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Matsuo, Y., Shibuya, K., Nakamura, M., Narabayashi, M., Sakanaka, K., Ueki, N., Miyagi, K., Norihisa, Y., Mizowaki, T., Nagata, Y. and Hiraoka, M. (2012) Dose-Volume Metrics Associated with Radiation Pneumonitis after Stereotactic Body Radiation Therapy for Lung Cancer. International Journal of Radiation Oncology * Biology * Physics, 83, e545-e549.
https://doi.org/10.1016/j.ijrobp.2012.01.018

has been cited by the following article:

• TITLE: Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis

  AUTHORS: Wookjin Choi, Sadegh Riyahi, Seth J. Kligerman, Chia-Ju Liu, James G. Mechalakos, Wei Lu

  KEYWORDS: Radiation-Induced Lung Disease, Normal Lung Texture, Radiomics, CT, Stereotactic Body Radiotherapy

  JOURNAL NAME: International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, Vol.7 No.3, August 7, 2018

  ABSTRACT: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD). For these features to be clinically useful, they should be robust to tumor size variations and not correlated with the normal lung volume of interest, i.e., the volume of the peri-tumoral region (PTR). CT images of 14 lung cancer patients were studied. Different sizes of gross tumor volumes (GTVs) were simulated and placed in the lung contralateral to the tumor. 27 texture features [nine from intensity histogram, eight from the gray-level co-occurrence matrix (GLCM) and ten from the gray-level run-length matrix (GLRM)] were extracted from the PTR. The Bland-Altman analysis was applied to measure the normalized range of agreement (nRoA) for each feature when GTV size varied. A feature was considered as robust when its nRoA was less than the threshold (100%). Sixteen texture features were identified as robust. None of the robust features was correlated with the volume of the PTR. No feature showed statistically significant differences (P 0.05) on GTV locations. We identified 16 robust normal lung CT texture features that can be further examined for the prediction of RILD.