TY - JOUR
T1 - Machine Learning Methods Uncover Radiomorphologic Dose Patterns in Salivary Glands that Predict Xerostomia in Patients with Head and Neck Cancer
AU - Jiang, Wei
AU - Lakshminarayanan, Pranav
AU - Hui, Xuan
AU - Han, Peijin
AU - Cheng, Zhi
AU - Bowers, Michael
AU - Shpitser, Ilya
AU - Siddiqui, Sauleh
AU - Taylor, Russell H.
AU - Quon, Harry
AU - McNutt, Todd
N1 - Funding Information:
This work would not have been possible without the support from the Radiation Oncology Institute (Grant # ROI2016-912 ).
Funding Information:
Sources of support: This work was funded by the Radiation Oncology Institute.Conflicts of interest: All authors have no potential conflict of interest to disclose, except for the following 3 authors: Dr. McNutt reports grants from the Radiation Oncology Institute during the conduct of the study and grants from Canon Medical and Philips Health Care outside the submitted work. In addition, Dr. McNutt holds a pending patent for “Method and apparatus for determining treatment region and mitigating radiation toxicity” (20170259083) and patents for “Method, system, and computer-readable media for treatment plan risk analysis” and “System and method for medical data analysis and sharing” (20170083682 and 20160378919, respectively). Dr. Taylor holds US patent 8,688,618 B2, “Shape-based retrieval of prior patients for automation and quality control of radiation therapy treatment Plans” (filed June 22, 2009; issued April 1, 2014, with royalties paid to unknown). Mr. Bowers reports grants from Elekta during the conduct of the study.This work would not have been possible without the support from the Radiation Oncology Institute (Grant # ROI2016-912).
Publisher Copyright:
© 2018 The Authors
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Purpose: Patients with head-and-neck cancer (HNC) may experience xerostomia after radiation therapy (RT), which leads to compromised quality of life. The purpose of this study is to explore how the spatial pattern of radiation dose (radiomorphology) in the major salivary glands influences xerostomia in patients with HNC. Methods and materials: A data-driven approach using spatially explicit dosimetric predictors, voxel dose (ie, actual radiation dose in voxels in parotid glands [PG] and submandibular glands [SMG]) was used to predict whether patients would develop xerostomia 3 months after RT. Using planned radiation dose data and other nondose covariates including baseline xerostomia grade of 427 patients with HNC in our database, the machine learning methods were used to investigate the influence of dose patterns across subvolumes in PG and SMG on xerostomia. Results: Of the 3 supervised learning methods studied, ridge logistic regression yielded the best predictive performance. Ridge logistic regression was also preferred to evaluate the influence pattern of highly correlated dose on xerostomia, which showed a discriminative pattern of influence of doses in the PG and SMG on xerostomia. Moreover, the superior–anterior portion of the contralateral PG and medial portion of the ipsilateral PG were determined to be the most influential regions regarding dose effect on xerostomia. The area under the receiver operating characteristic curve from a 10-fold cross-validation was 0.70 ± 0.04. Conclusions: Radiomorphology, combined with machine learning methods, is able to suggest patterns of dose in PG and SMG that are the most influential on xerostomia. The influence pattern identified by this data-driven approach and machine learning methods may help improve RT treatment planning and reduce xerostomia after treatment.
AB - Purpose: Patients with head-and-neck cancer (HNC) may experience xerostomia after radiation therapy (RT), which leads to compromised quality of life. The purpose of this study is to explore how the spatial pattern of radiation dose (radiomorphology) in the major salivary glands influences xerostomia in patients with HNC. Methods and materials: A data-driven approach using spatially explicit dosimetric predictors, voxel dose (ie, actual radiation dose in voxels in parotid glands [PG] and submandibular glands [SMG]) was used to predict whether patients would develop xerostomia 3 months after RT. Using planned radiation dose data and other nondose covariates including baseline xerostomia grade of 427 patients with HNC in our database, the machine learning methods were used to investigate the influence of dose patterns across subvolumes in PG and SMG on xerostomia. Results: Of the 3 supervised learning methods studied, ridge logistic regression yielded the best predictive performance. Ridge logistic regression was also preferred to evaluate the influence pattern of highly correlated dose on xerostomia, which showed a discriminative pattern of influence of doses in the PG and SMG on xerostomia. Moreover, the superior–anterior portion of the contralateral PG and medial portion of the ipsilateral PG were determined to be the most influential regions regarding dose effect on xerostomia. The area under the receiver operating characteristic curve from a 10-fold cross-validation was 0.70 ± 0.04. Conclusions: Radiomorphology, combined with machine learning methods, is able to suggest patterns of dose in PG and SMG that are the most influential on xerostomia. The influence pattern identified by this data-driven approach and machine learning methods may help improve RT treatment planning and reduce xerostomia after treatment.
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U2 - 10.1016/j.adro.2018.11.008
DO - 10.1016/j.adro.2018.11.008
M3 - Article
C2 - 31011686
AN - SCOPUS:85060608692
SN - 2452-1094
VL - 4
SP - 401
EP - 412
JO - Advances in Radiation Oncology
JF - Advances in Radiation Oncology
IS - 2
ER -