Prediction of Malignancy and Pathological Types of Solid Lung Nodules on CT Scans Using a Volumetric SWIN Transformer.

Lung adenocarcinoma and squamous cell carcinoma are the two most common pathological lung cancer subtypes. Accurate diagnosis and pathological subtyping are crucial for lung cancer treatment. Solitary solid lung nodules with lobulation and spiculation signs are often indicative of lung cancer; however, in some cases, postoperative pathology finds benign solid lung nodules.

Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.

Background: The diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis.

Methods: 420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled.

CLSSL-ResNet: Predicting malignancy of solitary pulmonary nodules from CT images by chimeric label with self-supervised learning.

Background: Pulmonary granulomatous nodules (GN) with spiculation or lobulation have a similar morphological appearance to solid lung adenocarcinoma (SADC) under computed tomography (CT). However, these two kinds of solid pulmonary nodules (SPN) have different malignancies and are sometimes misdiagnosed.

Objective: This study aims to predict malignancies of SPNs by a deep learning model automatically.

Deep radiomic model based on the sphere-shell partition for predicting treatment response to chemotherapy in lung cancer.

Background: The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients.

Objectives: To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images.

Materials And Methods: This retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment.

The Computed Tomography Findings and Follow-up Course of Pulmonary Nocardiosis.

Objective: Our study aimed to elucidate the computed tomography (CT) features and follow-up course of pulmonary nocardiosis patients to improve the understanding and diagnostic accuracy of this disease.

Methods: The chest CT findings and clinical data of patients diagnosed with pulmonary nocardiosis by culture or histopathological examination in our hospital between 2010 and 2019 were retrospectively analyzed.

Results: A total of 34 cases of pulmonary nocardiosis were included in our study.

Radiomics Analysis of Multiphasic Computed Tomography Images for Distinguishing High-Risk Thymic Epithelial Tumors From Low-Risk Thymic Epithelial Tumors.

Objectives: The objective of this study is to preoperatively investigate the value of multiphasic contrast-enhanced computed tomography (CT)-based radiomics signatures for distinguishing high-risk thymic epithelial tumors (HTET) from low-risk thymic epithelial tumors (LTET) compared with conventional CT signatures.

Materials And Methods: Pathologically confirmed 305 thymic epithelial tumors (TETs), including 147 LTET (Type A/AB/B1) and 158 HTET (Type B2/B3/C), were retrospectively analyzed, and were randomly divided into training (n = 214) and validation cohorts (n = 91). All patients underwent nonenhanced, arterial contrast-enhanced, and venous contrast-enhanced CT analysis.

Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images.

The individual prognosis of chemotherapy is quite different in non-small cell lung cancer (NSCLC). There is an urgent need to precisely predict and assess the treatment response. To develop a deep multiple-instance learning (DMIL) based model for predicting chemotherapy response in NSCLC in pretreatment CT images.

Predicting chemotherapy response in non-small-cell lung cancer computed tomography radiomic features: Peritumoral, intratumoral, or combined?

Purpose: This study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC).

Methods: After excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.

Multi-scale segmentation squeeze-and-excitation UNet with conditional random field for segmenting lung tumor from CT images.

Background And Objective: Lung cancer counts among diseases with the highest global morbidity and mortality rates. The automatic segmentation of lung tumors from CT images is of vast significance. However, the segmentation faces several challenges, including variable shapes and different sizes, as well as complicated surrounding tissues.

The Assessment of Prostate Cancer Aggressiveness Using a Combination of Quantitative Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Objective: To explore the value of combining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters with apparent diffusion coefficient (ADC) values in the diagnosis of prostate cancer.

Methods: The clinical data of 146 patients with prostate lesions, including 87 patients with prostate cancer (PCa) and 59 with benign prostatic hyperplasia (BPH), were collected. After DCE-MRI and diffusion-weighted imaging (DWI) prostate scans, the magnitude of the DCE-MRI transfer constant ( ), rate constant ( ), the volume of the extravascular extracellular space ( ), and the ADC between the groups were compared, and the correlations between the DCE-MRI parameters and Gleason scores were analyzed.

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma.

To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images.

An antibiotic stewardship programme to reduce inappropriate antibiotic prescribing for acute respiratory infections in rural Chinese primary care facilities: study protocol for a clustered randomised controlled trial.

Background: Inappropriate prescribing of antibiotics for acute respiratory infections at the primary care level represents the major source of antibiotic misuse in healthcare, and is a major driver for antimicrobial resistance worldwide. In this study we will develop, pilot and evaluate the effectiveness of a comprehensive antibiotic stewardship programme in China's primary care hospitals to reduce inappropriate prescribing of antibiotics for acute respiratory infections among all ages.

Methods: We will use a parallel-group, cluster-randomised, controlled, superiority trial with blinded outcome evaluation but unblinded treatment (providers and patients).

Etiological distribution and clinical features of fever of unknown origin with pulmonary lesions in South China.

Introduction: Fever of unknown origin (FUO) with pulmonary lesions is a common syndrome in respiratory diseases, which can be caused by infection, cancer, connective tissue disease and other rare diseases of South China. In patients with FUO associated with pulmonary lesions, pathogeny should be identified and followed by an appropriate treatment strategy.

Objective: This study aimed to investigate the etiological distribution and clinical features of FUO with pulmonary lesions and to analyze the efficiency of different diagnostic methods for certain disease categories.

Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule.

Background: Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer sensitive to EGFR-targeted tyrosine kinase inhibitors. We aimed to develop and validate a computed tomography (CT)-based radiomics signature for prediction of EGFR mutation status in LADC appearing as a subsolid nodule.

Materials And Methods: A total of 467 eligible patients were divided into training and validation cohorts ( = 306 and 161, respectively).

Uncontrolled asthma phenotypes defined from parameters using quantitative CT analysis.

Objective: Asthma is a heterogeneous disease with diverse clinical phenotypes that have been identified via cluster analyses. However, the classification of phenotypes based on quantitative CT (qCT) is poorly understood. The study was conducted to investigate CT determination of uncontrolled asthma phenotypes.

CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma.

Objectives: Pulmonary granulomatous nodule (GN) with spiculated or lobulated appearance are indistinguishable from solid lung adenocarcinoma (SADC) based on CT morphological features, and partial false-positive findings on PET/CT. The objective of this study was to investigate the ability of quantitative CT radiomics for preoperatively differentiating solitary atypical GN from SADC.

Methods: 302 eligible patients (SADC = 209, GN = 93) were evaluated in this retrospective study and were divided into training (n = 211) and validation cohorts (n = 91).

Correlations between computed tomography and positron emission tomography/computed tomography findings and pathology in 6 cases of pulmonary epithelioid angiosarcoma.

Previous studies on primary pulmonary epithelioid angiosarcoma (PEA) have been mostly clinical or pathological case reports. We here summarize findings from computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) analyses of PEA to improve the diagnosis and differentiation of this rare tumor.We conducted a retrospective analysis of the clinical findings, radiological imaging, and pathological findings of 6 cases of primary PEA confirmed by surgery, biopsy, and pathology.

A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram.

Background: Lymph node metastasis (LNM) of lung cancer is an important factor related to survival and recurrence. The association between radiomics features of lung cancer and LNM remains unclear. We developed and validated a radiomics nomogram to predict LNM in solid lung adenocarcinoma.

Malignant solitary pulmonary nodules: assessment of mass growth rate and doubling time at follow-up CT.

Background: The differentiation of benign and malignant solitary pulmonary nodules (SPNs), especially subsolid nodules, is still challenging because of the small size, slow growth, and atypical imaging characteristics of these nodules. We aimed to determine the significance of mass growth rate (MGR) and mass doubling time (MDT) at follow-up CT of malignant SPNs.

Methods: This retrospective study included 167 patients (169 SPNs, diameter 8-30 mm).

Is a 5-mm diameter an appropriate cut-off value for the diagnosis of atypical adenomatous hyperplasia and adenocarcinoma in situ on chest computed tomography and pathological examination?

Background: Preinvasive lesions, such as atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), usually appear as pure ground-glass nodules (pGGNs) on thin-section computed tomography (TSCT). AAH is usually less than 5 mm wide on imaging and pathological examinations. We aimed to determine whether a 5-mm cut-off value was appropriate for the diagnosis of AAH and AIS.

Stage I synchronous multiple primary non-small cell lung cancer: CT findings and the effect of TNM staging with the 7th and 8th editions on prognosis.

Background: Studies have reported that up to 8% of non-small cell lung cancers (NSCLC) involve multiple lesions; no detailed study has assessed the prognosis of early synchronous multiple primary non-small cell lung cancer (SMPNSCLC) (T1N0M0, T2aN0M0). We aimed to assess the spiral CT manifestations of SMPNSCLC during stage I and evaluate the effect of TNM staging with the 7th and 8th editions on the prognosis.

Methods: We retrospectively analyzed the data of patients who were examined, operated, and pathologically confirmed as having NSCLC from January 1, 2009, to December 31, 2010, and were followed-up for 5 years.

Erlotinib intercalating pemetrexed/cisplatin versus erlotinib alone in Chinese patients with brain metastases from lung adenocarcinoma: a prospective, non-randomised, concurrent controlled trial (NCT01578668).

Objective: Erlotinib has a synergistic effect with pemetrexed for treating non-squamous non-small-cell lung cancer. We investigated the efficacy and safety of erlotinib (E) in combination with pemetrexed/cisplatin (E-P) in Chinese patients with lung adenocarcinoma with brain metastases.

Design: Patients who were erlotinib-naïve or pemetrexed-naïve were assigned in parallel to receive either E or E-P.

Quantitative Low-Dose Computed Tomography of the Lung Parenchyma and Airways for the Differentiation between Chronic Obstructive Pulmonary Disease and Asthma Patients.

Background: It is difficult to differentiate between chronic obstructive pulmonary disease (COPD) and asthma in clinics; therefore, for diagnostic purposes, imaging-based measurements could be beneficial to differentiate between the two diseases.

Objectives: We aim to analyze quantitative measurements of the lung and bronchial parameters that are provided by low-dose computed tomography (CT) to differentiate COPD and asthma from an imaging perspective.

Materials And Methods: 69 COPD patients, 52 asthma patients, and 20 healthy subjects were recruited to participate in CT imaging and pulmonary function tests (PFTs).

Coexistence of p16/CDKN2A homozygous deletions and activating EGFR mutations in lung adenocarcinoma patients signifies a poor response to EGFR-TKIs.

Objectives: Activating mutations in the epidermal growth factor receptor (EGFR) are strongly predictive of EGFR-tyrosine kinase inhibitor (TKI) activity in non-small cell lung cancer (NSCLC). However, primary resistance to EGFR-TKIs occurs in approximately 20-30% of NSCLC patients with EGFR mutations. The goal of this study was to determine whether p16/CDKN2A homozygous deletion (HD) is associated with primary resistance to EGFR-TKIs in lung adenocarcinoma patients with EGFR activating mutations.

Preoperative evaluation of stage T3, central-type non-small cell lung cancer with double sleeve lobectomy under complete video-assisted thoracoscopic surgery using spiral computed tomography post-processing techniques.

Background: To investigate the estimated value of spiral computed tomography (CT) post-processing techniques in preoperative stage T3, central-type non-small cell lung cancer (NSCLC) with double sleeve lobectomy under complete video-assisted thoracoscopic surgery (c-VATS).

Methods: Preoperative clinical date and CT reconstructed data of 10 patients who underwent double sleeve lobectomy with upper lobe stage T3, central-type NSCLC were retrospectively analysed and compared to surgical pathological results and cross-sectional CT data. The diagnostic criterions of tumour invasion of pulmonary artery and bronchus were divided into five grades, which included estimation of upper lobe pulmonary arteries and bronchi (40 branches, respectively).