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P. Wnuk
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MO08 - NSCLC - Early Stage (ID 117)
- Event: WCLC 2013
- Type: Mini Oral Abstract Session
- Track: Medical Oncology
- Presentations: 1
- Moderators:K. Nakagawa, J. Douillard
- Coordinates: 10/28/2013, 16:15 - 17:45, Bayside Gallery B, Level 1
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MO08.09 - PET-CT scanning derived Artificial Neural Network can<br /> predict mediastinal lymph nodes metastases in NSCLC<br /> patients. Preliminary report. (ID 1198)
17:05 - 17:10 | Author(s): P. Wnuk
- Abstract
- Presentation
Background
Mediastinal lymph nodes staging in NSCLC is of paramount importance. Although relatively precise, diagnostic modalities still employ certain level of invasiveness. Artificial Neural Network (ANN) is a well established predictor tool which, due to underlying distribution and relationship among the given variables, allow for construction of multidimensional models trained in prognosis of given outcome. Their performance in mediastinal staging based on radiological data only, currently remains unknown.Methods
Samples from 467 lymph nodes were obtained from 160 patients with primary NSCLC by means of endobronchial ultrasound guided-transbronchial needle aspiration (EBUS-TBNA), mediastinoscopy or lymphadenectomy during thoracotomy and microscopically analyzed. ANN models were created and prospectively validated on unmatched cohort of 50 consecutive patients (158 groups of lymph nodes). To identify factors correlated with nodal involvement single factor tests and logistic regression analysis were performed.Figure 1 Figure 1. The multilayer perceptron (MLP). Artificial Neural Network (ANN) structure for predicting metastatic involvement of mediastinal lymph nodes in NSCLC patients.Results
Size and standard uptake value (SUV) of the node along with primary tumour T characteristics were identified as the most sensitive variables regardless of the analysis conducted. Two ANN models predicted metastatic involvement with 89% and 92% accuracy. Single factor tests maintained high accuracy only for 2 out of 4 most sensitive variables (SUV >2.8 and length >15mm) in prospective validation. Additionally, logistic regression analysis allowed for construction of scoring model with certain parameters corresponding to risk thresholds of metastatic disease.Figure 1 Figure 2. Artificial Neural Network (ANN) characteristics. ROC curves for 2 manually designed ANNs (A); Sensitivity analyses of coefficients (B) and overall characteristics of ANNs performance (C).Conclusion
ANN is a repeatable and accurate diagnostic tool in mediastinal staging in NSCLC patients. Before its role in clinical practice will be established in large multi-centre study, findings of this preliminary report should be considered as exploratory only.Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.
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P1.06 - Poster Session 1 - Prognostic and Predictive Biomarkers (ID 161)
- Event: WCLC 2013
- Type: Poster Session
- Track: Biology
- Presentations: 1
- Moderators:
- Coordinates: 10/28/2013, 09:30 - 16:30, Exhibit Hall, Ground Level
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P1.06-025 - Determination of the activity of lysosomal enzymes and protease inhibitors is useful in the diagnostics of lung cancer. (ID 1977)
09:30 - 09:30 | Author(s): P. Wnuk
- Abstract
Background
Lysosomal proteolytic enzymes play an important role in carcinogenesis and metastasizing processes. Activation of lysosomes may result in an increased exfoliation of cancer cells, which in vivo may promote metastatic progression. It was also observed that increased activity of lysosomal enzymes is connected with an increased permeability of cellular membranes and vascular endothelium, in turn associated with promoting metastasizing, also in lung cancer.Methods
We evaluated the activity of selected lysosomal enzymes and one of protease inhibitors in serum, lung parenchyma and lung tumour, in 41 patients operated on with radical intent due to non-small cell lung cancer (NSCLC). Control group consisted of 44 healthy individuals. Cathepsin D, acid phosphatase, arylsulfatase and alpha-1-antitrypsin serum concentration was measured in patients before surgery, and on day 7, and 30 after operation. The concentration of these enzymes was also measured in the tumor and in healthy lung parenchyma. Obtained results were compared with control group, where concentration of enzymes was measured only in serum.Results
In NSCLC patients an elevated serum concentration of cathepsin D (p<0.001), acid phosphatase (p<0.001) and arylsulfatase (p<0.001) was observed, compared with the control group. Serum concentration of acid phosphatase (p=0.033) and arylsulfatase (p=0.004) was elevated in patients with metastases to regional lymph nodes. Concentration of acid phosphatase (p<0.001), arylsulfatase (p<0.001) and alpha-1-antitrypsin (p<0.001) was higher in pulmonary tumor than in the healthy lung parenchyma. Concentration of acid phosphatase (p=0.002) and arylsulfatase (p<0.001) in pulmonary tumor was also elevated in patients with metastases to regional lymph nodes. In lung cancer patients, postoperative concentration of acid phosphatase and arylsulfatase decreased significantly, as comperative values. Figure 1 Figure 1. Chosen biomarkers activity comparison. (A) Comparison of cathepsin D (Cat D) activity in NSCLC patients with (N1+N2) and without (N0) lymph node metastases at baseline, POD 7 and POD 30. (B) Comparison of arylsulfatase (AS) activity in NSCLC patients with (N1+N2) and without (N0) lymph node metastases at baseline, POD 7 and POD 30. P values for each comparisons were obtained with Mann-Whitney U tests; POD, post-operative day.Conclusion
Serum concentration of cathepsin D, acid phosphatase, arylsulfatase and alpha-1-antitrypsin is useful in the diagnostics of NSCLC. Moreover, serum acid phosphatase and arylsulphatase concentrations are useful in postoperative monitoring of these patients.
