Predicting Radiation Esophagitis Using 18F-FDG PET During Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer

Adapt-On-Demand: A Novel Strategy for Personalized Adaptive Radiation Therapy for Locally Advanced Lung Cancer

PURPOSE

Real-time adaptation of thoracic radiation plans is compelling because offline adaptive experiences show that tumor volumes and lung anatomy can change during therapy. We present and analyze a novel adaptive-on-demand (AOD) workflow combining online adaptive radiation therapy (o-ART) on the ETHOS system with image guided radiation therapy delivery on a Halcyon unit for conventional fractionated radiation therapy of locally advanced lung cancer (LALC).

METHODS AND MATERIALS

We analyzed 26 patients with LALC treated with the AOD workflow, adapting weekly. We timed segments of the workflow to evaluate efficiency in a real-world clinic. Target coverage and organ at risk (OAR) doses were compared between adaptive plans (ADP) and nonadaptive scheduled plans (SCH). Planning robustness was evaluated by the frequency of preplanning goals achieved in ADP plans, stratified by tumor volume change.

RESULTS

The AOD workflow was achievable within 30 minutes for most radiation fractions. Over the course of therapy, we observed an average 26.6% ± 23.3% reduction in internal target volume (ITV). Despite these changes, with o-ART, ITV and planning target volume (PTV) coverage (V100%) was 99.2% and 93.9% for all members of the cohort, respectively. This represented a 2.9% and 6.8% improvement over nonadaptive plans (P < .05), respectively. For tumors that grew >10%, V100% was 93.1% for o-ART and 76.4% for nonadaptive plans, representing a median 17.2% improvement in the PTV coverage (P < .05). In these plans, critical OAR constraints were met 94.1% of the time, whereas in nonadaptive plans, this figure was 81.9%. This represented reductions of 1.32 Gy, 1.34 Gy, or 1.75 Gy in the heart, esophagus, and lung, respectively. The effect was larger when tumors had shrunk more than 10%. Regardless of tumor volume alterations, the PTV/ITV coverage was achieved for all adaptive plans. Exceptional cases, where dose constraints were not met, were due to large initial tumor volumes or tumor growth.

CONCLUSIONS

The AOD workflow is efficient and robust in responding to anatomic changes in LALC patients, providing dosimetric advantages over standard therapy. Weekly adaptation was adequate to keep pace with changes. This approach is a feasible alternative to conventional offline replanning workflows for managing anatomy changes in LALC radiation therapy.

Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy

PURPOSE

The study aimed to predict acute radiation esophagitis (ARE) with grade  ≥ 2 for patients with locally advanced lung cancer (LALC) treated with intensity-modulated radiation therapy (IMRT) using multi-omics features, including radiomics and dosiomics.

METHODS

161 patients with stage IIIA-IIIB LALC who received chemoradiotherapy (CRT) or radiotherapy by IMRT with a prescribed dose from 45 to 70 Gy from 2015 to 2019 were enrolled retrospectively. All the toxicity gradings were given following the Common Terminology Criteria for Adverse Events V4.0. Multi-omics features, including radiomics, dosiomics (including dose-volume histogram dosimetric parameters), were extracted based on the planning CT image and three-dimensional dose distribution. All data were randomly divided into training cohorts (N = 107) and testing cohorts (N = 54). In the training cohorts, features with reliably high outcome relevance and low redundancy were selected under random patient subsampling. Four classification models (using clinical factors (CF) only, using radiomics features (RFs) only, dosiomics features (DFs) only, and the hybrid features (HFs) containing clinical factors, radiomics and dosiomics) were constructed employing the Ridge classifier using two-thirds of randomly selected patients as the training cohort. The remaining patient was treated as the testing cohort. A series of models were built with 30 times training-testing splits. Their performances were assessed using the area under the ROC curve (AUC) and accuracy.

RESULTS

Among all patients, 51 developed ARE grade  ≥ 2, with an incidence of 31.7%. Next, 8990 radiomics and 213 dosiomics features were extracted, and 3, 6, 12, and 13 features remained after feature selection in the CF, DF, RF and DF models, respectively. The RF and HF models achieved similar classification performance, with the training and testing AUCs of 0.796 ± 0.023 (95% confidence interval (CI [0.79, 0.80])/0.744 ± 0.044 (95% CI [0.73, 0.76]) and 0.801 ± 0.022 (95% CI [0.79, 0.81]) (p = 0.74), respectively. The model performances using CF and DF features were poorer, with training and testing AUCs of 0.573 ± 0.026 (95% CI [0.56, 0.58])/ 0.509 ± 0.072 (95% CI [0.48, 0.53]) and 0.679 ± 0.027 (95% CI [0.67, 0.69])/0.604 ± 0.041 (95% CI [0.53, 0.63]) compared with the above two models (p < 0.001), respectively.

CONCLUSIONS

In LALC patients treated with CRT IMRT, the ARE grade  ≥ 2 can be predicted using the pretreatment radiotherapy image features. To predict ARE, the multi-omics features had similar predictability with radiomics features; however, the dosiomics features and clinical factors had a limited classification performance.

Adaptive Radiation Therapy in the Treatment of Lung Cancer: An Overview of the Current State of the Field

Lung cancer treatment is constantly evolving due to technological advances in the delivery of radiation therapy. Adaptive radiation therapy (ART) allows for modification of a treatment plan with the goal of improving the dose distribution to the patient due to anatomic or physiologic deviations from the initial simulation. The implementation of ART for lung cancer is widely varied with limited consensus on who to adapt, when to adapt, how to adapt, and what the actual benefits of adaptation are. ART for lung cancer presents significant challenges due to the nature of the moving target, tumor shrinkage, and complex dose accumulation because of plan adaptation. This article presents an overview of the current state of the field in ART for lung cancer, specifically, probing topics of: patient selection for the greatest benefit from adaptation, models which predict who and when to adapt plans, best timing for plan adaptation, optimized workflows for implementing ART including alternatives to re-simulation, the best radiation techniques for ART including magnetic resonance guided treatment, algorithms and quality assurance, and challenges and techniques for dose reconstruction. To date, the clinical workflow burden of ART is one of the major reasons limiting its widespread acceptance. However, the growing body of evidence demonstrates overwhelming support for reduced toxicity while improving tumor dose coverage by adapting plans mid-treatment, but this is offset by the limited knowledge about tumor control. Progress made in predictive modeling of on-treatment tumor shrinkage and toxicity, optimizing the timing of adaptation of the plan during the course of treatment, creating optimal workflows to minimize staffing burden, and utilizing deformable image registration represent ways the field is moving toward a more uniform implementation of ART.

The increasing potential of nuclear medicine imaging for the evaluation and reduction of normal tissue toxicity from radiation treatments

Radiation therapy is an effective treatment modality for a variety of cancers. Despite several advances in delivery techniques, its main drawback remains the deposition of dose in normal tissues which can result in toxicity. Common practices of evaluating toxicity, using questionnaires and grading systems, provide little underlying information beyond subjective scores, and this can limit further optimization of treatment strategies. Nuclear medicine imaging techniques can be utilised to directly measure regional baseline function and function loss from internal/external radiation therapy within normal tissues in an in vivo setting with high spatial resolution. This can be correlated with dose delivered by radiotherapy techniques to establish objective dose-effect relationships, and can also be used in the treatment planning step to spare normal tissues more efficiently. Toxicity in radionuclide therapy typically occurs due to undesired off-target uptake in normal tissues. Molecular imaging using diagnostic analogues of therapeutic radionuclides can be used to test various interventional protective strategies that can potentially reduce this normal tissue uptake without compromising tumour uptake. We provide an overview of the existing literature on these applications of nuclear medicine imaging in diverse normal tissue types utilising various tracers, and discuss its future potential.

Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy

PURPOSE

Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change.

METHODS AND MATERIALS

Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1-to-week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MED_Plan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (P_HL).

RESULTS

Univariately, w1→w2 VE10% (P = .004), VE5% (P = .01) and MEex% (P = .02) significantly predicted ≥AE2. A model combining MED_W2 and w1→w2 VE10% had the best performance (AUC = 0.80; P_HL = 0.43), whereas the MED_Plan model had a lower accuracy (AUC = 0.67; P_HL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75).

CONCLUSIONS

A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis.

Clinical response assessment on DW-MRI compared with FDG-PET/CT after neoadjuvant chemoradiotherapy in patients with oesophageal cancer

PURPOSE

In about 30% of patients treated with neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection for locally advanced oesophageal cancer no vital tumour is found in the resection specimen. Accurate clinical response assessment is critical if deferral from surgery is considered in complete responders. Our study aimed to compare the performance of MRI and of FDG-PET/CT for the detection of residual disease after nCRT.

METHODS

Patients with oesophageal cancer eligible for nCRT and oesophagectomy were prospectively included. All patients underwent FDG-PET/CT and MRI before and between 6 and 8 weeks after nCRT. Two radiologists scored the MRI scans, and two nuclear medicine physicians scored the FDG-PET/CT scans using a 5-point score for residual disease. Histopathology after oesophagectomy represented the reference standard. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated for detection of residual tumour (ypT+), residual nodal disease (ypN+), and any residual disease (ypT+Nx/ypT0N+).

RESULTS

Seven out of 33 (21%) patients had a pathological complete response. The AUCs for individual readers to detect ypT+ were 0.71/0.70 on diffusion-weighted (DW)-MRI and 0.54/0.57 on FDG-PET/CT, and to detect ypN+ were 0.89/0.81 on DW-MRI and 0.75/0.71 on FDG-PET/CT. The AUCs/sensitivities/specificities for the individual readers to detect any residual disease were 0.74/92%/57% and 0.70/96%/43% on MRI; these were 0.49/69%/29% and 0.60/69%/43% on FDG-PET/CT, respectively.

CONCLUSION

MRI reached higher diagnostic accuracies than FDG-PET/CT for the detection of residual tumour in oesophageal cancer patients at 6 to 8 weeks after nCRT.

Machine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation

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A large cohort to predict radiation esophagitis in lung cancer patients was used.

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Modern machine learning models were implemented to predict radiation esophagitis.

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Previously published predictors of grade ≥ 3 radiation esophagitis may be unreliable.

Background and Purpose

Radiation esophagitis is a clinically important toxicity seen with treatment for locally-advanced non-small cell lung cancer. There is considerable disagreement among prior studies in identifying predictors of radiation esophagitis. We apply machine learning algorithms to identify factors contributing to the development of radiation esophagitis to uncover previously unidentified criteria and more robust dosimetric factors.

Materials and Methods

We used machine learning approaches to identify predictors of grade ≥ 3 radiation esophagitis in a cohort of 202 consecutive locally-advanced non-small cell lung cancer patients treated with definitive chemoradiation from 2008 to 2016. We evaluated 35 clinical features per patient grouped into risk factors, comorbidities, imaging, stage, histology, radiotherapy, chemotherapy and dosimetry. Univariate and multivariate analyses were performed using a panel of 11 machine learning algorithms combined with predictive power assessments.

Results

All patients were treated to a median dose of 66.6 Gy at 1.8 Gy per fraction using photon (89.6%) and proton (10.4%) beam therapy, most often with concurrent chemotherapy (86.6%). 11.4% of patients developed grade ≥ 3 radiation esophagitis. On univariate analysis, no individual feature was found to predict radiation esophagitis (AUC range 0.45–0.55, p ≥ 0.07). In multivariate analysis, all machine learning algorithms exhibited poor predictive performance (AUC range 0.46–0.56, p ≥ 0.07).

Conclusions

Contemporary machine learning algorithms applied to our modern, relatively large institutional cohort could not identify any reliable predictors of grade ≥ 3 radiation esophagitis. Additional patients are needed, and novel patient-specific and treatment characteristics should be investigated to develop clinically meaningful methods to mitigate this survival altering toxicity.

Combined tumor plus nontumor interim FDG-PET parameters are prognostic for response to chemoradiation in squamous cell esophageal cancer

We have investigated the prognostic value of two novel interim ¹⁸ F-fluorodeoxyglucose positron emission tomography (FDG-PET) parameters in patients undergoing chemoradiation (CRT) for esophageal squamous cell carcinoma (ESCC): one tumor parameter (maximal standardized uptake ratio rSUR) and one normal tissue parameter (change of FDG uptake within irradiated nontumor-affected esophagus ∆SUV_NTO ). PET data of 134 European and Chinese patients were analyzed. Parameter establishment was based on 36 patients undergoing preoperative CRT plus surgery, validation was performed in 98 patients receiving definitive CRT. Patients received PET imaging prior and during fourth week of CRT. Clinical parameters, baseline PET parameters, and interim PET parameters (rSUR and ∆SUV_NTO ) were analyzed and compared to event-free survival (EFS), overall survival (OS), loco-regional control (LRC) and freedom from distant metastases (FFDM). Combining rSUR and ∆SUV_NTO revealed a strong prognostic impact on EFS, OS, LRC and FFDM in patients undergoing preoperative CRT. In the definitive CRT cohort, univariate analysis with respect to EFS revealed several staging plus both previously established interim PET parameters as significant prognostic factors. Multivariate analyses revealed only rSUR and ∆SUV_NTO as independent prognostic factors (p = 0.003, p = 0.008). Combination of these parameters with the cutoff established in preoperative CRT revealed excellent discrimination of patients with a long or short EFS (73% vs. 17% at 2 years, respectively) and significantly discriminated all other endpoints (OS, p < 0.001; LRC, p < 0.001; FFDM, p = 0.02), even in subgroups. Combined use of interim FDG-PET derived parameters ∆SUV_NTO and rSUR seems to have predictive potential, allowing to select responders for definitive CRT and omission of surgery.

Image-guided adaptive radiotherapy in patients with locally advanced non-small cell lung cancer: the art of PET

With a worldwide annual incidence of 1.8 million cases, lung cancer is the most diagnosed form of cancer in men and the third most diagnosed form of cancer in women. Histologically, 80-85% of all lung cancers can be categorized as non-small cell lung cancer (NSCLC). For patients with locally advanced NSCLC, standard of care is fractionated radiotherapy combined with chemotherapy. With the aim of improving clinical outcome of patients with locally advanced NSCLC, combined and intensified treatment approaches are increasingly being used. However, given the heterogeneity of this patient group with respect to tumor biology and subsequent treatment response, a personalized treatment approach is required to optimize therapeutic effect and minimize treatment induced toxicity. Medical imaging, in particular positron emission tomography (PET), before and during the course radiotherapy is increasingly being used to personalize radiotherapy. In this setting, PET imaging can be used to improve delineation of target volumes, employ molecularly-guided dose painting strategies, early response monitoring, prediction and monitoring of treatment-related toxicity. The concept of PET image-guided adaptive radiotherapy (IGART) is an interesting approach to personalize radiotherapy for patients with locally advanced NSCLC, which might ultimately contribute to improved clinical outcomes and reductions in frequency of treatment-related adverse events in this patient group. In this review, we provide a comprehensive overview of available clinical data supporting the use of PET imaging for IGART in patients with locally advanced NSCLC.

Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels

Radiation esophagitis (RE) is a common adverse event associated with radiotherapy for non-small cell lung cancer (NSCLC). While plasma cytokine levels have been correlated with other forms of radiation-induced toxicity, their association with RE has been less well studied. We analyzed data from 126 patients treated on 4 prospective clinical trials. Logistic regression models based on combinations of dosimetric factors [maximum dose to 2 cubic cm (D2cc) and generalized equivalent uniform dose (gEUD)], clinical variables, and pretreatment plasma levels of 30 cytokines were developed. Cross-validated estimates of area under the receiver operating characteristic curve (AUC) and log likelihood were used to assess prediction accuracy. Dose-only models predicted grade 3 RE with AUC values of 0.750 (D2cc) and 0.727 (gEUD). Combining clinical factors with D2cc increased the AUC to 0.779. Incorporating pretreatment cytokine measurements, modeled as direct associations with RE and as potential interactions with the dose-esophagitis association, produced AUC values of 0.758 and 0.773, respectively. D2cc and gEUD correlated with grade 3 RE with odds ratios (ORs) of 1.094/Gy and 1.096/Gy, respectively. Female gender was associated with a higher risk of RE, with ORs of 1.09 and 1.112 in the D2cc and gEUD models, respectively. Older age was associated with decreased risk of RE, with ORs of 0.992/year and 0.991/year in the D2cc and gEUD models, respectively. Combining clinical with dosimetric factors but not pretreatment cytokine levels yielded improved prediction of grade 3 RE compared to prediction by dose alone. Such multifactorial modeling may prove useful in directing radiation treatment planning.

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.

The Use of Imaging in the Prediction and Assessment of Cancer Treatment Toxicity

Multimodal imaging is commonly used in the management of patients with cancer. Imaging plays pivotal roles in the diagnosis, initial staging, treatment response assessment, restaging after treatment and the prognosis of many cancers. Indeed, it is difficult to imagine modern precision cancer care without the use of multimodal molecular imaging, which is advancing at a rapid pace with innovative developments in imaging sciences and an improved understanding of the complex biology of cancer. Cancer therapy often leads to undesirable toxicity, which can range from an asymptomatic subclinical state to severe end organ damage and even death. Imaging is helpful in the portrayal of the unwanted effects of cancer therapy and may assist with optimal clinical decision-making, clinical management, and overall improvements in the outcomes and quality of life for patients.

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.

Acute radiation oesophagitis associated with 2-deoxy-2-[18F]fluoro-d-glucose uptake on positron emission tomography/CT during chemo-radiation therapy in patients with non-small-cell lung cancer

INTRODUCTION

Acute radiation oesophagitis (ARO) is frequently experienced by patients receiving concurrent chemo-radiation therapy (cCRT) for non-small-cell lung cancer (NSCLC). We investigated ARO symptoms (CTCAE v3.0), radiation dose and oesophageal FDG PET/CT uptake.

METHOD

Candidates received cCRT (60 Gy, 2 Gy/fx) and sequential FDG PET/CT (baseline FDG₀ , FDG_wk2 and FDG_wk4 ). Mean and maximum standardized uptake value (SUVmean and SUVmax) and radiation dose (O_mean and O_max ) were calculated within the whole oesophagus and seven sub-regions (5-60 Gy).

RESULTS

Forty-four patients underwent FDG₀ and FDG_wk2 , and 41 (93%) received FDG_wk4 , resulting in 129 PET/CT scans for analysis. Of 29 (66%) patients with ≥ grade 2 ARO, SUVmax (mean ± SD) increased from FDG₀ to FDG_wk4 (3.06 ± 0.69 to 3.83 ± 1.27, P = 0.0019) and FDG_wk2 to FDG_wk4 (3.10 ± 0.75 to 3.83 ± 1.27, P = 0.0046). Radiation dose (mean ± SD) was higher in grade ≥2 patients; O_mean (47.5 ± 20 vs 53.9 ± 10.2, P = 0.0061), O_max (13.7 ± 9.6 vs 20.1 ± 10.6, P = 0.0009) and V40 Gy (8.0 ± 8.2 vs 11.9 ± 7.3, P = 0.0185).

CONCLUSIONS

FDG_wk4 SUVmax and radiation dose were associated with ≥ grade 2 ARO. Compared to subjective assessments, future interim FDG PET/CT acquired for disease response assessment may also be utilized to objectively characterize ARO severity and image-guided oesophageal dose constraints.

Adaptive Dose Escalation using Serial Four-dimensional Positron Emission Tomography/Computed Tomography Scans during Radiotherapy for Locally Advanced Non-small Cell Lung Cancer

AIMS

Computed tomography (CT)-based radiotherapy dose escalation for locally advanced non-small cell lung cancer (LA-NSCLC) has had limited success. In this planning study, we investigated the potential for adaptive dose escalation using respiratory-gated 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography scans (4DPET/4DCT) acquired before and during a course of chemoradiotherapy (CRT).

MATERIALS AND METHODS

We prospectively enrolled patients with LA-NSCLC receiving curative intent CRT. Radiotherapy was delivered using intensity-modulated radiotherapy (IMRT) using the week 0 4DCT scan. Three alternative, dose-escalated IMRT plans were developed offline based on the week 0, 2 and 4 4DPET/4DCT scans. The FDG-avid primary (PET-T) and nodal disease (PET-N) volumes defined by the 50% of maximum standard uptake value threshold were dose escalated to as high as possible while respecting organ at risk constraints.

RESULTS

Thirty-two patients were recruited, 27 completing all scans. Twenty-five patients (93%) were boosted successfully above the clinical plan doses at week 0, 23 (85%) at week 2 and 20 (74%) at week 4. The median dose received by 95% of the planning target volume (D95) at week 0, 2 and 4 to PET-T were 74.4 Gy, 75.3 Gy and 74.1 Gy and to PET-N were 74.3 Gy, 71.0 Gy and 69.5 Gy.

CONCLUSIONS

Using 18F-FDG-4DPET/4DCT, it is feasible to dose escalate both primary and nodal disease in most patients. Choosing week 0 images to plan a course with an integrated boost to PET-avid disease allows for more patients to be successfully dose escalated with the highest boost dose.

(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.