Publications by authors named "Sun Tang"

Purpose: To propose and validate a CT radiomics model utilizing radiomic features from lymph nodes (LNs) with maximum short axis diameter (MSAD) < 1 cm for predicting small metastatic LN (sMLN) in patients with resectable esophageal squamous cell carcinoma (ESCC).

Methods: A total of 196 resectable patients with ESCC undergoing surgery were retrospectively enrolled, among whom 25% had sMLN. 146 out of 196 patients (from hospital 1) were randomly divided into the training (n = 116) and testing cohorts (n = 30) at an 8:2 ratio, while the remaining 50 patients from hospital 2 constituted the external validation cohort.

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Purpose: The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis.

Material And Methods: Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression.

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Background: Siamese network (SN) using longitudinal DCE-MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection.

Purpose: To identify pCR in early-stage NAC, using SN with longitudinal DCE-MRI and introducing IPS for phases selection.

Study Type: Multicenter, longitudinal.

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Introduction: To develop and validate a radiogenomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer compared to a genomics and radiomics model.

Methods: This retrospective study integrated transcriptomic data from The Cancer Genome Atlas with matched MRI data from The Cancer Imaging Archive for the same set of 111 patients with breast cancer, which were used as the training and testing groups. Fifteen patients from one hospital were enrolled as the external validation group.

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Objective: To investigate relationship of tumor stage-based gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) measured on computed tomography (CT) with early recurrence (ER) after esophagectomy.

Materials And Methods: Two hundred and four consecutive patients with resectable ESCC including 159 patients enrolled in the training cohort (TC) and 45 patients in validation cohort (VC) underwent contrast-enhanced CT less than 2 weeks before esophagectomy. GTV was retrospectively measured by multiplying sums of all tumor areas by section thickness.

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Radiomics transforms the medical images into high-dimensional quantitative features and provides potential information about tumor phenotypes and heterogeneity. We conducted a retrospective analysis to explore and validate radiomics model based on contrast-enhanced computed tomography (CECT) to predict recurrence of locally advanced oesophageal squamous cell cancer (SCC) within 2 years after trimodal therapy. This study collected CECT and clinical data of consecutive 220 patients with pathology-confirmed locally advanced oesophageal SCC (154 in the training cohort and 66 in the validation cohort).

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Background: Early recurrence of oesophageal squamous cell carcinoma (SCC) is defined as recurrence after surgery within 1 year, and appears as local recurrence, distant recurrence, and lymph node positive and disseminated recurrence. Contrast-enhanced computed tomography (CECT) is recommended for diagnosis of primary tumor and initial staging of oesophageal SCC, but it cannot be used to predict early recurrence. It is reported that radiomics can help predict preoperative stages of oesophageal SCC, lymph node metastasis before operation, and 3-year overall survival of oesophageal SCC patients following chemoradiotherapy by extracting high-throughput quantitative features from CT images.

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Background: Prediction of lymph node status in esophageal squamous cell carcinoma (ESCC) is critical for clinical decision making. In clinical practice, computed tomography (CT) has been frequently used to assist in the preoperative staging of ESCC. Texture analysis can provide more information to reflect potential biological heterogeneity based on CT.

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Objective: To investigate changes in CT manifestations and results of reverse transcription polymerase chain reaction (RT-PCR) testing between afferent and second-generation coronavirus disease 2019 (COVID-19) outside the original city (Wuhan) until recovery.

Methods: We collected 26 consecutive COVID-19 patients undergoing initial and follow-up CT scans together with RT-PCR until recovery from 2 hospitals outside the original city. Seventeen patients with afferent infection and 9 with second-generation infection were assigned to Group A and B, respectively.

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Liver cirrhosis is a common chronic progressive liver disease in clinical practice, and intravoxel incoherent motion (IVIM) is a promising magnetic resonance method to assess liver cirrhosis, so our purpose was to investigate association of liver-lobe-based IVIM-derived parameters with hepatitis-B-related cirrhosis and its severity, and esophageal and gastric fundic varices. Seventy-four patients with hepatitis-B-related cirrhotic and 25 healthy volunteers were enrolled and underwent upper abdominal IVIM diffusion-weighted imaging with b-values of 0, 20, 50, 80, 100, 200, 400, 600, and 800 s/mm. IVIM-derived parameters (D, pure molecular diffusion; D, pseudo diffusion; and f, perfusion fraction) of left lateral lobe (LLL), left medial lobe (LML), right lobe (RL), and caudate lobe (CL) were assessed statistically to show their associations with cirrhosis and its severity, and esophageal and gastric fundic varices.

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Background: Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed.

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