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Article: Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non–Small Cell Lung Cancer

TitleMachine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non–Small Cell Lung Cancer
Authors
Issue Date2019
PublisherAmerican Association for Cancer Research. The Journal's web site is located at http://clincancerres.aacrjournals.org/
Citation
Clinical Cancer Research, 2019, v. 25 n. 14, p. 4343-4350 How to Cite?
AbstractPurpose: Radiation pneumonitis is an important adverse event in patients with non–small cell lung cancer (NSCLC) receiving thoracic radiotherapy. However, the risk of radiation pneumonitis grade ≥ 2 (RP2) has not been well predicted. This study hypothesized that inflammatory cytokines or the dynamic changes during radiotherapy can improve predictive accuracy for RP2. Experimental Design: Levels of 30 inflammatory cytokines and clinical information in patients with stages I–III NSCLC treated with radiotherapy were from our prospective studies. Statistical analysis was used to select predictive cytokine candidates and clinical covariates for adjustment. Machine learning algorithm was used to develop the generalized linear model for predicting risk RP2. Results: A total of 131 patients were eligible and 17 (13.0%) developed RP2. IL8 and CCL2 had significantly (Bonferroni) lower expression levels in patients with RP2 than without RP2. But none of the changes in cytokine levels during radiotherapy was significantly associated with RP2. The final predictive GLM model for RP2 was established, including IL8 and CCL2 at baseline level and two clinical variables. Nomogram was constructed based on the GLM model. The model's predicting ability was validated in the completely independent test set (AUC = 0.863, accuracy = 80.0%, sensitivity = 100%, specificity = 76.5%). Conclusions: By machine learning, this study has developed and validated a comprehensive model integrating inflammatory cytokines with clinical variables to predict RP2 before radiotherapy that provides an opportunity to guide clinicians.
Persistent Identifierhttp://hdl.handle.net/10722/273856
ISSN
2017 Impact Factor: 10.199
2015 SCImago Journal Rankings: 5.314

 

DC FieldValueLanguage
dc.contributor.authorYu, H-
dc.contributor.authorWu, HM-
dc.contributor.authorWang, WL-
dc.contributor.authorJolly, S-
dc.contributor.authorJin, JY-
dc.contributor.authorHu, C-
dc.contributor.authorKong, FMS-
dc.date.accessioned2019-08-18T14:49:59Z-
dc.date.available2019-08-18T14:49:59Z-
dc.date.issued2019-
dc.identifier.citationClinical Cancer Research, 2019, v. 25 n. 14, p. 4343-4350-
dc.identifier.issn1078-0432-
dc.identifier.urihttp://hdl.handle.net/10722/273856-
dc.description.abstractPurpose: Radiation pneumonitis is an important adverse event in patients with non–small cell lung cancer (NSCLC) receiving thoracic radiotherapy. However, the risk of radiation pneumonitis grade ≥ 2 (RP2) has not been well predicted. This study hypothesized that inflammatory cytokines or the dynamic changes during radiotherapy can improve predictive accuracy for RP2. Experimental Design: Levels of 30 inflammatory cytokines and clinical information in patients with stages I–III NSCLC treated with radiotherapy were from our prospective studies. Statistical analysis was used to select predictive cytokine candidates and clinical covariates for adjustment. Machine learning algorithm was used to develop the generalized linear model for predicting risk RP2. Results: A total of 131 patients were eligible and 17 (13.0%) developed RP2. IL8 and CCL2 had significantly (Bonferroni) lower expression levels in patients with RP2 than without RP2. But none of the changes in cytokine levels during radiotherapy was significantly associated with RP2. The final predictive GLM model for RP2 was established, including IL8 and CCL2 at baseline level and two clinical variables. Nomogram was constructed based on the GLM model. The model's predicting ability was validated in the completely independent test set (AUC = 0.863, accuracy = 80.0%, sensitivity = 100%, specificity = 76.5%). Conclusions: By machine learning, this study has developed and validated a comprehensive model integrating inflammatory cytokines with clinical variables to predict RP2 before radiotherapy that provides an opportunity to guide clinicians.-
dc.languageeng-
dc.publisherAmerican Association for Cancer Research. The Journal's web site is located at http://clincancerres.aacrjournals.org/-
dc.relation.ispartofClinical Cancer Research-
dc.titleMachine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non–Small Cell Lung Cancer-
dc.typeArticle-
dc.identifier.emailKong, FMS: kong0001@hku.hk-
dc.identifier.authorityKong, FMS=rp02508-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1158/1078-0432.CCR-18-1084-
dc.identifier.pmid30992302-
dc.identifier.scopuseid_2-s2.0-85069042247-
dc.identifier.hkuros301458-
dc.identifier.volume25-
dc.identifier.issue14-
dc.identifier.spage4343-
dc.identifier.epage4350-
dc.publisher.placeUnited States-

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