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Enhancing pathologic staging diagnosis of lung cancer Using datamining techniques
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| Author:
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M. KARPAGAM, E. BRUMANCIA, KARUNYA RATHAN, K.GEETHA, C.RAJAN
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| Abstract:
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Lung cancer, also known as lung carcinoma is a malignant lung tumor characterized by uncontrolled cell growth in tissues of the lung. It is one of the leading cancers for both male and female all over the world. Most cancers that start in the lung, known as primary lung cancers, are carcinomas. The lung cancer pathologic staging is mainly based on the pathology report to illustrate the volume and/or the extent of the original tumor and also it indicated that whether the metastasis has spread over. Pathologic staging is important in concern of lung cancer to figure out patient’s prognosis which helps the experts to treat the patient efficiently. The lung cancer pathologic staging diagnosis entails pathology report. This preparation of pathological report requires a model of tissue from the patient’s lung. The complication in the preparation of report is that it necessitates a surgery biopsy which puts the certain patient’s health in danger especially for smokers. To overcome this complication, the proposed work involves obtaining the knowledge for pathologic staging diagnosis from clinical data which replaces the preparation of pathology report to avoid surgery. The data mining techniques are used to predict T (Primary Tumor), N (Lymph nodes), M(Metastasis) values from the clinical data and further those values are used for pathological staging process.
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| Keyword:
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Metastasis, TCGA
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.03.220
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| Download:
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