A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall‐cell lung cancer (NSCLC) for response‐adapted radiotherapy. Issue 8 (21st June 2018)

• Record Type: Journal Article
• Authors: Luo, Yi; McShan, Daniel L.; Matuszak, Martha M.; Ray, Dipankar; Lawrence, Theodore S.; Jolly, Shruti; Kong, Feng‐Ming; Ten Haken, Randall K.; El Naqa, Issam
• Is Part Of: Medical physics. Volume 45:Issue 8(2018)
• Page Start: 3980
• Page End: 3995
• Publication Date: 2018-06-21
• Subjects: joint prediction of LC and RP2 -- multiobjective Bayesian networks -- nonsmall‐cell lung cancer -- response‐adapted radiotherapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13029
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response‐adapted personalized treatment planning. Methods: A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty‐eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO‐BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross‐validation (CV) was used to guide the MO‐BN structure learning. Area under the free‐response receiver operating characteristic (AU‐FROC) curve was used to evaluate joint prediction performance. Results: Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter‐relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO‐BN. The joint LC/RP2 prediction yielded an AU‐FROC of 0.80 (95% CI: 0.70–0.86) upon internal CV. This improved to 0.85 (0.75–0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during‐treatment MO‐BN. This MO‐BN model outperformed combined single‐objective Bayesian networks (SO‐BNs) during‐treatment [0.78 (0.67–0.84)]. AU‐FROC values in the evaluation of the MO‐BN and individual SO‐BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during‐treatment, respectively. Conclusions: MO‐BNs can reveal possible biophysical cross‐talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during‐treatment information. The developed MO‐BNs can be an important component of decision support systems for personalized response‐adapted radiotherapy.