An all integer-based spiking neural network with dynamic threshold adaptation.

Spiking Neural Networks (SNNs) are typically regards as the third generation of neural networks due to their inherent event-driven computing capabilities and remarkable energy efficiency. However, training an SNN that possesses fast inference speed and comparable accuracy to modern artificial neural networks (ANNs) remains a considerable challenge. In this article, a sophisticated SNN modeling algorithm incorporating a novel dynamic threshold adaptation mechanism is proposed.

Automatic prediction model for online diaphragm motion tracking based on optical surface monitoring by machine learning.

Background: The aim of this study was to establish a correlation model between external surface motion and internal diaphragm apex movement using machine learning and to realize online automatic prediction of the diaphragm motion trajectory based on optical surface monitoring.

Methods: The optical body surface parameters and kilovoltage (kV) X-ray fluoroscopic images of 7 liver tumor patients were captured synchronously for 50 seconds. The location of the diaphragm apex was manually delineated by a radiation oncologist and automatically detected with a convolutional network model in fluoroscopic images.

Breath-Hold CBCT-Guided CBCT-to-CT Synthesis via Multimodal Unsupervised Representation Disentanglement Learning.

Adaptive radiation therapy (ART) aims to deliver radiotherapy accurately and precisely in the presence of anatomical changes, in which the synthesis of computed tomography (CT) from cone-beam CT (CBCT) is an important step. However, because of serious motion artifacts, CBCT-to-CT synthesis remains a challenging task for breast-cancer ART. Existing synthesis methods usually ignore motion artifacts, thereby limiting their performance on chest CBCT images.

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT.

LGEANet: LSTM-global temporal convolution-external attention network for respiratory motion prediction.

Purpose: To develop a deep learning network that treats the three-dimensional respiratory motion signals as a whole and considers the inter-dimensional correlation between signals of different directions for accurate respiratory tumor motion prediction.

Methods: We propose a deep learning framework, named as LSTM-Global Temporal Convolution-External Attention Network (LGEANet). In LGEANet, we first feed each of the univariate time series into the Long Short-Term Memory (LSTM) module respectively and utilize the strength of the global temporal convolutional layer to discover the temporal pattern of the univariate signals from hidden states of the LSTM.

Prognostic generalization of multi-level CT-dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma.

Purpose: To investigate the prognostic performance of multi-level computed tomography (CT)-dose fusion dosiomics at the image-, matrix-, and feature-levels from the gross tumor volume (GTV) at nasopharynx and the involved lymph node for nasopharyngeal carcinoma (NPC) patients.

Methods: Two hundred and nineteen NPC patients (175 vs. 44 for training vs.

Multiscale Local Enhancement Deep Convolutional Networks for the Automated 3D Segmentation of Gross Tumor Volumes in Nasopharyngeal Carcinoma: A Multi-Institutional Dataset Study.

Purpose: Accurate segmentation of gross target volume (GTV) from computed tomography (CT) images is a prerequisite in radiotherapy for nasopharyngeal carcinoma (NPC). However, this task is very challenging due to the low contrast at the boundary of the tumor and the great variety of sizes and morphologies of tumors between different stages. Meanwhile, the data source also seriously affect the results of segmentation.

Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study.

Purpose: We developed a deep learning model to achieve automatic multitarget delineation on planning CT (pCT) and synthetic CT (sCT) images generated from cone-beam CT (CBCT) images. The geometric and dosimetric impact of the model was evaluated for breast cancer adaptive radiation therapy.

Methods: We retrospectively analyzed 1,127 patients treated with radiotherapy after breast-conserving surgery from two medical institutions.

Rectum Protection by Rectal Gel Injection in Cervical Cancer Brachytherapy: A Dosimetric Study Deformable Surface Dose Accumulation and Machine-Learning-Based Discriminative Modeling.

Purpose: This retrospective study aimed to evaluate the dosimetric effects of a rectal insertion of on rectal protection using deformable dose accumulation and machine learning-based discriminative modelling.

Materials And Methods: Sixty-two patients with cervical cancer enrolled in a clinical trial, who received a injection of 20 g into their rectum for rectal protection high-dose rate brachytherapy (HDR-BT, 6 Gy/f), were studied. The cumulative equivalent 2-Gy fractional rectal surface dose was deformably summed using an in-house-developed topography-preserved point-matching deformable image registration method.

Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases.

Objectives: To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases.

Materials And Methods: A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis.

Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.

Purpose: The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning-based synthetic computed tomography (sCT) generation in the complex head and neck region.

Methods: Four sequences of MR images (T1, T2, T1C, and T1DixonC-water) were collected from 45 patients with nasopharyngeal carcinoma. Seven conditional generative adversarial network (cGAN) models were trained with different sequences (single channel) and different combinations (multi-channel) as inputs.

Neuroprotective Effect of Optimized Yinxieling Formula in 6-OHDA-Induced Chronic Model of Parkinson's Disease through the Inflammation Pathway.

Parkinson's disease (PD) is characterized by progressive degeneration of dopaminergic neurons in the substantia nigra (SN)-striatum circuit, which is associated with glial activation and consequent chronic neuroinflammation. Optimized Yinxieling Formula (OYF) is a Chinese medicine that exerts therapeutical effect and antiinflammation property on psoriasis. Our previous study has proven that pretreatment with OYF could regulate glia-mediated inflammation in an acute mouse model of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine.

Subregional Radiomics Analysis of PET/CT Imaging with Intratumor Partitioning: Application to Prognosis for Nasopharyngeal Carcinoma.

Purpose: This work aims to identify intratumoral habitats with distinct heterogeneity based on 2-deoxy-2-[F]fluro-D-glucose positron emission tomography (PET)/X-ray computed tomography (CT) imaging, and to develop a subregional radiomics approach to predict progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC).

Procedures: In total, 128 NPC patients (85 vs. 43 for primary vs.

Inter-regulation of IGFBP1 and FOXO3a unveils novel mechanism in ursolic acid-inhibited growth of hepatocellular carcinoma cells.

Background: Ursolic acid (UA), a natural pentacyclic triterpenoid, exerts anti-tumor effects in various cancer types including hepatocellular carcinoma (HCC). However, the molecular mechanisms underlying this remain largely unknown.

Methods: Cell viability and cell cycle were examined by MTT and Flow cytometry assays.

[Monte Carlo simulation and validation of the multi-leaf collimator of Varian 23EX accelerator].

Objective: To simulate the multi-leaf collimator of Varian linear accelerator using Monte Carlo method.

Methods: The multi-leaf collimator model was established using the DYNVMLC module of BEAMnrc and validated by comparison of Monte Carlo simulation and actual measurement results.

Results: The simulation results were well consistent with the actual measurement results with a bias of less than 3%.