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Back to main AI Blog

solitary pulmonary nodule

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.

Most used hashtags:

machine learningdeep learningradiomicsmrix-ray computed tomographyai newsCOVID-19interviewmammographybreast cancerneural networksdiagnostic imagingtomographyMDCTradiographyworkflow

Latest posts

Radiologists with and without deep learning–based computer-aided diagnosis

Shoji Kido October 2022

When radiologists encounter pulmonary nodules/masses in computed tomography (CT) images, they diagnose malignancy based on lesion characteristics (e.g., spiculation and calcification). However, accurate characterization requires careful observation and can be difficult, especially for inexperienced radiologists. In addition, the assessments may vary among radiologists, resulting in the low reproductivity of findings. We investigated if commercially-available deep learning (DL)-based computer-aided diagnosis (CAD)

Read More →

Radiomic nomogram predicts pathology invasiveness

European Radiology March 2022

The purpose of this retrospective diagnostic study was to develop and validate a preoperative CT-based nomogram combined with radiomic and clinical-radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. The authors were able to demonstrate that a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. Key points The radiomic

Read More →

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest

European Radiology November 2021

The aim of this study was to compare the performance of a deep learning (DL)-based method used for diagnosing pulmonary nodules compared with the diagnostic approach of the radiologist in computed tomography (CT) of the chest. The authors included a total of 150 pathologically confirmed pulmonary nodules that were assessed and reported by radiologists. The study found that the DL-based

Read More →

Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph

European Radiology April 2020

The authors of this retrospective study performed test-retest reproducibility analyses for a deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs with short-term intervals, in order to analyze influential factors on test-retest variations. The test, which included patients with pulmonary nodules resected in 2017, showed that DLAD was robust to the test-retest variation. Key points The deep learning–based

Read More →

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation

European Radiology December 2019

The aim of this study was to investigate the natural history of pulmonary pure ground-glass nodules (pGGNs) with deep learning (DL)-assisted nodule segmentation. The authors concluded that DL can assist in accurately explaining the natural history of pGGNs and that pGGNs with a lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key points The

Read More →

Radiologists with and without deep learning–based computer-aided diagnosis

Shoji Kido October 2022

When radiologists encounter pulmonary nodules/masses in computed tomography (CT) images, they diagnose malignancy based on lesion characteristics (e.g., spiculation and calcification). However, accurate characterization requires careful observation and can be difficult, especially for inexperienced radiologists. In addition, the assessments may vary among radiologists, resulting in the low reproductivity of findings. We investigated if commercially-available deep learning (DL)-based computer-aided diagnosis (CAD)

Read More →

Radiomic nomogram predicts pathology invasiveness

European Radiology March 2022

The purpose of this retrospective diagnostic study was to develop and validate a preoperative CT-based nomogram combined with radiomic and clinical-radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. The authors were able to demonstrate that a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. Key points The radiomic

Read More →

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest

European Radiology November 2021

The aim of this study was to compare the performance of a deep learning (DL)-based method used for diagnosing pulmonary nodules compared with the diagnostic approach of the radiologist in computed tomography (CT) of the chest. The authors included a total of 150 pathologically confirmed pulmonary nodules that were assessed and reported by radiologists. The study found that the DL-based

Read More →

Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph

European Radiology April 2020

The authors of this retrospective study performed test-retest reproducibility analyses for a deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs with short-term intervals, in order to analyze influential factors on test-retest variations. The test, which included patients with pulmonary nodules resected in 2017, showed that DLAD was robust to the test-retest variation. Key points The deep learning–based

Read More →

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation

European Radiology December 2019

The aim of this study was to investigate the natural history of pulmonary pure ground-glass nodules (pGGNs) with deep learning (DL)-assisted nodule segmentation. The authors concluded that DL can assist in accurately explaining the natural history of pGGNs and that pGGNs with a lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key points The

Read More →

Become A Member Today!

You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

Check out our different membership options.

If you don’t find a fitting membership send us an email here.

①

Membership

for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Exclusive option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 4
  • Content e-mails for all ESR journals
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.
②

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.
③

ESR Friends

For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.
Apply now

The membership type best fitting for you will be selected automatically during the application process.

Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.