tailieuchung - On Predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue

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On Predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue

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Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. |

Đang chuẩn bị liên kết để tải về tài liệu:
On Predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue

Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. |

Outcome and predicting chemotoxicity in elderly patients with advanced non-small cell lung cancer

A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: A large population-based study

Nomogram model for predicting causespecific mortality in patients with stage I small-cell lung cancer: A competing risk analysis

An inflammation-related nomogram for predicting the survival of patients with non-small cell lung cancer after pulmonary lobectomy

Nomogram based on homogeneous and heterogeneous associated factors for predicting bone metastases in patients with different histological types of lung cancer

Immunotherapy for non-small cell lung cancers: Biomarkers for predicting responses and strategies to overcome resistance

Expressions of MUC1 and vascular endothelial growth factor mRNA in blood are biomarkers for predicting efficacy of gefitinib treatment in non-small cell lung cancer

Prognostic nomogram for predicting longterm cancer-specific survival in patients with lung carcinoid tumors

Evaluation of log odds of positive lymph nodes in predicting the survival of patients with non-small cell lung cancer treated with neoadjuvant therapy and surgery: A SEER cohort-based study

The yield of immediate post lung biopsy CT in predicting iatrogenic pneumothorax

Đang chuẩn bị liên kết để tải về tài liệu: On Predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue

Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. |

Outcome and predicting chemotoxicity in elderly patients with advanced non-small cell lung cancer

A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: A large population-based study

Nomogram model for predicting causespecific mortality in patients with stage I small-cell lung cancer: A competing risk analysis

An inflammation-related nomogram for predicting the survival of patients with non-small cell lung cancer after pulmonary lobectomy

Nomogram based on homogeneous and heterogeneous associated factors for predicting bone metastases in patients with different histological types of lung cancer

Immunotherapy for non-small cell lung cancers: Biomarkers for predicting responses and strategies to overcome resistance

Expressions of MUC1 and vascular endothelial growth factor mRNA in blood are biomarkers for predicting efficacy of gefitinib treatment in non-small cell lung cancer

Prognostic nomogram for predicting longterm cancer-specific survival in patients with lung carcinoid tumors

Evaluation of log odds of positive lymph nodes in predicting the survival of patients with non-small cell lung cancer treated with neoadjuvant therapy and surgery: A SEER cohort-based study

The yield of immediate post lung biopsy CT in predicting iatrogenic pneumothorax

Đang chuẩn bị liên kết để tải về tài liệu:
On Predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue