A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients.

Background: This study aimed to develop a multi-modality model by incorporating pretreatment computed tomography (CT) radiomics and pathomics features along with clinical variables to predict pathologic complete response (pCR) to neoadjuvant chemoimmunotherapy in patients with locally advanced esophageal cancer (EC).

Method: A total of 223 EC patients who underwent neoadjuvant chemoimmunotherapy followed by surgical intervention between August 2021 and December 2023 were included in this study. Radiomics features were extracted from contrast-enhanced CT images using PyrRadiomics, while pathomics features were derived from whole-slide images (WSIs) of pathological specimens using a fine-tuned deep learning model (ResNet-50).

Multi-omics models for predicting prognosis in non-small cell lung cancer patients following chemotherapy and radiotherapy: A multi-center study.

Background And Purpose: Quantifying tumor heterogeneity from various dimensions is crucial for precise treatment. This study aimed to develop and validate multi-omics models based on the computed tomography images, pathological images, dose and clinical information to predict treatment response and overall survival of non-small cell lung cancer (NSCLC) patients undergoing chemotherapy and radiotherapy.

Materials And Methods: This retrospective study included 220 NSCLC patients from three centers.

A novel nanodrug for the sensitization of photothermal chemotherapy for breast cancer .

Owing to the complexity of tumor treatment, clinical tumor treatment has evolved from a single treatment mode to multiple combined treatment modes. Reducing the tolerance of tumors to heat and the toxicity of chemotherapy drugs to the body, as well as increasing the sensitivity of tumors to photothermal therapy and chemotherapy drugs, are key issues that urgently need to be addressed in the current cancer treatment. In this work, polylactic acid-based drug nanoparticles (PLA@DOX/GA/ICG) were synthesized with good photothermal conversion ability by encapsulating the water-soluble anticancer drug doxorubicin (DOX), photothermal conversion agent indocyanine green (ICG) and liposoluble drug gambogic acid (GA) using a double emulsion method.

Polysaccharides in fruits: Biological activities, structures, and structure-activity relationships and influencing factors-A review.

Fruits are a rich source of polysaccharides, and an increasing number of studies have shown that polysaccharides from fruits have a wide range of biological functions. Here, we thoroughly review recent advances in the study of the bioactivities, structures, and structure-activity relationships of fruit polysaccharides, especially highlighting the structure-activity influencing factors such as extraction methods and chemical modifications. Different extraction methods cause differences in the primary structures of polysaccharides, which in turn lead to different polysaccharide biological activities.

Carbon Quantum Dots: Properties, Preparation, and Applications.

Carbon quantum dots are a novel form of carbon material. They offer numerous benefits including particle size adjustability, light resistance, ease of functionalization, low toxicity, excellent biocompatibility, and high-water solubility, as well as their easy accessibility of raw materials. Carbon quantum dots have been widely used in various fields.

Classification and prediction of chemoradiotherapy response and survival from esophageal carcinoma histopathology images.

Whole slide imaging (WSI) of Hematoxylin and Eosin-stained biopsy specimens has been used to predict chemoradiotherapy (CRT) response and overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients. This retrospective study collected 279 specimens in 89 non-surgical ESCC patients through endoscopic biopsy between January 2010 and January 2019. These patients were divided into a CRT response group (CR + PR group) and a CRT non-response group (SD + PD group).

Integration of dosimetric parameters, clinical factors, and radiomics to predict symptomatic radiation pneumonitis in lung cancer patients undergoing combined immunotherapy and radiotherapy.

Purpose: This study aimed to combine clinical/dosimetric factors and handcrafted/deep learning radiomic features to establish a predictive model for symptomatic (grade ≥ 2) radiation pneumonitis (RP) in lung cancer patients who received immunotherapy followed by radiotherapy.

Materials And Methods: This study retrospectively collected data of 73 lung cancer patients with prior receipt of ICIs who underwent thoracic radiotherapy (TRT). Of these 73 patients, 41 (56.

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma.

Background: To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images.

Methods: This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response.

Whole slide imaging-based deep learning to predict the treatment response of patients with non-small cell lung cancer.

Background: This study developed and validated a deep learning (DL) model based on whole slide imaging (WSI) for predicting the treatment response to chemotherapy and radiotherapy (CRT) among patients with non-small cell lung cancer (NSCLC).

Methods: We collected the WSI of 120 nonsurgical patients with NSCLC treated with CRT from three hospitals in China. Based on the processed WSI, two DL models were established: a tissue classification model which was used to select tumor-tiles, and another model which predicted the treatment response of the patients based on the tumor-tiles (predicting the treatment response of each tile).

Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology.

The pathological diagnosis of intracranial germinoma (IG), oligodendroglioma, and low-grade astrocytoma on intraoperative frozen section (IFS) and hematoxylin-eosin (HE)-staining section directly determines patients' treatment options, but it is a difficult task for pathologists. We aimed to investigate whether whole-slide imaging (WSI)-based deep learning can contribute new precision to the diagnosis of IG, oligodendroglioma, and low-grade astrocytoma. Two types of WSIs (500 IFSs and 832 HE-staining sections) were collected from 379 patients at multiple medical centers.

Identifying individuals with attention-deficit/hyperactivity disorder based on multisite resting-state functional magnetic resonance imaging: A radiomics analysis.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age-inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting-state functional magnetic resonance (rs-fMRI) have more discriminative power for the diagnosis of ADHD.

Ultrasound Image-Based Deep Features and Radiomics for the Discrimination of Small Fat-Poor Angiomyolipoma and Small Renal Cell Carcinoma.

We evaluated the performance of ultrasound image-based deep features and radiomics for differentiating small fat-poor angiomyolipoma (sfp-AML) from small renal cell carcinoma (SRCC). This retrospective study included 194 patients with pathologically proven small renal masses (diameter ≤4 cm; 67 in the sfp-AML group and 127 in the SRCC group). We obtained 206 and 364 images from the sfp-AML and SRCC groups with experienced radiologist identification, respectively.

Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: A retrospective cohort study.

Objective: We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in patients with locally advanced cervical cancer (LACC) undergoing intensity-modulated radiation therapy and brachytherapy.

Design: A retrospective cohort study.

Setting: Xiangya Hospital of Central South University, Changsha, Hunan, China.

Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions.

Purpose: This study aimed to investigate whether models built from radiomics features based on multiphase contrast-enhanced MRI can identify microscopic pre-hepatocellular carcinoma lesions.

Methods: We retrospectively studied 54 small hepatocellular carcinoma (SHCC, diameter < 2 cm) patients and 70 patients with hepatocellular cysts or haemangiomas from September 2018 to June 2021. For the former, two MRI scans were collected within 12 months of each other; the 2 scan was used to confirm the diagnosis.

Two neuroanatomical subtypes of males with autism spectrum disorder revealed using semi-supervised machine learning.

Background: Clinical and etiological varieties remain major obstacles to decompose heterogeneity in autism spectrum disorders (ASD). Recently, neuroimaging raised new hope to identify neurosubtypes of ASD for further understanding the biological mechanisms behind the disorder.

Methods: In this study, brain structural MRI data and clinical measures of 221 male subjects with ASD and 257 healthy controls were selected from 7 independent sites from the Autism Brain Image Data Exchange database (ABIDE).

Energizing Intention to Visit Rural Destinations: How Social Media Disposition and Social Media Use Boost Tourism Through Information Publicity.

This study aimed to examine the impact of information publicity on the intention of tourists to visit rural destinations in developing countries. Based on the theory of planned behavior (TPB), we examined the indirect effect of information publicity on intention to visit subjective norms and further investigated the moderating effect of social media disposition and social media use. The study used data from a time-lagged design with three waves which supported the hypothesized model.

Surface values, volumetric measurements and radiomics of structural MRI for the diagnosis and subtyping of attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is diagnosed subjectively based on an individual's behaviour and performance. The clinical community has no objective biomarker to inform the diagnosis and subtyping of ADHD. This study aimed to explore the potential diagnostic biomarkers of ADHD among surface values, volumetric metrics and radiomic features that were extracted from structural MRI images.

A Comparison of the Distortion in the Same Field MRI and MR-Linac System With a 3D Printed Phantom.

Purpose: A 3D printed geometric phantom was developed that can be scanned with computed tomography (CT) and magnetic resonance imaging (MRI) to measure the geometric distortion and determine the relevant dose changes.

Materials And Methods: A self-designed 3D printed photosensitive resin phantom was used, which adopts grid-like structures and has 822 1 cm squares. The scanning plan was delivered by three MRI scanners: the Elekta Unity MR-Linac 1.

Benchmarking of Deformable Image Registration for Multiple Anatomic Sites Using Digital Data Sets With Ground-Truth Deformation Vector Fields.

Purpose: This study aimed to evaluate the accuracy of deformable image registration (DIR) algorithms using data sets with different levels of ground-truth deformation vector fields (DVFs) and to investigate the correlation between DVF errors and contour-based metrics.

Methods And Materials: Nine pairs of digital data sets were generated through contour-controlled deformations based on 3 anonymized patients' CTs (head and neck, thorax/abdomen, and pelvis) with low, medium, and high deformation intensity for each site using the ImSimQA software. Image pairs and their associated contours were imported to MIM-Maestro, Raystation, and Velocity systems, followed by DIR and contour propagation.

Dosimetric Factors and Radiomics Features Within Different Regions of Interest in Planning CT Images for Improving the Prediction of Radiation Pneumonitis.

Purpose: This study aimed to establish machine learning models using dosimetric factors and radiomics features within 5 regions of interest (ROIs) in treatment planning computed tomography images to improve the prediction of symptomatic radiation pneumonitis (RP) (grade ≥2).

Methods And Materials: This study retrospectively collected data on 79 patients with lung cancer (25 RP ≥2) who underwent chemoradiotherapy between 2015 and 2018. We defined 5 ROIs in planning computed tomography images: gross tumor volume (GTV), planning tumor volume (PTV), PTV-GTV, total lung (TL)-GTV, and TL-PTV.

MRI Radiomics for Prediction of Tumor Response and Downstaging in Rectal Cancer Patients after Preoperative Chemoradiation.

Purpose: This study aimed to investigate radiomic features extracted from magnetic resonance imaging (MRI) scans performed before and after neoadjuvant chemoradiotherapy (nCRT) in predicting response of locally advanced rectal cancer (LARC).

Methods And Materials: Thirty-nine patients who underwent nCRT for LARC were included, with 294 radiomic features extracted from MRI that was performed before (pre-CRT) and 6 to 8 weeks after completing nCRT (post-CRT). Based on tumor regression grade (TRG), 26 patients were classified as having a histopathologic good response (GR; TRG 0-1) and 13 as non-GR (TRG 2-3).

Clinical feasibility of MR-assisted CT-based cervical brachytherapy using MR-to-CT deformable image registration.

Purpose: The purpose of this study is to evaluate the feasibility of using deformable image registration algorithms to improve high-dose-rate high-risk clinical target volume (HR-CTV) delineation between preapplicator implantation MRI (pre-MRI) and postapplicator implantation CT (post-CT) in the treatment of locally advanced cervical cancer (LACC).

Method And Materials: Twenty-six patients were identified for the study. Regions of interest were segmented on MRI and CT.

Cone-beam computed tomography-based delta-radiomics for early response assessment in radiotherapy for locally advanced lung cancer.

Cone-beam computed tomography (CBCT) images acquired during radiotherapy may allow early response assessment. Previous studies have reported inconsistent findings on an association of CBCT-measured tumor volume changes with clinical outcomes. The purpose of this pilot study was twofold: (1) to characterize changes in CBCT-based radiomics features during treatment; and (2) to quantify the potential association of CBCT-based delta-radiomics features with overall survival in locally advanced lung cancer.

Characterizing mechanical and medical imaging properties of polyvinyl chloride-based tissue-mimicking materials.

Polyvinyl chloride (PVC) is a commonly used tissue-mimicking material (TMM) for phantom construction using 3D printing technology. PVC-based TMMs consist of a mixture of PVC powder and dioctyl terephthalate as a softener. In order to allow the clinical use of a PVC-based phantom use across CT and magnetic resonance imaging (MRI) imaging platforms, we evaluated the mechanical and physical imaging characteristics of ten PVC samples.