AIMC Topic: Adenocarcinoma
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Selective prediction for extracting unstructured clinical data.
Journal of the American Medical Informatics Association : JAMIA
Dec 22, 2023
OBJECTIVE: While there are currently approaches to handle unstructured clinical data, such as manual abstraction and structured proxy variables, these methods may be time-consuming, not scalable, and imprecise. This article aims to determine whether ...
ApoA-I and ApoB levels, and ApoB-to-ApoA-I ratio as candidate pre-treatment biomarkers of pathomorphological response to neoadjuvant therapy in gastric and esophago-gastric junction adenocarcinoma.
Polski przeglad chirurgiczny
Sep 20, 2023
<b><br>Introduction:</b> Neoadjuvant chemotherapy (NAC) is a part of the current standard of care in a locally advanced gastric adenocarcinoma (GA) and esophagogastric junction adenocarcinoma (EGJA), but only patients with good path...
Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning.
JCO clinical cancer informatics
Aug 1, 2023
PURPOSE: Quantifying treatment response to gastroesophageal junction (GEJ) adenocarcinomas is crucial to provide an optimal therapeutic strategy. Routinely taken tissue samples provide an opportunity to enhance existing positron emission tomography-c...
Deep Learning-based Image Cytometry Using a Bit-pattern Kernel-filtering Algorithm to Avoid Multi-counted Cell Determination.
Anticancer research
Aug 1, 2023
BACKGROUND/AIM: In pathology, the digitization of tissue slide images and the development of image analysis by deep learning have dramatically increased the amount of information obtainable from tissue slides. This advancement is anticipated to not o...
Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis.
Clinical and translational medicine
Jul 1, 2023
INTRODUCTION: Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. ...
DEMoS: a deep learning-based ensemble approach for predicting the molecular subtypes of gastric adenocarcinomas from histopathological images.
Bioinformatics (Oxford, England)
Sep 2, 2022
MOTIVATION: The molecular subtyping of gastric cancer (adenocarcinoma) into four main subtypes based on integrated multiomics profiles, as proposed by The Cancer Genome Atlas (TCGA) initiative, represents an effective strategy for patient stratificat...
[Two Cases of Robot-Assisted Total Pelvic Exenteration and Intracorporeal Ileal Conduit for Locally Advanced Rectal Cancer].
Hinyokika kiyo. Acta urologica Japonica
May 1, 2022
We describe two cases of locally advanced rectal cancer (LARC) treated with robot-assisted total pelvic exenteration (Ra-TPE) and intracorporeal ileal conduit (ICIC). The first case was in a 71-year-old man with LARC (RbP, T4bN2bM0, cStage IIIc). He ...
Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review.
Annals of surgery
Mar 1, 2022
OBJECTIVE: To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC.
Comparing outcomes of robotic versus open mesorectal excision for rectal cancer.
BJS open
Nov 9, 2021
BACKGROUND: The outcomes of robot-assisted mesorectal excision for rectal cancer, compared with open resection, have not been fully characterized.
Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm.
Technology in cancer research & treatment
Jan 1, 2021
The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. A...
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