NCCN Guidelines® Insights: Mesothelioma: Pleural, Version 1.2024.

Mesothelioma is a rare cancer that originates from the mesothelial surfaces of the pleura and other sites, and is estimated to occur in approximately 3,500 people in the United States annually. Pleural mesothelioma is the most common type and represents approximately 85% of these cases. The NCCN Guidelines for Mesothelioma: Pleural provide recommendations for the diagnosis, evaluation, treatment, and follow-up for patients with pleural mesothelioma. These NCCN Guidelines Insights highlight significant updates to the NCCN Guidelines for Mesothelioma: Pleural, including revised guidance on disease classification and systemic therapy options.

Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non-Small Cell Lung Cancer (NSCLC) provide recommendations for the treatment of patients with NSCLC, including diagnosis, primary disease management, surveillance for relapse, and subsequent treatment. The panel has updated the list of recommended targeted therapies based on recent FDA approvals and clinical data. This selection from the NCCN Guidelines for NSCLC focuses on treatment recommendations for advanced or metastatic NSCLC with actionable molecular biomarkers.

Mesothelioma: Peritoneal, Version 2.2023, NCCN Clinical Practice Guidelines in Oncology.

Mesothelioma is a rare cancer originating in mesothelial surfaces of the peritoneum, pleura, and other sites. These NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) focus on peritoneal mesothelioma (PeM). The NCCN Guidelines for PeM provide recommendations for workup, diagnosis, and treatment of primary as well as previously treated PeM. The diagnosis of PeM may be delayed because PeM mimics other diseases and conditions and because the disease is so rare. The pathology section was recently updated to include new information about markers used to identify mesothelioma, which is difficult to diagnose. The term "malignant" is no longer used to classify mesotheliomas, because all mesotheliomas are now defined as malignant.

NCCN Guidelines® Insights: Non-Small Cell Lung Cancer, Version 2.2023.

The NCCN Guidelines for Non-Small Cell Lung Cancer (NSCLC) provide recommendations for management of disease in patients with NSCLC. These NCCN Guidelines Insights focus on neoadjuvant and adjuvant (also known as perioperative) systemic therapy options for eligible patients with resectable NSCLC.

Non-Small Cell Lung Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology.

NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non-Small Cell Lung Cancer (NSCLC) provide recommended management for patients with NSCLC, including diagnosis, primary treatment, surveillance for relapse, and subsequent treatment. Patients with metastatic lung cancer who are eligible for targeted therapies or immunotherapies are now surviving longer. This selection from the NCCN Guidelines for NSCLC focuses on targeted therapies for patients with metastatic NSCLC and actionable mutations.

NCCN Guidelines Insights: Non-Small Cell Lung Cancer, Version 2.2021.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non-Small Cell Lung Cancer (NSCLC) address all aspects of management for NSCLC. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines regarding targeted therapies, immunotherapies, and their respective biomarkers.

NCCN Guidelines Insights: Non-Small Cell Lung Cancer, Version 1.2020.

The NCCN Guidelines for Non-Small Cell Lung Cancer (NSCLC) address all aspects of management for NSCLC. These NCCN Guidelines Insights focus on recent updates in immunotherapy. For the 2020 update, all of the systemic therapy regimens have been categorized using a new preference stratification system; certain regimens are now recommended as "preferred interventions," whereas others are categorized as either "other recommended interventions" or "useful under certain circumstances."

NCCN Guidelines Insights: Non-Small Cell Lung Cancer, Version 5.2018.

The NCCN Guidelines for Non-Small Cell Lung Cancer (NSCLC) address all aspects of management for NSCLC. These NCCN Guidelines Insights focus on recent updates to the targeted therapy and immunotherapy sections in the NCCN Guidelines. For the 2018 update, a new section on biomarkers was added.

Non-Small Cell Lung Cancer, Version 5.2017, NCCN Clinical Practice Guidelines in Oncology.

This selection from the NCCN Guidelines for Non-Small Cell Lung Cancer (NSCLC) focuses on targeted therapies and immunotherapies for metastatic NSCLC, because therapeutic recommendations are rapidly changing for metastatic disease. For example, new recommendations were added for atezolizumab, ceritinib, osimertinib, and pembrolizumab for the 2017 updates.

Outcomes targeting the PD-1/PD-L1 axis in conjunction with stereotactic radiation for patients with non-small cell lung cancer brain metastases.

Anti-PD-1/PD-L1 therapies have demonstrated activity in patients with advanced stage non-small cell lung cancer (NSCLC). However, little is known about the safety and feasibility of patients receiving anti-PD-1/PD-L1 therapy and stereotactic radiation for the treatment of brain metastases. Data were analyzed retrospectively from NSCLC patients treated with stereotactic radiation either before, during or after anti-PD-1/PD-L1 therapy with nivolumab (anti-PD-1) or durvalumab (anti-PD-L1). Seventeen patients treated with stereotactic radiosurgery (SRS) or fractionated stereotactic radiation therapy (FSRT) to 49 brain metastases over 21 sessions were identified. Radiation was administered prior to, during and after anti-PD-1/PD-L1 therapy in 22 lesions (45%), 13 lesions (27%), and 14 lesions (29%), respectively. The 6 months Kaplan-Meier (KM) distant brain control rate was 48% following stereotactic radiation. Six and 12 month KM rates of OS from the date of stereotactic radiation and the date of cranial metastases diagnosis were 48/41% and 81/51%, respectively. The 6 month rate of distant brain control following stereotactic radiation for patients treated with stereotactic radiation during or prior to anti-PD-1/PD-L1 therapy was 57% compared to 0% among patients who received anti-PD-1/PD-L1 therapy before stereotactic radiation (p = 0.05). A Karnofsky Performance Status (KPS) of <90 was found to be predictive of worse OS following radiation treatment on both univariate and multivariate analyses (MVA, p = 0.01). In our series, stereotactic radiation to NSCLC brain metastases was well tolerated in patients who received anti-PD-1/PD-L1 therapy. Prospective evaluation to determine how these two modalities can be used synergistically to improve distant brain control and OS is warranted.

NCCN Guidelines Insights: Non-Small Cell Lung Cancer, Version 4.2016.

These NCCN Guidelines Insights focus on recent updates in the 2016 NCCN Guidelines for Non-Small Cell Lung Cancer (NSCLC; Versions 1-4). These NCCN Guidelines Insights will discuss new immunotherapeutic agents, such as nivolumab and pembrolizumab, for patients with metastatic NSCLC. For the 2016 update, the NCCN panel recommends immune checkpoint inhibitors as preferred agents (in the absence of contraindications) for second-line and beyond (subsequent) therapy in patients with metastatic NSCLC (both squamous and nonsquamous histologies). Nivolumab and pembrolizumab are preferred based on improved overall survival rates, higher response rates, longer duration of response, and fewer adverse events when compared with docetaxel therapy.

Non-Small Cell Lung Cancer, Version 6.2015.

These NCCN Guidelines Insights focus on recent updates to the 2015 NCCN Guidelines for Non-Small Cell Lung Cancer (NSCLC). Appropriate targeted therapy is very effective in patients with advanced NSCLC who have specific genetic alterations. Therefore, it is important to test tumor tissue from patients with advanced NSCLC to determine whether they have genetic alterations that make them candidates for specific targeted therapies. These NCCN Guidelines Insights describe the different testing methods currently available for determining whether patients have genetic alterations in the 2 most commonly actionable genetic alterations, notably anaplastic lymphoma kinase (ALK) gene rearrangements and sensitizing epidermal growth factor receptor (EGFR) mutations.

Non-small cell lung cancer, version 1.2015.

This selection from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non-Small Cell Lung Cancer (NSCLC) focuses on the principles of radiation therapy (RT), which include the following: (1) general principles for early-stage, locally advanced, and advanced/metastatic NSCLC; (2) target volumes, prescription doses, and normal tissue dose constraints for early-stage, locally advanced, and advanced/palliative RT; and (3) RT simulation, planning, and delivery. Treatment recommendations should be made by a multidisciplinary team, including board-certified radiation oncologists who perform lung cancer RT as a prominent part of their practice.

Structure-specific rigid dose accumulation dosimetric analysis of ablative stereotactic MRI-guided adaptive radiation therapy in ultracentral lung lesions.

BACKGROUND: Definitive local therapy with stereotactic ablative radiation therapy (SABR) for ultracentral lung lesions is associated with a high risk of toxicity, including treatment related death. Stereotactic MR-guided adaptive radiation therapy (SMART) can overcome many of the challenges associated with SABR treatment of ultracentral lesions. METHODS: We retrospectively identified 14 consecutive patients who received SMART to ultracentral lung lesions from 10/2019 to 01/2021. Patients had a median distance from the proximal bronchial tree (PBT) of 0.38 cm. Tumors were most often lung primary (64.3%) and HILUS group A (85.7%). A structure-specific rigid registration approach was used for cumulative dose analysis. Kaplan-Meier log-rank analysis was used for clinical outcome data and the Wilcoxon Signed Rank test was used for dosimetric data. RESULTS: Here we show that SMART dosimetric improvements in favor of delivered plans over predicted non-adapted plans for PBT, with improvements in proximal bronchial tree DMax of 5.7 Gy (p = 0.002) and gross tumor 100% prescription coverage of 7.3% (p = 0.002). The mean estimated follow-up is 17.2 months and 2-year local control and local failure free survival rates are 92.9% and 85.7%, respectively. There are no grade ≥ 3 toxicities. CONCLUSIONS: SMART has dosimetric advantages and excellent clinical outcomes for ultracentral lung tumors. Daily plan adaptation reliably improves target coverage while simultaneously reducing doses to the proximal airways. These results further characterize the therapeutic window improvements for SMART. Structure-specific rigid dose accumulation dosimetric analysis provides insights that elucidate the dosimetric advantages of SMART more so than per fractional analysis alone.

Stereotactic MR-guided Adaptive Radiation Therapy (SMART) is a type of radiation therapy for cancer. With SMART, treatment can be adapted based on daily changes in the body seen via imaging. SMART can safely deliver radiation to lung tumors near the center of the body which are risky to treat, due to potential damage to nearby organs. We looked at 14 patients who received SMART to determine how much changing the radiation plan each day improved our ability to safely deliver high doses. We found that SMART not only improved our ability to cover the entirety of the tumor with the dose originally intended, but also reduced dose to nearby organs. Treatment resulted in excellent control of the tumor with few side effects. SMART shows promise for safer and more effective treatment for lung tumors in this part of the body.

Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy.

Background: Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies. To investigate such collaborative decision-making process, we conducted a Human-AI interaction case study on response-adaptive radiotherapy (RT). Methods: We designed and conducted a two-phase study for two disease sites and two treatment modalities-adaptive RT for non-small cell lung cancer (NSCLC) and adaptive stereotactic body RT for hepatocellular carcinoma (HCC)-in which clinicians were asked to consider mid-treatment modification of the dose per fraction for a number of retrospective cancer patients without AI-support (Unassisted Phase) and with AI-assistance (AI-assisted Phase). The AI-CDSS graphically presented trade-offs in tumor control and the likelihood of toxicity to organs at risk, provided an optimal recommendation, and associated model uncertainties. In addition, we asked for clinicians' decision confidence level and trust level in individual AI recommendations and encouraged them to provide written remarks. We enrolled 13 evaluators (radiation oncology physicians and residents) from two medical institutions located in two different states, out of which, 4 evaluators volunteered in both NSCLC and HCC studies, resulting in a total of 17 completed evaluations (9 NSCLC, and 8 HCC). To limit the evaluation time to under an hour, we selected 8 treated patients for NSCLC and 9 for HCC, resulting in a total of 144 sets of evaluations (72 from NSCLC and 72 from HCC). Evaluation for each patient consisted of 8 required inputs and 2 optional remarks, resulting in up to a total of 1440 data points. Results: AI-assistance did not homogeneously influence all experts and clinical decisions. From NSCLC cohort, 41 (57%) decisions and from HCC cohort, 34 (47%) decisions were adjusted after AI assistance. Two evaluations (12%) from the NSCLC cohort had zero decision adjustments, while the remaining 15 (88%) evaluations resulted in at least two decision adjustments. Decision adjustment level positively correlated with dissimilarity in decision-making with AI [NSCLC: ρ = 0.53 ( p < 0.001); HCC: ρ = 0.60 ( p < 0.001)] indicating that evaluators adjusted their decision closer towards AI recommendation. Agreement with AI-recommendation positively correlated with AI Trust Level [NSCLC: ρ = 0.59 ( p < 0.001); HCC: ρ = 0.7 ( p < 0.001)] indicating that evaluators followed AI's recommendation if they agreed with that recommendation. The correlation between decision confidence changes and decision adjustment level showed an opposite trend [NSCLC: ρ = -0.24 ( p = 0.045), HCC: ρ = 0.28 ( p = 0.017)] reflecting the difference in behavior due to underlying differences in disease type and treatment modality. Decision confidence positively correlated with the closeness of decisions to the standard of care (NSCLC: 2 Gy/fx; HCC: 10 Gy/fx) indicating that evaluators were generally more confident in prescribing dose fractionations more similar to those used in standard clinical practice. Inter-evaluator agreement increased with AI-assistance indicating that AI-assistance can decrease inter-physician variability. The majority of decisions were adjusted to achieve higher tumor control in NSCLC and lower normal tissue complications in HCC. Analysis of evaluators' remarks indicated concerns for organs at risk and RT outcome estimates as important decision-making factors. Conclusions: Human-AI interaction depends on the complex interrelationship between expert's prior knowledge and preferences, patient's state, disease site, treatment modality, model transparency, and AI's learned behavior and biases. The collaborative decision-making process can be summarized as follows: (i) some clinicians may not believe in an AI system, completely disregarding its recommendation, (ii) some clinicians may believe in the AI system but will critically analyze its recommendations on a case-by-case basis; (iii) when a clinician finds that the AI recommendation indicates the possibility for better outcomes they will adjust their decisions accordingly; and (iv) When a clinician finds that the AI recommendation indicate a worse possible outcome they will disregard it and seek their own alternative approach.

Consolidation Osimertinib Versus Durvalumab Versus Observation After Concurrent Chemoradiation in Unresectable EGFR-Mutant NSCLC: A Multicenter Retrospective Cohort Study.

INTRODUCTION: Durvalumab improves survival when used as consolidation therapy after chemoradiation (CRT) in patients with stage III NSCLC. The optimal consolidation therapy for patients with EGFR-mutant (EGFRmut) stage III NSCLC remains unknown. METHODS: In this multi-institutional, international retrospective analysis across 24 institutions, we evaluated outcomes in patients with stage III EGFRmut NSCLC treated with concurrent CRT followed by consolidation therapy with osimertinib, durvalumab, or observation between 2015 and 2022. Kaplan-Meier method was used to estimate real-world progression-free survival (rwPFS, primary end point) and overall survival (secondary end point). Treatment-related adverse events (trAEs) during consolidation treatment were defined using Common Terminology Criteria for Adverse Events version 5.0. Multivariable Cox regression analysis was used. RESULTS: Of 136 patients with stage III EGFRmut NSCLC treated with definitive concurrent CRT, 56 received consolidation durvalumab, 33 received consolidation osimertinib, and 47 was on observation alone. Baseline characteristics were similar across the three cohorts. With a median follow-up of 46 months for the entire cohort, the median duration of treatment was not reached (NR) for osimertinib (interquartile range: NR-NR) and was 5.5 (interquartile range: 2.4-10.8) months with durvalumab. After adjusting for nodal status, stage III A/B/C, and age, patients treated with consolidation osimertinib had significantly longer 24-month rwPFS compared to those treated with durvalumab or in the observation cohorts (osimertinib: 86%, durvalumab: 30%, observation: 27%, p < 0.001 for both comparisons). There was no difference in rwPFS between the durvalumab and the observation cohorts. No significant difference in overall survival across the three cohorts was detected, likely due to the limited follow-up. Any-grade trAE occurred in 52% (2 [6.1%] grade ≥3) and 48% (10 [18%] grade ≥3) of patients treated with osimertinib and durvalumab, respectively. Of 45 patients who progressed on consolidation durvalumab, 37 (82%) subsequently received EGFR tyrosine kinase inhibitors. Of these, 14 (38%) patients developed trAEs including five patients with pneumonitis (14%; 2 [5.4%] grade ≥3) and five patients with diarrhea (14%; 1 [2.7%] grade ≥3). CONCLUSIONS: This study suggests that among patients with stage III unresectable NSCLC with a sensitizing EGFR mutation, consolidation osimertinib was associated with a significantly longer rwPFS compared to durvalumab or observation. No unanticipated safety signals were observed with consolidation osimertinib.

Dependence of ventilation image derived from 4D CT on deformable image registration and ventilation algorithms.

Ventilation imaging using 4D CT is a convenient and low-cost functional imaging methodology which might be of value in radiotherapy treatment planning to spare functional lung volumes. Deformable image registration (DIR) is needed to calculate ventilation imaging from 4D CT. This study investigates the dependence of calculated ventilation on DIR methods and ventilation algorithms. DIR of the normal end expiration and normal end inspiration phases of the 4D CT images was used to correlate the voxels between the two respiratory phases. Three different DIR algorithms, optical flow (OF), diffeomorphic demons (DD), and diffeomorphic morphons (DM) were retrospectively applied to ten esophagus and ten lung cancer cases with 4D CT image sets that encompassed the entire lung volume. The three ventilation extraction methods were used based on either the Jacobian, the change in volume of the voxel, or directly calculated from Hounsfield units. The ventilation calculation algorithms used are the Jacobian, ΔV, and HU method. They were compared using the Dice similarity coefficient (DSC) index and Bland-Altman plots. Dependence of ventilation images on the DIR was greater for the ΔV and the Jacobian methods than for the HU method. The DSC index for 20% of low-ventilation volume for ΔV was 0.33 ± 0.03 (1 SD) between OF and DM, 0.44 ± 0.05 between OF and DD, and 0.51 ± 0.04 between DM and DD. The similarity comparisons for Jacobian were 0.32 ± 0.03, 0.44 ± 0.05, and 0.51 ± 0.04, respectively, and for HU they were 0.53 ± 0.03, 0.56 ± 0.03, and 0.76 ± 0.04, respectively. Dependence of extracted ventilation on the ventilation algorithm used showed good agreement between the ΔV and Jacobian methods, but differed significantly for the HU method. DSC index for using OF as DIR was 0.86 ± 0.01 between ΔV and Jacobian, 0.28 ± 0.04 between ΔV and HU, and 0.28 ± 0.04 between Jacobian and HU, respectively. When using DM or DD as DIR, similar values were obtained when comparing the different ventilation calculation methods. The similarity values for the 20% high-ventilation volume were close to those found for the 20% low-ventilation volume. The results obtained with DSC index were confirmed when using the Bland-Altman plots for comparing the ventilation images. Our data suggest that ventilation calculated from 4D CT depends on the DIR algorithm employed. Similarities between ΔV and Jacobian are higher than between ΔV and HU, and Jacobian and HU.

Deep Learning-Guided Dosimetry for Mitigating Local Failure of Patients With Non-Small Cell Lung Cancer Receiving Stereotactic Body Radiation Therapy.

PURPOSE: Non-small cell lung cancer (NSCLC) stereotactic body radiation therapy with 50 Gy/5 fractions is sometimes considered controversial, as the nominal biologically effective dose (BED) of 100 Gy is felt by some to be insufficient for long-term local control of some lesions. In this study, we analyzed such patients using explainable deep learning techniques and consequently proposed appropriate treatment planning criteria. These novel criteria could help planners achieve optimized treatment plans for maximal local control. METHODS AND MATERIALS: A total of 535 patients treated with 50 Gy/5 fractions were used to develop a novel deep learning local response model. A multimodality approach, incorporating computed tomography images, 3-dimensional dose distribution, and patient demographics, combined with a discrete-time survival model, was applied to predict time to failure and the probability of local control. Subsequently, an integrated gradient-weighted class activation mapping method was used to identify the most significant dose-volume metrics predictive of local failure and their optimal cut-points. RESULTS: The model was cross-validated, showing an acceptable performance (c-index: 0.72, 95% CI, 0.68-0.75); the testing c-index was 0.69. The model's spatial attention was concentrated mostly in the tumors' periphery (planning target volume [PTV] - internal gross target volume [IGTV]) region. Statistically significant dose-volume metrics in improved local control were BED Dnear-min ≥ 103.8 Gy in IGTV (hazard ratio [HR], 0.31; 95% CI, 015-0.63), V104 ≥ 98% in IGTV (HR, 0.30; 95% CI, 0.15-0.60), gEUD ≥ 103.8 Gy in PTV-IGTV (HR, 0.25; 95% CI, 0.12-0.50), and Dmean ≥ 104.5 Gy in PTV-IGTV (HR, 0.25; 95% CI, 0.12-0.51). CONCLUSIONS: Deep learning-identified dose-volume metrics have shown significant prognostic power (log-rank, P = .003) and could be used as additional actionable criteria for treatment planning in NSCLC stereotactic body radiation therapy patients receiving 50 Gy in 5 fractions. Although our data do not confirm or refute that a significantly higher BED for the prescription dose is necessary for tumor control in NSCLC, it might be clinically effective to escalate the nominal prescribed dose from BED 100 to 105 Gy.

Magnetic Resonance-Guided Stereotactic Body Radiation Therapy/Hypofractionated Radiation therapy for Metastatic and Primary Central and Ultracentral Lung Lesions.

Introduction: The recent results from the Nordic-HILUS study indicate stereotactic body radiation therapy (SBRT) is associated with high-grade toxicity for ultracentral (UC) tumors. We hypothesized that magnetic resonance-guided SBRT (MRgSBRT) or hypofractionated radiation therapy (MRgHRT) enables the safe delivery of high-dose radiation to central and UC lung lesions. Methods: Patients with UC or central lesions were treated with MRgSBRT/MRgHRT with real-time gating or adaptation. Central lesions were defined as per the Radiation Therapy Oncology Group and UC as per the HILUS study definitions: (1) group A or tumors less than 1 cm from the trachea and/or mainstem bronchi; or (2) group B or tumors less than 1 cm from the lobar bronchi. The Kaplan-Meier estimate and log-rank test were used to estimate survival. Associations between toxicities and other patient factors were tested using the Mann-Whitney U test and Fisher's exact test. Results: A total of 47 patients were included with a median follow-up of 22.9 months (95% confidence interval: 16.4-29.4). Most (53%) had metastatic disease. All patients had central lesions and 55.3% (n = 26) had UC group A. The median distance from the proximal bronchial tree was 6.0 mm (range: 0.0-19.0 mm). The median biologically equivalent dose (α/ß = 10) was 105 Gy (range: 75-151.2). The most common radiation schedule was 60 Gy in eight fractions (40.4%). Most (55%) had previous systemic therapy, 32% had immunotherapy and 23.4% had previous thoracic radiation therapy. There were 16 patients who underwent daily adaptation. The 1-year overall survival was 82% (median = not reached), local control 87% (median = not reached), and progression-free survival 54% (median = 15.1 mo, 95% confidence interval: 5.1-25.1). Acute toxicity included grade 1 (26%) and grade 2 (21%) with only two patients experiencing grade 3 (4.3%) in the long term. No grade 4 or 5 toxicities were seen. Conclusions: Previous studies noted high rates of toxicity after SBRT to central and UC lung lesions, with reports of grade 5 toxicities. In our cohort, the use of MRgSBRT/MRgHRT with high biologically effective doses was well tolerated, with two grade 3 toxicities and no grade 4/5.

Early-Stage Primary Lung Neuroendocrine Tumors Treated With Stereotactic Body Radiation Therapy: A Multi-Institution Experience.

PURPOSE: Current guidelines recommend surgery as standard of care for primary lung neuroendocrine tumor (LNET). Given that LNET is a rare clinical entity, there is a lack of literature regarding treatment of LNET with stereotactic body radiation therapy (SBRT). We hypothesized that SBRT could lead to effective locoregional tumor control and long-term outcomes. METHODS AND MATERIALS: We retrospectively reviewed 48 tumors in 46 patients from 11 institutions with a histologically confirmed diagnosis of LNET, treated with primary radiation therapy. Data were collected for patients treated nonoperatively with primary radiation therapy between 2006 and 2020. Patient records were reviewed for lesion characteristics and clinical risk factors. Kaplan-Meier analysis, log-rank tests, and Cox multivariate models were used to compare outcomes. RESULTS: Median age at treatment was 71 years and mean tumor size was 2 cm. Thirty-two lesions were typical carcinoid histology, 7 were atypical, and 9 were indeterminate. The most common SBRT fractionation schedule was 50 to 60 Gy in 5 daily fractions. Overall survival at 3, 6, and 9 years was 64%, 43%, and 26%, respectively. Progression-free survival at 3, 6, and 9 years was 88%, 78%, and 78%, respectively. Local control at 3, 6, and 9 years was 97%, 91%, and 91%, respectively. There was 1 regional recurrence in a paraesophageal lymph node. No grade 3 or higher toxicity was identified. CONCLUSIONS: This is the largest series evaluating outcomes in patients with LNET treated with SBRT. This treatment is well tolerated, provides excellent locoregional control, and should be offered as an alternative to surgical resection for patients with early-stage LNET, particularly those who may not be ideal surgical candidates.