RAdiotherapy Dose Accumulation Routine (RADAR)-A Novel Dose Accumulation Script With Built-In Uncertainty.

Purpose: To incorporate uncertainty into dose accumulation for reirradiation.

Methods And Materials: The RAdiotherapy Dose Accumulation Routine (RADAR) script for the Eclipse treatment planning system (Varian Medical Systems) is described, and the voxel-wise ellipsoid search algorithm is introduced as a means of incorporating uncertainty. RADAR is first demonstrated on a test patient reirradiated to the spine, illustrating the effect of the uncertainty algorithm.

Geometric evaluation and quantifying dosimetric impact of diverse deformable image registration algorithms on abdomen images with biomechanically modeled deformations.

Purpose: Deformable image registration (DIR) has been increasingly used in radiation therapy (RT). The accuracy of DIR algorithms and how it impacts on the RT plan dosimetrically were examined in our study for abdominal sites using biomechanically modeled deformations.

Methods: Five pancreatic cancer patients were enrolled in this study.

Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy.

Background And Purpose: Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma.

Materials And Methods: Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma.

Research progress on the improvement of cardiovascular diseases through the autonomic nervous system regulation of the NLRP3 inflammasome pathway.

Cardiovascular disease stands as a leading global cause of mortality. Nucleotide-binding Oligomerization Domain-like Receptor Protein 3 (NLRP3) inflammasome is widely acknowledged as pivotal factor in specific cardiovascular disease progression, such as myocardial infarction, heart failure. Recent investigations underscore a close interconnection between autonomic nervous system (ANS) dysfunction and cardiac inflammation.

Automated planning of stereotactic spine re-irradiation using cumulative dose limits.

Background And Purpose: The lack of dedicated tools in commercial planning systems currently restricts efficient review and planning for re-irradiation. The aim of this study was to develop an automated re-irradiation planning framework based on cumulative doses.

Materials And Methods: We performed a retrospective study of 14 patients who received spine SBRT re-irradiation near a previously irradiated treatment site.

Clinical Experience With an Offline Adaptive Radiation Therapy Head and Neck Program: Dosimetric Benefits and Opportunities for Patient Selection.

Purpose: The objective of this study was to develop a linear accelerator (LINAC)-based adaptive radiation therapy (ART) workflow for the head and neck that is informed by automated image tracking to identify major anatomic changes warranting adaptation. In this study, we report our initial clinical experience with the program and an investigation into potential trigger signals for ART.

Methods And Materials: Offline ART was systematically performed on patients receiving radiation therapy for head and neck cancer on C-arm LINACs.

Creating patient-specific digital phantoms with a longitudinal atlas for evaluating deformable CT-CBCT registration in adaptive lung radiotherapy.

Background: Quality assurance of deformable image registration (DIR) is challenging because the ground truth is often unavailable. In addition, current approaches that rely on artificial transformations do not adequately resemble clinical scenarios encountered in adaptive radiotherapy.

Purpose: We developed an atlas-based method to create a variety of patient-specific serial digital phantoms with CBCT-like image quality to assess the DIR performance for longitudinal CBCT imaging data in adaptive lung radiotherapy.

RMSim: controlled respiratory motion simulation on static patient scans.

This work aims to generate realistic anatomical deformations from static patient scans. Specifically, we present a method to generate these deformations/augmentations via deep learning driven respiratory motion simulation that provides the ground truth for validating deformable image registration (DIR) algorithms and driving more accurate deep learning based DIR.We present a novel 3D Seq2Seq deep learning respiratory motion simulator (RMSim) that learns from 4D-CT images and predicts future breathing phases given a static CT image.

Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy.

Purpose: To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART).

Methods: To address the unique needs of ART, we designed Seq2Morph, a novel deep learning-based deformable image registration (DIR) network. Seq2Morph was built upon VoxelMorph which is a general-purpose framework for learning-based image registration.

Fraxinellone Induces Hepatotoxicity in Zebrafish through Oxidative Stress and the Transporters Pathway.

Fraxinellone (FRA), a major active component from Cortex Dictamni, produces hepatotoxicity via the metabolization of furan rings by CYP450. However, the mechanism underlying the hepatotoxicity of FRA remains unclear. Therefore, zebrafish larvae at 72 h post fertilization were used to evaluate the metabolic hepatotoxicity of FRA and to explore the underlying molecular mechanisms.

Subfamily-specific differential contribution of individual monomers and the tether sequence to mouse L1 promoter activity.

Background: The internal promoter in L1 5'UTR is critical for autonomous L1 transcription and initiating retrotransposition. Unlike the human genome, which features one contemporarily active subfamily, four subfamilies (A_I, Gf_I and Tf_I/II) have been amplifying in the mouse genome in the last one million years. Moreover, mouse L1 5'UTRs are organized into tandem repeats called monomers, which are separated from ORF1 by a tether domain.

Deep learning driven predictive treatment planning for adaptive radiotherapy of lung cancer.

Background And Purpose: To develop a novel deep learning algorithm of sequential analysis, Seq2Seq, for predicting weekly anatomical changes of lung tumor and esophagus during definitive radiotherapy, incorporate the potential tumor shrinkage into a predictive treatment planning paradigm, and improve the therapeutic ratio.

Methods And Materials: Seq2Seq starts with the primary tumor and esophagus observed on the planning CT to predict their geometric evolution during radiotherapy on a weekly basis, and subsequently updates the predictions with new snapshots acquired via weekly CBCTs. Seq2Seq is equipped with convolutional long short term memory to analyze the spatial-temporal changes of longitudinal images, trained and validated using a dataset including sixty patients.

Total saponins from stems and leaves of Panax quinquefolius L. ameliorate podophyllotoxin-induced myelosuppression and gastrointestinal toxicity.

Podophyllotoxin (POD), a natural lignan distributed in podophyllum species, possesses significant antitumor and antiviral activities. But POD often causes serious side effects, such as myelosuppression, gastrointestinal toxicity, neurotoxicity, hepatic and renal dysfunction, and even death, which not only hinder its clinical application but also threaten the patient's health. Therefore, an effective treatment against POD-induced toxicity is important.

Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy.

Purpose: Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy.

Methods: To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short-Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVFs) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs.

Multi-class medical image segmentation using one-vs-rest graph cuts and majority voting.

Semi-automatic image segmentation is still a valuable tool in clinical applications since it retains the expert oversights legally required. However, semi-automatic methods for simultaneous multi-class segmentation are difficult to be clinically implemented due to the complexity of underlining algorithms. We purpose an efficient one-vs-rest graph cut approach of which the complexity only grows linearly as the number of classes increases.

Consistency evaluation of Chaihuang granules based on pharmacokinetics and metabolomics.

Consistency evaluation of Traditional Chinese Medicinal preparations (TCMPs) with complex chemical composition is challenging. Chaihuang granules (CHG), as a well-known TCMP, consists of Chaihu (Bupleuri Radix) and Huangqin (Scutellariae Radix) extract. In this work, we used pharmacokinetics and metabolomics to evaluate consistency of CHG products from two different manufacturers.

Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy.

Purpose: Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change.

Methods And Materials: Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT).

Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.

Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatment plans to reduce radiation-induced esophageal toxicity in an adaptive radiotherapy (ART) workflow. We have developed a novel machine learning framework to predict the patient-specific spatial presentation of the esophagus in the weeks following treatment, using magnetic resonance imaging (MRI)/ cone-beam CT (CBCT) scans acquired earlier in the 6 week radiotherapy course.

Self-derived organ attention for unpaired CT-MRI deep domain adaptation based MRI segmentation.

To develop and evaluate a deep learning method to segment parotid glands from MRI using unannotated MRI and unpaired expert-segmented CT datasets. We introduced a new self-derived organ attention deep learning network for combined CT to MRI image-to-image translation (I2I) and MRI segmentation, all trained as an end-to-end network. The expert segmentations available on CT scans were combined with the I2I translated pseudo MR images to train the MRI segmentation network.

Predictive dose accumulation for HN adaptive radiotherapy.

During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT).

PSIGAN: Joint Probabilistic Segmentation and Image Distribution Matching for Unpaired Cross-Modality Adaptation-Based MRI Segmentation.

We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images. Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution using a new structure discriminator. The structure discriminator computes structure of interest focused adversarial loss by combining the generated pseudo MRI with probabilistic segmentations produced by a simultaneously trained segmentation sub-network.

Quantification of accumulated dose and associated anatomical changes of esophagus using weekly Magnetic Resonance Imaging acquired during radiotherapy of locally advanced lung cancer.

Background And Purpose: Minimizing acute esophagitis (AE) in locally advanced non-small cell lung cancer (LA-NSCLC) is critical given the proximity between the esophagus and the tumor. In this pilot study, we developed a clinical platform for quantification of accumulated doses and volumetric changes of esophagus via weekly Magnetic Resonance Imaging (MRI) for adaptive radiotherapy (RT).

Material And Methods: Eleven patients treated via intensity-modulated RT to 60-70 Gy in 2-3 Gy-fractions with concurrent chemotherapy underwent weekly MRIs.

Library of deep-learning image segmentation and outcomes model-implementations.

An open-source library of implementations for deep-learning-based image segmentation and outcomes models based on radiotherapy and radiomics is presented. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. Inclusion of deep-learning-based image segmentation and outcomes models in the same library provides a fully automated and reproduceable pipeline to estimate prognosis.