<h3>Purpose/Objective(s)</h3> Dosimetric predictors of toxicity in patients treated with definitive chemoradiation (CRT) for locally advanced non-small cell lung cancer have been identified, though these are often identified through trial and error or difficult to understand statistical methods. This study utilizes machine learning (ML) evaluated using explainable artificial intelligence (EAI) techniques to empirically characterize dosimetric predictors of toxicity in patients with LA-NSCLC treated with CRT as part of a prospective clinical trial. <h3>Materials/Methods</h3> A secondary analysis was performed to utilize ML EAI to identify dosimetric predictors of treatment-related toxicity in the RTOG 0617 trial. We trained a machine-learning based, gradient-boosted tree (GBT) models of ≥ grade 3 pulmonary, cardiac, and esophageal toxicities using Zubrod performance status, T stage, N stage, group stage, radiation technique, receipt of cetuximab, receipt of consolidative chemotherapy, and available dosimetric parameters. Shapley additive explanation (SHAP) values based on the GBT models were used to identify inflection points to select dosimetric thresholds. Identified thresholds were validated using univariate (UVA) and multivariable (MVA) logistic regression. <h3>Results</h3> Of the patients included on the trial, 471 patients had available dosimetry data. A total of 27 (5.7%), 56 (11.9%), and 45 (9.6%) patients experienced ≥ grade 3 pulmonary, esophageal, and cardiac toxicities, respectively. For pulmonary toxicities, using SHAP plots, lung V5≥57% and ≥82% were identified as an inflection points predictive of pneumonitis, which was validated using MVA, with odds ratios (OR) of 4.04 (p=0.004) and 5.11 (p=0.009), respectively. Upon inspection of lung V20, values ≥30% (OR: 3.23; p=0.011) and ≥39% (OR: 5.42; p=0.003) were predictive. Using mean lung dose, values ≥21 Gy were associated with increased toxicity (OR: 3.52; p=0.004). Use of 3DCRT was significantly associated with increased pneumonitis only in the V5 ≥57%, V5 ≥82%, V20 ≥39% MVAs. When assessing esophagitis, esophagus V20 ≥36% (OR: 4.76; p=0.010) and ≥74% (OR: 4.82; p=0.002) were associated with increased risk, as were mean values ≥21 Gy (OR: 4.66; p=0.004) and ≥32 Gy (OR: 3.30; p<0.001). In all of these analyses, radiation technique did not have a significant impact but receipt of consolidative chemotherapy was protective against esophagitis. On evaluation of cardiac dosimetric parameters, only heart V40 ≥57% (OR: 2.04; p=0.032) was predictive of cardiotoxicity. Only group stage was also significantly associated with cardiotoxicity. <h3>Conclusion</h3> Using validation with logistic regression, we demonstrated the feasibility of ML EAI approaches to identify dosimetric predictors of toxicity. Using this method, new dosimetric thresholds were identified, pending validation in larger datasets. Our findings support the utilization of ML EAI for empiric determination of radiation treatment planning considerations.