Publications by authors named "Fa-jin Lv"
Rationale And Objectives: To explore the clinical and computed tomography (CT) characteristics of early-stage lung adenocarcinoma (LADC) that presents with an irregular shape.
Materials And Methods: The CT data of 575 patients with stage IA LADC and 295 with persistent inflammatory lesion (PIL) manifesting as subsolid nodules (SSNs) were analyzed retrospectively. Among these patients, we selected 233 patients with LADC and 140 patients with PIL, who showed irregular SSNs, hereinafter referred to as irregular LADC (I-LADC) and irregular PIL (I-PIL), respectively.
Progressive changes in non-neoplastic ground-glass nodules on follow-up computed tomography (CT).
Quant Imaging Med Surg
December 2024
Background: Non-neoplastic ground-glass nodules (GGNs) generally decrease in size or density during follow-up; however, some exhibit the opposite effect (and show progressive changes), which can lead to unnecessary resection. This study sought to determine the progressive changes in non-neoplastic GGNs using follow-up computed tomography (CT).
Methods: This cross-sectional study included 70 patients diagnosed with pathologically confirmed non-neoplastic GGNs from January 2017 to March 2023.
Article Synopsis
- The study evaluates how well an AI algorithm, originally designed for conventional CT images, can detect ground-glass nodules on virtual monochromatic images, aiming to find the best energy level for clinical use.
- Researchers analyzed chest CT images from patients with different stages of adenocarcinoma, using a commercial AI system to process various energy levels of virtual images.
- Results showed that while the AI performed well on both images, 80 keV was the most effective energy level for identifying invasive adenocarcinoma, with no significant difference in performance between conventional and virtual images.
Background: Bronchiolar adenoma (BA) is frequently misdiagnosed as peripheral lung cancer (PLC) because it resembles PLC. Computed tomography (CT) examination is an effective tool for detecting and diagnosing lung diseases. To date, there has been no comprehensive study on the differential diagnosis of BAs and PLCs using thin-section computed tomography (TSCT) based on a large sample, and the efficiency of CT in diagnosing BAs has not been verified.
View Article and Find Full Text PDFRationale And Objectives: To investigate the clinical and computed tomography characteristics of inflammatory solid pulmonary nodules (SPNs) with morphology suggesting malignancy, hereinafter referred to as atypical inflammatory SPNs (AI-SPNs).
Materials And Methods: The CT data of 515 patients with SPNs who underwent surgical resection were retrospectively analyzed. These patients were divided into inflammatory and malignant groups and their clinical and imaging features were compared.
Background: The nodule mass is an important indicator for evaluating the invasiveness of neoplastic ground-glass nodules (GGNs); however, the efficacy of nodule mass acquired by artificial intelligence (AI) has not been validated. This study thus aimed to determine the efficacy of nodule mass measured by AI in predicting the invasiveness of neoplastic GGNs.
Methods: From May 2019 to September 2023, a retrospective study was conducted on 755 consecutive patients comprising 788 pathologically confirmed neoplastic GGNs, among which 259 were adenocarcinoma in situ (AIS), 282 minimally invasive adenocarcinoma (MIA), and 247 invasive adenocarcinoma (IAC).
Article Synopsis
- - The study focused on the ability of deep learning models using CT scans to tell apart benign and malignant small pulmonary nodules (SPNs) that are 8 mm or smaller.
- - Researchers developed and tested multiple models, finding that Model 4 was the most effective in distinguishing between benign and malignant SPNs, outperforming traditional algorithms.
- - Ultimately, the study suggests that these CT-based deep learning models are reliable tools for identifying the nature of small pulmonary nodules, which can aid in better diagnosis and treatment.
Article Synopsis
- An interactive, non-invasive AI system was developed to predict the malignancy risk associated with cystic renal lesions (CRLs) by utilizing a combination of 3D segmentation and classification models.
- The study analyzed data from 715 patients and used advanced techniques like a 3D-ResNet50 network and gated recurrent unit (GRU) for feature extraction and classification.
- Results showed outstanding performance in distinguishing benign from malignant CRLs, achieving high accuracy and sensitivity, thus enhancing clinical decision-making in diagnosing these lesions.
Article Synopsis
- The study examines CT characteristics of air-containing spaces in ground glass nodules (GGNs) to help differentiate between neoplastic (cancerous) and non-neoplastic (non-cancerous) types.
- It analyzed data from 1328 patients with GGNs over 7 years and found that neoplastic GGNs were more common in females, larger in size, and exhibited specific patterns like air bronchogram and bubble-like lucency (BLL) more often than non-neoplastic GGNs.
- BLL was identified as having high specificity for neoplastic nodules and was more frequent in larger and part-solid neoplastic GGNs, indicating its potential as a reliable diagnostic marker.
Background: The solid component of subsolid nodules (SSNs) is closely associated with the invasiveness of lung adenocarcinoma, and its accurate assessment is crucial for selecting treatment method. Therefore, this study aimed to evaluate the accuracy of solid component size within SSNs measured on multiplanar volume rendering (MPVR) and compare it with the dimensions of invasive components on pathology.
Methods: A pilot study was conducted using a chest phantom to determine the optimal MPVR threshold for the solid component within SSN, and then clinical validation was carried out by retrospective inclusion of patients with pathologically confirmed solitary SSN from October 2020 to October 2021.
Purpose: To determine the value of intrapulmonary concomitant lesions in differentiating non-neoplastic and neoplastic ground-glass nodules (GGNs).
Patients And Methods: From January 2014 to March 2022, 395 and 583 patients with confirmed non-neoplastic and neoplastic GGNs were retrospectively enrolled. Their clinical and chest CT data were evaluated.
Objective: To investigate the dynamic changes during follow-up computed tomography (CT), histological subtypes, gene mutation status, and surgical prognosis for different morphological presentations of solitary lung adenocarcinomas (SLADC).
Materials And Methods: This retrospective study compared dynamic tumor changes and volume doubling time (VDT) in 228 patients with SLADC (morphological types I-IV) who had intermittent growth during follow-ups. The correlation between the morphological classification and histological subtypes, gene mutation status, and surgical prognosis was evaluated.
Differentiation of pulmonary solid nodules attached to the pleura detected by thin-section CT.
Insights Imaging
September 2023
Background: Pulmonary solid pleura-attached nodules (SPANs) are not very commonly detected and thus not well studied and understood. This study aimed to identify the clinical and CT characteristics for differentiating benign and malignant SPANs.
Results: From January 2017 to March 2023, a total of 295 patients with 300 SPANs (128 benign and 172 malignant) were retrospectively enrolled.
Background: Some peripheral small cell lung cancers (pSCLCs) and benign lung tumors (pBLTs) have similar morphological features but different treatment and prognosis.
Purpose: To determine the significance of marginal vessels in differentiating pSCLCs and pBLTs.
Material And Methods: A total of 57 and 95 patients with pathological confirmed nodular (≤3 cm) pSCLC and pBLT with similar morphological features were enrolled in this study retrospectively.
Pure ground-glass nodules (pGGNs) may represent a diverse range of histologic entities of varying aggressiveness. The purpose of this study was to evaluate the use of the reticulation sign on thin-section CT images for predicting the invasiveness of pGGNs. This retrospective study included 795 patients (mean age, 53.
View Article and Find Full Text PDFObjectives: Noncontrast computed tomography (NCCT) imaging markers are associated with early perihematomal edema (PHE) growth. The aim of this study was to compare the predictive value of different NCCT markers in predicting early PHE expansion.
Methods: ICH patients who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan within 36 h between July 2011 and March 2017 were included in this study.
Article Synopsis
- The study aimed to evaluate how effective noncontrast computed tomography (NCCT) models using radiomics and machine learning are at predicting early perihematomal edema (PHE) growth in patients with spontaneous intracerebral hemorrhage (ICH).
- Researchers analyzed NCCT data from 214 ICH patients, applying multiple machine learning techniques to develop predictive models based on selected radiomics features.
- Among the models tested, the multilayer perceptron (MLP) demonstrated the highest accuracy for predicting PHE expansion, suggesting that these NCCT models can help identify patients at risk for complications following ICH.
Purpose: To investigate the influence factors for the various boundary manifestations of pulmonary non-neoplastic ground glass nodules (GGNs) on computed tomography (CT).
Materials And Methods: From January 2015 to March 2022, a total of 280 patients with 318 non-neoplastic GGNs were enrolled. The correlations between degree of inflammatory cell infiltration and relative density (ΔCT) and the boundary manifestations of lesions were evaluated, respectively.
Background: Transition of the CT values from nodule to peripheral normal lung is related to pathological changes and may be a potential indicator for differential diagnosis. This study investigated the significance of the standard deviation (SD) values in the lesion-lung boundary zone when differentiating between benign and neoplastic subsolid nodules (SSNs).
Methods: From January 2012 to July 2021, a total of 229 neoplastic and 84 benign SSNs confirmed by pathological examination were retrospectively and nonconsecutively enrolled in this study.
Article Synopsis
- * The researchers analyzed data from 180 SPSNs, extracting over 1000 radiomics features from non-enhanced CT images to build predictive models using machine learning techniques, specifically a support vector machine (SVM).
- * Results showed that the combined model integrating radiomics and clinical factors performed best, achieving high accuracy in distinguishing between benign and malignant SPSNs, with an area under the curve (AUC) of 0.940 in training and 0.903 in testing sets. *
Background: The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard.
View Article and Find Full Text PDFArticle Synopsis
- * Analyzing 169 ICH patients, researchers found that prehospital UND occurred in 17.2% of cases, showing significantly worse hospital admission scores and 3-month recovery outcomes compared to those without UND.
- * The results suggest that prehospital UND is a significant predictor of poor functional outcomes, highlighting the need for more research on assessing and managing ICH before hospital admission.
Background: Previous studies confirmed that ground-glass nodules (GGNs) with certain CT manifestations had a higher probability of malignancy. However, differentiating patchy ground-glass opacities (GGOs) and GGNs has not been discussed solely. This study aimed to investigate the differences between the CT features of benign and malignant patchy GGOs to improve the differential diagnosis.
View Article and Find Full Text PDFUsing nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion.
View Article and Find Full Text PDFPurpose: To clarify the clinical and computed tomography (CT) indicators in distinguishing pulmonary nodules caused by fungal infection from lung cancers.
Methods: From January 2013 to April 2022, 68 patients with solitary fungal nodules (64 were solid and 4 were mixed ground-glass nodules) and 140 cases with solid cancerous nodules with similar size were enrolled. Their clinical characteristics and CT manifestations of the solid nodules were summarized and compared, respectively.