Lung cancer is still one of the most common malignant tumors in the world. With the increase of its incidence and the development of medical technology, the overall survival of lung cancer patients has significantly extended compared to before. The incidence of brain and meningeal metastases from lung cancer has also been rising year by year, but patients with brain and meningeal metastases from lung cancer have a poor prognosis and a very high mortality rate, and the diagnosis is mainly based on computed tomography (CT), magnetic resonance imaging (MRI) and other imaging examinations. However, the imaging features are diverse and the specificity is low, which makes it easy to be misdiagnosed and missed. Therefore, accurately identifying brain and meningeal metastases and timely targeted treatment is crucial for improving patient prognosis. This paper analyzed the diagnosis and treatment of a case of lung cancer with no obvious recurrence and metastasis in nearly 7-year long-term follow-up after radical lung cancer surgery, but the patient with abnormal behavior, impaired consciousness and epilepsy in the past 5 months, and multiple punctate calcifications in the brain found by head CT and MRI. This paper consider that the patient's mental and behavioral symptoms were caused by brain and meningeal metastasis of lung cancer after excluding infectious disease and ineffective treatment of autoimmune encephalitis, and further pathological biopsy and genetic detection confirmed the diagnosis of metastatic lung adenocarcinoma with epidermal growth factor receptor (EGFR) L858R gene mutation, and the patient's symptoms were significantly improved after targeted therapy by Osimertinib. This paper also searched the relevant literatures of brain calcifications in databases such as China National Knowledge Infrastructure (CNKI), Wanfang, UpToDate, PubMed, etc., and found that intracerebral calcifications exist in a variety of diseases, including infectious, genetic and neurodegenerative diseases, vascular diseases, metabolic diseases and tumors. However, brain calcification in brain and meningeal metastases are often underestimated, and the consequent risk is misdiagnosis and delayed treatment. Therefore, brain and meningeal metastases manifested as brain calcification should not be ignored in patients with a history of previous tumors.
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Humans ; Lung Neoplasms/pathology* ; Brain Neoplasms/diagnostic imaging* ; Meningeal Neoplasms/diagnostic imaging* ; Calcinosis/diagnostic imaging* ; Male ; Middle Aged ; Tomography, X-Ray Computed ; Magnetic Resonance Imaging

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

Non-small cell lung cancer (NSCLC) can be detected with enlarged lymph nodes on imaging, but their benignity and malignancy are difficult to determine directly, making it difficult to stage the tumor and design radiotherapy target volumes. The clinical diagnosis of malignant lymph nodes is often based on the short diameter of lymph nodes ≥1 cm or the maximum standard uptake value ≥2.5, but the sensitivity and specificity of these criteria are too low to meet the clinical needs. In recent years, many advances have been made in diagnosing benign and malignant lymph nodes using other imaging parameters, and with the development of radiomics, deep learning and other technologies, models of mining the image information of enlarged lymph node regions further improve the diagnostic accuracy. The purpose of this paper is to review recent advances in imaging-based diagnosis of benign and malignant enlarged lymph nodes in NSCLC for more accurate and noninvasive assessment of lymph node status in clinical practice.
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Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Diagnostic Imaging ; Lung Neoplasms/pathology* ; Lymph Nodes/pathology* ; Sensitivity and Specificity

BACKGROUND: At present, more and more studies predict invasive adenocarcinoma (IAC) through three-dimensional features of pulmonary nodules, but few studies have confirmed that three-dimensional features have more advantages in diagnosing IAC than traditional two-dimensional features of pulmonary nodules. This study analyzed the differences of chest computed tomography (CT) features between IAC and minimally invasive adenocarcinoma (MIA) from three-dimensional and two-dimensional levels, and compared the ability of diagnosing IAC. The non-invasive adenocarcinoma group includes precursor glandular lesions (PGL) and minimally invasive adenocarcinoma (MIA). METHODS: The clinical data of 1,045 patients with ground glass opacity (GGO) from January to December 2019 were collected. Then the correlation between preoperative CT image characteristics and pathological results were analyzed retrospectively. The independent influencing factors for the identification of IAC were screened out according to two-dimensional and three-dimensional classification by multivariate Logistic regression and the cut-off point for the identification of IAC was found out through the receiver operating characteristic (ROC) curve. At last, the ability of diagnosing IAC was evaluated by Yoden index. RESULTS: The diameter of nodule, the diameter of solid component, the diameter of mediastinal window nodule in two-dimensional factors, and the volume of nodule, the volume of solid part and the average CT value in three-dimensional factors were independent risk factors for the diagnosis of IAC. These factors were arranged by Yoden index: solid partial volume (0.601)>nodule volume (0.536)>solid component diameter (0.525)>nodule diameter (0.518)>mediastinal window nodule diameter (0.488)>proportion of solid component volume (0.471)>1-tumor disappearance ratio (TDR) (0.468)>consolidation tumor ratio (CTR) (0.394)>average CT value (0.380). CONCLUSIONS The CT features of three-dimensional are better than two-dimensional in the diagnosis of IAC, and the size of solid components is better than the overall size of nodules.
Humans ; Lung Neoplasms/pathology* ; Retrospective Studies ; Neoplasm Invasiveness ; Multiple Pulmonary Nodules/diagnostic imaging* ; Adenocarcinoma/pathology*

Objective: Solid and micropapillary pattern are highly invasive histologic subtypes in lung adenocarcinoma and are associated with poor prognosis while the biopsy sample is not enough for the accurate histological diagnosis. This study aims to assess the correlation and predictive efficacy between metabolic parameters in (18)F-fluorodeoxy glucose positron emission tomography/computed tomography ((18)F-FDG PET-CT), including the maximum SUV (SUV(max)), metabolic tumor volume (MTV), total lesion glycolysis (TLG) and solid and micropapillary histological subtypes in lung adenocarcinoma. Methods: A total of 145 resected lung adenocarcinomas were included. The clinical data and preoperative (18)F-FDG PET-CT data were retrospectively analyzed. Mann-Whitney U test was used for the comparison of the metabolic parameters between solid and micropapillary subtype group and other subtypes group. Receiver operating characteristic (ROC) curve and areas under curve (AUC) were used for evaluating the prediction efficacy of metabolic parameters for solid or micropapillary patterns. Univariate and multivariate analyses were conducted to determine the prediction factors of the presence of solid or micropapillary subtypes. Results: Median SUV(max) and TLG in solid and papillary predominant subtypes group (15.07 and 34.98, respectively) were significantly higher than those in other subtypes predominant group (6.03 and 10.16, respectively, P<0.05). ROC curve revealed that SUV(max) and TLG had good efficacy for prediction of solid and micropapillary predominant subtypes [AUC=0.811(95% CI: 0.715~0.907) and 0.725(95% CI: 0.610~0.840), P<0.05]. Median SUV(max) and TLG in lung adenocarcinoma with the solid or micropapillary patterns (11.58 and 22.81, respectively) were significantly higher than those in tumors without solid and micropapillary patterns (4.27 and 6.33, respectively, P<0.05). ROC curve revealed that SUV(max) and TLG had good efficacy for predicting the presence of solid or micropapillary patterns [AUC=0.757(95% CI: 0.679~0.834) and 0.681(95% CI: 0.595~0.768), P<0.005]. Multivariate logistic analysis showed that the clinical stage (Stage Ⅲ-Ⅳ), SUV(max) ≥10.27 and TLG≥7.12 were the independent predictive factors of the presence of solid or micropapillary patterns (P<0.05). Conclusions: Preoperative SUV(max) and TLG of lung adenocarcinoma have good prediction efficacy for the presence of solid or micropapillary patterns, especially for the solid and micropapillary predominant subtypes and are independent factors of the presence of solid or micropapillary patterns.
Adenocarcinoma of Lung/diagnostic imaging* ; Fluorodeoxyglucose F18/metabolism* ; Humans ; Lung Neoplasms/pathology* ; Multimodal Imaging/methods* ; Positron Emission Tomography Computed Tomography ; Positron-Emission Tomography/methods* ; Prognosis ; Radiopharmaceuticals ; Retrospective Studies ; Tomography, X-Ray Computed/methods* ; Tumor Burden

The International Agency for Research on Cancer (IARC) published the World Health Organization (WHO) classification of thoracic tumors (5th edition) in May 2021, only six years after the 4th edition of WHO Classification. With the application of low-dose spiral computed tomography (CT) as an early screening method for lung tumors in recent years, lung adenocarcinoma has become the main type of disease in many hospital surgical treatments. The WHO classification serves as the authoritative guide for pathological diagnosis, and any slight change in the classification is at the heart of pathologists, clinicians and patients. Adenocarcinoma in situ is a newly added type of adenocarcinoma diagnosis in the 4th edition of the WHO classification, and it is also the focus of clinical treatment and research at home and abroad in recent years. Because its catalog position has been adjusted in the 5th edition of the WHO classification, there has been a huge controversy and discussion among clinicians and patients that "adenocarcinoma in situ was excluded from the category of malignant tumors". This article will briefly explain the origin of the diagnosis of lung adenocarcinoma in situ, the adjustment of the new classification catalog, and whether adenocarcinoma in situ is benign or malignant.
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Adenocarcinoma in Situ/pathology* ; Adenocarcinoma of Lung/diagnostic imaging* ; Humans ; Lung Neoplasms/pathology* ; Neoplasm Staging

OBJECTIVE: To investigate the computed tomography findings, clinicopathological features, genetic characteristics and prognosis of in situ adenocarcinoma (AIS) and minimally invasive adenocarcinoma (MIA) of the lung. METHODS: We retrospectively analyzed the data including computed tomography (CT) images, histopathological findings, Ki-67 immunostaining, and genetic mutations in patients with lung adenocarcinoma undergoing surgery at our hospital between 2014 and 2019. RESULTS: Of the total of 480 patients with lung adenocarcinoma we reviewed, 73 (15.2%) had AIS (=28) or MIA (=45) tumors. The age of the patients with MIA was significantly younger than that of patients with AIS ( < 0.02). CT scans identified pure ground-glass nodules in 46.4% of AIS cases and in 44.4% of MIA cases. Multiple GGOs were more common in MIA than in AIS cases ( < 0.05), and bluured tumor margins was less frequent in AIS cases ( < 0.05). No significant difference was found in EGFR mutations between MIA and AIS cases. A Ki-67 labeling index (LI) value ≥2.8% did not differentiate MIA from AIS. The follow-up time in MIA group was significantly shorter than that in AIS group, but no recurrence or death occurred. CONCLUSIONS Despite similar surgical outcomes and favorable survival outcomes, the patients with AIS and MIA show differences in terms of age, CT findings, EGFR mutations and Ki-67 LI.
Adenocarcinoma of Lung ; diagnostic imaging ; pathology ; ErbB Receptors ; genetics ; Humans ; Ki-67 Antigen ; genetics ; Lung Neoplasms ; diagnostic imaging ; pathology ; Mutation ; Prognosis ; Retrospective Studies ; Tomography, X-Ray Computed

BACKGROUND: Pneumonic-type lung carcinoma is a special type of lung cancer both clinically and radiologically. Here we present our experience on pneumonic-type lung carcinoma in an attempt to investigate the clinical, radiological and pathological features, diagnostic procedures, treatment, and prognosis of this type of tumor. METHODS: Pathologically confirmed lung cancer with a chest CT characterized by ground glass opacity or consolidation was defined as pneumonic-type lung carcinoma. Cases with advanced pneumonic-type lung carcinoma admitted to Peking Union Medical College Hospital (PUMCH) from January 1, 2013 to August 30, 2018 were enrolled. Retrospective analysis of clinical data and survival follow-up of these patients was conducted. RESULTS: A total of 46 cases were enrolled, all of which were adenocarcinoma. Cough (41/46, 89.1%) and expectoration (35/46, 76.1%) were the most prominent symptoms. The most frequent chest CT findings were ground glass attenuation (87.0%), patchy consolidation (84.8%), and multiple ground-glass nodules (84.8%). Multiple cystic changes (40%) and cavitation (13%) were also quite frequent. Ipsilateral and contralateral intrapulmonary metastasis were noted in 95.3% and 84.8% of cases respectively. The median duration from symptom onset to diagnosis was 214 days (95%CI: 129-298). Both surgical lung biopsy and CT-guided percutaneous lung biopsy had a diagnostic yield of 100%. Transbronchial lung biopsy (TBLB) combined with bronchoalveolar lavage (BAL) had a diagnostic yield of 80.9% (17/21). Sputum cytology had a diagnostic yield of 45% (9/20). Twenty-six cases were invasive mucinous adenocarcinoma (26/46, 56.5%) and the remainder were unable to identify pathological subtypes due to lack of adequate biopsy sample size. EGFR mutation was detected in 15.8% (6/38) of patients and ALK rearrangement was detected in 3.0% (1/33) of patients. The median overall survival for these patients was 522 d (95%CI: 424-619). In patients without EGFR mutation or ALK rearrangement, chemotherapy significantly improved survival (HR=0.155, P=0.002,2). The median overall survival was 547 d (95%CI: 492-602 d) with chemotherapy and 331 d (95%CI: 22-919) without chemotherapy. CONCLUSIONS Diagnosis of pneumonic-type carcinoma is usually delayed due to clinical and radiological features mimicking pulmonary infection. TBLB combined with BAL has a quite high diagnostic yield. The most frequent histological type is invasive mucinous adenocarcinoma. The incidence of EGFR mutation or ALK rearrangement is low in pneumonic-type carcinoma. For patients without cancer driver genes, chemotherapy is recommended to improve overall survival.
Aged ; Anaplastic Lymphoma Kinase ; genetics ; metabolism ; Antineoplastic Agents ; therapeutic use ; Carcinoma ; diagnostic imaging ; drug therapy ; genetics ; pathology ; ErbB Receptors ; genetics ; metabolism ; Female ; Gene Rearrangement ; Humans ; Lung Neoplasms ; diagnostic imaging ; drug therapy ; genetics ; pathology ; Male ; Middle Aged ; Mutation ; Neoplasm Staging ; Retrospective Studies ; Survival Analysis ; Tomography, X-Ray Computed

Biopsy, Needle ; Female ; Humans ; Immunohistochemistry ; In Situ Hybridization ; Lung Neoplasms ; diagnostic imaging ; metabolism ; pathology ; Middle Aged ; Situs Inversus ; diagnostic imaging ; metabolism ; pathology

Humans ; Lung Neoplasms ; diagnostic imaging ; pathology ; Tomography, X-Ray Computed