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Adenocarcinoma

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Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Radiology
Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improv...

Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.

JCO clinical cancer informatics
PURPOSE: Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans...

A novel single-port robot for total gastrectomy to treat gastric cancer: A case report (with video).

Asian journal of endoscopic surgery
Multiport robots are now widely used for total gastrectomy for gastric cancer, while there is almost a void of research on whether single-port (SP) robots can be used for total gastrectomy. Here, we report a case of a 75-year-old female patient who w...

Selective prediction for extracting unstructured clinical data.

Journal of the American Medical Informatics Association : JAMIA
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
<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
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
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
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)
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
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 ...