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  1. NTU Scholars
  2. 醫學院
  3. 病理學科所
Please use this identifier to cite or link to this item: https://scholars.lib.ntu.edu.tw/handle/123456789/627200
Title: CT-Based Radiomic Analysis for Preoperative Prediction of Tumor Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodule
Authors: Kao, Tzu-Ning
MIN-SHU HSIEH 
Chen, Li-Wei
Yang, Chi-Fu Jeffrey
Chuang, Ching-Chia
Chiang, Xu-Heng
Chen, Yi-Chang
Lee, Yi-Hsuan
Hsu, Hsao-Hsun
CHUNG-MING CHEN 
MONG-WEI LIN 
Chen, Jin-Shing
Keywords: ground-glass nodule; invasiveness; lung adenocarcinoma; lung cancer surgery; radiomic feature analysis
Issue Date: 29-Nov-2022
Publisher: MDPI
Journal Volume: 14
Journal Issue: 23
Source: Cancers
Abstract: 
It remains a challenge to preoperatively forecast whether lung pure ground-glass nodules (pGGNs) have invasive components. We aimed to construct a radiomic model using tumor characteristics to predict the histologic subtype associated with pGGNs. We retrospectively reviewed clinicopathologic features of pGGNs resected in 338 patients with lung adenocarcinoma between 2011-2016 at a single institution. A radiomic prediction model based on forward sequential selection and logistic regression was constructed to differentiate adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma. The study cohort included 133 (39.4%), 128 (37.9%), and 77 (22.8%) patients with AIS, MIA, and invasive adenocarcinoma (acinar 55.8%, lepidic 33.8%, papillary 10.4%), respectively. The majority (83.7%) underwent sublobar resection. There were no nodal metastases or tumor recurrence during a mean follow-up period of 78 months. Three radiomic features-cluster shade, homogeneity, and run-length variance-were identified as predictors of histologic subtype and were selected to construct a prediction model to classify the AIS/MIA and invasive adenocarcinoma groups. The model achieved accuracy, sensitivity, specificity, and AUC of 70.6%, 75.0%, 70.0%, and 0.7676, respectively. Applying the developed radiomic feature model to predict the histologic subtypes of pGGNs observed on CT scans can help clinically in the treatment selection process.
URI: https://scholars.lib.ntu.edu.tw/handle/123456789/627200
ISSN: 2072-6694
DOI: 10.3390/cancers14235888
SDG/Keyword: [SDGs]SDG3
Appears in Collections:病理學科所

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臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

總館學科館員 (Main Library)
醫學圖書館學科館員 (Medical Library)
社會科學院辜振甫紀念圖書館學科館員 (Social Sciences Library)

開放取用是從使用者角度提升資訊取用性的社會運動,應用在學術研究上是透過將研究著作公開供使用者自由取閱,以促進學術傳播及因應期刊訂購費用逐年攀升。同時可加速研究發展、提升研究影響力,NTU Scholars即為本校的開放取用典藏(OA Archive)平台。(點選深入了解OA)

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