Background: The purpose of this retrospective study was to evaluate the value of contrast-enhanced computed tomography (CE-CT) image features at baseline and after neoadjuvant chemotherapy in predicting histopathological response in patients with adenocarcinoma of the gastroesophageal junction (GEJ).

Methods: A total of 105 patients with a diagnosis of adenocarcinoma of the GEJ were examined by CE-CT at baseline and preoperatively after neoadjuvant chemotherapy. All patients underwent surgical resection. Histopathological parameters and tumor regression grading according to Becker et al. were collected in 93 patients. Line profiles of the primary tumor area in baseline and preoperative CE-CT were generated using ImageJ. Maximum tumor density and tumor-to-wall density delta were calculated and correlated with the histopathological tumor response. In addition, tumor response was assessed according to standard RECIST measurements in all patients and by endoscopy in 72 patients.

Results: Baseline and change in baseline to preoperative CE-CT parameters showed no significant differences between responders (Becker grade 1a, 1b) and non-responders (Becker grade 2, 3). After neoadjuvant therapy, responders and non-responders showed significant differences in maximum density and tumor-to-wall density delta values. Line profile measurements showed excellent inter-rater agreement. In comparison, neither RECIST nor endoscopy showed significant differences between these groups.

Conclusions: Posttreatment CE-CT can predict histopathological therapy response to neoadjuvant treatment in adenocarcinoma of GEJ patients with high accuracy and thus may improve patient management.

Download full-text PDF

Source
http://dx.doi.org/10.3390/cancers17020216DOI Listing

Publication Analysis

Top Keywords

image features
8
neoadjuvant treatment
8
adenocarcinoma gastroesophageal
8
gastroesophageal junction
8
high accuracy
8
neoadjuvant chemotherapy
8
adenocarcinoma gej
8
baseline preoperative
8
preoperative ce-ct
8
density tumor-to-wall
8

Similar Publications

Nevoid basal cell carcinoma syndrome (Gorlin syndrome): a case report.

J Med Case Rep

January 2025

Department of Dermatology and Venereology, Faculty of Medicine, University of Aleppo, Aleppo, Syria.

Background: Basal cell nevus syndrome, also known as Gorlin or Gorlin-Goltz syndrome, is a hereditary condition caused by mutation in the PATCHED gene. The syndrome presents with a wide range of clinical manifestations, including basal cell carcinomas, jaw cysts, and skeletal anomalies. Diagnosis is based on specific criteria, and treatment typically includes surgical removal of basal cell carcinomas.

View Article and Find Full Text PDF

Blood-based epigenome-wide association study and prediction of alcohol consumption.

Clin Epigenetics

January 2025

Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.

Alcohol consumption is an important risk factor for multiple diseases. It is typically assessed via self-report, which is open to measurement error through recall bias. Instead, molecular data such as blood-based DNA methylation (DNAm) could be used to derive a more objective measure of alcohol consumption by incorporating information from cytosine-phosphate-guanine (CpG) sites known to be linked to the trait.

View Article and Find Full Text PDF

Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.

View Article and Find Full Text PDF

Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'.

View Article and Find Full Text PDF

An automatic cervical cell classification model based on improved DenseNet121.

Sci Rep

January 2025

Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.

The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!