Prognostic Impact of the Findings on Thin-Section Computed Tomography in stage I lung adenocarcinoma with visceral pleural invasion
Autor: | Tong-Fu Yu, Hai Li, Yu-Dong Zhang, Mei Yuan, Teng Zhang, Jin-Yuan Liu |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Male
Percentile medicine.medical_specialty Lung Neoplasms Pleural Neoplasms lcsh:Medicine Adenocarcinoma of Lung Computed tomography Article 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Text mining medicine Humans Neoplasm Invasiveness Pleural Neoplasm lcsh:Science Neoplasm Staging Multidisciplinary Lung medicine.diagnostic_test business.industry lcsh:R Odds ratio Middle Aged Prognosis medicine.disease medicine.anatomical_structure 030220 oncology & carcinogenesis Stage I Lung Adenocarcinoma Pleura Adenocarcinoma Female lcsh:Q Radiology Tomography X-Ray Computed business |
Zdroj: | Scientific Reports, Vol 8, Iss 1, Pp 1-9 (2018) Scientific Reports |
ISSN: | 2045-2322 |
DOI: | 10.1038/s41598-018-22853-1 |
Popis: | Visceral pleural invasion (VPI) in stageI lung adenocarcinoma is an independent negative prognostic factor. However, no studies proved any morphologic pattern could be referred to as a prognostic factor. Thus, we aim to investigate the potential prognostic impact of VPI by extracting high-dimensional radiomics features on thin-section computed tomography (CT). A total of 327 surgically resected pathological-N0M0 lung adenocarcinoma 3 cm or less in size were evaluated. Radiomics signature was generated by calculating the contribution weight of each feature and validated using repeated leaving-one-out ten-fold cross-validation approach. The accuracy of proposed radiomics signature for predicting VPI achieved 90.5% with ROC analysis (AUC, 0.938, sensitivity, 90.6%, specificity, 93.2%, PPV: 91.2, NPV: 92.8). The cut-off value allowed separation of patients in the validation data into high-risk and low-risk groups with an odds ratio 12.01. Radiomics signature showed a concordance index of 0.895 and AIC value of 88.9% with regression analysis. Among these radiomics features, percentile 10%, wavEnLL_S_2, S_0_1_SumAverage represented as independent factors for determining VPI. Results suggested that radiomics signature on CT exhibited as an independent prognostic factor in discriminating VPI in lung adenocarcinoma and could potentially help to discriminate the prognosis difference in stage I lung adenocarcinoma. |
Databáze: | OpenAIRE |
Externí odkaz: | |
Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |