Automatic medical imaging segmentation via self-supervising large-scale convolutional neural networks.

Purpose: This study aims to develop a robust, large-scale deep learning model for medical image segmentation, leveraging self-supervised learning to overcome the limitations of supervised learning and data variability in clinical settings.

Methods And Materials: We curated a substantial multi-center CT dataset for self-supervised pre-training using masked image modeling with sparse submanifold convolution. We designed a series of Sparse Submanifold U-Nets (SS-UNets) of varying sizes and performed self-supervised pre-training.

Artificial intelligence-based motion tracking in cancer radiotherapy: A review.

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant.

Impact of Age on Overall Survival Among Children With Wilms Tumor: A Population-based Registry Analysis.

Objectives: International trials have reported conflicting findings on whether the association between age and worse overall survival (OS) among children with Wilms tumor (WT) is due to age as an independent prognostic factor or the observation of more advanced disease at older ages. We sought to further elucidate this relationship using a population-based registry analysis.

Methods: The Surveillance, Epidemiology, and End Results database was queried for all patients diagnosed with WT under the age of 20.

Landmark tracking in liver US images using cascade convolutional neural networks with long short-term memory.

Accurate tracking of anatomic landmarks is critical for motion management in liver radiation therapy. Ultrasound (US) is a safe, low-cost technology that is broadly available and offer real-time imaging capability. This study proposed a deep learning-based tracking method for the US image-guided radiation therapy.

Artificial intelligence in tumor subregion analysis based on medical imaging: A review.

Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications.

A review on medical imaging synthesis using deep learning and its clinical applications.

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

Stereotactic ablative radiotherapy (SABR) for treatment of central and ultra-central lung tumors.

Objectives: Treatment of central and ultra-central lung tumors with stereotactic ablative radiotherapy (SABR) remains controversial due to risks of treatment-related toxicities compared with peripheral tumors. Here we report our institution's experience in treating central and ultra-central lung tumor patients with SABR.

Materials And Methods: We retrospectively reviewed outcomes in 68 patients with single lung tumors, 34 central and 34 peripheral, all treated with SABR consisting of 50 Gy in 4-5 fractions.

Feasibility and potential utility of multicomponent exhaled breath analysis for predicting development of radiation pneumonitis after stereotactic ablative radiotherapy.

Introduction: In this prospective pilot study, we evaluated the feasibility and potential utility of measuring multiple exhaled gases as biomarkers of radiation pneumonitis (RP) in patients receiving stereotactic ablative radiotherapy (SABR) for lung tumors.

Methods: Breath analysis was performed for 26 patients receiving SABR for lung tumors. Concentrations of exhaled nitric oxide (eNO), carbon monoxide (eCO), nitrous oxide (eN2O), and carbon dioxide (eCO2) were measured before and immediately after each fraction using real-time, infrared laser spectroscopy.

An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage.

Circulating tumor DNA (ctDNA) is a promising biomarker for noninvasive assessment of cancer burden, but existing ctDNA detection methods have insufficient sensitivity or patient coverage for broad clinical applicability. Here we introduce cancer personalized profiling by deep sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying ctDNA. We implemented CAPP-Seq for non-small-cell lung cancer (NSCLC) with a design covering multiple classes of somatic alterations that identified mutations in >95% of tumors.