Phase I Trial of SPECT-Guided Liver-Directed Radiotherapy for Patients with Low Functional Liver Volume.

BACKGROUND: Traditional constraints specify that 700 cc of liver should be spared a hepatotoxic dose when delivering liver-directed radiotherapy to reduce the risk of inducing liver failure. We investigated the role of single photon emission computed tomography (SPECT) to identify and preferentially avoid functional liver during liver-directed radiation treatment planning in patients with preserved liver function but limited functional liver volume after receiving prior hepatotoxic chemotherapy or surgical resection. METHODS: This phase I trial with a 3 + 3 design evaluated the safety of liver-directed radiotherapy using escalating functional liver radiation dose constraints in patients with liver metastases. Dose limiting toxicities (DLTs) were assessed 6-8 weeks and 6 months after completing radiotherapy. RESULTS: All twelve patients had colorectal liver metastases and received prior hepatotoxic chemotherapy. Eight patients underwent prior liver resection. Median computed tomography (CT) anatomical non-tumor liver volume was 1,584 cc (range 764-2,699 cc). Median SPECT functional liver volume was 1,117 cc (range 570-1,928cc). Median non-target CT and SPECT liver volumes below the volumetric dose constraint were 997 cc (range 544-1,576 cc) and 684 cc (range 429-1,244 cc), respectively. The prescription dose was 67.5-75 Gy in 15 fractions or 75-100 Gy in 25 fractions. No DLTs were observed during follow-up. One-year in-field control was 57%. One-year overall survival was 73%. CONCLUSION: Liver-directed radiotherapy can be safely delivered to high doses when incorporating functional SPECT into the radiation treatment planning process which may enable sparing of lower volumes of liver than traditionally accepted in patients with preserved liver function. TRIAL REGISTRATION: NCT02626312.

Long-Term Patient-Reported Dyspareunia After Definitive Chemoradiation for Anal Cancer: Using the Anterior Vaginal Wall as an Organ-at-Risk to Define an Actionable Dosimetric Goal.

Purpose: Chemoradiation therapy (CRT) is the standard treatment for squamous cell carcinoma of the anus (SCCA). This study aimed to investigate the relationship between vaginal dosimetry and long-term patient-reported dyspareunia after treatment. We further aimed to use the anterior vaginal wall (AVW) as an organ at risk to define an actionable dosimetric clinical goal to decrease the risk of patient-reported dyspareunia. Methods and Materials: Women with SCCA treated with intensity modulated radiation therapy-based CRT were surveyed at least 2 years after successfully completing therapy. A Female Sexual Function Index (FSFI) pain subscore ≤4 was used to define dyspareunia. Dosimetric parameters were calculated for both the full vaginal canal and AVW. Multivariable linear regression models were created to identify predictors of FSFI pain subscore using backward selection to identify final variables include in the models. An actionable dosimetric predictor for dyspareunia was established using the Youden index method for cutoff optimization. Results: Of 184 women who were contacted, 90 (49%) returned completed surveys. Of those who completed surveys, 51 (56.7%) reported being sexually active, and 47 had dosimetric data available for review. Of sexually active respondents, 32 (68%) had an FSFI pain subscore ≤4. Multiple regression models were generated using the full vaginal canal and AVW as organs at risk, and both models showed similar predictive relationships with volumetric dose parameters emerging as the best dosimetric predictors for dysparenuia. Age over 65 years was also associated with higher FSFI pain subscores (eg, less pain with intercourse) in both models. AVW V35 Gy < 60% was identified as the optimal cutoff to reduce the risk of patient-reported dyspareunia. Conclusions: Increased dose to the vaginal canal is significantly associated with worse patient-reported dyspareunia following CRT for SCCA. Minimizing dose to the AVW to V35 Gy < 60% may reduce the risk of this quality of life-limiting toxicity. Further prospective evaluation is needed to validate these findings.

Random Forest Modeling of Acute Toxicity in Anal Cancer: Effects of Peritoneal Cavity Contouring Approaches on Model Performance.

PURPOSE: Our purpose was to analyze the effect on gastrointestinal (GI) toxicity models when their dose-volume metrics predictors are derived from segmentations of the peritoneal cavity after different contouring approaches. METHODS AND MATERIALS: A random forest machine learning approach was used to predict acute grade ≥3 GI toxicity from dose-volume metrics and clinicopathologic factors for 246 patients (toxicity incidence = 9.5%) treated with definitive chemoradiation for squamous cell carcinoma of the anus. Three types of random forest models were constructed based on different bowel bag segmentation approaches: (1) physician-delineated after Radiation Therapy Oncology Group (RTOG) guidelines, (2) autosegmented by a deep learning model (nnU-Net) following RTOG guidelines, and (3) autosegmented but spanning the entire bowel space. Each model type was evaluated using repeated cross-validation (100 iterations; 50%/50% training/test split). The performance of the models was assessed using area under the precision-recall curve (AUPRC) and the receiver operating characteristic curve (AUROCC), as well as optimal F1 score. RESULTS: When following RTOG guidelines, the models based on the nnU-Net auto segmentations (mean values: AUROCC, 0.71 ± 0.07; AUPRC, 0.42 ± 0.09; F1 score, 0.46 ± 0.08) significantly outperformed (P < .001) those based on the physician-delineated contours (mean values: AUROCC, 0.67 ± 0.07; AUPRC, 0.34 ± 0.08; F1 score, 0.36 ± 0.07). When spanning the entire bowel space, the performance of the autosegmentation models improved considerably (mean values: AUROCC, 0.87 ± 0.05; AUPRC, 0.70 ± 0.09; F1 score, 0.68 ± 0.09). CONCLUSIONS: Random forest models were superior at predicting acute grade ≥3 GI toxicity when based on RTOG-defined bowel bag autosegmentations rather than physician-delineated contours. Models based on autosegmentations spanning the entire bowel space show further considerable improvement in model performance. The results of this study should be further validated using an external data set.

Deep learning-based automatic segmentation of cardiac substructures for lung cancers.

PURPOSE: Accurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning-based auto-segmentation models for cardiac substructures. MATERIALS AND METHODS: Nineteen cardiac substructures (whole heart, 4 heart chambers, 6 great vessels, 4 valves, and 4 coronary arteries) in 100 patients treated for non-small cell lung cancer were manually delineated by two radiation oncologists. The valves and coronary arteries were delineated as planning risk volumes. An nnU-Net auto-segmentation model was trained, validated, and tested on this dataset with a split ratio of 75:5:20. The auto-segmented contours were evaluated by comparing them with manually drawn contours in terms of Dice similarity coefficient (DSC) and dose metrics extracted from clinical plans. An independent dataset of 42 patients was used for subjective evaluation of the auto-segmentation model by 4 physicians. RESULTS: The average DSCs were 0.95 (+/- 0.01) for the whole heart, 0.91 (+/- 0.02) for 4 chambers, 0.86 (+/- 0.09) for 6 great vessels, 0.81 (+/- 0.09) for 4 valves, and 0.60 (+/- 0.14) for 4 coronary arteries. The average absolute errors in mean/max doses to all substructures were 1.04 (+/- 1.99) Gy and 2.20 (+/- 4.37) Gy. The subjective evaluation revealed that 94% of the auto-segmented contours were clinically acceptable. CONCLUSION: We demonstrated the effectiveness of our nnU-Net model for delineating cardiac substructures, including coronary arteries. Our results indicate that this model has promise for studies regarding radiation dose to cardiac substructures.

Automated contouring and statistical process control for plan quality in a breast clinical trial.

Background and purpose: Automatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess planning consistency in retrospective data from a breast radiotherapy clinical trial. Materials and methods: A deep learning auto-contouring model was created and tested quantitatively and qualitatively on 104 post-lumpectomy patients' computed tomography images (nnUNet; train/test: 80/20). The auto-contouring model was then applied to 127 patients enrolled in a clinical trial. Statistical process control was used to assess the consistency of the mean dose to auto-contours between plans and treatment modalities by setting control limits within three standard deviations of the data's mean. Two physicians reviewed plans outside the limits for possible planning inconsistencies. Results: Mean Dice similarity coefficients comparing manual and auto-contours was above 0.7 for breast clinical target volume, supraclavicular and internal mammary nodes. Two radiation oncologists scored 95% of contours as clinically acceptable. The mean dose in the clinical trial plans was more variable for lymph node auto-contours than for breast, with a narrower distribution for volumetric modulated arc therapy than for 3D conformal treatment, requiring distinct control limits. Five plans (5%) were flagged and reviewed by physicians: one required editing, two had clinically acceptable variations in planning, and two had poor auto-contouring. Conclusions: An automated contouring model in a statistical process control framework was appropriate for assessing planning consistency in a breast radiotherapy clinical trial.

Dose Escalation for Pancreas SBRT: Potential and Limitations of using Daily Online Adaptive Radiation Therapy and an Iterative Isotoxicity Automated Planning Approach.

Purpose: To determine the dosimetric limitations of daily online adaptive pancreas stereotactic body radiation treatment by using an automated dose escalation approach. Methods and Materials: We collected 108 planning and daily computed tomography (CT) scans from 18 patients (18 patients × 6 CT scans) who received 5-fraction pancreas stereotactic body radiation treatment at MD Anderson Cancer Center. Dose metrics from the original non-dose-escalated clinical plan (non-DE), the dose-escalated plan created on the original planning CT (DE-ORI), and the dose-escalated plan created on daily adaptive radiation therapy CT (DE-ART) were analyzed. We developed a dose-escalation planning algorithm within the radiation treatment planning system to automate the dose-escalation planning process for efficiency and consistency. In this algorithm, the prescription dose of the dose-escalation plan was escalated before violating any organ-at-risk (OAR) dose constraint. Dose metrics for 3 targets (gross target volume [GTV], tumor vessel interface [TVI], and dose-escalated planning target volume [DE-PTV]) and 9 OARs (duodenum, large bowel, small bowel, stomach, spinal cord, kidneys, liver, and skin) for the 3 plans were compared. Furthermore, we evaluated the effectiveness of the online adaptive dose-escalation planning process by quantifying the effect of the interfractional dose distribution variations among the DE-ART plans. Results: The median D95% dose to the GTV/TVI/DE-PTV was 33.1/36.2/32.4 Gy, 48.5/50.9/40.4 Gy, and 53.7/58.2/44.8 Gy for non-DE, DE-ORI, and DE-ART, respectively. Most OAR dose constraints were not violated for the non-DE and DE-ART plans, while OAR constraints were violated for the majority of the DE-ORI patients due to interfractional motion and lack of adaptation. The maximum difference per fraction in D95%, due to interfractional motion, was 2.5 ± 2.7 Gy, 3.0 ± 2.9 Gy, and 2.0 ± 1.8 Gy for the TVI, GTV, and DE-PTV, respectively. Conclusions: Most patients require daily adaptation of the radiation planning process to maximally escalate delivered dose to the pancreatic tumor without exceeding OAR constraints. Using our automated approach, patients can receive higher target dose than standard of care without violating OAR constraints.

Stereotactic Versus Conventional Radiation Therapy for Patients With Pancreatic Cancer in the Modern Era.

PURPOSE: Patients with pancreatic cancer often receive radiation therapy before undergoing surgical resection. We compared the clinical outcomes differences between stereotactic body radiation therapy (SBRT) and 3-dimensional (3D)/intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS: We retrospectively collected data from the University of Texas MD Anderson Cancer Center. Patients with borderline resectable/potentially resectable or locally advanced pancreatic cancer receiving neoadjuvant SBRT (median, 36.0 Gy/5fx), 3D conformal radiation (median, 50.4 Gy/28 fx) or IMRT (median, 50.4 Gy/28 fx) were included. Overall survival (OS) and progression-free survival were analyzed using Cox regression. RESULTS: In total, 104 patients were included in our study. Fifty-seven patients (54.8%) were treated with SBRT, and 47 patients (45.2%) were treated with 3D/IMRT. Patients in the SBRT group were slightly older (median age: 70.3 vs 62.7 in the 3D/IMRT group). Both groups had similar proportions of patients with locally advanced pancreatic cancer (SBRT: 30, 52.6%; 3D/IMRT: 24, 51.1%). All patients were treated with chemotherapy. Patients in the SBRT group underwent more surgical resection compared with the 3D/IMRT group (38.6% vs 23.4%, respectively). At a median follow-up of 22 months, a total of 60 patients (57.7%) died: 25 (25/57, 43.9%) in the SBRT group, and 35 (35/47, 74.5%) in the 3D/IMRT group. Median OS was slightly higher in the SBRT group (29.6 months vs 24.1 months in the 3D/IMRT group). On multivariable Cox regression, the choice of radiation therapy technique was not associated with differences in OS (adjusted hazard ratios [aHR] = 0.5; 95% confidence interval [CI], 0.2%-1.3%, P = .18). Moreover, patients that underwent surgical resection had better OS (aHR = 0.3, 95% CI, 0.1%-0.8%, P = .01). Furthermore, progression-free survival was also similar between patients treated with SBRT and those treated with 3D/IMRT (aHR = 0.9, 95% CI, 0.5%-1.8%, P = .81). CONCLUSIONS: SBRT was associated with similar clinical outcomes compared with conventional radiation techniques, despite being delivered over a shorter period of time which would spare patients prolonged treatment burden. Future prospective data are still needed to better assess the role of SBRT in patients with pancreatic cancer.

Dosimetric Uncertainties Resulting From Interfractional Anatomic Variations for Patients Receiving Pancreas Stereotactic Body Radiation Therapy and Cone Beam Computed Tomography Image Guidance.

PURPOSE: To estimate the effects of interfractional anatomic changes on dose to organs at risk (OARs) and tumors, as measured with cone beam computed tomography (CBCT) image guidance for pancreatic stereotactic body radiation therapy. METHODS AND MATERIALS: We evaluated 11 patients with pancreatic cancer whom were treated with stereotactic body radiation therapy (33-40 Gy in 5 fractions) using daily CT-on-rails (CTOR) image guidance immediately before treatment with breath-hold motion management. CBCT alignment was simulated in the treatment planning software by aligning the original planning CT to each fractional CTOR image set via fiducial markers. CTOR data sets were used to calculate fractional doses after alignment by applying the rigid shift of the planning CT and CTOR image sets to the planning treatment isocenter and recalculating the fractional dose. Accumulated dose to the gross tumor volume (GTV), tumor vessel interface, duodenum, small bowel, and stomach were calculated by summing the 5 fractional absolute dose-volume histograms into a single dose-volume histogram for comparison with the original planned dose. RESULTS: Four patients had a GTV D100% of at least 1.5 Gy less than the fractional planned value in several fractions; 4 patients had fractional underestimation of duodenum dose by 1.0 Gy per fraction. The D1.0 cm3 <35 Gy constraint was violated for at least 1 OAR in 3 patients, with either the duodenum (n = 2) or small bowel (n = 1) D1.0 cm3 being higher on the accumulated dose distribution (P = .01). D100% was significantly lower according to accumulated dose GTV (P = .01) and tumor vessel interface (P = .02), with 4 and 2 patients having accumulated D100%  ≥4 Gy lower than the planned value for the GTV and tumor vessel interface, respectively. CONCLUSIONS: For some patients, CBCT image guidance based on fiducial alignment may cause large dosimetric uncertainties for OARs and target structures, according to accumulated dose.

Development of a rational strategy for integration of lactate dehydrogenase A suppression into therapeutic algorithms for head and neck cancer.

BACKGROUND: Lactate dehydrogenase (LDH) is a critical metabolic enzyme. LDH A (LDHA) overexpression is a hallmark of aggressive malignancies and has been linked to tumour initiation, reprogramming and progression in multiple tumour types. However, successful LDHA inhibition strategies have not materialised in the translational and clinical space. We sought to develop a rational strategy for LDHA suppression in the context of solid tumour treatment. METHODS: We utilised a doxycycline-inducible short hairpin RNA (shRNA) system to generate LDHA suppression. Lactate and LDH activity levels were measured biochemically and kinetically using hyperpolarised 13C-pyruvate nuclear magnetic resonance spectroscopy. We evaluated effects of LDHA suppression on cellular proliferation and clonogenic survival, as well as on tumour growth, in orthotopic models of anaplastic thyroid carcinoma (ATC) and head and neck squamous cell carcinoma (HNSCC), alone or in combination with radiation. RESULTS: shRNA suppression of LDHA generated a time-dependent decrease in LDH activity with transient shifts in intracellular lactate levels, a decrease in carbon flux from pyruvate into lactate and compensatory shifts in metabolic flux in glycolysis and the Krebs cycle. LDHA suppression decreased cellular proliferation and temporarily stunted tumour growth in ATC and HNSCC xenografts but did not by itself result in tumour cure, owing to the maintenance of residual viable cells. Only when chronic LDHA suppression was combined with radiation was a functional cure achieved. CONCLUSIONS: Successful targeting of LDHA requires exquisite dose and temporal control without significant concomitant off-target toxicity. Combinatorial strategies with conventional radiation are feasible as long as the suppression is targeted, prolonged and non-toxic.

Development and application of an elastic net logistic regression model to investigate the impact of cardiac substructure dose on radiation-induced pericardial effusion in patients with NSCLC.

BACKGROUND: Typically, cardiac substructures are neither delineated nor analyzed during radiation treatment planning. Therefore, we developed a novel machine learning model to evaluate the impact of cardiac substructure dose for predicting radiation-induced pericardial effusion (PCE). MATERIALS AND METHODS: One-hundred and forty-one stage III NSCLC patients, who received radiation therapy in a prospective clinical trial, were included in this analysis. The impact of dose-volume histogram (DVH) metrics (mean and max dose, V5Gy[%]-V70Gy[%]) for the whole heart, left and right atrium, and left and right ventricle, on pericardial effusion toxicity (≥grade 2, CTCAE v4.0 grading) were examined. Elastic net logistic regression, using repeat cross-validation (n = 100 iterations, 75%/25% training/test set data split), was conducted with cardiac-based DVH metrics as covariates. The following model types were constructed and analyzed: (i) standard model type, which only included whole-heart DVH metrics; and (ii) a model type trained with both whole-heart and substructure DVH metrics. Model performance was analyzed on the test set using area under the curve (AUC), accuracy, calibration slope and calibration intercept. A final fitted model, based on the optimal model type, was developed from the entire study population for future comparisons. RESULTS: Grade 2 PCE incidence was 49.6% (n = 70). Models using whole heart and substructure dose had the highest performance (median values: AUC = 0.820; calibration slope/intercept = 1.356/-0.235; accuracy = 0.743) and outperformed the standard whole-heart only model type (median values: AUC = 0.799; calibration slope/intercept = 2.456/-0.729; accuracy = 0.713). The final fitted elastic net model showed high performance in predicting PCE (median values: AUC = 0.879; calibration slope/intercept = 1.352/-0.174; accuracy = 0.801). CONCLUSIONS: We developed and evaluated elastic net regression toxicity models of radiation-induced PCE. We found the model type that included cardiac substructure dose had superior predictive performance. A final toxicity model that included cardiac substructure dose metrics was developed and reported for comparison with external datasets.

Differences in Normal Tissue Response in the Esophagus Between Proton and Photon Radiation Therapy for Non-Small Cell Lung Cancer Using In Vivo Imaging Biomarkers.

PURPOSE: To determine whether there exists any significant difference in normal tissue toxicity between intensity modulated radiation therapy (IMRT) or proton therapy for the treatment of non-small cell lung cancer. METHODS AND MATERIALS: A total of 134 study patients (n=49 treated with proton therapy, n=85 with IMRT) treated in a randomized trial had a previously validated esophageal toxicity imaging biomarker, esophageal expansion, quantified during radiation therapy, as well as esophagitis grade (Common Terminology Criteria for Adverse Events version 3.0), on a weekly basis during treatment. Differences between the 2 modalities were statically analyzed using the imaging biomarker metric value (Kruskal-Wallis analysis of variance), as well as the incidence and severity of esophagitis grade (χ2 and Fisher exact tests, respectively). The dose-response of the imaging biomarker was also compared between modalities using esophageal equivalent uniform dose, as well as delivered dose to an isotropic esophageal subvolume. RESULTS: No statistically significant difference in the distribution of esophagitis grade, the incidence of grade ≥3 esophagitis (15 and 11 patients treated with IMRT and proton therapy, respectively), or the esophageal expansion imaging biomarker between cohorts (P>.05) was found. The distribution of imaging biomarker metric values had similar distributions between treatment arms, despite a slightly higher dose volume in the proton arm (P>.05). Imaging biomarker dose-response was similar between modalities for dose quantified as esophageal equivalent uniform dose and delivered esophageal subvolume dose. Regardless of treatment modality, there was high variability in imaging biomarker response, as well as esophagitis grade, for similar esophageal doses between patients. CONCLUSIONS: There was no significant difference in esophageal toxicity from either proton- or photon-based radiation therapy as quantified by esophagitis grade or the esophageal expansion imaging biomarker.

A Novel Methodology using CT Imaging Biomarkers to Quantify Radiation Sensitivity in the Esophagus with Application to Clinical Trials.

Personalized cancer therapy seeks to tailor treatment to an individual patient's biology. Therefore, a means to characterize radiosensitivity is necessary. In this study, we investigated radiosensitivity in the normal esophagus using an imaging biomarker of radiation-response and esophageal toxicity, esophageal expansion, as a method to quantify radiosensitivity in 134 non-small-cell lung cancer patients, by using K-Means clustering to group patients based on esophageal radiosensitivity. Patients within the cluster of higher response and lower dose were labelled as radiosensitive. This information was used as a variable in toxicity prediction modelling (lasso logistic regression). The resultant model performance was quantified and compared to toxicity prediction modelling without utilizing radiosensitivity information. The esophageal expansion-response was highly variable between patients, even for similar radiation doses. K-Means clustering was able to identify three patient subgroups of radiosensitivity: radiosensitive, radio-normal, and radioresistant groups. Inclusion of the radiosensitive variable improved lasso logistic regression models compared to model performance without radiosensitivity information. Esophageal radiosensitivity can be quantified using esophageal expansion and K-Means clustering to improve toxicity prediction modelling. Finally, this methodology may be applied in clinical trials to validate pre-treatment biomarkers of esophageal toxicity.

(18)F-Fluorodeoxyglucose Positron Emission Tomography Can Quantify and Predict Esophageal Injury During Radiation Therapy.

PURPOSE: We sought to investigate the ability of mid-treatment (18)F-fluorodeoxyglucose positron emission tomography (PET) studies to objectively and spatially quantify esophageal injury in vivo from radiation therapy for non-small cell lung cancer. METHODS AND MATERIALS: This retrospective study was approved by the local institutional review board, with written informed consent obtained before enrollment. We normalized (18)F-fluorodeoxyglucose PET uptake to each patient's low-irradiated region (<5 Gy) of the esophagus, as a radiation response measure. Spatially localized metrics of normalized uptake (normalized standard uptake value [nSUV]) were derived for 79 patients undergoing concurrent chemoradiation therapy for non-small cell lung cancer. We used nSUV metrics to classify esophagitis grade at the time of the PET study, as well as maximum severity by treatment completion, according to National Cancer Institute Common Terminology Criteria for Adverse Events, using multivariate least absolute shrinkage and selection operator (LASSO) logistic regression and repeated 3-fold cross validation (training, validation, and test folds). This 3-fold cross-validation LASSO model procedure was used to predict toxicity progression from 43 asymptomatic patients during the PET study. Dose-volume metrics were also tested in both the multivariate classification and the symptom progression prediction analyses. Classification performance was quantified with the area under the curve (AUC) from receiver operating characteristic analysis on the test set from the 3-fold analyses. RESULTS: Statistical analysis showed increasing nSUV is related to esophagitis severity. Axial-averaged maximum nSUV for 1 esophageal slice and esophageal length with at least 40% of axial-averaged nSUV both had AUCs of 0.85 for classifying grade 2 or higher esophagitis at the time of the PET study and AUCs of 0.91 and 0.92, respectively, for maximum grade 2 or higher by treatment completion. Symptom progression was predicted with an AUC of 0.75. Dose metrics performed poorly at classifying esophagitis (AUC of 0.52, grade 2 or higher mid treatment) or predicting symptom progression (AUC of 0.67). CONCLUSIONS: Normalized uptake can objectively, locally, and noninvasively quantify esophagitis during radiation therapy and predict eventual symptoms from asymptomatic patients. Normalized uptake may provide patient-specific dose-response information not discernible from dose.

Objectively Quantifying Radiation Esophagitis With Novel Computed Tomography-Based Metrics.

PURPOSE: To study radiation-induced esophageal expansion as an objective measure of radiation esophagitis in patients with non-small cell lung cancer (NSCLC) treated with intensity modulated radiation therapy. METHODS AND MATERIALS: Eighty-five patients had weekly intra-treatment CT imaging and esophagitis scoring according to Common Terminlogy Criteria for Adverse Events 4.0, (24 Grade 0, 45 Grade 2, and 16 Grade 3). Nineteen esophageal expansion metrics based on mean, maximum, spatial length, and volume of expansion were calculated as voxel-based relative volume change, using the Jacobian determinant from deformable image registration between the planning and weekly CTs. An anatomic variability correction method was validated and applied to these metrics to reduce uncertainty. An analysis of expansion metrics and radiation esophagitis grade was conducted using normal tissue complication probability from univariate logistic regression and Spearman rank for grade 2 and grade 3 esophagitis endpoints, as well as the timing of expansion and esophagitis grade. Metrics' performance in classifying esophagitis was tested with receiver operating characteristic analysis. RESULTS: Expansion increased with esophagitis grade. Thirteen of 19 expansion metrics had receiver operating characteristic area under the curve values >0.80 for both grade 2 and grade 3 esophagitis endpoints, with the highest performance from maximum axial expansion (MaxExp1) and esophageal length with axial expansion ≥30% (LenExp30%) with area under the curve values of 0.93 and 0.91 for grade 2, 0.90 and 0.90 for grade 3 esophagitis, respectively. CONCLUSIONS: Esophageal expansion may be a suitable objective measure of esophagitis, particularly maximum axial esophageal expansion and esophageal length with axial expansion ≥30%, with 2.1 Jacobian value and 98.6 mm as the metric value for 50% probability of grade 3 esophagitis. The uncertainty in esophageal Jacobian calculations can be reduced with anatomic correction methods.