Lung Cancer

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography...

Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network.

. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (R...

Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom Study.

We attempted to determine the optimal radiation dose to maintain image quality using a deep learning application in a physical human phantom. Three ...

Comparison of Global Evaluative Assessment of Robotic Surgery with objective performance indicators for the assessment of skill during robotic-assisted thoracic surgery.

BACKGROUND: The Global Evaluative Assessment of Robotic Skills is a popular but ultimately subjective assessment tool in robotic-assisted surgery. An ...

Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas.

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural...

Deep learning in MRI-guided radiation therapy: A systematic review.

Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI m...

Automating Ground Truth Annotations for Gland Segmentation Through Immunohistochemistry.

Microscopic evaluation of glands in the colon is of utmost importance in the diagnosis of inflammatory bowel disease and cancer. When properly trained...

75% radiation dose reduction using deep learning reconstruction on low-dose chest CT.

OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to co...

Impact of Iron Supplementation on Hospital Length of Stay for Pneumonia or Skin and Skin Structure Infections: A Retrospective Cohort Study.

Pathogenic organisms utilize iron to survive and replicate and have evolved many processes to extract iron from human hosts. The goal of this study w...

Artificial intelligence and Italian culture: an understanding of how artificial intelligence can transform the radiation therapy landscape.

The aim is to support the perception of artificial intelligence in the radiation therapy landscape.

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has b...

Real-Time Motion Analysis With 4D Deep Learning for Ultrasound-Guided Radiotherapy.

Motion compensation in radiation therapy is a challenging scenario that requires estimating and forecasting motion of tissue structures to deliver the...

Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy.

PURPOSE: The purpose of this study was to create and evaluate deep learning-based models to detect and classify errors of multi-leaf collimator (MLC) ...

Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images.

We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin-sta...

Infrastructure tools to support an effective Radiation Oncology Learning Health System.

PURPOSE: Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry,...

Automated prognosis of renal function decline in ADPKD patients using deep learning.

An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention. Current bio...

Unlocking the Power of ChatGPT, Artificial Intelligence, and Large Language Models: Practical Suggestions for Radiation Oncologists.

Recent advances in artificial intelligence (AI), such as generative AI and large language models (LLMs), have generated significant excitement about t...

Perioperative and oncological outcomes of robot-assisted laparoscopic partial nephrectomy for cystic and solid renal masses: Evidence from controlled trials.

To evaluate the outcomes of robot-assisted partial nephrectomy (RAPN) for solid and cystic renal tumors. We systematically searched the Cochrane Libra...

Circulating Plasma Exosomal PD-L1 Predicts Prognosis of Head and Neck Squamous Cell Carcinoma After Radiation Therapy.

PURPOSE: Radiation therapy is widely used to treat head and neck squamous cell carcinoma (HNSCC). This study evaluated the association between circula...

Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

OBJECTIVES: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation ex...