Radiomic features add incremental benefit to conventional radiological feature-based differential diagnosis of lung nodules.

Purpose: To investigate the incremental benefit of adding radiomic features to conventional semantic radiological feature-based differential diagnosis between benign and malignant lung nodules.

Methods: From May 2017 to March 2021, 393 patients with 465 pathologically confirmed lung nodules were enrolled with 54 patients with 54 lung nodules as external testing. Based on manually segmented lung nodules, 1409 radiomics features were extracted.

Baseline computed tomography imaging findings could assist in early diagnosis of visceral pleural invasion for newly discovered early subpleural non-small cell lung cancer: T1 or T2.

Background: Preoperative accurate visceral pleural infiltration (VPI) diagnosis for T1-size non-small cell lung cancer (NSCLC) is significant for clinical decision-making. The study aimed to explore the diagnostic efficacy of computed tomography (CT) imaging features and serum biomarkers in diagnosing VPI in newly discovered subpleural NSCLC ≤3 cm.

Methods: There were 447 patients with NSCLC ≤3 cm retrospectively enrolled and assigned to the VPI group (n=81) and the non-VPI group (n=366) based on elastic fiber staining results.

Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis.

Background: Radiogenomics is an emerging technology that integrates genomics and medical image-based radiomics, which is considered a promising approach toward achieving precision medicine.

Objective: The aim of this study was to quantitatively analyze the research status, dynamic trends, and evolutionary trajectory in the radiogenomics field using bibliometric methods.

Methods: The relevant literature published up to 2023 was retrieved from the Web of Science Core Collection.

Deregulation of immune response contributing to fulminant hepatitis in HEV infected pregnant women.

Hepatitis E virus (HEV) infection in pregnant women is associated with a wide spectrum of adverse consequences for both mother and fetus. The high mortality in this population appears to be associated with hormonal changes and consequent immunological changes. This study conducted an analysis of immune responses in pregnant women infected with HEV manifesting varying severity.

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.

Background: In low-dose computed tomography (LDCT) lung cancer screening, soft tissue is hardly appreciable due to high noise levels. While deep learning-based LDCT denoising methods have shown promise, they typically rely on structurally aligned synthesized paired data, which lack consideration of the clinical reality that there are no aligned LDCT and normal-dose CT (NDCT) images available. This study introduces an LDCT denoising method using clinically structure-unaligned but paired data sets (LDCT and NDCT scans from the same patients) to improve lesion detection during LDCT lung cancer screening.

Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma.

Background: Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC.

Methods: Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio.

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images.

Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion.In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation.

Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions.

Objectives: To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications.

Materials And Methods: From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19).

Spectral CT assists differentiation of osteoblastic bone metastasis from bone island in newly diagnosed cancer patients.

Objectives: To investigate measurements derived from plain and enhanced spectral CT in differentiating osteoblastic bone metastasis (OBM) from bone island (BI).

Materials And Methods: From January to November 2020, 73 newly diagnosed cancer patients with 201 bone lesions (OBM = 92, BI = 109) having received spectral CT were retrospectively enrolled. Measurements including CT values of 40-140 keV, slope of the spectral curve, effective atomic number (Z), water (calcium) density, calcium (water) density, and Iodine (calcium) density were derived from manually segmented lesions on plain and enhanced spectral CT, and then analyzed using Student t-test and Pearson's correlation.

A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.

Background: Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition.

Utility of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) to predict prognosis and survival risk in laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy.

Purpose: To investigate the predictive power of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) in prognosis and survival risk of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy.

Materials And Methods: Forty-five patients with laryngeal or hypopharyngeal squamous cell carcinoma were retrospectively enrolled. All patients had undergone pretreatment IVIM examination, subsequently, mean apparent diffusion coefficient (ADCmean), maximum ADC (ADCmax), minimum ADC (ADCmin) and ADCrange (ADCmax - ADCmean) by mono-exponential model, true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f) by bi-exponential model, distributed diffusion coefficient (DDC), and diffusion heterogeneity index (α) by stretched exponential model were measured.

Added value of histogram analysis of ADC in predicting radiation-induced temporal lobe injury of patients with nasopharyngeal carcinoma treated by intensity-modulated radiotherapy.

Background: This study evaluated the predictive potential of histogram analysis derived from apparent diffusion coefficient (ADC) maps in radiation-induced temporal lobe injury (RTLI) of nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT).

Results: Pretreatment diffusion-weighted imaging (DWI) of the temporal lobes of 214 patients with NPC was retrospectively analyzed to obtain ADC histogram parameters. Of the 18 histogram parameters derived from ADC maps, 7 statistically significant variables in the univariate analysis were included in the multivariate logistic regression analysis.

PPA1, an energy metabolism initiator, plays an important role in the progression of malignant tumors.

Inorganic pyrophosphatase (PPA1) encoded by PPA1 gene belongs to Soluble Pyrophosphatases (PPase) family and is expressed widely in various tissues of Homo sapiens, as well as significantly in a variety of malignancies. The hydrolysis of inorganic pyrophosphate (PPi) to produce orthophosphate (Pi) not only dissipates the negative effects of PPi accumulation, but the energy released by this process also serves as a substitute for ATP. PPA1 is highly expressed in a variety of tumors and is involved in proliferation, invasion, and metastasis during tumor development, through the JNK/p53, Wnt/β-catenin, and PI3K/AKT/GSK-3β signaling pathways.

Induction TPF followed by concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in locally advanced hypopharyngeal cancer: a preliminary analysis of a randomized phase 2 trial.

Purpose: Concurrent chemoradiotherapy (CCRT) is a standard treatment choice for locally advanced hypopharyngeal carcinoma. The aim of this study was to investigate whether induction chemotherapy (IC) followed by CCRT is superior to CCRT alone to treat locally advanced hypopharyngeal carcinoma.

Methods And Materials: Patients (n = 142) were randomized to receive two cycles of paclitaxel/cisplatin/5-fluorouracil (TPF) IC followed by CCRT or CCRT alone.

Segmentation-guided Denoising Network for Low-dose CT Imaging.

Background: To reduce radiation exposure and improve diagnosis in low-dose computed tomography, several deep learning (DL)-based image denoising methods have been proposed to suppress noise and artifacts over the past few years. However, most of them seek an objective data distribution approximating the gold standard and neglect structural semantic preservation. Moreover, the numerical response in CT images presents substantial regional anatomical differences among tissues in terms of X-ray absorbency.

Prediction model based on preoperative CT findings for carotid artery invasion in patients with head and neck masses.

Objectives: To investigate the performance of a model in predicting carotid artery (CA) invasion in patients with head and neck masses using computed tomography (CT).

Methods: This retrospective study included patients with head and neck masses who underwent CT and surgery between January 2013 and July 2021. Patient characteristics and ten CT features were assessed by two radiologists.

Magnetic resonance imaging-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma after intensity-modulated radiotherapy.

Background: To develop a model based on magnetic resonance imaging (MRI) radiomics and clinical features for predicting radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT).

Methods: Two hundred and sixteen patients with NPC were retrospectively included. Radiomics features were extracted and selected.

Improved GAN: Using a transformer module generator approach for material decomposition.

Dual-energy computed tomography (CT) can be used for material decomposition, allowing for the precise quantitative mapping of body substances; this has a wide range of clinical applications, including disease diagnosis, treatment response evaluation and prognosis prediction. However, dual-energy CT has not yet become the mainstream technique in most clinical settings due to its limited accessibility. To fully take advantage of material quantification, researchers have attempted to use deep learning to generate material decomposition maps from conventional single-energy CT images, mainly by synthesizing another single-energy CT image from a conventional single-energy CT image to form a dual-energy CT image first and then generate material decomposition maps.

Sarcopenia Was a Poor Prognostic Predictor for Patients With Advanced Lung Cancer Treated With Immune Checkpoint Inhibitors.

Background: It remains not well known whether skeletal muscle mass (SMM) loss has any impact on the effectiveness of immune checkpoint inhibitors (ICIs) in patients with advanced lung cancer. We aimed to evaluate the association between SMM and clinical outcome of patients with advanced lung cancer receiving ICIs as first line or second line.

Materials And Methods: From March 1st, 2019 to March 31st, 2021 at our hospital, 34 patients with advanced lung cancer treated with first-line or second-line ICIs were enrolled retrospectively.

Breast Amide Proton Transfer Imaging at 3 T: Diagnostic Performance and Association With Pathologic Characteristics.

Background: Amide proton transfer (APT) imaging has been increasingly applied in tumor characterization. However, its value in evaluating breast cancer remains undetermined.

Purpose: To assess the diagnostic performance of APT imaging in breast cancer and its association with prognostic histopathologic characteristics.

A MRI-based radiomics model predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

Objectives: To develop and validate a radiomics-based model for predicting radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) by pretreatment MRI of the temporal lobe.

Methods: A total of 216 patients with diagnosed NPC were retrospectively reviewed. Patients were randomly allocated to the training (n = 136) and the validation cohort (n = 80).

Demonstration of fast and equilibrium human muscle creatine CEST imaging at 3 T.

Purpose: Creatine chemical exchange saturation transfer (CrCEST) MRI is used increasingly in muscle imaging. However, the CrCEST measurement depends on the RF saturation duration (Ts) and relaxation delay (Td), and it is challenging to compare the results of different scan parameters. Therefore, this study aims to evaluate the quasi-steady-state (QUASS) CrCEST MRI on clinical 3T scanners.

Baseline Amide Proton Transfer Imaging at 3T Fails to Predict Early Response to Induction Chemotherapy in Nasopharyngeal Carcinoma.

Background: Early identification of nasopharyngeal carcinoma (NPC) patients with high risk of failure to induction chemotherapy (IC) would facilitate prompt individualized treatment decisions and thus reduce toxicity and improve overall survival rate. This study aims to investigate the value of amide proton transfer (APT) imaging in predicting short-term response of NPC to IC and its potential correlation with well-established prognosis-related clinical characteristics.

Methods And Materials: A total of 80 pathologically confirmed NPC patients receiving pre-treatment APT imaging at 3T were retrospectively enrolled.

Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment.

Background: Accurate pretreatment prediction for disease progression of nasopharyngeal carcinoma is key to intensify therapeutic strategies to high-risk individuals. Our aim was to evaluate the value of baseline MRI-based radiomics machine-learning models in predicting the disease progression in nasopharyngeal carcinoma patients who achieved complete response after treatment.

Methods: In this retrospective study, 171 patients with pathologically confirmed nasopharyngeal carcinoma were included.