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Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study.
Kong, Yan; Su, Mingming; Zhu, Yan; Li, Xuan; Zhang, Jinmeng; Gu, Wenchao; Yang, Fei; Zhou, Jialiang; Ni, Jianjiao; Yang, Xi; Zhu, Zhengfei; Huang, Jianfeng.
Afiliação
  • Kong Y; Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
  • Su M; Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
  • Zhu Y; Department of Medical Oncology, Affiliated Huishan Hospital of Xinglin College, Nantong University, Wuxi Huishan District People's Hospital, 214187, Wuxi, Jiangsu, China.
  • Li X; Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
  • Zhang J; Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
  • Gu W; Department of Medical Oncology, Affiliated Huishan Hospital of Xinglin College, Nantong University, Wuxi Huishan District People's Hospital, 214187, Wuxi, Jiangsu, China.
  • Yang F; Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
  • Zhou J; Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China.
  • Ni J; Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
  • Yang X; Department of Diagnostic and Interventional Radiology, University of Tsukuba, 305-8577, Ibaraki, Japan.
  • Zhu Z; Department of Radiation Oncology, University of Miami, 33136, Miami, FL, USA.
  • Huang J; Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
Strahlenther Onkol ; 2024 Mar 18.
Article em En | MEDLINE | ID: mdl-38498173
ABSTRACT

OBJECTIVE:

This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients. MATERIALS AND

METHODS:

The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value.

RESULTS:

The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05).

CONCLUSION:

Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients. CLINICAL RELEVANCE STATEMENT Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article