Lung Cancer

Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.

To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentation of high-risk clinical target volume (HRCTV) and organ at risk (OAR) in high-dose-rate brachytherapy for cervical cancer patients.We used 73 computed tomography scans and 62 magnetic resonance imaging scans from 135 (103 for training, 16 for valid...

Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis.

The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, focusing on early immune infiltration. Feature selection was performed on The Cancer Genome Atlas (TCGA) data using least absolute shrinkage and selection Operator (LASSO), random forest (RF), and support vector machine (SVM) algorithms. Six ML algorith...

A deep learning-informed interpretation of why and when dose metrics outside the PTV can affect the risk of distant metastasis in SBRT NSCLC patients.

PURPOSE: Recent papers suggested a correlation between the risk of distant metastasis (DM) and dose outside the PTV, though conclusions in different p...

Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study.

BACKGROUND: The optimal number of lymph nodes to be dissected during lung cancer surgery to minimise the postoperative recurrence risk remains undeter...

Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists want?

BACKGROUND AND PURPOSE: During the ESTRO 2023 physics workshop on "AI for the fully automated radiotherapy treatment chain", the topic of deep learnin...

Advances in spatial resolution and radiation dose reduction using super-resolution deep learning-based reconstruction for abdominal computed tomography: A phantom study.

RATIONALE AND OBJECTIVES: This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it th...

Whole slide image-based weakly supervised deep learning for predicting major pathological response in non-small cell lung cancer following neoadjuvant chemoimmunotherapy: a multicenter, retrospective, cohort study.

OBJECTIVE: Develop a predictive model utilizing weakly supervised deep learning techniques to accurately forecast major pathological response (MPR) in...

Radiomics-Based Support Vector Machine Distinguishes Molecular Events Driving the Progression of Lung Adenocarcinoma.

INTRODUCTION: An increasing number of early-stage lung adenocarcinomas (LUAD) are detected as lung nodules. The radiological features related to LUAD ...

Machine learning to predict distant metastasis and prognostic analysis of moderately differentiated gastric adenocarcinoma patients: a novel focus on lymph node indicators.

BACKGROUND: Moderately differentiated gastric adenocarcinoma (MDGA) has a high risk of metastasis and individual variation, which strongly affects pat...

Deep Learning-Based Denoising Enables High-Quality, Fully Diagnostic Neuroradiological Trauma CT at 25% Radiation Dose.

RATIONALE AND OBJECTIVES: Traumatic neuroradiological emergencies necessitate rapid and accurate diagnosis, often relying on computed tomography (CT)....

Benchmarking deep learning-based low-dose CT image denoising algorithms.

BACKGROUND: Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography ...

Unsupervised Machine Learning to Identify Risk Factors of Pyeloplasty Failure in Ureteropelvic Junction Obstruction.

In adult patients with ureteropelvic junction obstruction (UPJO), little data exist on predicting pyeloplasty outcome, and there is no unified defini...

Deciphering the cytotoxicity of micro- and nanoplastics in Caco-2 cells through meta-analysis and machine learning.

Plastic pollution, driven by micro- and nanoplastics (MNPs), poses a major environmental threat, exposing humans through various routes. Despite human...

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 (NSCLC) on H&E is generating sufficient ground tr...

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 lumps in the lungs which are too small to be picke...

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 patients but also their families and communities. v...

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-counting detector computed tomography (PCD-CT), the...

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 tomography (CT) radiomics and machine learning fo...

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort.

INTRODUCTION: The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related ...

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 metastases (BMs). The delivery of drugs to the central ner...