI）, T2 weighted images（T2WI）, T1 weighted contrast-enhanced images（T1CE）, and a combination of T1WI and T2WI. The ImageNet-pretrained ResNet101 model was fine-tuned for the classification task. Statistical analysis was conducted using SPSS 22.0. The Dice coefficient loss was used to evaluate performance of segmentation task. Diagnostic performance was assessed using receiver operating characteristic（ROC） curves. Gradient-weighted class activation mapping（Grad-CAM） was imported to visualize the model's function. Results:The DICE score of the segmentation model reached 0.876 in the testing set. The AUC values of classification models in testing set were as follows: T1WI: 0.78（95%CI 0.67-0.81）, T2WI: 0.75（95%CI 0.72-0.86）, T1CE: 0.84（95%CI 0.76-0.87）, and T1WI+T2WI: 0.93（95%CI 0.85-0.94）. The AUC values for the two clinicians were 0.77（95%CI 0.72-0.82） for the junior, and 0.84（95%CI 0.80-0.89） for the senior. Grad-CAM analysis revealed that the central region of the tumor was highly correlated with the model's classification decisions, while the correlation was lower in the peripheral regions. Conclusion:The deep learning model performed well in differentiating NPC from NPL based on conventional MRI. The T1WI+T2WI combination model exhibited the best performance. The model can assist in the early diagnosis of NPC and NPL, facilitating timely and standardized treatment, which may improve patient prognosis.
Humans ; Nasopharyngeal Carcinoma/diagnostic imaging* ; Deep Learning ; Magnetic Resonance Imaging ; Retrospective Studies ; Nasopharyngeal Neoplasms/diagnostic imaging* ; Lymphoma/diagnostic imaging* ; Diagnosis, Differential ; ROC Curve ; Male ; Female ; Middle Aged ; Adult

Objective:To explore the clinical, imaging, and pathological features of glottic squamous cell carcinoma of the larynx and their relationship with prognosis. Methods:A retrospective analysis was conducted on the clinical, imaging, and pathological data of 130 patients with glottic squamous cell carcinoma of the larynx who were treated at the First People's Hospital of Yinchuan and the General Hospital of Ningxia Medical University from January 2018 to March 2023. Imaging examinations （CT and MRI） were used to evaluate the lesion boundary clarity, density, enhancement nature, and enhancement degree. Postoperative pathological examination was used to determine the pathological nature, immunohistochemistry, etc. Statistical methods such as χ² test, Spearman correlation analysis, multivariate logistic regression analysis, and Kaplan-Meier method were used to analyze the data. Results:Among the 130 patients, 127 were male and 3 were female, with an average age of （61.92±9.595） years. There was a correlation between clinical, imaging, and pathological features. Multivariate analysis showed that heterogeneous MRI density （OR=12.414;P=0.019） and squamous cell carcinoma as a subtype were correlated. The initial symptom of non-hoarseness （HR=6.045;P=0.010） and unclear MRI boundary （HR=12.559; P=0.029） were independent risk factors for poor prognosis in patients with glottic squamous cell carcinoma of the larynx. Conclusion:There is a correlation between the clinical, imaging, and pathological features of patients with glottic squamous cell carcinoma of the larynx, and they can affect prognosis. The initial symptom of non-hoarseness and unclear MRI boundary of the tumor are independent risk factors for poor prognosis.
Humans ; Laryngeal Neoplasms/diagnosis* ; Prognosis ; Male ; Female ; Retrospective Studies ; Middle Aged ; Carcinoma, Squamous Cell/diagnosis* ; Magnetic Resonance Imaging ; Glottis/pathology* ; Tomography, X-Ray Computed ; Aged

OBJECTIVES: To evaluate the effect of artificial intelligence-assisted compressed sensing (ACS) acceleration on MRI radiomic feature extraction and performance of diagnostic staging models for nasopharyngeal carcinoma (NPC) in comparison with conventional parallel imaging (PI). METHODS: A total of 64 patients with newly diagnosed NPC underwent 3.0T MRI using axial T1-weighted (T1W), T2-weighted (T2W), and contrast-enhanced T1-weighted (CE-T1W) sequences. Both PI and ACS protocols were performed using identical imaging parameters. The total scan time for the 3 sequences in ACS group was 227 s, representing a 30% reduction from 312 s in the PI group. Eighteen first-order and 75 texture features were extracted using Pyradiomics. Intraclass correlation coefficients (ICCs) were calculated to assess the agreement between the two acceleration methods. After feature selection using the least absolute shrinkage and selection operator (LASSO), random forest regression models were constructed to distinguish early-stage (T1 and T2) from advanced-stage (T3 and T4) NPC. The diagnostic performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. RESULTS: ACS-accelerated images demonstrated good radiomic reproducibility, with 86.0% (240/279) of features showing good agreement (ICC>0.75), with mean ICCs for T1W, T2W and CE-T1W sequences of 0.91±0.09, 0.89±0.13 and 0.88±0.11, respectively. The staging prediction models achieved similar AUCs for ACS and PI (0.89 vs 0.90, P=0.991). CONCLUSIONS The MRI radiomic features extracted using ACS and PI techniques are highly consistent, and the ACS-based model shows comparable diagnostic performance to the PI-based model, but ACS significantly reduces the scan time and provides an efficient and reliable acceleration strategy for radiomics in NPC.
Humans ; Nasopharyngeal Neoplasms/diagnosis* ; Magnetic Resonance Imaging/methods* ; Nasopharyngeal Carcinoma ; Neoplasm Staging ; Artificial Intelligence ; Carcinoma ; Female ; Male ; Middle Aged ; Adult ; Radiomics