Objective: To investigate the diagnostic efficiency of conventional serum tumor markers and their combination with chest CT for stage ⅠA lung cancer. Methods: A total of 1 155 patients with stage ⅠA lung cancer and 200 patients with benign lung lesions (confirmed by surgery) treated at the Cancer Hospital, Chinese Academy of Medical Sciences from January 2016 to October 2020 were retrospectively enrolled in this study. Six conventional serum tumor markers [carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), squamous cell carcinoma associated antigen (SCCA), cytokeratin 19 fragment (CYFRA21-1), neuron-specific enolase (NSE), and gastrin-releasing peptide precursor (ProGRP)] and chest thin-slice CT were performed on all patients one month before surgery. Pathology was taken as the gold standard to analyze the difference of positivity rates of tumor markers between the lung cancer group and the benign group, the moderate/poor differentiation group and the well differentiation group, the adenocarcinoma group and the squamous cell carcinoma group, the lepidic and non-lepidic predominant adenocarcinoma groups, the solid nodule group and the subsolid nodule group based on thin-slice CT, and subgroups of ⅠA1 to ⅠA3 lung cancers. The diagnostic performance of tumor markers and tumor markers combined with chest CT was analyzed using the receiver operating characteristic curve. Results: The positivity rates of six serum tumor markers in the lung cancer group and the benign group were 2.32%-20.08% and 0-13.64%, respectively; only the SCCA positivity rate in the lung cancer group was higher than that in the benign group (10.81% and 0, P=0.022). There were no significant differences in the positivity rates of other serum tumor markers between the two groups (all P>0.05). The combined detection of six tumor markers showed that the positivity rate of the lung cancer group was higher than that of the benign group (40.93% and 18.18%, P=0.004), and the positivity rate of the adenocarcinoma group was lower than that of the squamous cell carcinoma group (35.66% and 47.41%, P=0.045). The positivity rates in the poorly differentiated group and moderately differentiated group were higher than that in the well differentiated group (46.48%, 43.75% and 22.73%, P=0.025). The positivity rate in the non-lepidic adenocarcinoma group was higher than that in lepidic adenocarcinoma group (39.51% and 21.74%, P=0.001). The positivity rate of subsolid nodules was lower than that of solid nodules (30.01% vs 58.71%, P=0.038), and the positivity rates of stageⅠA1, ⅠA2 and ⅠA3 lung cancers were 33.33%, 48.96% and 69.23%, respectively, showing an increasing trend (P=0.005). The sensitivity and specificity of the combined detection of six tumor markers in the diagnosis of stage ⅠA lung cancer were 74.00% and 56.30%, respectively, and the area under the curve (AUC) was 0.541. The sensitivity and specificity of the combined detection of six serum tumor markers with CT in the diagnosis of stage ⅠA lung cancer were 83.0% and 78.3%, respectively, and the AUC was 0.721. Conclusions: For stage ⅠA lung cancer, the positivity rates of commonly used clinical tumor markers are generally low. The combined detection of six markers can increase the positivity rate. The positivity rate of markers tends to be higher in poorly differentiated lung cancer, squamous cell carcinoma, or solid nodules. Tumor markers combined with thin-slice CT showed limited improvement in diagnostic efficiency for early lung cancer.
Humans ; Lung Neoplasms/diagnostic imaging* ; Biomarkers, Tumor ; Retrospective Studies ; Antigens, Neoplasm ; Keratin-19 ; Carcinoembryonic Antigen ; Adenocarcinoma/diagnostic imaging* ; Carcinoma, Squamous Cell/diagnostic imaging* ; Phosphopyruvate Hydratase ; Tomography, X-Ray Computed

Objective: To investigate the diagnostic efficiency of conventional serum tumor markers and their combination with chest CT for stage ⅠA lung cancer. Methods: A total of 1 155 patients with stage ⅠA lung cancer and 200 patients with benign lung lesions (confirmed by surgery) treated at the Cancer Hospital, Chinese Academy of Medical Sciences from January 2016 to October 2020 were retrospectively enrolled in this study. Six conventional serum tumor markers [carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), squamous cell carcinoma associated antigen (SCCA), cytokeratin 19 fragment (CYFRA21-1), neuron-specific enolase (NSE), and gastrin-releasing peptide precursor (ProGRP)] and chest thin-slice CT were performed on all patients one month before surgery. Pathology was taken as the gold standard to analyze the difference of positivity rates of tumor markers between the lung cancer group and the benign group, the moderate/poor differentiation group and the well differentiation group, the adenocarcinoma group and the squamous cell carcinoma group, the lepidic and non-lepidic predominant adenocarcinoma groups, the solid nodule group and the subsolid nodule group based on thin-slice CT, and subgroups of ⅠA1 to ⅠA3 lung cancers. The diagnostic performance of tumor markers and tumor markers combined with chest CT was analyzed using the receiver operating characteristic curve. Results: The positivity rates of six serum tumor markers in the lung cancer group and the benign group were 2.32%-20.08% and 0-13.64%, respectively; only the SCCA positivity rate in the lung cancer group was higher than that in the benign group (10.81% and 0, P=0.022). There were no significant differences in the positivity rates of other serum tumor markers between the two groups (all P>0.05). The combined detection of six tumor markers showed that the positivity rate of the lung cancer group was higher than that of the benign group (40.93% and 18.18%, P=0.004), and the positivity rate of the adenocarcinoma group was lower than that of the squamous cell carcinoma group (35.66% and 47.41%, P=0.045). The positivity rates in the poorly differentiated group and moderately differentiated group were higher than that in the well differentiated group (46.48%, 43.75% and 22.73%, P=0.025). The positivity rate in the non-lepidic adenocarcinoma group was higher than that in lepidic adenocarcinoma group (39.51% and 21.74%, P=0.001). The positivity rate of subsolid nodules was lower than that of solid nodules (30.01% vs 58.71%, P=0.038), and the positivity rates of stageⅠA1, ⅠA2 and ⅠA3 lung cancers were 33.33%, 48.96% and 69.23%, respectively, showing an increasing trend (P=0.005). The sensitivity and specificity of the combined detection of six tumor markers in the diagnosis of stage ⅠA lung cancer were 74.00% and 56.30%, respectively, and the area under the curve (AUC) was 0.541. The sensitivity and specificity of the combined detection of six serum tumor markers with CT in the diagnosis of stage ⅠA lung cancer were 83.0% and 78.3%, respectively, and the AUC was 0.721. Conclusions: For stage ⅠA lung cancer, the positivity rates of commonly used clinical tumor markers are generally low. The combined detection of six markers can increase the positivity rate. The positivity rate of markers tends to be higher in poorly differentiated lung cancer, squamous cell carcinoma, or solid nodules. Tumor markers combined with thin-slice CT showed limited improvement in diagnostic efficiency for early lung cancer.
Humans ; Lung Neoplasms/diagnostic imaging* ; Biomarkers, Tumor ; Retrospective Studies ; Antigens, Neoplasm ; Keratin-19 ; Carcinoembryonic Antigen ; Adenocarcinoma/diagnostic imaging* ; Carcinoma, Squamous Cell/diagnostic imaging* ; Phosphopyruvate Hydratase ; Tomography, X-Ray Computed

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: To explore the application value of diffusion kurtosis imaging (DKI) in the differential diagnosis of rectal tumors and evaluating the prognostic factors associated with rectal adenocarcinoma. Methods: A total of 105 patients with rectal tumors admitted in the First Affiliated Hospital of Zhengzhou University from December 2018 to August 2020 were retrospectively analyzed. All patients underwent high-resolution magnetic resonance DKI scanning. The mean diffusivity (MD), mean kurtosis (MK) and apparent diffusion coefficient (ADC) were measured and the relationship of these parameters with pathological types and prognostic factors of rectal tumor were analyzed. The diagnostic efficacy of MD, MK, and ADC for positive circumferential resection margin (CRM) and extramural venous invasion (EMVI) of rectal adenocarcinoma was evaluated by the receiver operating characteristic (ROC) curve. Results: MD and ADC were only related to pathological type. The MD and ADC were (2.091±0.390)×10(-3) and (1.478±0.265)×10(-3) mm(2)/s in mucinous adenocarcinoma, higher than (1.136±0.182)×10(-3) and (0.767±0.077)×10(-3) mm(2)/s in unspecified adenocarcinoma and (1.617±0.697)×10(-3) and (0.940±0.179)×10(-3) mm(2)/s in tubulo-villous adenoma. The MD and ADC in unspecified adenocarcinoma were lower than those in tubule-villous adenoma (P<0.05). Nevertheless, MK was associated with pathological type, N stage, CRM and EMVI. The MK was 0.566±0.110 in mucinous adenocarcinoma, lower than 0.982±0.135 in unspecified adenocarcinoma and 0.827±0.121 in tubulo-villous adenoma. The MK in unspecified adenocarcinoma was higher than that in intubulo-villous adenoma. The MK was 0.984±0.107 in pN1-2, higher than 0.881±0.146 in pN0. The MK was 0.990±0.142 in positive CRM, higher than 0.862±0.114 in negative CRM. The MK was 0.996±0.140 in positive EMVI, higher than 0.832±0.100 in negative EMVI (P<0.05). The ROC curves showed that the AUCs of MD, MK and ADC in diagnosing positive CRM were 0.459, 0.653 and 0.408, respectively; with MK=1.006 as the optimal diagnostic threshold, the diagnostic sensitivity and specificity were 51.9% and 81.0%, respectively. The AUCs of MD, MK and ADC values in diagnosing positive EMVI were 0.510, 0.662 and 0.388, respectively; with MK=1.010 as the optimal diagnostic threshold, the diagnostic sensitivity and specificity were 50.9% and 87.5%, respectively. Conclusions: DKI quantitative parameter is helpful for discriminating rectal tubulo-villous adenoma, unspecified adenocarcinoma, and mucinous adenocarcinoma, and is helpful for predicting the prognosis of patients with rectal adenocarcinoma. High MK is associated with positive CRM and EMVI.
Humans ; Adenocarcinoma/diagnostic imaging* ; Adenocarcinoma, Mucinous/diagnostic imaging* ; Adenoma, Villous/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging/methods* ; Prognosis ; Rectal Neoplasms/pathology* ; Retrospective Studies ; Sensitivity and Specificity

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

Objective To investigate the correlations of diffusion weighted imaging (DWI) with pathological grading,typing and clinical staging of cervical adenocarcinoma. Methods The data of 95 patients with cervical adenocarcinoma from May 2011 to February 2018 in Cancer Hospital Chinese Academy of Medical Sciences were collected for retrospective analysis.Before treatment,conventional MRI and DWI (b=0,800 s/mm²) were performed,and the apparent diffusion coefficient (ADC) value of cervical adenocarcinoma was measured.The ADC values were compared among different pathological grades,types,and clinical stages. Results The mean ADC value was (1.00±0.25)×10^-3 mm²/s in the poorly differentiation group,(1.09±0.25)×10^-3 mm²/s in the moderately differentiation group,and (1.22±0.20)×10^-3 mm²/s in the well differentiation group,which showed significant difference between the poorly and well differentiation groups (P=0.002).The mean ADC values were (1.04±0.24) ×10^-3 mm²/s and (1.21±0.26)×10^-3 mm²/s in the endocervical adenocarcinoma (usual type) group and mucinous carcinoma group,respectively,which showed significant difference (P=0.005). Conclusions The worse differentiation of cervical adenocarcinoma corresponded to the lower ADC value.The ADC value of mucinous carcinoma was higher than that of endocervical adenocarcinoma (usual type).
Adenocarcinoma/pathology* ; Diffusion Magnetic Resonance Imaging/methods* ; Female ; Humans ; Magnetic Resonance Imaging/methods* ; Retrospective Studies ; Uterine Cervical Neoplasms/diagnostic imaging*

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

Fetal adenocarcinoma of the lung (FLAC) is an extremely rare subtype of lung cancer, accounting for only 0.1% to 0.5% of primary pulmonary malignancy. In 2011, international multidisciplinary classification of lung adenocarcinoma developed by the International Association for the Study of Lung Cancer (IALSC), the American Thoracic Society (ATS) and the European Respiratory Society (ERS) classified FLAC as a variant of invasive adenocarcinoma. FLAC has been further divided into low-grade fetal adenocarcinoma (L-FLAC) and high-grade fetal adenocarcinoma (H-FLAC) as these two categories exhibit different clinicopathological features and biological behaviors. Here we report a case of high-grade fetal adenocarcinoma and summarize clinicopathologic features of fetal lung adenocarcinoma.
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Adenocarcinoma of Lung ; diagnostic imaging ; pathology ; surgery ; Fetus ; Humans ; Male ; Middle Aged ; Neoplasm Grading ; Prognosis ; Tomography, X-Ray Computed

Objective To evaluate the value of texture features derived from intravoxel incoherent motion (IVIM) parameters for differentiating pancreatic neuroendocrine tumor (pNET) from pancreatic adenocarcinoma (PAC).Methods Eighteen patients with pNET and 32 patients with PAC were retrospectively enrolled in this study. All patients underwent diffusion-weighted imaging with 10 b values used (from 0 to 800 s/mm ). Based on IVIM model, perfusion-related parameters including perfusion fraction (f), fast component of diffusion (D) and true diffusion parameter slow component of diffusion (D) were calculated on a voxel-by-voxel basis and reorganized into gray-encoded parametric maps. The mean value of each IVIM parameter and texture features [Angular Second Moment (ASM), Inverse Difference Moment (IDM), Correlation, Contrast and Entropy] values of IVIM parameters were measured. Independent sample t-test or Mann-Whitney U test were performed for the between-group comparison of quantitative data. Regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic efficiency.Results The mean f value of the pNET group were significantly higher than that of the PAC group (27.0% vs. 19.0%, P = 0.001), while the mean values of D and D showed no significant differences between the two groups. All texture features (ASM, IDM, Correlation, Contrast and Entropy) of each IVIM parameter showed significant differences between the pNET and PAC groups (P=0.000-0.043). Binary logistic regression analysis showed that texture ASM of D and texture Correlation of D were considered as the specific imaging variables for the differential diagnosis of pNET and PAC. ROC analysis revealed that multiple texture features presented better diagnostic performance than IVIM parameters (AUC 0.849-0.899 vs. 0.526-0.776), and texture ASM of D combined with Correlation of D in the model of logistic regression had largest area under ROC curve for distinguishing pNET from PAC (AUC 0.934, cutoff 0.378, sensitivity 0.889, specificity 0.854).Conclusions Texture analysis of IVIM parameters could be an effective and noninvasive tool to differentiate pNET from PAC.
Adenocarcinoma ; diagnostic imaging ; Adult ; Aged ; Algorithms ; Diagnosis, Differential ; Diffusion Magnetic Resonance Imaging ; Female ; Humans ; Male ; Middle Aged ; Motion ; Pancreatic Neoplasms ; diagnostic imaging ; Retrospective Studies

Adenocarcinoma of Lung ; diagnostic imaging ; genetics ; mortality ; ErbB Receptors ; genetics ; Exons ; genetics ; Humans ; Kaplan-Meier Estimate ; Lung Neoplasms ; diagnostic imaging ; genetics ; mortality ; Magnetic Resonance Imaging ; Positron Emission Tomography Computed Tomography ; Survival Analysis ; Tomography, X-Ray Computed