Objectives: To study the prognostic significance of tumour budding (TB) compared with the grading of lung adenocarcinoma (LAC).
Materials And Methods: The postoperative haematoxylin and eosin-stained histological slices of 207 surgically treated LAC patients were retrospectively reviewed by a lung pathologist. Two groups were formed from the cohort: the high-grade TB group (≥10 buds) and low-grade TB group (0-9 buds).
Purpose: This study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions.
Methods: A comprehensive phantom CT dataset (three dose levels, six reconstruction methods, amounting to 9240 slices) was acquired and used to train a convolutional neural network (CNN) to output an estimate of local image noise standard deviations (SD) from a single CT scan input. The CNN model consisting of seven convolutional layers was trained on the phantom image dataset representing a range of scan parameters and was tested with phantom images acquired in a variety of different scan conditions, as well as publicly available chest CT images to produce clinical noise SD maps.
Objectives: Pleural mesothelioma (PM) is an aggressive malignancy with limited treatment options. The first-line therapy has remained unchanged for two decades and consists of pemetrexed in combination with cisplatin. Immune-checkpoint inhibitors (nivolumab plus ipilimumab) have high response rates, resulting in recent updates in treatment recommendations by the U.
View Article and Find Full Text PDFAim: To predict the differentiation between invasive growth patterns and new grades of lung adenocarcinoma (LAC) using computed tomography (CT).
Materials And Methods: The CT features of 180 surgically treated LAC patients were compared retrospectively to pathological invasive subtypes and tumour grades as defined by the new grading system published in 2021 by the World Health Organization. Two radiologists reviewed the images semi-quantitatively and independently.