Humans ; Consensus ; Lung Neoplasms/pathology* ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Biopsy

A 54-year-old, non-smoking woman was diagnosed as stage ⅣB adenocarcinoma with widespread bone metastasis (cT4N2M1c) in the First Affiliated Hospital, Zhejiang University School of Medicine. Immunohistochemistry result showed the presence of anaplastic lymphoma kinase (ALK) gene rearrangement; next-generation sequencing (NGS) indicated EML4-ALK fusion (E6:A20) with concurrent CCDC148-ALK (C1:A20), PKDCC-ALK (Pintergenic:A20)and VIT-ALK (V15:A20) fusions. After 32 weeks of alectinib treatment, the patient complained cough and exertional chest distress but had no sign of infection. Computed tomography (CT) showed bilateral diffuse ground glass opacities, suggesting a diagnosis of alectinib-related interstitial lung disease (ILD). Following corticosteroid treatment and discontinuation of alectinib, clinical presentations and CT scan gradually improved, but the primary lung lesions enlarged during the regular follow-up. The administration of crizotinib was then initiated and the disease was stable for 25 months without recurrence of primary lung lesions and ILD.
Female ; Humans ; Middle Aged ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; Crizotinib/therapeutic use* ; Lung Neoplasms/drug therapy* ; Anaplastic Lymphoma Kinase/therapeutic use* ; Lung Diseases, Interstitial/diagnosis*

Objective: Based on the diagnostic model established and validated by the machine learning algorithm, to investigate the value of seven tumor-associated autoantibodies (TAABs), namely anti-p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1 and CAGE antibodies in the diagnosis of non-small cell lung cancer (NSCLC) and to differentiate between NSCLC and benign lung nodules. Methods: This was a retrospective study of clinical cases. Model building queue: a total of 227 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from November 2018 to June 2021 were collected as the NSCLC group, and 120 cases of benign lung nodules, 122 cases of pneumonia and 120 healthy individuals were selected as the control groups. External validation queue: a total of 100 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from May 2022 to December 2022 were collected as the NSCLC group, and 36 cases of benign lung nodules, 32 cases of pneumonia and 44 healthy individuals were selected as the control groups. In addition, NSCLC was divided into early (stage 0-ⅠB) and mid-to-late (stage ⅡA-ⅢB) subgroups. The levels of 7-TAABs were detected by enzyme immunoassay, and serum concentrations of CEA and CYFRA21-1 were detected by electrochemiluminescence. Four machine learning algorithms, XGBoost, Lasso logistic regression, Naïve Bayes, and Support Vector Machine are used to establish classification models. And the best performance model was chosen based on evaluation metrics and a multi-indicator combination model was established. In addition, an online risk evaluation tool was generated to assist clinical applications. Results: Except for p53, the levels of rest six TAABs, CEA and CYFRA21-1 were significantly higher in the NSCLC group (P<0.05). Serum levels of anti-SOX2 [1.50 (0.60, 10.85) U/ml vs. 0.8 (0.20, 2.10) U/ml, Z=2.630, P<0.05] and MAGEA1 antibodies [0.20 (0.10, 0.43) U/ml vs. 0.10 (0.10, 0.20) U/ml, Z=2.289, P<0.05], CEA [3.13 (2.12, 5.64) ng/ml vs. 2.11 (1.25, 3.09) ng/ml, Z=3.970, P<0.05] and CYFRA21-1 [4.31(2.37, 7.14) ng/ml vs. 2.53(1.92, 3.48) ng/ml, Z=3.959, P<0.05] were significantly higher in patients with mid-to late-stage NSCLC than in early stages. XGBoost model was used to establish a multi-indicator combined detection model (after removing p53). 6-TAABs combined with CYFRA21-1 was the best combination model for the diagnosis of NSCLC and early NSCLC. The optimal diagnostic thresholds were 0.410, 0.701 and 0.744, and the AUC was 0.828, 0.757 and 0.741, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in model building queue, and the AUC was 0.760, 0.710 and 0.660, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in external validation queue. Conclusion: In the diagnosis of NSCLC, 6-TAABs is superior to that of traditional tumor markers CEA and CYFRA21-1, and can compensate for the shortcomings of traditional tumor markers. For the differential diagnosis of NSCLC and benign lung nodule, "6-TAABs+CYFRA21-1" is the most cost-effective combination, and plays an important role in prevention and screening for early lung cancer.
Humans ; Carcinoma, Non-Small-Cell Lung/surgery* ; Lung Neoplasms/diagnosis* ; Retrospective Studies ; Autoantibodies ; Bayes Theorem ; Tumor Suppressor Protein p53 ; Carcinoembryonic Antigen ; Antigens, Neoplasm ; Biomarkers, Tumor ; Algorithms ; Pneumonia

Objective: Based on the diagnostic model established and validated by the machine learning algorithm, to investigate the value of seven tumor-associated autoantibodies (TAABs), namely anti-p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1 and CAGE antibodies in the diagnosis of non-small cell lung cancer (NSCLC) and to differentiate between NSCLC and benign lung nodules. Methods: This was a retrospective study of clinical cases. Model building queue: a total of 227 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from November 2018 to June 2021 were collected as the NSCLC group, and 120 cases of benign lung nodules, 122 cases of pneumonia and 120 healthy individuals were selected as the control groups. External validation queue: a total of 100 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from May 2022 to December 2022 were collected as the NSCLC group, and 36 cases of benign lung nodules, 32 cases of pneumonia and 44 healthy individuals were selected as the control groups. In addition, NSCLC was divided into early (stage 0-ⅠB) and mid-to-late (stage ⅡA-ⅢB) subgroups. The levels of 7-TAABs were detected by enzyme immunoassay, and serum concentrations of CEA and CYFRA21-1 were detected by electrochemiluminescence. Four machine learning algorithms, XGBoost, Lasso logistic regression, Naïve Bayes, and Support Vector Machine are used to establish classification models. And the best performance model was chosen based on evaluation metrics and a multi-indicator combination model was established. In addition, an online risk evaluation tool was generated to assist clinical applications. Results: Except for p53, the levels of rest six TAABs, CEA and CYFRA21-1 were significantly higher in the NSCLC group (P<0.05). Serum levels of anti-SOX2 [1.50 (0.60, 10.85) U/ml vs. 0.8 (0.20, 2.10) U/ml, Z=2.630, P<0.05] and MAGEA1 antibodies [0.20 (0.10, 0.43) U/ml vs. 0.10 (0.10, 0.20) U/ml, Z=2.289, P<0.05], CEA [3.13 (2.12, 5.64) ng/ml vs. 2.11 (1.25, 3.09) ng/ml, Z=3.970, P<0.05] and CYFRA21-1 [4.31(2.37, 7.14) ng/ml vs. 2.53(1.92, 3.48) ng/ml, Z=3.959, P<0.05] were significantly higher in patients with mid-to late-stage NSCLC than in early stages. XGBoost model was used to establish a multi-indicator combined detection model (after removing p53). 6-TAABs combined with CYFRA21-1 was the best combination model for the diagnosis of NSCLC and early NSCLC. The optimal diagnostic thresholds were 0.410, 0.701 and 0.744, and the AUC was 0.828, 0.757 and 0.741, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in model building queue, and the AUC was 0.760, 0.710 and 0.660, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in external validation queue. Conclusion: In the diagnosis of NSCLC, 6-TAABs is superior to that of traditional tumor markers CEA and CYFRA21-1, and can compensate for the shortcomings of traditional tumor markers. For the differential diagnosis of NSCLC and benign lung nodule, "6-TAABs+CYFRA21-1" is the most cost-effective combination, and plays an important role in prevention and screening for early lung cancer.
Humans ; Carcinoma, Non-Small-Cell Lung/surgery* ; Lung Neoplasms/diagnosis* ; Retrospective Studies ; Autoantibodies ; Bayes Theorem ; Tumor Suppressor Protein p53 ; Carcinoembryonic Antigen ; Antigens, Neoplasm ; Biomarkers, Tumor ; Algorithms ; Pneumonia

Pulmonary enteric adenocarcinoma (PEAC), as a rare histologic subtype of primary lung adenocarcinoma, is defined as an adenocarcinoma in which the enteric component exceeds 50%. It is named after its shared morphological and immunohistochemical features with colorectal cancer. While with such similarity, the differential diagnosis of PEAC and lung metastatic colorectal cancer is a great challenge in the clinic. PEAC may originate from the intestinal metaplasia of respiratory basal cells stimulated by risk factors such as smoking. Current studies have found that KRAS is a relatively high-frequency mutation gene, and other driver gene mutations are rare. In terms of immunohistochemistry, in pulmonary enteric adenocarcinoma, the positive rate was 88.2% (149/169) for CK7, 78.1% (132/169) for CDX2, 48.2% (82/170) for CK20 and 38.8% (66/170) for TTF1. As for clinical features, the average age of onset for pulmonary enteric adenocarcinoma was 62 years, male patients accounted for 56.5% (35/62), smokers accounted for 78.8% (41/52), and 41.4% (24/58) of the primary lesion was located in the upper lobe of the right lung. In terms of treatment, conventional non-small cell lung cancer (NSCLC) regimens rather than colorectal cancer regimens are now recommended. There is still an urgent need for more basic and clinical research, in-depth exploration of its molecular feature and pathogenesis from the level of omics and other aspects, to help diagnosis and differential diagnosis, and find the optimal chemotherapy regimen, possibly effective targeted therapy and even immunotherapy.
Adenocarcinoma/pathology* ; Adenocarcinoma of Lung/pathology* ; Biomarkers, Tumor ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Colonic Neoplasms/pathology* ; Diagnosis, Differential ; Humans ; Lung Neoplasms/genetics* ; Male ; Middle Aged

Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Consensus ; Lung Neoplasms/diagnosis* ; Asian People ; China

OBJECTIVE: To evaluate the diagnostic value of the serum tumor markers carcinoembryonic antigen (CEA), cytokeratin-19-fragment (CYFRA21-1), squamous cell carcinoma associated antigen (SCCAg), neuron-specificenolase (NSE) and pro-gastrin-releasing peptide (ProGRP) for lung cancers of different pathological types. METHODS: This study was conducted among patients with established diagnoses of lung adenocarcinoma (LADC, n=137), lung squamous cell carcinoma (LSCC, n=82), small cell lung carcinoma (SCLC, n=59), and benign chest disease (BCD, n=102). The serum tumor markers were detected for all the patients for comparison of the positivity rates and their serum levels. ROC curve was used for analysis of the diagnostic efficacy of these tumor markers either alone or in different combinations. RESULTS: In patients with LADC, the positivity rate and serum level of CEA were significantly higher than those in the other groups (P < 0.05); the patients with LSCC had the highest positivity rate and serum level of SCCAg among the 4 groups (P < 0.05). The positivity rates and serum levels of ProGRP and NSE were significantly higher in SCLC group than in the other groups (P < 0.05). CYFRA21-1 showed the highest positivity rate and serum level in LADC group and LSCC group. With the patients with BCD as control, CEA showed a diagnostic sensitivity of 62.8% and a specificity of 93.1% for LADC, and the sensitivity and specificity of SCCAg for diagnosing LSCC were 64.6% and 91.2%, respectively. CYFRA21-1 had the highest diagnostic sensitivity for LADC and LSCC. The diagnostic sensitivity and specificity of ProGRP for SCLC were 83.1% and 98.0%, respectively. When combined, CYFRA21-1 and CEA showed a high sensitivity (78.8%) and specificity (86.3%) for diagnosing LADC with an AUC of 0.891; CYFRA21-1 and SCCAg had a high sensitivity (84.1%) and specificity (87.3%) for diagnosing LSCC with an AUC of 0.912. NSE combined with ProGRP was highly sensitive (88.1%) and specific (98.0%) for diagnosis of SCLC, with an AUC of 0.952. For lung cancers of different pathological types, the combination of all the 5 tumor markers showed no significant differences in the diagnostic power from a combined detection with any two of the markers (P>0.05). CONCLUSION CEA, CYFRA21-1, SCCAg, NSE and ProGRP are all related to the pathological type of lung cancers and can be used in different combinations as useful diagnostic indicators for lung cancers.
Antigens, Neoplasm ; Biomarkers, Tumor ; Carcinoembryonic Antigen ; Humans ; Keratin-19 ; Lung Neoplasms/pathology* ; Peptide Fragments ; Peptide Hormones ; Recombinant Proteins ; Small Cell Lung Carcinoma/diagnosis*

Lung cancer, with the highest incidence in China, is the leading cause of death in cancer patients. Of these, about 85% are patients with non-small cell lung cancer (NSCLC). Therefore, the diagnosis and treatment of patients with lung cancer have always been a top priority nowadays. Fluid biopsy has many advantages, such as safety, convenience, repeatability, low trauma and so on, which are not available in traditional invasive biopsy. In recent years, with the rapid progress of molecular biological detection technology, fluid biopsy, as a new technology, has become the focus of attention. What's more, it contributes to the development of precision treatment and individualized treatment of lung cancer. Liquid biopsy mainly detects circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) and exosomes in peripheral blood. We will make an introduce to the detection and clinical applications of ctDNA, CTCs and exocrine in this article, in order that it can provide insights into future clinical treatment for NSCLC.
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Biomarkers, Tumor ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Circulating Tumor DNA ; Humans ; Liquid Biopsy ; Lung Neoplasms/diagnosis*

diagnosis of lung cancer and to establish the optimal cut-off values.METHODS: We retrospectively reviewed the medical records of 1,176 subjects with CYFRA 21-2 and CEA data; they were classified into 93 lung cancer cases and 1,083 total controls, including 146 age-matched controls. Multivariate analysis was used to determine the relationship between the concentration of each tumor marker and lung cancer diagnosis. The diagnostic efficiencies of tumor markers were evaluated using receiver operating characteristic curve analysis and areas under the curve (AUCs) were calculated. The optimal cut-offs for CYFRA 21-1 and CEA were also estimated.RESULTS: Age, CYFRA 21-1, and CEA concentrations were independently associated with lung cancer diagnosis. Diagnostic efficiency of each tumor marker and its' combination was different according to the histological types of lung cancer. For non-small cell lung cancer, the AUCs for the two-marker combination were the highest: 0.8661 and 0.7559 for total and age-matched controls, respectively. For squamous cell carcinoma, the AUCs for CYFRA 21-1 were the highest: 0.9245 and 0.8428 for total and age-matched controls, respectively. The sensitivity and specificity of CYFRA 21-1 and CEA for lung cancer diagnosis were improved when the cutoffs determined based on this study were applied.CONCLUSIONS: CYFRA 21-1 and CEA could be useful markers for diagnosing lung cancer and single or combination of markers may be useful according to different histological types of lung cancer.]]>
Area Under Curve ; Biomarkers, Tumor ; Carcinoembryonic Antigen ; Carcinoma, Non-Small-Cell Lung ; Carcinoma, Squamous Cell ; Diagnosis ; Keratin-19 ; Lung Neoplasms ; Lung ; Medical Records ; Multivariate Analysis ; Retrospective Studies ; ROC Curve ; Sensitivity and Specificity

Immune cell infiltration is of great significance for the diagnosis and prognosis of cancer. In this study, we collected gene expression data of non-small cell lung cancer (NSCLC) and normal tissues included in TCGA database, obtained the proportion of 22 immune cells by CIBERSORT tool, and then evaluated the infiltration of immune cells. Subsequently, based on the proportion of 22 immune cells, a classification model of NSCLC tissues and normal tissues was constructed using machine learning methods. The AUC, sensitivity and specificity of classification model built by random forest algorithm reached 0.987, 0.98 and 0.84, respectively. In addition, the AUC, sensitivity and specificity of classification model of lung adenocarcinoma and lung squamous carcinoma tissues constructed by random forest method 0.827, 0.75 and 0.77, respectively. Finally, we constructed a prognosis model of NSCLC by combining the immunocyte score composed of 8 strongly correlated features of 22 immunocyte features screened by LASSO regression with clinical features. After evaluation and verification, C-index reached 0.71 and the calibration curves of three years and five years were well fitted in the prognosis model, which could accurately predict the degree of prognostic risk. This study aims to provide a new strategy for the diagnosis and prognosis of NSCLC based on the classification model and prognosis model established by immune cell infiltration.
Algorithms ; Carcinoma, Non-Small-Cell Lung ; diagnosis ; physiopathology ; Humans ; Lung Neoplasms ; diagnosis ; physiopathology ; Machine Learning ; Prognosis