Dual-energy computed tomography for evaluating nodal staging in lung adenocarcinoma: correlation with surgical pathology.

Purpose: To ascertain the performance of dual-energy CT (DECT) with iodine quantification in differentiating malignant mediastinal and hilar lymph nodes (LNs) from benign ones, focusing on patients with lung adenocarcinoma.

Materials And Methods: In this study, patients with suspected lung cancer received a preoperative contrast-enhanced DECT scan from Jun 2018 to Dec 2020. Quantitative DECT parameters and the size were compared between metastatic and benign LNs.

CT findings of 144 in-hospital patients with influenza pneumonia: A retrospective analysis.

Background/purpose: Patients with influenza infection during their period of admission may have worse computed tomography (CT) manifestation according to the clinical status. This study aimed to evaluate the CT findings of in-hospital patients due to clinically significant influenza pneumonia with correlation of clinical presentations.

Methods: In this retrospective, single center case series, 144 patients were included.

An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification.

Background And Objective: Lung cancer has the highest cancer-related mortality worldwide, and lung nodule usually presents with no symptom. Low-dose computed tomography (LDCT) was an important tool for lung cancer detection and diagnosis. It provided a complete three-dimensional (3-D) chest image with a high resolution.

Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning.

Lymph node metastasis also called nodal metastasis (Nmet), is a clinically primary task for physicians. The survival and recurrence of lung cancer are related to the Nmet staging from Tumor-Node-Metastasis (TNM) reports. Furthermore, preoperative Nmet prediction is still a challenge for the patient in managing the surgical plan and making treatment decisions.

Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning.

Lymph node metastasis (LNM) identification is the most clinically important tasks related to survival and recurrence from lung cancer. However, the preoperative prediction of nodal metastasis remains a challenge to determine surgical plans and pretreatment decisions in patients with cancers. We proposed a novel deep prediction method with a size-related damper block for nodal metastasis (Nmet) identification from the primary tumor in lung cancer generated by gemstone spectral imaging (GSI) dual-energy computer tomography (CT).

Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis.

Three-dimensional (3-D) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) consists of a large number of images in different enhancement phases which are used to identify and characterize breast lesions. The purpose of this study was to develop a computer-assisted algorithm for tumor segmentation and characterization using both kinetic information and morphological features of 3-D breast DCE-MRI. An integrated color map created by intersecting kinetic and area under the curve (AUC) color maps was used to detect potential breast lesions, followed by the application of a region growing algorithm to segment the tumor.

A 2-year follow-up of swallowing function after radiation therapy in patients with nasopharyngeal carcinoma.

Objective: To evaluate over a 2-year period the serial swallowing function of patients with nasopharyngeal carcinoma (NPC) after completing radiotherapy (RT).

Design: Prospective longitudinal follow-up.

Setting: University hospital.

Intrapulmonary bronchogenic cysts: computed tomography, clinical and histopathologic correlations.

Background/purpose: Bronchogenic cysts (BCs) are usually located in the mediastinum and they occur less commonly in the lung parenchyma. This study investigated the findings from computed tomography (CT) images, clinical presentation and histopathologic findings of intrapulmonary BCs.

Methods: From the last 7 years, the CT images of 20 patients (12 females, 8 males; mean age, 38.

Dysphagia in patients with nasopharyngeal cancer after radiation therapy: a videofluoroscopic swallowing study.

This study evaluated swallowing status and the factors influencing swallowing in patients with nasopharyngeal carcinoma (NPC) after radiation therapy. During the period from July 1995 to June 1999, this cross-sectional study used videofluoroscopic swallowing study (VFSS) to evaluate 184 NPC patients who had completed radiation therapy [113 cases had completed radiation therapy < or = 12 months prior to evaluation (acute group) and 71 cases had completed radiation therapy > 12 months prior to evaluation (chronic group)]. The numbers of patients with tumors in each of the four stages were as follows: 24 in stage I, 45 in stage II, 41 in stage III, and 74 in stage IV.