AI Medical Compendium Journal:
Thoracic cancer

Showing 11 to 20 of 24 articles

Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor.

Thoracic cancer
BACKGROUND: Rapid intraoperative diagnosis for unconfirmed pulmonary tumor is extremely important for determining the optimal surgical procedure (lobectomy or sublobar resection). Attempts to diagnose malignant tumors using mass spectrometry (MS) hav...

Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics.

Thoracic cancer
BACKGROUND: To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological characteristics for lung cancer screening in patients with small pulmonary nodules (SPN).

Expanding TNM for lung cancer through machine learning.

Thoracic cancer
BACKGROUND: Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will improve patient stratification and survival prediction. Here, we introduce machine learning for incorp...

Identifying sarcopenia in advanced non-small cell lung cancer patients using skeletal muscle CT radiomics and machine learning.

Thoracic cancer
BACKGROUND: Sarcopenia has been confirmed as a poor prognostic indicator of lung cancer. However, the lack of abdominal computed tomography (CT) hindered the application to assess the status of sarcopenia. The purpose of this study was to assess the ...

Intraoperative localization of small pulmonary nodules to assist surgical resection: A novel approach using a surgical navigation puncture robot system.

Thoracic cancer
BACKGROUND: Localization and resection of nonvisible, nonpalpable pulmonary nodules during video-assisted thoracoscopic surgery is challenging. In this study we developed a surgical navigation puncture robot system in order to locate small pulmonary ...

Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes.

Thoracic cancer
BACKGROUND: The aim of this study was to investigate the influence of convolution kernel and iterative reconstruction on the diagnostic performance of radiomics and deep learning (DL) in lung adenocarcinomas.

Successful radiotherapy in postoperative recurrence of a primary mediastinal yolk sac tumor: A case report.

Thoracic cancer
A woman in her 60s was evaluated for anterior chest pain. Computed tomography (CT) revealed a 50 mm mass with irregular contrast enhancement in the anterior mediastinum. α-fetoprotein (AFP) level was elevated to 1188 ng/mL. A germ cell tumor was diag...