RESUMEN

Previous studies have associated tumor size with metastasis and prognosis in lung carcinoma; however, a precise cut-off for predicting distant metastasis in lung adenocarcinoma remains unclear. The aim of this study was to determine the cut-off point for predicting distant metastasis in lung adenocarcinoma. A cross-sectional study was conducted at Dr. Moewardi Hospital, Surakarta, Indonesia, from January 2022 to September 2023. Total sampling was employed, involving patients over 18 years old with a confirmed diagnosis of lung adenocarcinoma based on lung computed tomography (CT) scan findings, who had not yet received chemotherapy and had confirmed metastasis outside the lung. The study's dependent variable was the incidence of distant metastasis, while the independent variable was lung adenocarcinoma size. Two experienced thoracic radiologists measured lung adenocarcinoma size by assessing the longest axis using chest multi-slice computed tomography (MSCT) in the lung window setting. Receiver operating characteristic (ROC) curve analysis determined the optimal tumor size cut-off for predicting distant metastasis. Of 956 thoracic cancer patients, 108 were diagnosed with lung adenocarcinoma. After applying the inclusion and exclusion criteria, 89 patients were eligible. In the present study, tumor size predicted 68.1% of distant metastasis cases, with a cut-off point of 7.25 cm, yielding a sensitivity of 61.9% and a specificity of 61.5%. Tumors >7.25 cm had a 2.60-fold higher risk of distant metastasis compared to smaller tumors, with larger tumors more likely to spread to various sites. In conclusion, lung adenocarcinomas larger than 7.25 cm have a 2.60-fold increased risk of distant metastasis, making tumor size a crucial predictive factor. The study provides valuable insights for radiologists and can improve diagnosis accuracy and treatment planning by emphasizing tumor size as a key factor in managing lung adenocarcinoma.

Asunto(s)

Adenocarcinoma del Pulmón , Neoplasias Pulmonares , Tomografía Computarizada por Rayos X , Humanos , Masculino , Femenino , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/secundario , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico por imagen , Persona de Mediana Edad , Estudios Transversales , Indonesia/epidemiología , Anciano , Metástasis de la Neoplasia , Pronóstico , Adulto , Curva ROC , Valor Predictivo de las Pruebas

RESUMEN

BACKGROUND: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma. METHODS: This retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient. Classification models were developed using random forest (RF) and cross-validation, including three one-versus-others models and one three-class model. For each model, features were ranked by normalized Gini importance, and a minimal subset was selected with a cumulative importance exceeding 0.9. These selected features were then used to train the final models. The models' performance metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity, were computed. AUC and accuracy were compared to determine the final optimal method. RESULTS: The study comprised 193 patients (mean age 54 ± 11 years, 65 men), including 65 AIS, 54 MIA, and 74 IAC, divided into one training cohort (N = 154) and one test cohort (N = 39). The final three-class RF model outperformed three individual one-versus-others models in distinguishing each class from the other two. For the multiclass classification model, the AUC, accuracy, sensitivity, and specificity were 0.87, 0.79, 0.62, and 0.88 for AIS; 0.90, 0.79, 0.54, and 0.89 for MIA; and 0.87, 0.69, 0.73, and 0.67 for IAC, respectively. CONCLUSIONS: A radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma.

Asunto(s)

Adenocarcinoma del Pulmón , Neoplasias Pulmonares , Aprendizaje Automático , Tomografía Computarizada por Rayos X , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Estudios Retrospectivos , Femenino , Masculino , Tomografía Computarizada por Rayos X/métodos , Persona de Mediana Edad , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Anciano , Invasividad Neoplásica/diagnóstico por imagen , Sensibilidad y Especificidad , Radiómica

RESUMEN

PURPOSE: We aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC). METHODS: A total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared. The diagnostic performance of the different models was evaluated using receiver operating characteristic curve analysis RESULTS: The pGGNs were classified into grade 1 (117), 2 (34), and 3 (0), according to the IASLC grading system. In all four radiomic models, pGGNs of grade 2 had significantly higher radiomic scores than those of grade 1 (P < 0.05). The AUC of the GTV and GTV + 5/10/15-mm PTV were 0.869, 0.910, 0.951, and 0.872 in the training cohort and 0.700, 0.715, 0.745, and 0.724 in the validation cohort, respectively. CONCLUSIONS: The radiomic features we extracted from the GTV and PTV of pGGNs could effectively be used to differentiate grade-1 and grade-2 tumors. In particular, the radiomic features from the PTV increased the efficiency of the diagnostic model, with GTV + 10 mm PTV exhibiting the highest efficacy.

Asunto(s)

Neoplasias Pulmonares , Tomografía Computarizada por Rayos X , Humanos , Estudios Retrospectivos , Masculino , Femenino , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/clasificación , Tomografía Computarizada por Rayos X/métodos , Persona de Mediana Edad , Anciano , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/clasificación , Carga Tumoral , Clasificación del Tumor , Nódulos Pulmonares Múltiples/diagnóstico por imagen , Nódulos Pulmonares Múltiples/patología , Nódulos Pulmonares Múltiples/clasificación , Radiómica

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Ovine pulmonary adenocarcinoma (OPA) is an infectious, neoplastic lung disease of sheep that causes significant animal welfare and economic issues throughout the world. Understanding OPA pathogenesis is key to developing tools to control its impact. Central to this need is the availability of model systems that can monitor and track events after Jaagsiekte sheep retrovirus (JSRV) infection. Here, we report the development of an experimentally induced OPA model intended for this purpose. Using three different viral dose groups (low, intermediate and high), localised OPA tumour development was induced by bronchoscopic JSRV instillation into the segmental bronchus of the right cardiac lung lobe. Pre-clinical OPA diagnosis and tumour progression were monitored by monthly computed tomography (CT) imaging and trans-thoracic ultrasound scanning. Post mortem examination and immunohistochemistry confirmed OPA development in 89% of the JSRV-instilled animals. All three viral doses produced a range of OPA lesion types, including microscopic disease and gross tumours; however, larger lesions were more frequently identified in the low and intermediate viral groups. Overall, 31% of JSRV-infected sheep developed localised advanced lesions. Of the sheep that developed localised advanced lesions, tumour volume doubling times (calculated using thoracic CT 3D reconstructions) were 14.8 ± 2.1 days. The ability of ultrasound to track tumour development was compared against CT; the results indicated a strong significant association between paired CT and ultrasound measurements at each time point (R2 = 0.799, p < 0.0001). We believe that the range of OPA lesion types induced by this model replicates aspects of naturally occurring disease and will improve OPA research by providing novel insights into JSRV infectivity and OPA disease progression.

Asunto(s)

Adenocarcinoma del Pulmón , Modelos Animales de Enfermedad , Retrovirus Ovino Jaagsiekte , Neoplasias Pulmonares , Adenomatosis Pulmonar Ovina , Animales , Retrovirus Ovino Jaagsiekte/patogenicidad , Ovinos , Adenomatosis Pulmonar Ovina/virología , Adenomatosis Pulmonar Ovina/patología , Adenocarcinoma del Pulmón/virología , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/diagnóstico por imagen , Neoplasias Pulmonares/virología , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico por imagen , Infecciones por Retroviridae/virología , Infecciones por Retroviridae/patología , Infecciones por Retroviridae/veterinaria , Tomografía Computarizada por Rayos X

RESUMEN

We examined the association between texture features using three-dimensional (3D) io-dine density histogram on delayed phase of dual-energy CT (DECT) and expression of programmed death-ligand 1 (PD-L1) using immunostaining methods in non-small cell lung cancer. Consecutive 37 patients were scanned by DECT. Unenhanced and enhanced (3 min delay) images were obtained. 3D texture analysis was performed for each nodule to obtain 7 features (max, min, median, mean, standard deviation, skewness, and kurtosis) from iodine density mapping and extracellular volume (ECV). A pathologist evaluated a tumor proportion score (TPS, %) using PD-L1 immunostaining: PD-L1 high (TPS ≥ 50%) and low or negative expression (TPS < 50%). Associations between PD-L1 expression and each 8 parameter were evaluated using logistic regression analysis. The multivariate logistic regression analysis revealed that skewness and ECV were independent indicators associated with high PD-L1 expression (skewness: odds ratio [OR] 7.1 [95% CI 1.1, 45.6], p = 0.039; ECV: OR 6.6 [95% CI 1.1, 38.4], p = 0.037). In the receiver-operating characteristic analysis, the area under the curve of the combination of skewness and ECV was 0.83 (95% CI 0.67, 0.93) with sensitivity of 64% and specificity of 96%. Skewness from 3D iodine density histogram and ECV on dual energy CT were significant factors for predicting PD-L1 expression.

Asunto(s)

Antígeno B7-H1 , Yodo , Neoplasias Pulmonares , Tomografía Computarizada por Rayos X , Humanos , Antígeno B7-H1/metabolismo , Masculino , Femenino , Tomografía Computarizada por Rayos X/métodos , Anciano , Persona de Mediana Edad , Neoplasias Pulmonares/metabolismo , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Yodo/metabolismo , Imagenología Tridimensional/métodos , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/metabolismo , Adenocarcinoma del Pulmón/patología , Anciano de 80 o más Años , Curva ROC

RESUMEN

AIMS: The purpose of the study was to build a radiomics model using Dual-energy CT (DECT) to predict pathological grading of invasive lung adenocarcinoma. MATERIALS AND METHODS: The retrospective study enrolled 107 patients (80 low-grade and 27 high-grade) with invasive lung adenocarcinoma before surgery. Clinical features, radiographic characteristics, and quantitative parameters were measured. Virtual monoenergetic images at 50kev and 150kev were reconstructed for extracting DECT radiomics features. To select features for constructing models, Pearson's correlation analysis, intraclass correlation coefficients, and least absolute shrinkage and selection operator penalized logistic regression were performed. Four models, including the DECT radiomics model, the clinical-DECT model, the conventional CT radiomics model, and the mixed model, were established. Area under the curve (AUC) and decision curve analysis were used to measure the performance and the clinical value of the models. RESULTS: The radiomics model based on DECT exhibited outstanding performance in predicting tumor differentiation, with an AUC of 0.997 and 0.743 in the training and testing sets, respectively. Incorporating tumor density, lobulation, and effective atomic number at AP, the clinical-DECT model showed a comparable performance with an AUC of 0.836 in both the training and testing sets. In comparison to the conventional CT radiomics model (AUC of 0.998 in the training and 0.529 in the testing set) and the mixed model (AUC of 0.988 in the training and 0.707 in the testing set), the DECT radiomics model demonstrated a greater AUC value and provided patients with a more significant net benefit in the testing set. CONCLUSIONS: In contrast to the conventional CT radiomics model, the DECT radiomics model produced greater predictive performance in pathological grading of invasive lung adenocarcinoma.

Asunto(s)

Adenocarcinoma del Pulmón , Neoplasias Pulmonares , Clasificación del Tumor , Tomografía Computarizada por Rayos X , Humanos , Masculino , Tomografía Computarizada por Rayos X/métodos , Femenino , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Estudios Retrospectivos , Persona de Mediana Edad , Anciano , Imagen Radiográfica por Emisión de Doble Fotón/métodos , Adulto , Invasividad Neoplásica/diagnóstico por imagen , Pulmón/diagnóstico por imagen , Pulmón/patología , Valor Predictivo de las Pruebas , Radiómica

RESUMEN

OBJECTIVE: To explore the potential of intratumoral metabolism and its heterogeneous parameters, as measured by preoperative fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging, to predict mediastinal occult lymph node metastasis in cN0 lung invasive adenocarcinoma. SUBJECTS AND METHODS: Seventy five patients were consecutively enrolled from January 2018 to December 2022. All patients underwent 18F-FDG PET/CT scans within two weeks before surgery, and had mediastinal lymph node metastasis confirmed by pathologic diagnosis after surgery. Metabolic parameters including the maximum standardized uptake value (SUVmax), mean SUV (SUVmean), maximum average SUV (SUVpeak), tumor metabolic volume (MTV), and metabolic heterogeneity (HF) were measured. The relationship between primary focal metabolism, its heterogeneity parameters, and occult mediastinal lymph node metastasis was analyzed using an independent-sample t-test, analysis of covariance, and Mann-Whitney U test. A multivariate logistic regression model was used to analyze independent risk factors for mediastinal lymph node metastasis, while the receiver operating characteristic (ROC) curve assessed the predictive value of metabolic heterogeneity parameters for mediastinal occult lymph node metastasis. RESULTS: A total of 20 out of 75 patients (26.7%) were pathologically confirmed to have mediastinal lymph node metastasis. Analysis of covariance showed that the SUVmax, SUVmean, SUVpeak and MTV were significantly higher in patients with metastasis than in those without (all P<0.05). The metabolic heterogeneity parameters HF2 and HF3 were significantly higher in patients with mediastinal lymph node metastasis than in those without (P=0.013, 0.001), but not HF1. Multivariate Logistic regression analysis identified that tumor size, SUVmax, SUVpeak, lymph node SUVmax, and HF2 of the primary tumor as independent risk factors for mediastinal lymph node metastasis. Metabolic heterogeneity 3 demonstrated high predictive value for mediastinal occult lymph node metastasis (AUC=0.720, P=0.004). CONCLUSION: Metabolism and heterogeneity, as measured by preoperative 18F-FDG PET/CT in lung invasive adenocarcinoma, potentially have clinical value for predicting mediastinal occult lymph node metastasis.

Asunto(s)

Adenocarcinoma del Pulmón , Fluorodesoxiglucosa F18 , Neoplasias Pulmonares , Metástasis Linfática , Mediastino , Tomografía Computarizada por Tomografía de Emisión de Positrones , Humanos , Masculino , Femenino , Metástasis Linfática/diagnóstico por imagen , Persona de Mediana Edad , Anciano , Mediastino/diagnóstico por imagen , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/metabolismo , Neoplasias Pulmonares/patología , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/metabolismo , Valor Predictivo de las Pruebas , Invasividad Neoplásica , Periodo Preoperatorio , Adulto , Estudios Retrospectivos

RESUMEN

Objective.In this study, we propose a semi-supervised learning (SSL) scheme using a patch-based deep learning (DL) framework to tackle the challenge of high-precision classification of seven lung tumor growth patterns, despite having a small amount of labeled data in whole slide images (WSIs). This scheme aims to enhance generalization ability with limited data and reduce dependence on large amounts of labeled data. It effectively addresses the common challenge of high demand for labeled data in medical image analysis.Approach.To address these challenges, the study employs a SSL approach enhanced by a dynamic confidence threshold mechanism. This mechanism adjusts based on the quantity and quality of pseudo labels generated. This dynamic thresholding mechanism helps avoid the imbalance of pseudo-label categories and the low number of pseudo-labels that may result from a higher fixed threshold. Furthermore, the research introduces a multi-teacher knowledge distillation (MTKD) technique. This technique adaptively weights predictions from multiple teacher models to transfer reliable knowledge and safeguard student models from low-quality teacher predictions.Main results.The framework underwent rigorous training and evaluation using a dataset of 150 WSIs, each representing one of the seven growth patterns. The experimental results demonstrate that the framework is highly accurate in classifying lung tumor growth patterns in histopathology images. Notably, the performance of the framework is comparable to that of fully supervised models and human pathologists. In addition, the framework's evaluation metrics on a publicly available dataset are higher than those of previous studies, indicating good generalizability.Significance.This research demonstrates that a SSL approach can achieve results comparable to fully supervised models and expert pathologists, thus opening new possibilities for efficient and cost-effective medical images analysis. The implementation of dynamic confidence thresholding and MTKD techniques represents a significant advancement in applying DL to complex medical image analysis tasks. This advancement could lead to faster and more accurate diagnoses, ultimately improving patient outcomes and fostering the overall progress of healthcare technology.

Asunto(s)

Adenocarcinoma del Pulmón , Procesamiento de Imagen Asistido por Computador , Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Procesamiento de Imagen Asistido por Computador/métodos , Aprendizaje Automático Supervisado , Aprendizaje Profundo

RESUMEN

OBJECTIVE: To establish a prediction model of lung cancer classification by computed tomography (CT) radiomics with the serum tumor markers (STM) of lung cancer. MATERIALS AND METHODS: Two-hundred NSCLC patients were enrolled in our study. Clinical data including age, sex, and STM (squamous cell carcinoma [SCC], neuron-specific enolase [NSE], carcinoembryonic antigen [CEA], pro-gastrin-releasing peptide [PRO-GRP], and cytokeratin 19 fragment [cYFRA21-1]) were collected. A radiomics signature was generated from the training set using the least absolute shrinkage and selection operator (LASSO) algorithm. The risk factors were identified using multivariate logistic regression analysis, and a radiomics nomogram based on the radiomics signature and clinical features was constructed. The capability of the nomogram was evaluated using the training set and validated using the validation set. A correction curve and the Hosmer-Lemeshow test were used to evaluate the predictive performance of the radiomics model for the training and test sets. RESULTS: Twenty-nine of 1234 radiomics parameters were screened as important factors for establishing the radiomics model. The training (area under the curve [AUC] = 0.925; 95% confidence interval [CI]: 0.885-0.966) and validation sets (AUC = 0.921; 95% CI: 0.854-0.989) showed that the CT radiomics signature, combined with STM, accurately predicted lung squamous cell carcinoma and lung adenocarcinoma. Moreover, the logistic regression model showed good performance based on the Hosmer-Lemeshow test in the training (P = 0.954) and test sets (P = 0.340). Good calibration curve consistency also indicated the good performance of the nomogram. CONCLUSION: The combination of the CT radiomics signature and lung cancer STM performed well for the pathological classification of NSCLC. Compared with the radiomics signature method, the nomogram based on the radiomics signature and clinical factors had better performance for the differential diagnosis of NSCLC.

Asunto(s)

Adenocarcinoma del Pulmón , Biomarcadores de Tumor , Carcinoma de Células Escamosas , Neoplasias Pulmonares , Nomogramas , Tomografía Computarizada por Rayos X , Humanos , Masculino , Femenino , Tomografía Computarizada por Rayos X/métodos , Biomarcadores de Tumor/sangre , Persona de Mediana Edad , Neoplasias Pulmonares/sangre , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico , Carcinoma de Células Escamosas/sangre , Carcinoma de Células Escamosas/diagnóstico por imagen , Carcinoma de Células Escamosas/diagnóstico , Carcinoma de Células Escamosas/patología , Anciano , Adenocarcinoma del Pulmón/sangre , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/diagnóstico , Adulto , Curva ROC , Queratina-19/sangre , Radiómica

RESUMEN

A 69-year-old man was diagnosed with an abnormal shadow on a chest X-ray during a routine check-up. Computed tomography (CT) showed a 36 mm solid nodule at left S1+2, and 3 dimentional (3D)-CT showed the left B1+2 branching from the left main bronchus. Bronchoscopy showed branching of B1+2, B3~5, and inferior lobar bronchus from the left main bronchus, and a biopsy from the peripheral area of B1+2 confirmed the diagnosis of lung adenocarcinoma. Subsequently, video-assisted thoracoscopic surgery was performed for the lung adenocarcinoma (cT2aN0M0, ⅠB). The dorsal pleura was incised and B1+2, which branches from the left main bronchus dorsal to the pulmonary artery, was identified. After dissecting B1+2, the fissure between the upper division and lower lobes was separated, followed by left upper lobectomy with ND2a-1. The preoperative understanding of the anatomical abnormalities obtained using 3D-CT allowed the surgery to be performed safely.

Asunto(s)

Bronquios , Neoplasias Pulmonares , Neumonectomía , Humanos , Masculino , Anciano , Neoplasias Pulmonares/cirugía , Neoplasias Pulmonares/diagnóstico por imagen , Bronquios/cirugía , Bronquios/anomalías , Bronquios/diagnóstico por imagen , Tomografía Computarizada por Rayos X , Adenocarcinoma/cirugía , Adenocarcinoma/diagnóstico por imagen , Adenocarcinoma del Pulmón/cirugía , Adenocarcinoma del Pulmón/diagnóstico por imagen

RESUMEN

ABSTRACT: Relapsing polychondritis (RP) is an uncommon autoimmune disease that causes inflammation of the cartilage and proteoglycan-rich structures, including the ear, nose, and airway. Paraneoplastic RP is a subset of RP that occurs in some individuals following the detection and treatment of certain types of cancers. FDG PET/CT helps with early diagnosis of RP, identifying inflammatory areas even in the absence of symptoms, and guiding the selection of appropriate biopsy sites. Here, we present a case of adenocarcinoma of the lung presenting with paraneoplastic symptoms of RP as initial presentation, and symptoms were resolved after 3 cycles of chemotherapy.

Asunto(s)

Adenocarcinoma del Pulmón , Adenocarcinoma , Fluorodesoxiglucosa F18 , Neoplasias Pulmonares , Policondritis Recurrente , Tomografía Computarizada por Tomografía de Emisión de Positrones , Humanos , Policondritis Recurrente/diagnóstico por imagen , Policondritis Recurrente/complicaciones , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/complicaciones , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/complicaciones , Adenocarcinoma/diagnóstico por imagen , Adenocarcinoma/complicaciones , Síndromes Paraneoplásicos/diagnóstico por imagen , Masculino , Persona de Mediana Edad , Tomografía Computarizada por Rayos X

RESUMEN

The objective of this study was to develop a nomogram model based on the natural progression of tumor and other radiological features to discriminate between solitary nodular pulmonary mucinous adenocarcinoma and non-mucinous adenocarcinomas. A retrospective analysis was conducted on 15,655 cases of lung adenocarcinoma diagnosed at our institution between January 2010 and June 2023. Primary nodular invasive mucinous adenocarcinomas and non-mucinous adenocarcinomas with at least two preoperative CT scans were included. These patients were randomly assigned to training and validation sets. Univariate and multivariate analyses were employed to compare tumor growth rates and clinical radiological characteristics between the two groups in the training set. A nomogram model was constructed based on the results of multivariate analysis. The diagnostic value of the model was evaluated in both the training and validation sets using calibration curves and receiver operating characteristic curves (ROC). The study included 174 patients, with 58 cases of mucinous adenocarcinoma and 116 cases of non-mucinous adenocarcinoma. The nomogram model incorporated the maximum tumor diameter, the consolidation/tumor ratio (CTR), and the specific growth rate (SGR) to generate individual scores for each patient, which were then accumulated to obtain a total score indicative of the likelihood of developing mucinous or non-mucinous adenocarcinoma. The model demonstrated excellent discriminative ability with an area under the receiver operating characteristic curve of 0.784 for the training set and 0.833 for the testing set. The nomogram model developed in this study, integrating SGR with other radiological and clinical parameters, provides a valuable and accurate tool for differentiating between solitary nodular pulmonary mucinous adenocarcinoma and non-mucinous adenocarcinomas. This prognostic model offers a robust and objective basis for personalized management of patients with pulmonary adenocarcinomas.

Asunto(s)

Adenocarcinoma Mucinoso , Neoplasias Pulmonares , Nomogramas , Humanos , Femenino , Masculino , Adenocarcinoma Mucinoso/diagnóstico , Adenocarcinoma Mucinoso/patología , Adenocarcinoma Mucinoso/diagnóstico por imagen , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico por imagen , Persona de Mediana Edad , Anciano , Estudios Retrospectivos , Tomografía Computarizada por Rayos X , Nódulo Pulmonar Solitario/patología , Nódulo Pulmonar Solitario/diagnóstico , Nódulo Pulmonar Solitario/diagnóstico por imagen , Curva ROC , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/diagnóstico , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adulto , Anciano de 80 o más Años

RESUMEN

To compare the pathological results and long-term survival results of early surgery and surgery after at least one year follow-up for ground-glass component predominant lung adenocarcinoma patients. From January 1, 2013 to August 31, 2017, a total of 279 patients with ground-glass nodules (GGNs) undergoing surgical resection and pathologically proved to be pulmonary adenocarcinoma were included in this study. All patients were divided into early surgery group (ES Group) (210 cases) and surgery after follow-up group (FS Group) (69 cases). Patients in FS group experienced at least one year surveillance. Clinical and imaging features were analyzed by using univariate analysis. After analysis, there was no statistical difference in pathological results and long-term prognosis between the two groups. In the follow-up group, grown GGNs have proved to have more aggressive pathological results. The one-year follow-up may be a feasible management method for patients with ground-glass component predominant GGN.

Asunto(s)

Adenocarcinoma del Pulmón , Neoplasias Pulmonares , Humanos , Masculino , Femenino , Persona de Mediana Edad , Pronóstico , Neoplasias Pulmonares/cirugía , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/mortalidad , Neoplasias Pulmonares/diagnóstico por imagen , Adenocarcinoma del Pulmón/cirugía , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/mortalidad , Adenocarcinoma del Pulmón/diagnóstico por imagen , Anciano , Estudios de Seguimiento , Estudios Retrospectivos , Adulto , Nódulo Pulmonar Solitario/cirugía , Nódulo Pulmonar Solitario/patología , Nódulo Pulmonar Solitario/diagnóstico por imagen , Adenocarcinoma/cirugía , Adenocarcinoma/patología , Adenocarcinoma/mortalidad

RESUMEN

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. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists' diagnoses. RESULTS: The DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS. CONCLUSIONS: A CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists.

Asunto(s)

Adenocarcinoma del Pulmón , Aprendizaje Profundo , Neoplasias Pulmonares , Tomografía Computarizada por Rayos X , Humanos , Masculino , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/diagnóstico , Femenino , Tomografía Computarizada por Rayos X/métodos , Persona de Mediana Edad , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico , Diagnóstico Diferencial , Anciano , Estudios Retrospectivos , Nódulo Pulmonar Solitario/diagnóstico por imagen , Nódulo Pulmonar Solitario/patología , Nódulo Pulmonar Solitario/diagnóstico , Adulto , Granuloma/diagnóstico por imagen , Granuloma/patología , Granuloma/diagnóstico

RESUMEN

After the recommendation of computed tomography as a routine procedure for lung cancer screening, an increasing number of young adults have been diagnosed with pulmonary ground-glass opacity (GGO). Up to 63% of pulmonary nodules with a GGO component can be malignant. Since young cancer patients have limited exposure to environmental mutagens, they have special characteristics and needs. This study sought to compare the clinicopathological characteristics of young and old patients with GGO-associated lung adenocarcinoma (GGO-LUAD). Clinicopathological data from 203 patients who underwent video-assisted thoracoscopic surgery between January 2018 and April 2020 for pulmonary GGO component nodules were reviewed. Lung nonmucinous adenocarcinoma patients younger than 40 years old and older than 40 years old were enrolled: 103 patients ≤ 40 years old and 100 patients > 40 years old. The relevant clinicopathological features, including sex, smoking status, tumor size, pathological characteristics, radiographic features and prognosis of pulmonary nodules, were evaluated. Univariate analyses were applied for comparisons between groups. The differences in baseline characteristics (sex, smoking status, tumor location) between the different age groups were not significant. Young patients were more likely to have tumors < 1 cm in size, while older patients predominantly had tumors > 2 cm in size. The mean percentage of invasive adenocarcinoma was greater in the elderly group. Young and older patients seemed to have similar subtypes of adenocarcinoma (p > 0.05) but had different degrees of differentiation (p < 0.001). The 3-year overall survival (OS) and recurrence-free survival (RFS) of the young group were 100% and 99.03%, respectively, while the 3-years OS and RFS of the older group were 99% and 98%, respectively. Our work revealed that young patients with malignant pulmonary nodules and GGOs have distinct pathological subtypes. Patients with GGOs of different ages have different clinicopathological characteristics. The 3-year prognosis of young patients with malignant pulmonary nodules with GGOs is satisfactory.

Asunto(s)

Adenocarcinoma del Pulmón , Neoplasias Pulmonares , Tomografía Computarizada por Rayos X , Humanos , Masculino , Femenino , Adulto , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/mortalidad , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/mortalidad , Persona de Mediana Edad , Anciano , Factores de Edad , Pronóstico , Estudios Retrospectivos , Cirugía Torácica Asistida por Video

RESUMEN

AIMS: Synchronous multiple pure ground-glass opacities (SMpGGOs) are observed more commonly. Nevertheless whether characteristics of SMpGGOs are similar to those of solitary pure ground-glass opacities (SpGGOs), remains unknown. This retrospective study aimed to compare radiographic characteristics between SMpGGOs and SpGGOs. MATERIALS AND METHODS: We retrospectively included patients along with SpGGOs or SMpGGOs at XXX between August 2018 and June 2020. They were enrolled in two groups (SpGGOs and SMpGGOs). The clinical records, pathologic features, and radiographic manifestations of two groups were collected and compared with SPSS 21.0. RESULTS: 138 patients (58 patients with 58 SpGGOs, 80 patients with 187 SMpGGOs) were evaluated. The threshold values of maximal diameters and mean computed tomography value for adenocarcinoma were 5.5 mm (sensitivity 86.4%, specificity 55.6%, AUC 0.777) and -615.0 Hu in SMpGGOs (sensitivity 61.4%, specificity 66.7%, AUC 0.651) for SMpGGOs, whereas 12.5 mm (sensitivity 54.5%, specificity 100%, AUC 0.851) and -531.9 Hu (sensitivity 43.2%, specificity 100%, AUC 0.724) in SpGGOs. CONCLUSION: The threshold values of maximal diameters and mean computed tomography value for adenocarcinoma in SMpGGOs may be smaller than those in SpGGOs (5.5 mm vs. 12.5mm, -615.0 Hu vs. -531.9 Hu).

Asunto(s)

Adenocarcinoma del Pulmón , Neoplasias Pulmonares , Tomografía Computarizada por Rayos X , Humanos , Masculino , Femenino , Estudios Retrospectivos , Persona de Mediana Edad , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Tomografía Computarizada por Rayos X/métodos , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Anciano , Sensibilidad y Especificidad , Pulmón/diagnóstico por imagen , Pulmón/patología , Adulto

RESUMEN

BACKGROUND: Ground glass opacity is observed frequently in the early stages of lung adenocarcinoma and is associated with a favorable prognosis and a low incidence of lymph node metastasis. However, the necessity of lymph node sampling in these patients is questionable, although current guidelines still recommend it. METHODS: Radiologic and clinical data were retrospectively collected and analyzed for 2,298 patients with lung cancer who underwent surgical resection for lesions ≤15 mm during 2022. Based on the consolidation tumor ratios, patients were categorized into 4 groups (pure ground glass opacity, ground glass opacity-predominant, solid-predominant, and pure solid). The incidence of lymph node metastasis in each group was examined. RESULTS: A total of 2,298 patients with a median age of 54.0 years were enrolled in this study. Tumors were categorized into 4 types: 1,427 (62.1%) were pure ground glass opacity, which constituted the majority, while 421 (18.3%) were ground glass opacity-predominant, 330 (14.4%) were solid-predominant, and the remaining 120 (5.2%) were pure solid. Significant positive correlations were revealed between the consolidation tumor ratio group and pathologic grade (P < .001, ρ = 0.307), T stage (P < .001, ρ = 0.270), and N stage (P < .001, ρ = 0.105). Among the included cases, only 7 cases with metastasis were in the pure solid group. Within this group, 113 cases (94.2%) were N0, 5 cases (4.2%) were N1, and 2 cases (1.7%) were N2. CONCLUSION: Lymph node metastasis exclusively occurred in the pure solid group, suggesting that for nodules <15 mm, lymph node sampling may be crucial for pure solid nodules but less so for those containing ground glass opacities.

Asunto(s)

Adenocarcinoma del Pulmón , Neoplasias Pulmonares , Ganglios Linfáticos , Metástasis Linfática , Humanos , Persona de Mediana Edad , Masculino , Femenino , Estudios Retrospectivos , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/cirugía , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/cirugía , Metástasis Linfática/diagnóstico por imagen , Metástasis Linfática/patología , Anciano , Ganglios Linfáticos/patología , Ganglios Linfáticos/diagnóstico por imagen , Adulto , Estadificación de Neoplasias , Escisión del Ganglio Linfático , Tomografía Computarizada por Rayos X , Pronóstico , Anciano de 80 o más Años , Neumonectomía/métodos , Adenocarcinoma/patología , Adenocarcinoma/diagnóstico por imagen , Adenocarcinoma/cirugía

RESUMEN

BACKGROUND: An accurate and non-invasive approach is urgently needed to distinguish tuberculosis granulomas from lung adenocarcinomas. This study aimed to develop and validate a nomogram based on contrast enhanced-compute tomography (CE-CT) to preoperatively differentiate tuberculosis granuloma from lung adenocarcinoma appearing as solitary pulmonary solid nodules (SPSN). METHODS: This retrospective study analyzed 143 patients with lung adenocarcinoma (mean age: 62.4 ± 6.5 years; 54.5% female) and 137 patients with tuberculosis granulomas (mean age: 54.7 ± 8.2 years; 29.2% female) from two centers between March 2015 and June 2020. The training and internal validation cohorts included 161 and 69 patients (7:3 ratio) from center No.1, respectively. The external testing cohort included 50 patients from center No.2. Clinical factors and conventional radiological characteristics were analyzed to build independent predictors. Radiomics features were extracted from each CT-volume of interest (VOI). Feature selection was performed using univariate and multivariate logistic regression analysis, as well as the least absolute shrinkage and selection operator (LASSO) method. A clinical model was constructed with clinical factors and radiological findings. Individualized radiomics nomograms incorporating clinical data and radiomics signature were established to validate the clinical usefulness. The diagnostic performance was assessed using the receiver operating characteristic (ROC) curve analysis with the area under the receiver operating characteristic curve (AUC). RESULTS: One clinical factor (CA125), one radiological characteristic (enhanced-CT value) and nine radiomics features were found to be independent predictors, which were used to establish the radiomics nomogram. The nomogram demonstrated better diagnostic efficacy than any single model, with respective AUC, accuracy, sensitivity, and specificity of 0.903, 0.857, 0.901, and 0.807 in the training cohort; 0.933, 0.884, 0.893, and 0.892 in the internal validation cohort; 0.914, 0.800, 0.937, and 0.735 in the external test cohort. The calibration curve showed a good agreement between prediction probability and actual clinical findings. CONCLUSION: The nomogram incorporating clinical factors, radiological characteristics and radiomics signature provides additional value in distinguishing tuberculosis granuloma from lung adenocarcinoma in patients with a SPSN, potentially serving as a robust diagnostic strategy in clinical practice.

Asunto(s)

Adenocarcinoma del Pulmón , Granuloma , Neoplasias Pulmonares , Nomogramas , Tomografía Computarizada por Rayos X , Humanos , Femenino , Persona de Mediana Edad , Masculino , Tomografía Computarizada por Rayos X/métodos , Estudios Retrospectivos , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Diagnóstico Diferencial , Granuloma/diagnóstico por imagen , Granuloma/patología , Anciano , Tuberculosis Pulmonar/diagnóstico por imagen , Periodo Preoperatorio , Radiómica

RESUMEN

BACKGROUND: Assessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions. OBJECTIVE: Developing a predictive model to assess the aggressiveness of pure ground glass nodules in lung adenocarcinoma is the study's goal. METHODS: A comprehensive search for studies on the relationship between computed tomography(CT) characteristics and the aggressiveness of pure ground glass nodules was conducted using databases such as PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM, up to December 20, 2023. Two independent researchers were responsible for screening literature, extracting data, and assessing the quality of the studies. Meta-analysis was performed using Stata 16.0, with the training data derived from this analysis. To identify publication bias, Funnel plots and Egger tests and Begg test were employed. This meta-analysis facilitated the creation of a risk prediction model for invasive adenocarcinoma in pure ground glass nodules. Data on clinical presentation and CT imaging features of patients treated surgically for these nodules at the Third Affiliated Hospital of Kunming Medical University, from September 2020 to September 2023, were compiled and scrutinized using specific inclusion and exclusion criteria. The model's effectiveness for predicting invasive adenocarcinoma risk in pure ground glass nodules was validated using ROC curves, calibration curves, and decision analysis curves. RESULTS: In this analysis, 17 studies were incorporated. Key variables included in the model were the largest diameter of the lesion, average CT value, presence of pleural traction, and spiculation. The derived formula from the meta-analysis was: 1.16×the largest lesion diameter + 0.01 × the average CT value + 0.66 × pleural traction + 0.44 × spiculation. This model underwent validation using an external set of 512 pure ground glass nodules, demonstrating good diagnostic performance with an ROC curve area of 0.880 (95% CI: 0.852-0.909). The calibration curve indicated accurate predictions, and the decision analysis curve suggested high clinical applicability of the model. CONCLUSION: We established a predictive model for determining the invasiveness of pure ground-glass nodules, incorporating four key radiological indicators. This model is both straightforward and effective for identifying patients with a high likelihood of invasive adenocarcinoma.

Asunto(s)

Neoplasias Pulmonares , Invasividad Neoplásica , Tomografía Computarizada por Rayos X , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Tomografía Computarizada por Rayos X/métodos , Medición de Riesgo , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/patología , Nódulos Pulmonares Múltiples/diagnóstico por imagen , Nódulos Pulmonares Múltiples/patología

RESUMEN

OBJECTIVE: This study developed and validated a nomogram utilizing clinical and multi-slice spiral computed tomography (MSCT) features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma. Additionally, we assessed the predictive accuracy of Ki-67 expression levels, as determined by our model, in estimating the prognosis of stage IA lung adenocarcinoma. MATERIALS AND METHODS: We retrospectively analyzed data from 395 patients with pathologically confirmed stage IA lung adenocarcinoma. A total of 322 patients were divided into training and internal validation groups at a 6:4 ratio, whereas the remaining 73 patients composed the external validation group. According to the pathological results, the patients were classified into high and low Ki-67 labeling index (LI) groups. Clinical and CT features were subjected to statistical analysis. The training group was used to construct a predictive model through logistic regression and to formulate a nomogram. The nomogram's predictive ability and goodness-of-fit were assessed. Internal and external validations were performed, and clinical utility was evaluated. Finally, the recurrence-free survival (RFS) rates were compared. RESULTS: In the training group, sex, age, tumor density type, tumor-lung interface, lobulation, spiculation, pleural indentation, and maximum nodule diameter differed significantly between patients with high and low Ki-67 LI. Multivariate logistic regression analysis revealed that sex, tumor density, and maximum nodule diameter were significantly associated with high Ki-67 expression in stage IA lung adenocarcinoma. The calibration curves closely resembled the standard curves, indicating the excellent discrimination and accuracy of the model. Decision curve analysis revealed favorable clinical utility. Patients with a nomogram-predicted high Ki-67 LI exhibited worse RFS. CONCLUSION: The nomogram utilizing clinical and CT features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma demonstrated excellent performance, clinical utility, and prognostic significance, suggesting that this nomogram is a noninvasive personalized approach for the preoperative prediction of Ki-67 expression.

Asunto(s)

Adenocarcinoma del Pulmón , Antígeno Ki-67 , Neoplasias Pulmonares , Estadificación de Neoplasias , Nomogramas , Humanos , Antígeno Ki-67/metabolismo , Masculino , Femenino , Persona de Mediana Edad , Estudios Retrospectivos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/metabolismo , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/cirugía , Adenocarcinoma del Pulmón/diagnóstico por imagen , Adenocarcinoma del Pulmón/metabolismo , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/cirugía , Pronóstico , Anciano , Tomografía Computarizada Espiral/métodos , Adulto