Oncology/Hematology

Lung Cancer

Latest AI and machine learning research in lung cancer for healthcare professionals.

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Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning.

The main bottleneck in training a robust tumor segmentation algorithm for non-small cell lung cancer...

An enhanced Garter Snake Optimization-assisted deep learning model for lung cancer segmentation and classification using CT images.

An early detection of lung tumors is critical for better treatment results, and CT scans can reveal ...

Antiproliferative and Pro-apoptotic Activities of vent. Leaves on the Human Breast Adenocarcinoma Cell Line (MCF-7).

BACKGROUND: Breast cancer is the most common cancer among women worldwide, impacting not only the pa...

Pulmonary nodule visualization and evaluation of AI-based detection at various ultra-low-dose levels using photon-counting detector CT.

BACKGROUND: Radiation dose should be as low as reasonably achievable. With the invention of photon-c...

Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.

BACKGROUND: To design a pulmonary ground-glass nodules (GGN) classification method based on computed...

Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer withF-FDG PET/CT images.

. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastas...

Development of a prognostic model for NSCLC based on differential genes in tumour stem cells.

Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic d...

MR Cranial Bone Imaging: Evaluation of Both Motion-Corrected and Automated Deep Learning Pseudo-CT Estimated MR Images.

BACKGROUND AND PURPOSE: CT imaging exposes patients to ionizing radiation. MR imaging is radiation f...

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma.

BACKGROUND AND OBJECTIVE: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging ...

Low-dose computed tomography perceptual image quality assessment.

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality...

Data privacy-aware machine learning approach in pancreatic cancer diagnosis.

PROBLEM: Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its adv...

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major c...

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.

The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning alg...

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images.

Delayed diagnosis and treatment resistance result in high pancreatic ductal adenocarcinoma (PDAC) mo...

Integrated machine learning survival framework to decipher diverse cell death patterns for predicting prognosis in lung adenocarcinoma.

Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and m...

Artificial intelligence for response prediction and personalisation in radiation oncology.

Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multiface...

Identification of cancer stem cell-related genes through single cells and machine learning for predicting prostate cancer prognosis and immunotherapy.

BACKGROUND: Cancer stem cells (CSCs) are a subset of cells within tumors that possess the unique abi...

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