Radiomic signatures of brain metastases on MRI: utility in predicting pathological subtypes of lung cancer.

Background: The pathological sub-classification of lung cancer is crucial in diagnosis, treatment and prognosis for patients. Quick and timely identification of pathological subtypes from imaging examinations rather than histological tests could help guiding therapeutic strategies. The aim of the study is to construct a non-invasive radiomics-based model for predicting the subtypes of lung cancer on brain metastases (BMs) from multiple magnetic resonance imaging (MRI) sequences.

Feasibility of Sub-milliSievert Low-dose Computed Tomography with Deep Learning Image Reconstruction in Evaluating Pulmonary Subsolid Nodules: A Prospective Intra-individual Comparison Study.

Rationale And Objectives: To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to noise.

Materials And Methods: Patients undergoing both standard-dose CT (SDCT) and LDCT between March and June 2023 were prospectively enrolled. LDCT images were reconstructed with high-strength DLIR (DLIR-H), medium-strength DLIR (DLIR-M), adaptive statistical iterative reconstruction-V level 50% (ASIR-V-50%), and filtered back projection (FBP); SDCT with FBP as the reference standard.

Obesity and survival in advanced non-small cell lung cancer patients treated with chemotherapy, immunotherapy, or chemoimmunotherapy: a multicenter cohort study.

Background: The association of body mass index (BMI) with survival outcomes in patients with advanced non-small cell lung cancer (NSCLC) treated with first-line chemotherapy, immunotherapy, or chemoimmunotherapy is controversial. We aimed to investigate these associations, including associations in male and female patients specifically, in a multicenter cohort study.

Methods: We retrospectively analyzed data from seven cohorts comprising 7021 advanced non-small cell lung cancer patients who received chemotherapy (three cohorts), immunotherapy (two cohorts), and chemoimmunotherapy (two cohorts) from five data sources, including a de-identified nationwide (US-based) NSCLC clinico-genomic database and two randomized, double-blind, phase 3 clinical trials.

Automatic detection of pulmonary embolism on computed tomography pulmonary angiogram scan using a three-dimensional convolutional neural network.

Objective: To propose a convolutional neural network (EmbNet) for automatic pulmonary embolism detection on computed tomography pulmonary angiogram (CTPA) scans and to assess its diagnostic performance.

Methods: 305 consecutive CTPA scans between January 2019 and December 2021 were enrolled in this study (142 for training, 163 for internal validation), and 250 CTPA scans from a public dataset were used for external validation. The framework comprised a preprocessing step to segment the pulmonary vessels and the EmbNet to detect emboli.

Identifying autism spectrum disorder from multi-modal data with privacy-preserving.

The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance.

Combination of Contrast-enhanced FlAIR and Contrast-enhanced T1WI: A Quick and Efficient Method in Detecting Brain Metastases of Lung Cancers.

Background: Some patients with suspected brain metastases (BM) could not tolerate longer scanning examinations according to the standardized MRI protocol.

Objective: The purpose of this study was to evaluate the clinical value of contrast-enhanced fast fluid-attenuated inversion recovery (CE FLAIR) imaging in combination with contrast-enhanced T1 weighted imaging (CE T1WI) in detecting BM of lung cancer and explore a quick and effective MRI protocol.

Material And Methods: In 201 patients with lung cancers and suspected BM, T1WI and FLAIR were performed before and after administration of gadopentetate dimeglumine.

Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning.

Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images.

A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test.

Background: Chest computed tomography (CT) is a critical tool in the diagnosis of pulmonary cryptococcosis as approximately 30% of normal immunity individuals may not exhibit any significant symptoms or laboratory findings. Pulmonary cryptococcosis granuloma and lung adenocarcinoma can appear similar on noncontrast chest CT. This study evaluates the use of an integrated model that was developed based on radiomic features combined with demographic and radiological features to differentiate pulmonary cryptococcosis nodules from lung adenocarcinomas.

Risk factors for pulmonary embolism in lung cancer patients with lower limb deep venous thrombosis: a case-control study.

Background: There is growing evidence that misdiagnosis contributes to the high mortality rate in lung cancer patients complicated with pulmonary embolism (PE). This current study analyzed predictors of PE in lung cancer patients with lower extremity deep venous thrombosis (DVT) with the aim of personalizing the treatment and management of patients with PE.

Methods: This retrospective case-control study included lung cancer patients with DVT at the emergency department of Shanghai Chest Hospital from January 2018 to December 2019.

Clinicopathological features and survival of rare primary pulmonary lymphoepithelial carcinoma: A cohort from a single center.

Background: Primary pulmonary lymphoepithelial carcinoma (PLEC) is a rare subtype of nonsmall cell lung cancer. This study aimed to investigate the clinicopathological and prognostic characteristics of resected primary PLEC.

Materials And Methods: In this retrospective study, 95 consecutive patients with primary PLEC, who received radical surgical resection treatment, were examined from October 2009 to January 2022.

Impact of surgery and adjuvant chemotherapy on the survival of stage I lung adenocarcinoma patients with tumor spread through air spaces.

Objectives: Tumor spread through air spaces (STAS) is a unique mechanism of lung cancer metastasis; however, its clinical value for stage I lung adenocarcinoma (ADC) remains unclear at present. We investigated the (1) prognosis of patients after sublobar resection compared with lobectomy for stage I lung adenocarcinoma with STAS; and (2) potential benefits of adjuvant chemotherapy (ACT) for patients with stage I ADC and STAS.

Methods: A total of 3328 consecutive patients with stage I ADC were retrospectively identified between 2014 and 2018 at our institution; among them, 600 were diagnosed with STAS.

An Interpretable Deep Embedding Model for Few and Imbalanced Biomedical Data.

In healthcare, training examples are usually hard to obtain (e.g., cases of a rare disease), or the cost of labelling data is high.

Prognostic and predictive value of radiomic signature in stage I lung adenocarcinomas following complete lobectomy.

Background: The overall survival (OS) of stage I operable lung cancer is relatively low, and not all patients can benefit from adjuvant chemotherapy. This study aimed to develop and validate a radiomic signature (RS) for prediction of OS and adjuvant chemotherapy candidates in stage I lung adenocarcinoma.

Methods: A total of 474 patients from 2 centers were divided into 1 training (n = 287), 1 internal validation (n = 122), and 1 external validation (n = 65) cohorts.

Clinical, radiologic, and physiologic features of idiopathic pulmonary fibrosis (IPF) with and without emphysema.

Background: Idiopathic pulmonary fibrosis (IPF) can combine with emphysema, a condition termed as IPF with emphysema (IPFE). We compared the clinical, radiologic, and physiologic features of IPF and IPFE.

Research Design And Methods: Newly diagnosed IPF    and IPFE    patients were recruited between January 2018 and September 2020.

Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.

Background: Accurate preoperative prediction of the invasiveness of lung nodules on computed tomography (CT) can avoid unnecessary invasive procedures and costs for low-risk patients. While previous studies approached this task using cross-sectional data, this study aimed to utilize the commonly available longitudinal data of lung nodules through sequential modelling based on long short-term memory (LSTM) networks.

Methods: We retrospectively included 171 patients with lung nodules that were followed-up at least once and pathologically diagnosed with adenocarcinoma for model development.

A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.

The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g.

Computed tomography versus frozen sections for distinguishing lung adenocarcinoma: A cohort study of concordance rate.

Background: Computed tomography (CT) imaging can help to predict the pathological invasiveness of early-stage lung adenocarcinoma and guide surgical resection. This retrospective study investigated whether CT imaging could distinguish pre-invasive lung adenocarcinoma from IAC. It also compared final pathology prediction accuracy between CT imaging and intraoperative frozen section analysis.

Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.

Background: Risk prediction models of lung nodules have been built to alleviate the heavy interpretative burden on clinicians. However, the malignancy scores output by those models can be difficult to interpret in a clinically meaningful manner. In contrast, the modeling of lung nodule growth may be more readily useful.

Accurate prediction of epidermal growth factor receptor mutation status in early-stage lung adenocarcinoma, using radiomics and clinical features.

Objectives: To develop a nomogram based on CT radiomics and clinical features to predict the epidermal growth factor receptor (EGFR) mutations in early-stage lung adenocarcinomas.

Methods: A retrospective analysis of postoperative patients with pathologically confirmed lung adenocarcinoma, which had been tested for EGFR mutations was performed from January 2015 to December 2015. Patients were randomly assigned to training and validation cohorts.

Universal Nanoparticle Counting Platform for Tetraplexed Biomarkers by Integrating Immunorecognition and Nucleic Acid Hybridization in One Assay.

The development of a simple and universal strategy for simultaneous quantification of proteins and nucleic acid biomarkers in one assay is valuable, particularly for disease diagnosis and pathogenesis studies. Herein, a universal and amplification-free quantum dot-doped nanoparticle counting platform was developed by integrating immunorecognition and nucleic acid hybridization in one assay. The assay can be performed at room temperature, which is friendly for routine analysis.

Contrast-enhanced CT longitudinal tail sign as a marker of positive resection margins in adenoid cystic carcinoma of the central airway.

Background: Due to submucosal infiltration's biological nature along the airway, adenoid cystic carcinoma (ACC) frequently leaves positive surgical margins. This study evaluated the clinicopathologic, and computed tomography (CT) features for predicting surgical margin status in central airway ACC.

Methods: We retrospectively analyzed the files of 71 patients with ACC of the central airway proven by histopathology and surgery who had presented between January 2010 and December 2018.

Pre-Immunotherapy Contrast-Enhanced CT Texture-Based Classification: A Useful Approach to Non-Small Cell Lung Cancer Immunotherapy Efficacy Prediction.

To investigate the utility of the pre-immunotherapy contrast-enhanced CT-based texture classification in predicting response to non-small cell lung cancer (NSCLC) immunotherapy treatment. Sixty-three patients with 72 lesions who received immunotherapy were enrolled in this study. We extracted textures including histogram, absolute gradient, run-length matrix, gray-level co-occurrence matrix, autoregressive model, and wavelet transform from pre-immunotherapy contrast-enhanced CT by using Mazda software.

Nonamplification Multiplexed Assay of Endonucleases and DNA Methyltransferases by Colocalized Particle Counting.

Restriction endonucleases (ENases) and DNA methyltransferases (MTases) are important enzymes in biological processes, and detection of ENases/MTases activity is significant for biological and pharmaceutical studies. However, available nonamplification methods with a versatile design, desirable sensitivity, and signal production mode of unbiased quantification toward multiple nucleases are rare. By combining deliberately designed hairpin DNA probes with the colocalized particle counting technique, we present a nonamplification, separation-free method for multiplexed detection of ENases and MTases.