AIMC Topic: Lung Neoplasms
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Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and/or deep learning-based radiomics features.
Radiation oncology (London, England)
Nov 17, 2022
BACKGROUND: This study was designed to establish radiation pneumonitis (RP) prediction models using dosiomics and/or deep learning-based radiomics (DLR) features based on 3D dose distribution.
Reply to: Letter to the Editor: Comment on ''Radiomics with Artificial Intelligence for the Prediction of Early Recurrence in Patients with Clinical Stage IA Lung Cancer'' by Usuzaki, Takuma et al.
Annals of surgical oncology
Nov 14, 2022
Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study.
EBioMedicine
Nov 14, 2022
BACKGROUND: This study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predicting major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer ...
Deep learning-based harmonization of CT reconstruction kernels towards improved clinical task performance.
European radiology
Nov 10, 2022
OBJECTIVES: To develop a deep learning-based harmonization framework, assessing whether it can improve performance of radiomics models given different kernels in different clinical tasks and additionally generalize to mitigate the effects of new/unob...
Artificial intelligence in lung cancer: current applications and perspectives.
Japanese journal of radiology
Nov 9, 2022
Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinica...
Deep learning-based framework for slide-based histopathological image analysis.
Scientific reports
Nov 9, 2022
Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images (WSIs) analysis. Major applications in digital pathology using machine learning include automatic ca...
Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer.
Nature communications
Nov 8, 2022
Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 quantification by immunohistochemistry (IHC) are a challenge. In contrast, hematoxyl...
Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade.
Frontiers in immunology
Nov 7, 2022
Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligand (PD-L1) blockade. We aimed to use deep-learning algorithm to estimate clinical benefit in patients...
Robotic Esophagectomy Compared With Open Esophagectomy Reduces Sarcopenia within the First Postoperative Year: A Propensity Score-Matched Analysis.
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
Nov 4, 2022
INTRODUCTION: Sarcopenia is a known risk factor for adverse outcomes after esophageal cancer (EC) surgery. Robot-assisted minimally invasive esophagectomy (RAMIE) offers numerous advantages, including reduced morbidity and mortality. However, no evid...
The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.
BMC cancer
Nov 1, 2022
BACKGROUND: Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.
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