Predictive capacity of protein serum biomarkers in the differential diagnosis of small cell and non-small cell lung cancer in patients with suspicious lung lesions

Predictive capacity of protein serum biomarkers in the differential diagnosis of small cell and non-small cell lung cancer in patients with suspicious lung lesions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive capacity of protein serum biomarkers in the differential diagnosis of small cell and non-small cell lung cancer in patients with suspicious lung lesions Luz Fernanda Sua, Silvia J. Serrano-Gomez, Marcela Nuñez, Saveria Sangiovanni, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2456846/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tumor biomarkers aid in the diagnosis, management, and prognosis in patients with cancer. In lung cancer, serum biomarkers are used at various timepoints. However, doubts remain about their accuracy for differential diagnosis and histological subtyping in patients with suspicious lung lesions. We conducted a diagnostic test study, selecting cases with malignant lung lesions and controls with benign lung lesions. Prior to lung biopsy, all patients had the following biomarkers measured in serum (Pro-GRP, NSE, CYFRA 21 − 1, SCC - Ag, CEA). Methods The predictive capacity of serum biomarkers evaluated to discriminate between lung cancer and benign pathology was measured using sensitivity, specificity, and Area Under the Curve (AUC). We also assessed their accuracy for distinguishing Small Cell Lung Cancer (SCLC) from Non-Small Cell Lung Cancer (NSCLC) and explored their ability to perform histological subtyping. Results A total of 93 patients were included, 60 with lung cancer and 30 with benign pathology. Serum levels of Pro-GRP and NSE were elevated in patients with SCLC (274 pg. / ml and 41.9 ng. / ml) compared to patients with NSCLC or nonmalignant lung disease (NMLD). The most accurate biomarkers for discriminating between malignant and benign pathology were CEA (AUC = 76.3%, sensitivity = 55.0% / specificity = 87.9%), and CYFRA 21 − 1(AUC = 76.2%, sensitivity = 55.1% / specificity = 87.9%). Pro-GRP had a poor predictive capacity alone for discriminating NSCLC from SCLC but increases in combination with CEA and CYFRA 21 − 1 (AUC = 80.4%, sensitivity = 70.6% / specificity = 81.8%). For SCLC the diagnostic efficacy of Pro-GRP increased by combining with other biomarkers such as NSE / CYFRA21–1 (AUC = 97.3%, sensitivity = 88.8% / specificity = 98.9%). Conclusions Individual biomarkers lacked the required sensitivity and specificity to perform a differential diagnosis or achieve histological subtyping on their own. They might be useful in parallel testing to aid the physician, but a tissue biopsy should not be delayed or postponed. lung cancer tumor markers differential diagnosis histological subtyping SCLC NSCLC Figures Figure 1 Background Lung cancer (LC) is the second most common cancer and the leading cause of cancer death worldwide (18.0% of total cancer deaths) with an estimated of 2.2 million new cases and 1.8 million deaths reported in 2020 (1). 5-year survival-rate ranges from 4–17% depending on the stage of the disease (2). Since LC tends to be asymptomatic,85% of cases are diagnosed at advanced stages and 57% of patients are diagnosed with metastatic disease (2,3). On the other hand, better prognosis has been reported for localized stages with a 5-year survival rate for the disease of 59% (3). It usually develops after the fifth decade of life, being more common in men than in women, and tobacco exposure is the single most important risk factor (4,5). LC can be divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), representing 15% and 85% of cases, respectively (6,7). It is important to distinguish between these two histological subtypes as they have different treatments and prognoses. NSCLC at early stages, is curable with surgery, however, SCLC is a rapidly growing, aggressive neoplasm that is treated with chemotherapy and radiotherapy (8–10). Aiming to achieve early detection, efforts have been made to perform screening in patients who fit the National Institutes of Health (NIH) sponsored National Lung Screening Trial (NLST) criteria using low dose computed tomography (CT). Although this strategy achieved a 20% reduction in LC mortality after 3 rounds of screening, it has a high rate of false positive results, increasing radiation exposure and health costs (11,12) thus, there is still a challenge for researchers to find a non-invasive, sensitive, and reliable biomarker that allows an early detection of the disease (13). Tumor-specific circulating proteins can be measured in samples such as whole blood, serum, obtained through minimally invasive approaches (11). They can be used for different purposes: in asymptomatic but high-risk patients as a screening tool, to support differential diagnosis in patients with suspicious symptoms or a lung lesion in thoracic images; at the time of diagnosis for histological classification, treatment strategy and prognosis, and during oncologic management through monitoring of therapy response, and for early detection of recurrence or progression (14). Their attraction relies in the fact that they are believed to be released early in the disease, can be measured quickly, with non-invasive techniques, plus they are non-expensive (5–7, 8). Limited number of individual serum biomarkers have been reported for lung cancer, although none have provided enough sensitivity and specificity for its use in the clinical practice (15). Some biomarkers such as cytokeratin-19 fragment (CYFRA 21 − 1), carcinoembryonic antigen (CEA) and squamous cell carcinoma antigen (SCC) has been mostly investigated in NSCLC (13,16,17). CYFRA 21 − 1 is the most sensitive tumor marker in NSCLC (18–20) and its serum concentrations have been correlated with clinical – pathological characteristics such as tumor size, lymph node status and the stage of disease (21,22). CEA is a serum glycoprotein and has been widely used as a biomarker for colorectal, breast and lung cancer (23) and provide additive information on the histology of lung cancer (24). SCC-Ag is the only marker used in NSCLC with a clear relationship to histology. There is a greater than 95% probability that abnormal serum SCC-Ag levels indicate NSCLC (25,26). On the other hand, Neuron Specific Enolase (NSE) is a cytosolic enzyme and is a better biomarker for SCLC given its neuroendocrine cellular origin (23,27) and has been used as a diagnostic, prognostic, and follow-up biomarker in SCLC (28). Pro-Gastrin Releasing Peptide (Pro-GRP) has been identified as a good biomarker for SCLC as higher levels has been detected even in patients with localized disease while it is not released to any appreciable extent in NSCLC or benign lung disease (25,29). Our aim was to analyze serum levels of 5 protein biomarkers (Pro-GRP, NSE, CEA, SCC-Ag and CYFRA 21 − 1) in patients with either lung cancer or lung benign disease and evaluate its predictive capacity in the differential diagnosis of patients with clinical suspicion of lung cancer and we evaluate its sensitivity and specificity for histological diagnosis. Methods Patient samples and determination of serum biomarkers Patients included in this study assisted at Fundación Valle del Lili (FVL) between 2018 and 2020. FVL is a non-profit university hospital that serves as a reference healthcare facility for the Southwestern region of Colombia. Two groups of patients were included:60 patients with malignant and 30 patients with benign lung lesions. Inclusion criteria for patients with malignant pathology were: (1) age > 18 years-old, (2) a lung lesion suggestive of malignity and a high probability of being a primary lesion by clinical history and CT scan, and (3) patients who were being taken to lung biopsy by thoracoscopy. Inclusion criteria for patients with benign pathology were: (1) age > 18 years-old, (2) a lung lesion with a high probability of being benign by clinical history and thoracic CT scan, and (3) patients who had indication of a lung biopsy by thoracoscopy for histological classification. All study participants gave written informed consent to participate in this study. The protocol for this study was approved by Ethics Committee in biomedical research of FVL with the No. 018-2018. Specimen Collection And Biomarker Analysis Blood samples were taken prior to the biopsy. All patients were tested for serum biomarkers Pro-GRP, NSE, CEA, SCC-Ag, CYFRA 21 − 1 using the Cobas® 8000-Roche diagnostics system following manufacturer’s recommendations. Histological analysis, the gold standard method for diagnosis was performed in all patients. Tissue samples were analyzed by the pathologist and immunohistochemistry (IHC) using the biomarkers Anti-NSE, CEA31, Cytokeratin 19-A53-B / A2.26, anti-p40-BC28 y pro GRP-mABM16, and was executed to establish the histological classification using the BenchMark ULTRA® system-Roche diagnostics system. Information about other diagnostic procedures (x-ray, bronchoscopy, computed tomography) were available for all the patients. Statistical analysis Demographic, clinical, and laboratory variables were collected prospectively, and registered into the software BD Clinic (Fundación Valle del Lili, Cali, Colombia). All statistical analyzes were performed using the R Project software version 4.1.3 (R Core Team, 2022). Normality was evaluated in the continuous variables using Shapiro Wilk's test. For those variables that did not fit the normal distribution, the median with interquartile range was used as a descriptive measure. Qualitative variables were described using absolute and relative frequencies. Comparisons between groups for the quantitative variables were performed using the Kruskal-Wallis H test and in pairs using the Mann-Whitney U test. Differences for the categorical variables were evaluated using Pearson's Chi-squared or Fisher's exact test. ROC (Receiver Operating Characteristic curve) curves were performed to compare the diagnostic efficacy of each biomarker in the discrimination between SCLC and NSCLC with respect to benign pulmonary disease. The optimal cut-off points for each biomarker were estimated using the values observed in the samples through the ROC analysis, using the method based on the Youden index. The results of the ROC analysis were tabulated as sensitivity, specificity, and precision (area under the curve, AUC) with 95% confidence. A secondary ROC analysis was performed to test the predictive capacity of serum biomarkers in discriminating between a malignant and benign pathology and according to lung cancer histology. p-values < 0.05 were considered statistically significant. Results In total, 93 patients were included in the study from which 9 (9.70%) had a diagnosis of SCLC, 51 (54.8%) of NSCLC and 33 (35.5%) non-malignant pulmonary disease (NMLD). Demographic and clinical characteristics of each group is presented in Table 1 . Table 1 Demographic and clinical characteristics of the patients enrolled in the study Characteristic Histological Classification Total (n = 93) SCLC (n = 9) NSCLC (n = 51) NMLD (n = 33) Age, years Median [IQR] 67.0 [17.0] 65.0 [13.0] 69.0 [14.5] 58.0 [22.0] Sex, n (%) Female 43 (46.2) 4 (44.4) 22 (43.1) 17 (51.5) Male 50 (53.8) 5 (55.6) 29 (56.9) 16 (48.5) Smoking history n (%) Yes 48 (51.6) 6 (66.7) 34 (66.7) 8 (24.2) No 45 (48.4) 3 (33.3) 17 (33.3) 25 (75.8) ECOG, n (%) 0 49 (52.7) 4 (44.4) 21 (41.2) 24 (72.7) 1 38 (40.9) 3 (33.3) 27 (52.9) 8 (24.3) > 1 6 (6.40) 2 (22.3) 3 (5.90) 1 (3.00) Tumor/lesion size (mm) Median [IQR] 40.0 [43.0] 40.0 [24.0] 44.0 [35.2] 11.5 [10.5] COPD, n (%) Yes 14 (15.1) 3 (33.3) 9 (17.6) 2 (6.10) No 79 (84.9) 6 (66.7) 42 (82.4) 31 (93.9) Creatinine levels (mg/dL) Median [IQR] 0.83 [0.33] 0.87 [0.28] 0.85 [0.30] 0.70 [0.34] NSCLC = Non-small-cell lung cancer; SCLC = Small-cell lung cancer; NMLD = Non-malignant lung disease; IQR = Interquartile range; COPD = Chronic obstructive pulmonary disease; ECOG = Eastern Cooperative Oncology Group. Measurement Of Serum Biomarkers Since altered renal function can affect the levels of some biomarkers such as Pro-GRP, median creatinine levels were measured and values above 1.30 mg/dL were found in all histological types. Measurements of serum biomarkers obtained from patients on the day of the lung biopsy are presented in Table 2 . The median serum levels of Pro-GRP, CEA, CYFRA 21 − 1, SCC-Ag and NSE in patients with SCLC and NSCLC were significantly higher compared to patients diagnosed with NMLD, although no statistically significant differences were found for Pro-GRP (p = 0.116) and SCC-Ag levels (p = 0.234) between benign vs. malign lesions nor by lung cancer histological type. Higher levels of CEA (5.84 ng/ml) and CYFRA 21 − 1 (4.27 ng/ml) biomarkers were observed NCSLC patients (p < 0.01) while higher levels of NSE (41.9 ng/ml) were observed in patients with SCLC (p < 0.01). Table 2 Serum biomarkers of the patients enrolled in the study Serum Biomarkers Histological Classification Total (n = 93) SCLC (n = 9) NSCLC (n = 51) NMLD (n = 33) P value (p) Pro-GRP (pg/ml) Median [IQR] 46.7 [27.3] 274 [555] 47.9 [28.8] 43.6 [22.5] 0.116 CEA, (ng/ml) Median [IQR] 3.19 [7.85] 2.64 [1.32] 5.84 (28.8) 1.82 [2.38] < 0.01 CYFRA 21 − 1, (ng/ml) Median [IQR] 3.01 [5.62] 3.06 [8.84] 4.27 [8.35] 1.74 [2.12] < 0.01 SCC-Ag, (ng/ml) Median [IQR] 1.23 [1.35] 1.34 [1.11] 1.02 [1.24] 1.37 [1.60] 0.234 NSE, (ng/ml) Median [IQR] 13.0 [11.1] 41.9 [46.6] 14.5 [16.8] 12.5 [4.30] < 0.01 NSCLC = Non-small-cell lung cancer; SCLC = Small-cell lung cancer; NMLD = Non-malignant lung disease; Pro-GRP = Gastrin-releasing peptide; CEA = Carcinoembryonic antigen; CYFRA21-1 = Cytokeratin 19 fragment; SCC-Ag = Squamous cell carcinoma antigen; NSE = Neuron-specific enolase. Figure 1 presents the distribution of the levels for each biomarker and the post-hoc comparisons per group. Some biomarkers were significantly different between a specific lung cancer histology and NMLD; for example, significantly higher level of CEA was observed in NSCLC compared to NMLD (5.84 ng/mL vs 1.82 ng/ml, p < 0.01) and similar results were observed for CYFRA 21 − 1 (NSCLC: 4.27 ng/ml vs. NMLD:1.74 ng /ml, p < 0.01). On the other hand, higher NSE levels were observed in patients with SCLC when compared to NMLD (SCLC: 41.9 ng/ml vs NMLD: 12.5 ng/ml, p = 0.012). Moreover, NSE levels were significantly higher in patients with SCLC compared to NSCLC (41.9 ng/mL vs 14.5 ng/mL, p = 0.013). Overall accuracy of serum biomarkers for lung cancer diagnosis. A ROC curve was used to analyze the effectiveness and diagnostic accuracy of these biomarkers, alone (Table 3 ) or in combination (Table 4 ) for lung cancer diagnosis (malign vs benign lesion) or to discriminate between NSCLC and SCLC. The following criteria was used for evaluation: - no diagnostic accuracy when the AUC 0.9. Table 3 Performance of different biomarkers in discriminating NSCLC + SCLC, NSCLC and SCLC types compared to non-malignant lung disease (NMLD) Biomarker Histology AUC % (95% CI) Cut-off † Sensitivity % (95% CI) Specificity % (95% CI) Pro GRP NSCLC + SCLC 60.2 (48.4–72.0) 68.1 30.0 (18.8–43.2) 93.9 (79.8–99.3) NSCLC 58.7 (46.4–71.1) 68.1 25.5 (14.3–39.6) 93.9 (79.8–99.3) SCLC 68.5 (47.4–89.6) 274 55.6 (21.2–86.3) 100 (89.4–100) CEA NSCLC + SCLC 76.3 (66.7–85.8) 4.32 55.0 (41.6–67.9) 87.9 (71.8–96.6) NSCLC 78.6 (69.0-88.2) 4.32 60.8 (46.1–74.2) 87.9 (71.8–96.6) SCLC 63.1 (41.5–84.7) 2.20 77.7 (40.0-97.2) 57.6 (39.2–74.5) CYFRA 21 − 1 NSCLC + SCLC 76.2 (66.6–85.8) 3.66 55.1 (41.7–68.9) 87.9 (71.8–96.6) NSCLC 76.7 (66.8–86.7) 3.66 56.9 (42.2–70.7) 87.9 (71.8–96.6) SCLC 72.9 (52.5–93.2) 7.67 44.4 (13.7–78.8) 97.0 (84.2–99.9) SCC-Ag NSCLC + SCLC 43.5 (31.1–55.8) 10.1 6.70 (1.80–16.2) 100 (89.4–100) NSCLC 41.3 (28.7–53.8) 10.1 5.90 (1.20–16.2) 100 (89.4–100) SCLC 55.9 (34.1–77.7) 1.21 88.9 (51.8–99.7) 42.4 (25.5–60.8) NSE NSCLC + SCLC 67.8 (56.9–78.8) 21.6 40.0 (27.6–53.5) 100 (89.4–100) NSCLC 66.2 (54.5–77.7) 21.6 37.7 (24.1–51.9) 100 (89.4–100) SCLC 77.4 (58.2–96.7) 41.9 55.6 (21.2–86.3) 100 (89.4–100) NSCLC = Non-small-cell lung cancer; SCLC = Small-cell lung cancer; AUC = Area Under the Curve; OR = Odds Ratio; CI = Confidence Interval; Pro-GRP = Gastrin-releasing peptide; CEA = Carcinoembryonic antigen; CYFRA21-1 = Cytokeratin 19 fragment; SCC-Ag = Squamous cell carcinoma antigen; NSE = Neuron-specific enolase. †: Estimation method based on Youden index Table 4 Performance different combinations of biomarkers in discriminating NSCLC and SCLC types with respect to non-malignant lung disease (NMLD) Biomarkers AUC % (95% CI) Cut-off † Sensitivity % (95% CI) Specificity % (95% CI) NSCLC CEA + CYFRA 21 − 1 78.7 (68.9–88.4) 0.54 60.8 (33.3–76.5) 84.8 (57.6–93.9) CEA + NSE 77.5 (67.6–87.4) 0.65 54.9 (17.6–70.6) 96.9 (72.7–99.5) CEA + Pro-GRP 79.8 (70.2–89.5) 0.53 70.6 (41.2–84.3) 84.8 (48.5–96.7) CYFRA 21 − 1 + NSE 77.6 (67.8–87.5) 0.50 70.6 (49.0-84.3) 78.8 (39.4–93.8) CYFRA 21 − 1 + Pro-GRP 79.4 (69.8–88.9) 0.49 72.5 (41.2–84.3) 75.7 (45.4–87.8) NSE + Pro-GRP 69.9 (59.0–81.0) 0.70 45.1 (23.5–58.8) 97.0 (60.6–99.1) CEA + CYFRA 21 − 1 + NSE 79.8 (70.5–89.2) 0.50 76.5 (49.0-86.3) 78.8 (33.3–90.9) CEA + NSE + Pro-GRP 80.0 (70.6–89.4) 0.69 54.9 (15.7–70.6) 96.9 (69.7–99.8) CEA + CYFRA 21 − 1 + Pro-GRP 80.4 (79.2–89.7) 0.55 70.6 (54.5–93.9) 81.8 (35.3–82.3) CYFRA 21 − 1 + NSE + Pro-GRP 79.3 (69.8–88.7) 0.56 66.7 (39.2–84.3) 81.8 (41.4–93.9) CEA + CYFRA 21 − 1 + NSE + Pro-GRP 81.1 (72.0–90.0) 0.52 74.5 (45.1–86.3) 78.8 (42.4–90.9) SCLC NSE + Pro-GRP 96.6 (89.8–100) 0.58 88.8 (66.7–100) 99.5 (60.6–100) NSE + CYFRA 21 − 1 79.1 (57.6–97.9) 0.16 77.8 (33.3–98.9) 78.8 (45.4–100) CYFRA 21 − 1 + Pro-GRP 83.1 (61.1–100) 0.33 77.8 (44.4–100) 99.3 (15.1–100) CYFRA 21 − 1 + Pro-GRP + NSE 97.3 (91.8–100) 0.56 88.8 (66.7–100) 98.9 (56.7–100) NSCLC = Non-small-cell lung cancer; SCLC = Small-cell lung cancer; AUC = Area Under the Curve; CI = Confidence Interval; Pro-GRP = Gastrin-releasing peptide; CEA = Carcinoembryonic antigen; CYFRA21-1 = Cytokeratin 19 fragment; SCC-Ag = Squamous cell carcinoma antigen; NSE = Neuron-specific enolase. †: Estimation method based on Youden index For each biomarker alone, a moderate diagnostic accuracy was observed for CEA in discriminating between malign lesion (NSCLC + SCLC) vs NMLD (AUC = 76.3%, sensitivity = 55.0% and specificity = 87.9%) and more specifically between NSCLC vs NMLD (AUC = 78.6%, sensitivity = 60.8% and specificity = 87.9%). Moderate accuracy was also observed for CYFRA 21 − 1 discriminating between malign lesion (NSCLC + SCLC) vs NMLD (AUC = 76.2%, sensitivity = 55.1% and specificity = 87.9%). Moreover, it also showed moderate accuracy when compared each histology to NMLD (NSCLC vs NMLD (AUC = 76.7%, sensitivity = 56.9% and specificity = 87.9%) and SCLC vs NMLD (AUC = 72.9%, sensitivity = 44.4% and specificity = 97.0%). No diagnostic accuracy was observed for SCC-Ag biomarker. For the other biomarkers (Pro-GRP and NSE), diagnostic accuracy was low when evaluated alone. Although, it was observed a moderate accuracy for NSE in discriminating between SCLC vs NMLD (AUC = 77.4%, sensitivity = 55.6% and specificity = 100%) (Table 2 ). The diagnostic value of Pro-GRP for NSCLC increases in combination with other biomarkers such as CEA and CYFRA 21 − 1 (AUC = 80.4%, sensitivity = 70.6% and specificity = 81.8%) or in combination with CEA, CYFRA 21 − 1 and NSE (AUC = 81.1%, sensitivity = 74.5% and specificity = 78.8%) that is significantly better than Pro-GRP alone (AUC = 58.7%, sensitivity = 25.5% and specificity = 93.9%). For SCLC, the diagnostic efficacy of Pro-GRP increased by combining with other biomarkers such as NSE and CYFRA21-1 (AUC = 97.3%, sensitivity = 88.8% and specificity = 98.9%) (Table 3 ). Discussion Lung cancer is the leading cause of cancer-related death worldwide and despite advances in diagnostic tools and treatments, a high percentage of patients are still diagnosed in advanced stages when the disease is inoperable and therapeutic options such as chemotherapy and radiotherapy do not serve for curative purposes (30). Screening studies with radiography and sputum cytology have not been useful in reducing mortality from this disease, which is the ultimate goal of a screening program (30). The concept of biomarkers is based on the biological properties of cancer as a systemic disease in which, as the disease progresses, it secretes proteins necessary for its growth and enhance its metastatic capacity (30). Biomarkers have been widely used in lung cancer, mainly to monitor the efficacy of therapy and for early detection of recurrences (31). It has been previously described how the low detection rate of true positives and the inability of any single biomarker to diagnose lung cancer due to the heterogeneity among individuals, differences in biochemical pathways and in the tumor biology, among other characteristics, make biomarkers mostly useful in the context of differential diagnosis rather than in screening programs (32,33). Even though the use of serum markers is still controversial specifically to distinguish between histological types of the disease given their low sensitivity (34). In our study, patients with SCLC had median values of NSE and Pro-GRP at least 2 times and 20 times higher, respectively, compared to patients with NSCLC or NMLD. This is consistent with the literature, in which median pro-GRP and NSE values have been reported around 28 and 4.5 times higher in the SCLC histology (35). Based on this trend, the probability of SCLC increases as the levels of pro-GRP and/or NSE elevates; in fact, pro-GRP levels higher than 300 ng/L are said to be 99% specific for detecting SCLC (33). Although in our study, all patients had creatinine levels in the normal range, it is important to consider that serum biomarkers, particularly pro-GRP and CYFRA 21 − 1 elevate in patients with chronic renal impairment, being a potential confounding factor (26) The predictive capacity of serum biomarkers analyzed in this study for diagnosing lung cancer was varied, being the most accurate CEA, with the limitation that it rises in many benign and malignant medical conditions (32,36,37). Additionally, and despite their low sensibility, serum biomarkers such as CEA, CYFRA 21 − 1 and NSE when analyzed alone, showed a high specificity (87.9% − 100%), ideal for ruling out cancer in patients with benign lesions. Histological subtyping, and specifically differentiating between SCLC and NSCLC is crucial in terms of prognosis and therapeutic targets. We observed that CEA and CYFRA 21.1 levels were higher in patients with malign pathology more specifically in patients with NSCLC suggesting its potential as a serum biomarker this histological type, however, we did find multiple outliers. Consistently different studies have used different combination of biomarkers to distinguish lung cancer patients. It has been reviewed the potential use of CYFRA 21.1, CEA, SCCA, tissue polypeptide antigen (TPA), and cancer antigen-125 (CA-125) as biomarkers for NSCLC and NSE for SCLC (27,38,39). Some of these biomarkers have also been associated with outcomes. For example, a recent meta-analysis reported a significant correlation between positive tests for CEA and nodal involvement and mortality, even in patients with stage I NSCLC (40,41). Correspondingly, NSE has been proven as a useful prognosis biomarker for survival, monitoring of treatment and relapse prediction (32,42) Due to the limitations that individual serum biomarkers might have to support lung cancer diagnosis, it has been proposed that assessing a combined panel of biomarkers delivers more accurate results. Pro-GRP has been studied in patients with lung cancer and has been suggested as a good biomarker for the differential histological diagnosis of lung cancer patients (30). In our study, the combination of Pro-GRP, CYFRA 21.1 and CEA showed a high diagnostic value of AUC of 80.4% with a sensitivity of 70.6% and specificity of 81.8% for NSCLC. Similarly, the combination of Pro-GRP, CYFRA 21.1 and NSE showed the highest diagnostic value of AUC of 97.3% with a sensitivity of 88.8% and specificity of 98.9% for SCLC. A recent publication that combined the same biomarkers of the present study, showed an average diagnostic performance of the individual biomarkers, with a significant increase in accuracy using a combined approach, reaching a sensibility of 88.5% and specificity of 82% (37). Our study has several limitations, although we included twice as many patients with malignant pathology compared to our sample size calculation, we were only able to find 9 patients with SCLC during the study period. Although this is consistent with the distribution of histological subtypes among the population, it probably affected the power of our study to detect a higher accuracy of biomarkers for discriminating between SCLC and NSCLC, as it has been reported previously in the literature. Furthermore, we did not design this study to evaluate different cutoff values for the biomarkers, nor did we realized serial testing of several biomarkers to increase the biomarkers specificity. Conclusions Serum biomarker measurement is a non-invasive procedure that could potentially aid in the differential diagnosis of LC. In our study, individual biomarkers lacked the required sensitivity and specificity to perform a differential diagnosis or achieve histological subtyping on their own, although patterns were seen since some biomarkers rose more than others in different histological subtypes. The predictive capacity of combined serum biomarkers after serial testing should be further explored. Abbreviations LC Lung cancer SCLC Small cell lung cancer NSCLC Non-small cell lung cancer SCC Squamous cell carcinoma LCC Large cell carcinoma CT Computed tomography TM Tumor markers Pro-GRP Pro-gastrin releasing peptide (Pro-GRP) NSE Neuron specific enolase CEA Carcinoembryonic antigen SCC-Ag Squamous cell carcinoma antigen CYFRA 21-1 Cytokeratin 19 fragment IHC Immunohistochemistry Sn Sensitivity Sp Specificity +LR Positive likelihood ratio -LR Negative likelihood ratio ROC Receiver operator characteristic AUC Area under the curve. Declarations Ethics approval and consent to participate This manuscript was written in compliance with the ethical standards of the institutional ethics committee and with the 1964 Helsinki Declaration. The protocol (IRB/EC protocol number 1210) for this study, was approved by Ethics Committee in biomedical research of the Fundacion Valle del Lili by letter IRB / EC No. 018-2018, which is available if needed with the Corresponding Author. All study participants gave written informed consent to participate in this study. Consent for publication Not applicable. Availability of data and materials All data and material are available for sharing if needed with the corresponding author, (Liliana Fernandez-Trujillo, [email protected] ). Competing interests The authors declare that they have no competing interests. This manuscript has not been published and is not under consideration for publication elsewhere. Additionally, all the authors have approved the contents of this paper and have agreed to the journal´s submission policies. Funding Support This study was supported by ABBVIE S.A.S. (Grant No. 897-2017), resource management was carried out by the Clinical Research Center of the Fundacion Valle del Lili. Author’ Contributions All authors have read and approved the manuscript, and significantly contributed to this paper. LFS: Substantial contributions to the conception and design of the work, acquisition, analysis, and interpretation of data for the work, critical review of intellectual content, final approval of the version to be published. SJSG: Substantial contributions to the manuscript writing, analysis, and interpretation of data for the work, critical review of intellectual content, final approval of the version to be published. MN: Substantial contribution to work analysis and interpretation of data and critical review of intellectual content. SS: Substantial contribution to work analysis and interpretation of data, writing, critical review of intellectual content, final approval of the version to be published. LFT: Substantial contributions to the conception and design of the work, acquisition, analysis, and interpretation of data for the work, critical review of intellectual content, final approval of the version to be published. Acknowledgements Thanks to the Research and Innovation Center of the Fundación Valle del Lili and ABBVIE S.A.S. for sponsoring this study. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. Wiley; 2021;71:209–49. Nasim F, Sabath BF, Eapen GA. Lung Cancer. Med Clin North Am. 2019;103:463–73. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. Wiley; 2021;71:7–33. Nasim F, Sabath BF, Eapen GA. Lung Cancer. Medical Clinics of North America. 2019;103:463–73. Mao Y, Yang D, He J, Krasna MJ. Epidemiology of Lung Cancer. Surg Oncol Clin N Am. 2016;25:439–45. Keogh A, Finn S, Radonic T. Emerging Biomarkers and the Changing Landscape of Small Cell Lung Cancer. Cancers (Basel) [Internet]. 2022;14:3772. 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Fang R, Zhu Y, Khadka VS, Zhang F, Jiang B, Deng Y. The Evaluation of Serum Biomarkers for Non-small Cell Lung Cancer (NSCLC) Diagnosis. Front Physiol. 2018;9. Lokshin A, Bast RC, Rodland K. Circulating Cancer Biomarkers. Cancers (Basel). 2021;13:802. Bigbee WL, Gopalakrishnan V, Weissfeld JL, Wilson DO, Dacic S, Lokshin AE, et al. A multiplexed serum biomarker immunoassay panel discriminates clinical lung cancer patients from high-risk individuals found to be cancer-free by CT screening. Journal of Thoracic Oncology. 2012;7:698–708. Cedrés S, Nuñez I, Longo M, Martinez P, Checa E, Torrejón D, et al. Serum tumor markers CEA, CYFRA21-1, and CA-125 are associated with worse prognosis in advanced non-small-cell lung cancer (NSCLC). Clin Lung Cancer. 2011;12:172–9. Crosbie PAJ, Shah R, Summers Y, Dive C, Blackhall F. Prognostic and predictive biomarkers in early stage NSCLC: CTCs and serum/plasma markers. Transl Lung Cancer Res. AME Publishing Company; 2013. page 382–97. Stieber P, Hasholzner U, Bodenmüller H, Nagel D, Sunder-Plassmann L, Dienemann H, et al. CYFRA 21-1. A new marker in lung cancer. Cancer. 1993;72:707–13. Niklinski J, Furman M, Rapellino M, Chyczewski L, Laudanski J, Oliaro A, et al. CYFRA 21-1 determination in patients with non-small cell lung cancer: clinical utility for the detection of recurrences. J Cardiovasc Surg (Torino). 1995;36:501–4. Molina R, Agusti C, Mañe JM, Filella X, Jo J, Joseph J, et al. CYFRA 21-1 in lung cancer: comparison with CEA, CA 125, SCC and NSE serum levels. Int J Biol Markers. 9:96–101. Molina R, Agusti C, Filella X, Jo J, Joseph J, Giménez N, et al. Study of a New Tumor Marker, CYFRA21-1, in Malignant and Nonmalignant Diseases. Tumor Biology. 1994;15:318–25. Sertić Milić H, Franjević A, Bubanović G, Marušić A, Nikolić I, Puljić I. Size, edge, and stage of NSCLC determine the release of CYFRA 21-1 in bloodstream. Wien Klin Wochenschr. 2015;127:465–71. Dal Bello MG, Filiberti RA, Alama A, Orengo AM, Mussap M, Coco S, et al. The role of CEA, CYFRA21-1 and NSE in monitoring tumor response to Nivolumab in advanced non-small cell lung cancer (NSCLC) patients. J Transl Med. BioMed Central Ltd.; 2019;17. Liu L, Teng J, Zhang L, Cong P, Yao Y, Sun G, et al. The Combination of the Tumor Markers Suggests the Histological Diagnosis of Lung Cancer. Biomed Res Int. Hindawi Limited; 2017;2017. Molina R, Auge JM, Filella X, Viñolas N, Alicarte J, Domingo JM, et al. Pro-gastrin-releasing peptide (proGRP) in patients with benign and malignant diseases: comparison with CEA, SCC, CYFRA 21-1 and NSE in patients with lung cancer. Anticancer Res. 25:1773–8. Nakahama H, Tanaka Y, Fujita Y, Fujii M, Sugita M. CYFRA 21-1 and ProGRP, tumor markers of lung cancer, are elevated in chronic renal failure patients. Respirology. 1998;3:207–10. Molina R, Filella X, Augé JM, Fuentes R, Bover I, Rifa J, et al. Tumor Markers (CEA, CA 125, CYFRA 21-1, SCC and NSE) in Patients with Non-Small Cell Lung Cancer as an Aid in Histological Diagnosis and Prognosis. Tumor Biology. 2003;24:209–18. Isgrò MA, Bottoni P, Scatena R. Neuron-Specific Enolase as a Biomarker: Biochemical and Clinical Aspects. 2015. page 125–43. Molina R, Holdenrieder S, Auge JM, Schalhorn A, Hatz R, Stieber P. Diagnostic relevance of circulating biomarkers in patients with lung cancer. Cancer Biomark. 2010;6:163–78. Patz EF, Campa MJ, Gottlin EB, Kusmartseva I, Guan XR, Herndon JE. Panel of Serum Biomarkers for the Diagnosis of Lung Cancer. Journal of Clinical Oncology. 2007;25:5578–83. Liu L, Teng J, Zhang L, Cong P, Yao Y, Sun G, et al. The Combination of the Tumor Markers Suggests the Histological Diagnosis of Lung Cancer. Biomed Res Int. Hindawi Limited; 2017;2017. Holdenrieder S. Biomarkers along the continuum of care in lung cancer. Scand J Clin Lab Invest. 2016;76:S40–5. Molina R, Augé JM, Bosch X, Escudero JM, Viñolas N, Marrades R, et al. Usefulness of Serum Tumor Markers, Including Progastrin-Releasing Peptide, in Patients with Lung Cancer: Correlation with Histology. Tumor Biology. 2009;30:121–9. Mauro C, Passerini R, Spaggiari L, Galetta D, Radice D, Lentati P, et al. New and old biomarkers in the differential diagnosis of lung cancer: Pro-gastrin-releasing peptide in comparison with neuron-specific enolase, carcinoembryonic antigen, and CYFRA 21-1. International Journal of Biological Markers. SAGE Publications Ltd; 2019;34:163–7. Cavalieri S, Morelli D, Martinetti A, Galli G, Nichetti F, de Braud F, et al. Clinical implications for pro-GRP in small cell lung cancer. A single center experience. Int J Biol Markers. 2018;33:55–61. Hammarström S. The carcinoembryonic antigen (CEA) family: structures, suggested functions and expression in normal and malignant tissues. Semin Cancer Biol. 1999;9:67–81. Molina R, Marrades RM, Augé JM, Escudero JM, Viñolas N, Reguart N, et al. Assessment of a Combined Panel of Six Serum Tumor Markers for Lung Cancer. Am J Respir Crit Care Med. 2016;193:427–37. Chi-Shing Cho W. Potentially useful biomarkers for the diagnosis, treatment and prognosis of lung cancer. Biomedicine & Pharmacotherapy. 2007;61:515–9. Jørgensen L, Osterlind K, Genollá J, Gomm S, Hernández J, Johnson P, et al. Serum neuron-specific enolase (S-NSE) and the prognosis in small-cell lung cancer (SCLC): a combined multivariable analysis on data from nine centres. Br J Cancer. 1996;74:463–7. Maeda R, Suda T, Hachimaru A, Tochii D, Tochii S, Takagi Y. Clinical significance of preoperative carcinoembryonic antigen level in patients with clinical stage IA non-small cell lung cancer. J Thorac Dis. 2017;9:176–86. Nasralla A, Lee J, Dang J, Turner S. Elevated preoperative CEA is associated with subclinical nodal involvement and worse survival in stage I non-small cell lung cancer: a systematic review and meta-analysis. J Cardiothorac Surg. 2020;15:318. Barak V, Holdenrieder S, Nisman B, Stieber P. Relevance of circulating biomarkers for the therapy monitoring and follow-up investigations in patients with non-small cell lung cancer. Cancer Biomarkers. 2010;6:191–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2456846","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":170219022,"identity":"674302ff-bdc9-4355-abfb-8b628565041b","order_by":0,"name":"Luz Fernanda Sua","email":"","orcid":"","institution":"Fundación Valle del Lili","correspondingAuthor":false,"prefix":"","firstName":"Luz","middleName":"Fernanda","lastName":"Sua","suffix":""},{"id":170219023,"identity":"c331a4fb-162e-4bd5-bacd-fd8c8c452b10","order_by":1,"name":"Silvia J. Serrano-Gomez","email":"","orcid":"","institution":"Instituto Nacional de Cancerología","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"J.","lastName":"Serrano-Gomez","suffix":""},{"id":170219025,"identity":"b0048825-426f-4fc2-8923-d25aa6a7041b","order_by":2,"name":"Marcela Nuñez","email":"","orcid":"","institution":"Instituto Nacional de Cancerología","correspondingAuthor":false,"prefix":"","firstName":"Marcela","middleName":"","lastName":"Nuñez","suffix":""},{"id":170219028,"identity":"0e86603c-c322-4657-bebc-41e7f0beac25","order_by":3,"name":"Saveria Sangiovanni","email":"","orcid":"","institution":"Fundación Valle del Lili","correspondingAuthor":false,"prefix":"","firstName":"Saveria","middleName":"","lastName":"Sangiovanni","suffix":""},{"id":170219030,"identity":"6c4a1df5-bb85-4a6f-8b00-c58274e61ff2","order_by":4,"name":"Liliana Fernandez-Trujillo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie2QsWoCQRCGZ1nYa0Zt9zh0X2EPixCUJI9y4eAsbQMpPBHW6vqEPEU6yw0DsfEhLgTshKS0ELIXkYCcJ+lC2K/6B+abnVkAj+cP0uY8t1UI8u+auzQtoWxQBGd7Be1BQdKQNCnA9uN/FJmeUQI2tdvFFWBQrMvtYtBTj+9CJjAcqxv+VrdgtdhLsUoBcXkRF6tRnz2llZJdPlvR16duaRkO1zITsmXodhaNSSdAOs5RyFOv7MwEUK1FuDM0MSHxswq54YBSiMiFBCVnZaUoaFC6ZomImYi6ZhQ/YOoUnWnN62/pdIg+N+a+h8GrCDdmoNScwH7cDbWaz2p/7AAe1W68pob+elT+a8Xj8Xj+J1+VmVQbJREqFQAAAABJRU5ErkJggg==","orcid":"","institution":"Fundación Valle del Lili","correspondingAuthor":true,"prefix":"","firstName":"Liliana","middleName":"","lastName":"Fernandez-Trujillo","suffix":""}],"badges":[],"createdAt":"2023-01-08 23:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2456846/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2456846/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":32050296,"identity":"431090b5-893d-444d-a59f-1e29c4d61d1c","added_by":"auto","created_at":"2023-01-25 15:46:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115425,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of pro-GRP, CEA, CYFRA 21-1, SCC-Ag and NSE levels in SCLC, NSCLC and NMLD (Non-Malignant Lung Disease).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2456846/v1/506d7d26513473cfad97171d.png"},{"id":34277384,"identity":"50d82889-7263-42c9-ad85-834b58a8a3b9","added_by":"auto","created_at":"2023-03-15 07:29:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":524676,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2456846/v1/309e8f5e-ba4e-4c71-95cb-97e1b1ca40e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive capacity of protein serum biomarkers in the differential diagnosis of small cell and non-small cell lung cancer in patients with suspicious lung lesions","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer (LC) is the second most common cancer and the leading cause of cancer death worldwide (18.0% of total cancer deaths) with an estimated of 2.2\u0026nbsp;million new cases and 1.8\u0026nbsp;million deaths reported in 2020 (1). 5-year survival-rate ranges from 4\u0026ndash;17% depending on the stage of the disease (2). Since LC tends to be asymptomatic,85% of cases are diagnosed at advanced stages and 57% of patients are diagnosed with metastatic disease (2,3). On the other hand, better prognosis has been reported for localized stages with a 5-year survival rate for the disease of 59% (3). It usually develops after the fifth decade of life, being more common in men than in women, and tobacco exposure is the single most important risk factor (4,5).\u003c/p\u003e \u003cp\u003eLC can be divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), representing 15% and 85% of cases, respectively (6,7). It is important to distinguish between these two histological subtypes as they have different treatments and prognoses. NSCLC at early stages, is curable with surgery, however, SCLC is a rapidly growing, aggressive neoplasm that is treated with chemotherapy and radiotherapy (8\u0026ndash;10).\u003c/p\u003e \u003cp\u003eAiming to achieve early detection, efforts have been made to perform screening in patients who fit the National Institutes of Health (NIH) sponsored National Lung Screening Trial (NLST) criteria using low dose computed tomography (CT). Although this strategy achieved a 20% reduction in LC mortality after 3 rounds of screening, it has a high rate of false positive results, increasing radiation exposure and health costs (11,12) thus, there is still a challenge for researchers to find a non-invasive, sensitive, and reliable biomarker that allows an early detection of the disease (13).\u003c/p\u003e \u003cp\u003eTumor-specific circulating proteins can be measured in samples such as whole blood, serum, obtained through minimally invasive approaches (11). They can be used for different purposes: in asymptomatic but high-risk patients as a screening tool, to support differential diagnosis in patients with suspicious symptoms or a lung lesion in thoracic images; at the time of diagnosis for histological classification, treatment strategy and prognosis, and during oncologic management through monitoring of therapy response, and for early detection of recurrence or progression (14). Their attraction relies in the fact that they are believed to be released early in the disease, can be measured quickly, with non-invasive techniques, plus they are non-expensive (5\u0026ndash;7, 8).\u003c/p\u003e \u003cp\u003eLimited number of individual serum biomarkers have been reported for lung cancer, although none have provided enough sensitivity and specificity for its use in the clinical practice (15). Some biomarkers such as cytokeratin-19 fragment (CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1), carcinoembryonic antigen (CEA) and squamous cell carcinoma antigen (SCC) has been mostly investigated in NSCLC (13,16,17). CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 is the most sensitive tumor marker in NSCLC (18\u0026ndash;20) and its serum concentrations have been correlated with clinical \u0026ndash; pathological characteristics such as tumor size, lymph node status and the stage of disease (21,22). CEA is a serum glycoprotein and has been widely used as a biomarker for colorectal, breast and lung cancer (23) and provide additive information on the histology of lung cancer (24). SCC-Ag is the only marker used in NSCLC with a clear relationship to histology. There is a greater than 95% probability that abnormal serum SCC-Ag levels indicate NSCLC (25,26). On the other hand, Neuron Specific Enolase (NSE) is a cytosolic enzyme and is a better biomarker for SCLC given its neuroendocrine cellular origin (23,27) and has been used as a diagnostic, prognostic, and follow-up biomarker in SCLC (28). Pro-Gastrin Releasing Peptide (Pro-GRP) has been identified as a good biomarker for SCLC as higher levels has been detected even in patients with localized disease while it is not released to any appreciable extent in NSCLC or benign lung disease (25,29).\u003c/p\u003e \u003cp\u003eOur aim was to analyze serum levels of 5 protein biomarkers (Pro-GRP, NSE, CEA, SCC-Ag and CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1) in patients with either lung cancer or lung benign disease and evaluate its predictive capacity in the differential diagnosis of patients with clinical suspicion of lung cancer and we evaluate its sensitivity and specificity for histological diagnosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient samples and determination of serum biomarkers\u003c/h2\u003e \u003cp\u003ePatients included in this study assisted at Fundaci\u0026oacute;n Valle del Lili (FVL) between 2018 and 2020. FVL is a non-profit university hospital that serves as a reference healthcare facility for the Southwestern region of Colombia.\u003c/p\u003e \u003cp\u003eTwo groups of patients were included:60 patients with malignant and 30 patients with benign lung lesions. Inclusion criteria for patients with malignant pathology were: (1) age\u0026thinsp;\u0026gt;\u0026thinsp;18 years-old, (2) a lung lesion suggestive of malignity and a high probability of being a primary lesion by clinical history and CT scan, and (3) patients who were being taken to lung biopsy by thoracoscopy. Inclusion criteria for patients with benign pathology were: (1) age\u0026thinsp;\u0026gt;\u0026thinsp;18 years-old, (2) a lung lesion with a high probability of being benign by clinical history and thoracic CT scan, and (3) patients who had indication of a lung biopsy by thoracoscopy for histological classification. All study participants gave written informed consent to participate in this study. The protocol for this study was approved by Ethics Committee in biomedical research of FVL with the No. 018-2018.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpecimen Collection And Biomarker Analysis\u003c/h3\u003e\n\u003cp\u003eBlood samples were taken prior to the biopsy. All patients were tested for serum biomarkers Pro-GRP, NSE, CEA, SCC-Ag, CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 using the Cobas\u0026reg; 8000-Roche diagnostics system following manufacturer\u0026rsquo;s recommendations.\u003c/p\u003e \u003cp\u003eHistological analysis, the gold standard method for diagnosis was performed in all patients. Tissue samples were analyzed by the pathologist and immunohistochemistry (IHC) using the biomarkers Anti-NSE, CEA31, Cytokeratin 19-A53-B / A2.26, anti-p40-BC28 y pro GRP-mABM16, and was executed to establish the histological classification using the BenchMark ULTRA\u0026reg; system-Roche diagnostics system. Information about other diagnostic procedures (x-ray, bronchoscopy, computed tomography) were available for all the patients.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDemographic, clinical, and laboratory variables were collected prospectively, and registered into the software BD Clinic (Fundaci\u0026oacute;n Valle del Lili, Cali, Colombia).\u003c/p\u003e \u003cp\u003eAll statistical analyzes were performed using the R Project software version 4.1.3 (R Core Team, 2022). Normality was evaluated in the continuous variables using Shapiro Wilk's test. For those variables that did not fit the normal distribution, the median with interquartile range was used as a descriptive measure. Qualitative variables were described using absolute and relative frequencies. Comparisons between groups for the quantitative variables were performed using the Kruskal-Wallis H test and in pairs using the Mann-Whitney U test. Differences for the categorical variables were evaluated using Pearson's Chi-squared or Fisher's exact test.\u003c/p\u003e \u003cp\u003eROC (Receiver Operating Characteristic curve) curves were performed to compare the diagnostic efficacy of each biomarker in the discrimination between SCLC and NSCLC with respect to benign pulmonary disease. The optimal cut-off points for each biomarker were estimated using the values observed in the samples through the ROC analysis, using the method based on the Youden index. The results of the ROC analysis were tabulated as sensitivity, specificity, and precision (area under the curve, AUC) with 95% confidence. A secondary ROC analysis was performed to test the predictive capacity of serum biomarkers in discriminating between a malignant and benign pathology and according to lung cancer histology. p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 93 patients were included in the study from which 9 (9.70%) had a diagnosis of SCLC, 51 (54.8%) of NSCLC and 33 (35.5%) non-malignant pulmonary disease (NMLD). Demographic and clinical characteristics of each group is presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of the patients enrolled in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eHistological Classification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNMLD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.0 [17.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.0 [13.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.0 [14.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.0 [22.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (51.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (48.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (24.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (75.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (72.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor/lesion size (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.0 [43.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.0 [24.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.0 [35.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5 [10.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (6.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (93.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine levels (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83 [0.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87 [0.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 [0.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70 [0.34]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNSCLC\u0026thinsp;=\u0026thinsp;Non-small-cell lung cancer; SCLC\u0026thinsp;=\u0026thinsp;Small-cell lung cancer; NMLD\u0026thinsp;=\u0026thinsp;Non-malignant lung disease; IQR\u0026thinsp;=\u0026thinsp;Interquartile range; COPD\u0026thinsp;=\u0026thinsp;Chronic obstructive pulmonary disease; ECOG\u0026thinsp;=\u0026thinsp;Eastern Cooperative Oncology Group.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMeasurement Of Serum Biomarkers\u003c/h3\u003e\n\u003cp\u003eSince altered renal function can affect the levels of some biomarkers such as Pro-GRP, median creatinine levels were measured and values above 1.30 mg/dL were found in all histological types. Measurements of serum biomarkers obtained from patients on the day of the lung biopsy are presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The median serum levels of Pro-GRP, CEA, CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1, SCC-Ag and NSE in patients with SCLC and NSCLC were significantly higher compared to patients diagnosed with NMLD, although no statistically significant differences were found for Pro-GRP (p\u0026thinsp;=\u0026thinsp;0.116) and SCC-Ag levels (p\u0026thinsp;=\u0026thinsp;0.234) between benign vs. malign lesions nor by lung cancer histological type. Higher levels of CEA (5.84 ng/ml) and CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 (4.27 ng/ml) biomarkers were observed NCSLC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) while higher levels of NSE (41.9 ng/ml) were observed in patients with SCLC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSerum biomarkers of the patients enrolled in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSerum\u003c/p\u003e \u003cp\u003eBiomarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eHistological Classification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNMLD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003cp\u003e(p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePro-GRP (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.7 [27.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 [555]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.9 [28.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.6 [22.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA, (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.19 [7.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64 [1.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.84 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82 [2.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1, (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.01 [5.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.06 [8.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.27 [8.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.74 [2.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCC-Ag, (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 [1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 [1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 [1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37 [1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSE, (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0 [11.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.9 [46.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5 [16.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5 [4.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNSCLC\u0026thinsp;=\u0026thinsp;Non-small-cell lung cancer; SCLC\u0026thinsp;=\u0026thinsp;Small-cell lung cancer; NMLD\u0026thinsp;=\u0026thinsp;Non-malignant lung disease; Pro-GRP\u0026thinsp;=\u0026thinsp;Gastrin-releasing peptide; CEA\u0026thinsp;=\u0026thinsp;Carcinoembryonic antigen; CYFRA21-1\u0026thinsp;=\u0026thinsp;Cytokeratin 19 fragment; SCC-Ag\u0026thinsp;=\u0026thinsp;Squamous cell carcinoma antigen; NSE\u0026thinsp;=\u0026thinsp;Neuron-specific enolase.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the distribution of the levels for each biomarker and the post-hoc comparisons per group. Some biomarkers were significantly different between a specific lung cancer histology and NMLD; for example, significantly higher level of CEA was observed in NSCLC compared to NMLD (5.84 ng/mL vs 1.82 ng/ml, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and similar results were observed for CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 (NSCLC: 4.27 ng/ml vs. NMLD:1.74 ng /ml, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). On the other hand, higher NSE levels were observed in patients with SCLC when compared to NMLD (SCLC: 41.9 ng/ml vs NMLD: 12.5 ng/ml, p\u0026thinsp;=\u0026thinsp;0.012). Moreover, NSE levels were significantly higher in patients with SCLC compared to NSCLC (41.9 ng/mL vs 14.5 ng/mL, p\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOverall accuracy of serum biomarkers for lung cancer diagnosis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA ROC curve was used to analyze the effectiveness and diagnostic accuracy of these biomarkers, alone (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) or in combination (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) for lung cancer diagnosis (malign vs benign lesion) or to discriminate between NSCLC and SCLC. The following criteria was used for evaluation: - no diagnostic accuracy when the AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.5, - low diagnostic accuracy when the AUC was between 0.5\u0026ndash;0.7, - moderate diagnostic accuracy when the AUC was between 0.7\u0026ndash;0.9 and high diagnostic accuracy when AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of different biomarkers in discriminating NSCLC\u0026thinsp;+\u0026thinsp;SCLC, NSCLC and SCLC types compared to non-malignant lung disease (NMLD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCut-off \u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePro GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u0026thinsp;+\u0026thinsp;SCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.2 (48.4\u0026ndash;72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.0 (18.8\u0026ndash;43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.9 (79.8\u0026ndash;99.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.7 (46.4\u0026ndash;71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.5 (14.3\u0026ndash;39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.9 (79.8\u0026ndash;99.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.5 (47.4\u0026ndash;89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.6 (21.2\u0026ndash;86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (89.4\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u0026thinsp;+\u0026thinsp;SCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.3 (66.7\u0026ndash;85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.0 (41.6\u0026ndash;67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.9 (71.8\u0026ndash;96.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.6 (69.0-88.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.8 (46.1\u0026ndash;74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.9 (71.8\u0026ndash;96.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.1 (41.5\u0026ndash;84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.7 (40.0-97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.6 (39.2\u0026ndash;74.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u0026thinsp;+\u0026thinsp;SCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.2 (66.6\u0026ndash;85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.1 (41.7\u0026ndash;68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.9 (71.8\u0026ndash;96.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.7 (66.8\u0026ndash;86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.9 (42.2\u0026ndash;70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.9 (71.8\u0026ndash;96.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.9 (52.5\u0026ndash;93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.4 (13.7\u0026ndash;78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.0 (84.2\u0026ndash;99.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCC-Ag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u0026thinsp;+\u0026thinsp;SCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5 (31.1\u0026ndash;55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.70 (1.80\u0026ndash;16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (89.4\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.3 (28.7\u0026ndash;53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.90 (1.20\u0026ndash;16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (89.4\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.9 (34.1\u0026ndash;77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.9 (51.8\u0026ndash;99.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.4 (25.5\u0026ndash;60.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u0026thinsp;+\u0026thinsp;SCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.8 (56.9\u0026ndash;78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.0 (27.6\u0026ndash;53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (89.4\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.2 (54.5\u0026ndash;77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.7 (24.1\u0026ndash;51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (89.4\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.4 (58.2\u0026ndash;96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.6 (21.2\u0026ndash;86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (89.4\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNSCLC\u0026thinsp;=\u0026thinsp;Non-small-cell lung cancer; SCLC\u0026thinsp;=\u0026thinsp;Small-cell lung cancer; AUC\u0026thinsp;=\u0026thinsp;Area Under the Curve; OR\u0026thinsp;=\u0026thinsp;Odds Ratio; CI\u0026thinsp;=\u0026thinsp;Confidence Interval; Pro-GRP\u0026thinsp;=\u0026thinsp;Gastrin-releasing peptide; CEA\u0026thinsp;=\u0026thinsp;Carcinoembryonic antigen; CYFRA21-1\u0026thinsp;=\u0026thinsp;Cytokeratin 19 fragment; SCC-Ag\u0026thinsp;=\u0026thinsp;Squamous cell carcinoma antigen; NSE\u0026thinsp;=\u0026thinsp;Neuron-specific enolase.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e\u0026dagger;: Estimation method based on Youden index\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance different combinations of biomarkers in discriminating NSCLC and SCLC types with respect to non-malignant lung disease (NMLD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off \u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity %\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u0026thinsp;+\u0026thinsp;CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.7 (68.9\u0026ndash;88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.8 (33.3\u0026ndash;76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.8 (57.6\u0026ndash;93.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u0026thinsp;+\u0026thinsp;NSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.5 (67.6\u0026ndash;87.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.9 (17.6\u0026ndash;70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.9 (72.7\u0026ndash;99.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u0026thinsp;+\u0026thinsp;Pro-GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.8 (70.2\u0026ndash;89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.6 (41.2\u0026ndash;84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.8 (48.5\u0026ndash;96.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;NSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.6 (67.8\u0026ndash;87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.6 (49.0-84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.8 (39.4\u0026ndash;93.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Pro-GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.4 (69.8\u0026ndash;88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.5 (41.2\u0026ndash;84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.7 (45.4\u0026ndash;87.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSE\u0026thinsp;+\u0026thinsp;Pro-GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.9 (59.0\u0026ndash;81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.1 (23.5\u0026ndash;58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.0 (60.6\u0026ndash;99.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u0026thinsp;+\u0026thinsp;CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;NSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.8 (70.5\u0026ndash;89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.5 (49.0-86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.8 (33.3\u0026ndash;90.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u0026thinsp;+\u0026thinsp;NSE\u0026thinsp;+\u0026thinsp;Pro-GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.0 (70.6\u0026ndash;89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.9 (15.7\u0026ndash;70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.9 (69.7\u0026ndash;99.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u0026thinsp;+\u0026thinsp;CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Pro-GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.4 (79.2\u0026ndash;89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.6 (54.5\u0026ndash;93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.8 (35.3\u0026ndash;82.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;NSE\u0026thinsp;+\u0026thinsp;Pro-GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.3 (69.8\u0026ndash;88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.7 (39.2\u0026ndash;84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.8 (41.4\u0026ndash;93.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u0026thinsp;+\u0026thinsp;CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;NSE\u0026thinsp;+\u0026thinsp;Pro-GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.1 (72.0\u0026ndash;90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.5 (45.1\u0026ndash;86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.8 (42.4\u0026ndash;90.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSE\u0026thinsp;+\u0026thinsp;Pro-GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.6 (89.8\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.8 (66.7\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.5 (60.6\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSE\u0026thinsp;+\u0026thinsp;CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.1 (57.6\u0026ndash;97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.8 (33.3\u0026ndash;98.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.8 (45.4\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Pro-GRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.1 (61.1\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.8 (44.4\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.3 (15.1\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Pro-GRP\u0026thinsp;+\u0026thinsp;NSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.3 (91.8\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.8 (66.7\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.9 (56.7\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNSCLC\u0026thinsp;=\u0026thinsp;Non-small-cell lung cancer; SCLC\u0026thinsp;=\u0026thinsp;Small-cell lung cancer; AUC\u0026thinsp;=\u0026thinsp;Area Under the Curve; CI\u0026thinsp;=\u0026thinsp;Confidence Interval; Pro-GRP\u0026thinsp;=\u0026thinsp;Gastrin-releasing peptide; CEA\u0026thinsp;=\u0026thinsp;Carcinoembryonic antigen; CYFRA21-1\u0026thinsp;=\u0026thinsp;Cytokeratin 19 fragment; SCC-Ag\u0026thinsp;=\u0026thinsp;Squamous cell carcinoma antigen; NSE\u0026thinsp;=\u0026thinsp;Neuron-specific enolase.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e\u0026dagger;: Estimation method based on Youden index\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor each biomarker alone, a moderate diagnostic accuracy was observed for CEA in discriminating between malign lesion (NSCLC\u0026thinsp;+\u0026thinsp;SCLC) vs NMLD (AUC\u0026thinsp;=\u0026thinsp;76.3%, sensitivity\u0026thinsp;=\u0026thinsp;55.0% and specificity\u0026thinsp;=\u0026thinsp;87.9%) and more specifically between NSCLC vs NMLD (AUC\u0026thinsp;=\u0026thinsp;78.6%, sensitivity\u0026thinsp;=\u0026thinsp;60.8% and specificity\u0026thinsp;=\u0026thinsp;87.9%). Moderate accuracy was also observed for CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 discriminating between malign lesion (NSCLC\u0026thinsp;+\u0026thinsp;SCLC) vs NMLD (AUC\u0026thinsp;=\u0026thinsp;76.2%, sensitivity\u0026thinsp;=\u0026thinsp;55.1% and specificity\u0026thinsp;=\u0026thinsp;87.9%). Moreover, it also showed moderate accuracy when compared each histology to NMLD (NSCLC vs NMLD (AUC\u0026thinsp;=\u0026thinsp;76.7%, sensitivity\u0026thinsp;=\u0026thinsp;56.9% and specificity\u0026thinsp;=\u0026thinsp;87.9%) and SCLC vs NMLD (AUC\u0026thinsp;=\u0026thinsp;72.9%, sensitivity\u0026thinsp;=\u0026thinsp;44.4% and specificity\u0026thinsp;=\u0026thinsp;97.0%). No diagnostic accuracy was observed for SCC-Ag biomarker. For the other biomarkers (Pro-GRP and NSE), diagnostic accuracy was low when evaluated alone. Although, it was observed a moderate accuracy for NSE in discriminating between SCLC vs NMLD (AUC\u0026thinsp;=\u0026thinsp;77.4%, sensitivity\u0026thinsp;=\u0026thinsp;55.6% and specificity\u0026thinsp;=\u0026thinsp;100%) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe diagnostic value of Pro-GRP for NSCLC increases in combination with other biomarkers such as CEA and CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 (AUC\u0026thinsp;=\u0026thinsp;80.4%, sensitivity\u0026thinsp;=\u0026thinsp;70.6% and specificity\u0026thinsp;=\u0026thinsp;81.8%) or in combination with CEA, CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 and NSE (AUC\u0026thinsp;=\u0026thinsp;81.1%, sensitivity\u0026thinsp;=\u0026thinsp;74.5% and specificity\u0026thinsp;=\u0026thinsp;78.8%) that is significantly better than Pro-GRP alone (AUC\u0026thinsp;=\u0026thinsp;58.7%, sensitivity\u0026thinsp;=\u0026thinsp;25.5% and specificity\u0026thinsp;=\u0026thinsp;93.9%). For SCLC, the diagnostic efficacy of Pro-GRP increased by combining with other biomarkers such as NSE and CYFRA21-1 (AUC\u0026thinsp;=\u0026thinsp;97.3%, sensitivity\u0026thinsp;=\u0026thinsp;88.8% and specificity\u0026thinsp;=\u0026thinsp;98.9%) (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLung cancer is the leading cause of cancer-related death worldwide and despite advances in diagnostic tools and treatments, a high percentage of patients are still diagnosed in advanced stages when the disease is inoperable and therapeutic options such as chemotherapy and radiotherapy do not serve for curative purposes (30).\u003c/p\u003e \u003cp\u003eScreening studies with radiography and sputum cytology have not been useful in reducing mortality from this disease, which is the ultimate goal of a screening program (30). The concept of biomarkers is based on the biological properties of cancer as a systemic disease in which, as the disease progresses, it secretes proteins necessary for its growth and enhance its metastatic capacity (30). Biomarkers have been widely used in lung cancer, mainly to monitor the efficacy of therapy and for early detection of recurrences (31). It has been previously described how the low detection rate of true positives and the inability of any single biomarker to diagnose lung cancer due to the heterogeneity among individuals, differences in biochemical pathways and in the tumor biology, among other characteristics, make biomarkers mostly useful in the context of differential diagnosis rather than in screening programs (32,33). Even though the use of serum markers is still controversial specifically to distinguish between histological types of the disease given their low sensitivity (34).\u003c/p\u003e \u003cp\u003eIn our study, patients with SCLC had median values of NSE and Pro-GRP at least 2 times and 20 times higher, respectively, compared to patients with NSCLC or NMLD. This is consistent with the literature, in which median pro-GRP and NSE values have been reported around 28 and 4.5 times higher in the SCLC histology (35). Based on this trend, the probability of SCLC increases as the levels of pro-GRP and/or NSE elevates; in fact, pro-GRP levels higher than 300 ng/L are said to be 99% specific for detecting SCLC (33). Although in our study, all patients had creatinine levels in the normal range, it is important to consider that serum biomarkers, particularly pro-GRP and CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 elevate in patients with chronic renal impairment, being a potential confounding factor (26)\u003c/p\u003e \u003cp\u003eThe predictive capacity of serum biomarkers analyzed in this study for diagnosing lung cancer was varied, being the most accurate CEA, with the limitation that it rises in many benign and malignant medical conditions (32,36,37). Additionally, and despite their low sensibility, serum biomarkers such as CEA, CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 and NSE when analyzed alone, showed a high specificity (87.9% \u0026minus;\u0026thinsp;100%), ideal for ruling out cancer in patients with benign lesions. Histological subtyping, and specifically differentiating between SCLC and NSCLC is crucial in terms of prognosis and therapeutic targets. We observed that CEA and CYFRA 21.1 levels were higher in patients with malign pathology more specifically in patients with NSCLC suggesting its potential as a serum biomarker this histological type, however, we did find multiple outliers. Consistently different studies have used different combination of biomarkers to distinguish lung cancer patients. It has been reviewed the potential use of CYFRA 21.1, CEA, SCCA, tissue polypeptide antigen (TPA), and cancer antigen-125 (CA-125) as biomarkers for NSCLC and NSE for SCLC (27,38,39). Some of these biomarkers have also been associated with outcomes. For example, a recent meta-analysis reported a significant correlation between positive tests for CEA and nodal involvement and mortality, even in patients with stage I NSCLC (40,41). Correspondingly, NSE has been proven as a useful prognosis biomarker for survival, monitoring of treatment and relapse prediction (32,42)\u003c/p\u003e \u003cp\u003eDue to the limitations that individual serum biomarkers might have to support lung cancer diagnosis, it has been proposed that assessing a combined panel of biomarkers delivers more accurate results. Pro-GRP has been studied in patients with lung cancer and has been suggested as a good biomarker for the differential histological diagnosis of lung cancer patients (30). In our study, the combination of Pro-GRP, CYFRA 21.1 and CEA showed a high diagnostic value of AUC of 80.4% with a sensitivity of 70.6% and specificity of 81.8% for NSCLC. Similarly, the combination of Pro-GRP, CYFRA 21.1 and NSE showed the highest diagnostic value of AUC of 97.3% with a sensitivity of 88.8% and specificity of 98.9% for SCLC. A recent publication that combined the same biomarkers of the present study, showed an average diagnostic performance of the individual biomarkers, with a significant increase in accuracy using a combined approach, reaching a sensibility of 88.5% and specificity of 82% (37).\u003c/p\u003e \u003cp\u003eOur study has several limitations, although we included twice as many patients with malignant pathology compared to our sample size calculation, we were only able to find 9 patients with SCLC during the study period. Although this is consistent with the distribution of histological subtypes among the population, it probably affected the power of our study to detect a higher accuracy of biomarkers for discriminating between SCLC and NSCLC, as it has been reported previously in the literature. Furthermore, we did not design this study to evaluate different cutoff values for the biomarkers, nor did we realized serial testing of several biomarkers to increase the biomarkers specificity.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSerum biomarker measurement is a non-invasive procedure that could potentially aid in the differential diagnosis of LC. In our study, individual biomarkers lacked the required sensitivity and specificity to perform a differential diagnosis or achieve histological subtyping on their own, although patterns were seen since some biomarkers rose more than others in different histological subtypes. The predictive capacity of combined serum biomarkers after serial testing should be further explored.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lung cancer\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSCLC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Small cell lung cancer\u003c/p\u003e\n\u003cp\u003eNSCLC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003eSCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eLCC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Large cell carcinoma\u003c/p\u003e\n\u003cp\u003eCT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Computed tomography\u003c/p\u003e\n\u003cp\u003eTM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tumor markers\u003c/p\u003e\n\u003cp\u003ePro-GRP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Pro-gastrin releasing peptide (Pro-GRP)\u003c/p\u003e\n\u003cp\u003eNSE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Neuron specific enolase\u003c/p\u003e\n\u003cp\u003eCEA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Carcinoembryonic antigen\u003c/p\u003e\n\u003cp\u003eSCC-Ag\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Squamous cell carcinoma antigen\u003c/p\u003e\n\u003cp\u003eCYFRA 21-1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cytokeratin 19 fragment\u003c/p\u003e\n\u003cp\u003eIHC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Immunohistochemistry\u003c/p\u003e\n\u003cp\u003eSn\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sensitivity\u003c/p\u003e\n\u003cp\u003eSp\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Specificity\u003c/p\u003e\n\u003cp\u003e+LR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Positive likelihood ratio\u003c/p\u003e\n\u003cp\u003e-LR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Negative likelihood ratio\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver operator characteristic\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Area under the curve.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript was written in compliance with the ethical standards of the institutional ethics committee and with the 1964 Helsinki Declaration. The protocol (IRB/EC protocol number 1210) for this study, was approved by Ethics Committee in biomedical research of the Fundacion Valle del Lili by letter IRB / EC No. 018-2018, which is available if needed with the Corresponding Author. All study participants gave written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and material are available for sharing if needed with the corresponding author, (Liliana Fernandez-Trujillo, [email protected]).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. This manuscript has not been published and is not under consideration for publication elsewhere. Additionally, all the authors have approved the contents of this paper and have agreed to the journal\u0026acute;s submission policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis study was supported by ABBVIE S.A.S. (Grant No. 897-2017), resource management was carried out by the Clinical Research Center of the Fundacion Valle del Lili.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u003cstrong\u003eAuthor\u0026rsquo; Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the manuscript, and significantly contributed to this paper. LFS: Substantial contributions to the conception and design of the work, acquisition, analysis, and interpretation of data for the work, critical review of intellectual content, final approval of the version to be published. SJSG: Substantial contributions to the manuscript writing, analysis, and interpretation of data for the work, critical review of intellectual content, final approval of the version to be published. \u0026nbsp;MN: Substantial contribution to work analysis and interpretation of data and critical review of intellectual content. SS: Substantial contribution to work analysis and interpretation of data, writing, critical review of intellectual content, final approval of the version to be published. LFT: Substantial contributions to the conception and design of the work, acquisition, analysis, and interpretation of data for the work, critical review of intellectual content, final approval of the version to be published.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to the Research and Innovation Center of the Fundaci\u0026oacute;n Valle del Lili and ABBVIE S.A.S. for sponsoring this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. Wiley; 2021;71:209\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eNasim F, Sabath BF, Eapen GA. Lung Cancer. 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Serum neuron-specific enolase (S-NSE) and the prognosis in small-cell lung cancer (SCLC): a combined multivariable analysis on data from nine centres. Br J Cancer. 1996;74:463\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eMaeda R, Suda T, Hachimaru A, Tochii D, Tochii S, Takagi Y. Clinical significance of preoperative carcinoembryonic antigen level in patients with clinical stage IA non-small cell lung cancer. J Thorac Dis. 2017;9:176\u0026ndash;86. \u003c/li\u003e\n\u003cli\u003eNasralla A, Lee J, Dang J, Turner S. Elevated preoperative CEA is associated with subclinical nodal involvement and worse survival in stage I non-small cell lung cancer: a systematic review and meta-analysis. J Cardiothorac Surg. 2020;15:318. \u003c/li\u003e\n\u003cli\u003eBarak V, Holdenrieder S, Nisman B, Stieber P. Relevance of circulating biomarkers for the therapy monitoring and follow-up investigations in patients with non-small cell lung cancer. Cancer Biomarkers. 2010;6:191\u0026ndash;6. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lung cancer, tumor markers, differential diagnosis, histological subtyping, SCLC, NSCLC","lastPublishedDoi":"10.21203/rs.3.rs-2456846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2456846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTumor biomarkers aid in the diagnosis, management, and prognosis in patients with cancer. In lung cancer, serum biomarkers are used at various timepoints. However, doubts remain about their accuracy for differential diagnosis and histological subtyping in patients with suspicious lung lesions. We conducted a diagnostic test study, selecting cases with malignant lung lesions and controls with benign lung lesions. Prior to lung biopsy, all patients had the following biomarkers measured in serum (Pro-GRP, NSE, CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1, SCC - Ag, CEA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe predictive capacity of serum biomarkers evaluated to discriminate between lung cancer and benign pathology was measured using sensitivity, specificity, and Area Under the Curve (AUC). We also assessed their accuracy for distinguishing Small Cell Lung Cancer (SCLC) from Non-Small Cell Lung Cancer (NSCLC) and explored their ability to perform histological subtyping.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 93 patients were included, 60 with lung cancer and 30 with benign pathology. Serum levels of Pro-GRP and NSE were elevated in patients with SCLC (274 pg. / ml and 41.9 ng. / ml) compared to patients with NSCLC or nonmalignant lung disease (NMLD). The most accurate biomarkers for discriminating between malignant and benign pathology were CEA (AUC\u0026thinsp;=\u0026thinsp;76.3%, sensitivity\u0026thinsp;=\u0026thinsp;55.0% / specificity\u0026thinsp;=\u0026thinsp;87.9%), and CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1(AUC\u0026thinsp;=\u0026thinsp;76.2%, sensitivity\u0026thinsp;=\u0026thinsp;55.1% / specificity\u0026thinsp;=\u0026thinsp;87.9%). Pro-GRP had a poor predictive capacity alone for discriminating NSCLC from SCLC but increases in combination with CEA and CYFRA 21\u0026thinsp;\u0026minus;\u0026thinsp;1 (AUC\u0026thinsp;=\u0026thinsp;80.4%, sensitivity\u0026thinsp;=\u0026thinsp;70.6% / specificity\u0026thinsp;=\u0026thinsp;81.8%). For SCLC the diagnostic efficacy of Pro-GRP increased by combining with other biomarkers such as NSE / CYFRA21\u0026ndash;1 (AUC\u0026thinsp;=\u0026thinsp;97.3%, sensitivity\u0026thinsp;=\u0026thinsp;88.8% / specificity\u0026thinsp;=\u0026thinsp;98.9%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIndividual biomarkers lacked the required sensitivity and specificity to perform a differential diagnosis or achieve histological subtyping on their own. They might be useful in parallel testing to aid the physician, but a tissue biopsy should not be delayed or postponed.\u003c/p\u003e","manuscriptTitle":"Predictive capacity of protein serum biomarkers in the differential diagnosis of small cell and non-small cell lung cancer in patients with suspicious lung lesions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-25 15:46:22","doi":"10.21203/rs.3.rs-2456846/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86654cea-716f-4d6e-9c91-6ed563e35b9d","owner":[],"postedDate":"January 25th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-15T07:29:37+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-25 15:46:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2456846","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2456846","identity":"rs-2456846","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}