Oncology/Hematology

Lung Cancer

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

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Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality o...

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation.

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiati...

Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with signifi...

Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.

Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadli...

Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images.

This study aims to develop a CT-based hybrid deep learning network to predict pathological subtypes ...

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

OBJECTIVE: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major pub...

Layer-selective deep representation to improve esophageal cancer classification.

Even though artificial intelligence and machine learning have demonstrated remarkable performances i...

Predicting Lymphovascular Invasion in Non-small Cell Lung Cancer Using Deep Convolutional Neural Networks on Preoperative Chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatmen...

First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation.

BACKGROUND: Living kidney donors are screened pre-donation to estimate the risk of end-stage kidney ...

New vision of HookEfficientNet deep neural network: Intelligent histopathological recognition system of non-small cell lung cancer.

BACKGROUND: Efficient and precise diagnosis of non-small cell lung cancer (NSCLC) is quite critical ...

Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.

BACKGROUND: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to...

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy.

BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed...

GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status.

Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status ...

Deep Learning for Histopathological Assessment of Esophageal Adenocarcinoma Precursor Lesions.

Histopathological assessment of esophageal biopsies is a key part in the management of patients with...

Personalized Composite Dosimetric Score-Based Machine Learning Model of Severe Radiation-Induced Lymphopenia Among Patients With Esophageal Cancer.

PURPOSE: Radiation-induced lymphopenia (RIL) is common among patients undergoing radiation therapy (...

Enhancing Precision in Cardiac Segmentation for Magnetic Resonance-Guided Radiation Therapy Through Deep Learning.

PURPOSE: Cardiac substructure dose metrics are more strongly linked to late cardiac morbidities than...

A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.

OBJECTIVES: At present, there are many limitations in the evaluation of lymph node metastasis of lun...

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