adenocarcinoma

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

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Latest posts

Enhanced CT-based radiomics predicts pathological complete response for advanced adenocarcinoma

This study shows that the combination of CT imaging and clinical factors pre-neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG) could help stratify potential responsiveness to NAC, which can also result in helping to provide a basis for clinicians to develop more customized treatment plans for patients. Key points Radiomics method can predict that AEG patients can

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Radiomic assessment of oesophageal adenocarcinoma

The use of radiomic models offers a possible way to improve oesophageal adenocarcinoma assessment through quantitative image analysis, but model selection becomes complicated due to the myriad available predictors as well as the uncertainty of their relevance and reproducibility. Therefore, the aim of this study was to review recent research in order to facilitate precedent-based model selection for prospective validation

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Differentiating the pathological subtypes of primary lung cancer for patients with brain metastases based on radiomics features from brain CT images

In this study, the aim of the authors was to investigate the feasibility and accuracy of differentiating the primary adenocarcinoma (AD) and squamous cell carcinoma (SCC) of non-small-cell lung cancer (NSCLC) for patients with brain metastases (BM) based on radiomics from brain contrast-enhanced computer tomography (CECT) images. Through this study, the authors discovered that brain CECT radiomics are promising in

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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS)

Read More →

Enhanced CT-based radiomics predicts pathological complete response for advanced adenocarcinoma

This study shows that the combination of CT imaging and clinical factors pre-neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG) could help stratify potential responsiveness to NAC, which can also result in helping to provide a basis for clinicians to develop more customized treatment plans for patients. Key points Radiomics method can predict that AEG patients can

Read More →

Radiomic assessment of oesophageal adenocarcinoma

The use of radiomic models offers a possible way to improve oesophageal adenocarcinoma assessment through quantitative image analysis, but model selection becomes complicated due to the myriad available predictors as well as the uncertainty of their relevance and reproducibility. Therefore, the aim of this study was to review recent research in order to facilitate precedent-based model selection for prospective validation

Read More →

Differentiating the pathological subtypes of primary lung cancer for patients with brain metastases based on radiomics features from brain CT images

In this study, the aim of the authors was to investigate the feasibility and accuracy of differentiating the primary adenocarcinoma (AD) and squamous cell carcinoma (SCC) of non-small-cell lung cancer (NSCLC) for patients with brain metastases (BM) based on radiomics from brain contrast-enhanced computer tomography (CECT) images. Through this study, the authors discovered that brain CECT radiomics are promising in

Read More →

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS)

Read More →

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  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

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