AI Medical Compendium Journal:
Cancer imaging : the official publication of the International Cancer Imaging Society

Showing 31 to 40 of 62 articles

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduc...

Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous...

Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence.

Cancer imaging : the official publication of the International Cancer Imaging Society
The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical ...

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal ca...

A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Automatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from ...

Artificial intelligence-based MRI radiomics and radiogenomics in glioma.

Cancer imaging : the official publication of the International Cancer Imaging Society
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots ma...

Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVES: To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting.

Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combining radiomics and deep learni...

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tom...

A transformer-based multi-task deep learning model for simultaneous infiltrated brain area identification and segmentation of gliomas.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The anatomical infiltrated brain area and the boundaries of gliomas have a significant impact on clinical decision making and available treatment options. Identifying glioma-infiltrated brain areas and delineating the tumor manually is a ...