Toward personalized dose-prescription in locally advanced non-small cell lung cancer: Validation of published normal tissue complication probability models

Thor, Maria; Deasy, Joseph O.; Iyer, Aditi; Bendau, Ethan; Fontanella, Andrew N.; Apte, Aditya; Yorke, Ellen; Jackson, Andrew

doi:10.1016/j.radonc.2019.05.011

Cited by 29 publications

(21 citation statements)

References 22 publications

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“…It also contrasts with dosimetric factors including eso mean, eso max, as well as eso V20, eso V35, eso V60 which have been analyzed retrospectively in [35], and using a mixture of retrospective and prospective collected datasets in [9]. It is important to note that we specifically examined grade 3 RE, whereas much of the prior literature has examined grade 2 esophagitis, a less clinically impactful toxicity [36,37].…”

Section: Discussionmentioning

confidence: 99%

“…We further attribute the difference in results between our current models and the prior literature to a combination of the different toxicity endpoint assessed (grade 3 vs. grade 2), variance in radiation techniques, and implementation of resampling. Previously published reports using models developed using a median cohort size of 141 (well below our actual cohort), and median rate of radiation esophagitis of 11.1%, used logistic regression approaches [1,9,12,17,31,32,34,36,37,39], while some other studies would fit Lyman-Kutcher-Burman (LKB) models, both using pre-IMRT era cohorts [14,33], which may not reflect the current risks of radiation-induced toxicity in a contemporary setting. A more recent study used lasso regularization using a smaller cohort of 94 patients, where 16% developed radiation esophagitis [35].…”

Section: Discussionmentioning

confidence: 99%

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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

Luna

Chao

Shinohara

et al. 2020

Clinical and Translational Radiation Oncology

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a b s t r a c tBackground 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Luna

Chao

Shinohara

et al. 2020

Clinical and Translational Radiation Oncology

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“…It is recommended to limit V20 to ≤30–35%, and MLD to ≤20–23 Gy in normofractionated radiotherapy to limit the risk of RIP to ≤20% in patients with NSCLC [ 124 ]. Hypofractionated radiotherapy with single doses of ≥2.5 Gy is associated with higher rates of RIP [ 124 , 136 ]. For SBRT, V20 > 10% and MLD > 6 Gy were associated with higher risk of grade 2–4 RIP [ 137 – 139 ].…”

Section: Introductionmentioning

confidence: 99%

Radiation-induced lung toxicity – cellular and molecular mechanisms of pathogenesis, management, and literature review

et al. 2020

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Lung, breast, and esophageal cancer represent three common malignancies with high incidence and mortality worldwide. The management of these tumors critically relies on radiotherapy as a major part of multi-modality care, and treatment-related toxicities, such as radiation-induced pneumonitis and/or lung fibrosis, are important dose limiting factors with direct impact on patient outcomes and quality of life. In this review, we summarize the current understanding of radiation-induced pneumonitis and pulmonary fibrosis, present predictive factors as well as recent diagnostic and therapeutic advances. Novel candidates for molecularly targeted approaches to prevent and/or treat radiation-induced pneumonitis and pulmonary fibrosis are discussed.

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“…Minimizing radiation-induced acute esophagitis (AE) in locally advanced non-small cell lung cancer (LA-NSCLC) is critical given the high rate of AE due to the proximity between the esophagus and the large tumors [1]. The esophagus is a mobile structure and the planned esophagus dose may not accurately represent the actual accumulated esophagus dose due to anatomical changes and setup uncertainties arising from e.g.…”

Section: Introductionmentioning

confidence: 99%

Quantification of accumulated dose and associated anatomical changes of esophagus using weekly Magnetic Resonance Imaging acquired during radiotherapy of locally advanced lung cancer

Alam

Thor

Rimner

et al. 2020

Physics and Imaging in Radiation Oncology

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Background and purpose: Minimizing acute esophagitis (AE) in locally advanced non-small cell lung cancer (LA-NSCLC) is critical given the proximity between the esophagus and the tumor. In this pilot study, we developed a clinical platform for quantification of accumulated doses and volumetric changes of esophagus via weekly Magnetic Resonance Imaging (MRI) for adaptive radiotherapy (RT). Material and methods: Eleven patients treated via intensity-modulated RT to 60-70 Gy in 2-3 Gy-fractions with concurrent chemotherapy underwent weekly MRIs. Eight patients developed AE grade 2 (AE2), 3-6 weeks after RT started. First, weekly MRI esophagus contours were rigidly propagated to planning CT and the distances between the medial esophageal axes were calculated as positional uncertainties. Then, the weekly MRI were deformably registered to the planning CT and the total dose delivered to esophagus was accumulated. Weekly Maximum Esophagus Expansion (MEex) was calculated using the Jacobian map. Eventually, esophageal dose parameters (Mean Esophagus Dose (MED), V 90% and D 5cc) between the planned and accumulated dose were compared. Results: Positional esophagus uncertainties were 6.8 ± 1.8 mm across patients. For the entire cohort at the end of RT: the median accumulated MED was significantly higher than the planned dose (24 Gy vs. 21 Gy p = 0.006). The median V 90% and D 5cc were 12.5 cm 3 vs. 11.5 cm 3 (p = 0.05) and 61 Gy vs. 60 Gy (p = 0.01), for accumulated and planned dose, respectively. The median MEex was 24% and was significantly associated with AE2 (p = 0.008). Conclusions: MRI is well suited for tracking esophagus volumetric changes and accumulating doses. Longitudinal esophagus expansion could reflect radiation-induced inflammation that may link to AE.

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Toward personalized dose-prescription in locally advanced non-small cell lung cancer: Validation of published normal tissue complication probability models

Cited by 29 publications

References 22 publications

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

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

Radiation-induced lung toxicity – cellular and molecular mechanisms of pathogenesis, management, and literature review

Quantification of accumulated dose and associated anatomical changes of esophagus using weekly Magnetic Resonance Imaging acquired during radiotherapy of locally advanced lung cancer

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