Is cardiac toxicity a relevant issue in the radiation treatment of esophageal cancer?

Beukema, Jannet C.; Luijk, Peter van; Widder, Joachim; Langendijk, Johannes A.; Muijs, Christina T.

doi:10.1016/j.radonc.2014.11.037

Cited by 128 publications

(92 citation statements)

References 34 publications

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“…Increased risks of radiation-induced heart disease (RIHD) including acute (pericarditis) and late (congestive heart failure, coronary artery disease (CAD), and myocardial infarction) cardiotoxicities have been linked to dose from thoracic radiation therapy (RT) for lymphoma, lung, breast, and esophageal cancers. [1][2][3][4] RIHD presents earlier than previously expected, beginning only a few years after RT and with elevated risk persisting for~20 yr. 4 Importantly, dose escalation evaluation for locally advanced non-small cell lung cancer in RTOG 0617 5 revealed that the volumes of the heart receiving ≥5 and ≥30 Gy were independent predictors of survival. 5 Furthermore, heart dose/volume metrics are significantly associated with quality of life.…”

Section: Introductionmentioning

confidence: 75%

Cardiac substructure segmentation with deep learning for improved cardiac sparing

et al. 2019

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Purpose Radiation dose to cardiac substructures is related to radiation‐induced heart disease. However, substructures are not considered in radiation therapy planning (RTP) due to poor visualization on CT. Therefore, we developed a novel deep learning (DL) pipeline leveraging MRI’s soft tissue contrast coupled with CT for state‐of‐the‐art cardiac substructure segmentation requiring a single, non‐contrast CT input. Materials/methods Thirty‐two left‐sided whole‐breast cancer patients underwent cardiac T2 MRI and CT‐simulation. A rigid cardiac‐confined MR/CT registration enabled ground truth delineations of 12 substructures (chambers, great vessels (GVs), coronary arteries (CAs), etc.). Paired MRI/CT data (25 patients) were placed into separate image channels to train a three‐dimensional (3D) neural network using the entire 3D image. Deep supervision and a Dice‐weighted multi‐class loss function were applied. Results were assessed pre/post augmentation and post‐processing (3D conditional random field (CRF)). Results for 11 test CTs (seven unique patients) were compared to ground truth and a multi‐atlas method (MA) via Dice similarity coefficient (DSC), mean distance to agreement (MDA), and Wilcoxon signed‐ranks tests. Three physicians evaluated clinical acceptance via consensus scoring (5‐point scale). Results The model stabilized in ~19 h (200 epochs, training error <0.001). Augmentation and CRF increased DSC 5.0 ± 7.9% and 1.2 ± 2.5%, across substructures, respectively. DL provided accurate segmentations for chambers (DSC = 0.88 ± 0.03), GVs (DSC = 0.85 ± 0.03), and pulmonary veins (DSC = 0.77 ± 0.04). Combined DSC for CAs was 0.50 ± 0.14. MDA across substructures was <2.0 mm (GV MDA = 1.24 ± 0.31 mm). No substructures had statistical volume differences (P > 0.05) to ground truth. In four cases, DL yielded left main CA contours, whereas MA segmentation failed, and provided improved consensus scores in 44/60 comparisons to MA. DL provided clinically acceptable segmentations for all graded patients for 3/4 chambers. DL contour generation took ~14 s per patient. Conclusions These promising results suggest DL poses major efficiency and accuracy gains for cardiac substructure segmentation offering high potential for rapid implementation into RTP for improved cardiac sparing.

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Section: Introductionmentioning

confidence: 75%

Cardiac substructure segmentation with deep learning for improved cardiac sparing

et al. 2019

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“…Only one of the studies described lung DVH parameters as risk factors for decreasing lung function based on multivariable analysis . Although an influence of heart dose on lung toxicity has been strongly suggested , only three studies reported on heart DVH parameters, two of which included them in the analysis . None of the studies reported on patient outcome measures such as dyspnea, self‐reported physical condition, or exercise capacity in addition to PFT results.…”

Section: Discussionmentioning

confidence: 99%

Pulmonary Function Changes After Radiotherapy for Lung or Esophageal Cancer: A Systematic Review Focusing on Dose-Volume Parameters

et al. 2017

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Background. Despite technical developments in treatment delivery, radiation-induced lung toxicity (RILT) remains a crucial problem in thoracic radiotherapy. Clinically based RILT scores have their limitations, and more objective measures such as pulmonary functions tests (PFTs) might help to improve treatment strategies. Purpose. To summarize the available evidence about the effect of dose to the lung in thoracic radiotherapy on forced expiratory volume in one second (FEV1) and diffusion capacity (DLCO) in patients with lung and esophageal cancer treated with curative intent. Material and methods. A systematic review following the PRISMA guidelines was performed, using MEDLINE and including clinical studies using (chemo)radiotherapy (CRT) or stereotactic ablative radiotherapy (SABR) for lung or CRT for esophageal cancer that reported both lung dose-volume histogram (DVH) parameters and changes in PFT results. Search terms included lung and esophageal neoplasms, respiratory function tests, and radiotherapy.

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“…[19–23] It has recently been suggested that the survival benefit imparted by the improved locoregional sterilization of disease by neoadjuvant chemoradiation therapy is allowing trimodality esophageal cancer patients to live long enough to develop and succumb to radiation induced cardiac comorbidities such as ischemic heart disease and heart failure. [24] It is plausible that using radiation therapies that have improved tumor targeting profiles with lower doses delivered to surrounding tissues, such as IMRT and PBT, could decrease the incidence of cardiac and pulmonary toxicities [25,26]. …”

Section: Discussionmentioning

confidence: 99%

Neoadjuvant Chemotherapy versus Chemoradiation Prior to Esophagectomy: Impact on Rate of Complete Pathologic Response and Survival in Esophageal Cancer Patients

Samson

Robinson

Bradley

et al. 2016

Journal of Thoracic Oncology

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Purpose To evaluate differences in pathologic complete response (pCR) rates and overall survival among patients receiving either neoadjuvant chemotherapy or chemoradiation prior to esophagectomy for locally advanced esophageal cancer. Patients and methods Esophageal cancer patients receiving either neoadjuvant chemotherapy or chemoradiation prior to esophagectomy were identified using the National Cancer Data Base (NCDB). Univariate analysis compared patient, tumor, and postoperative outcome characteristics. Logistic regression was performed to identify variables associated with achieving pCR. Kaplan-Meier analysis was performed to compare overall median survival by neoadjuvant therapy type and pCR status. Finally, a Cox proportional hazards model was fitted to identify variables associated with increased mortality hazard. Results From 2006 – 2012, 916/7,338 (12.5%) of patients received neoadjuvant chemotherapy while 6,422 (87.5%) received neoadjuvant chemoradiation. Neoadjuvant chemoradiation patients were more likely to achieve pCR (17.2% versus 6.4%, p<0.001) and less likely to have positive margins (5.6% versus 11.5%, p<0.001) than neoadjuvant chemotherapy patients, with no difference in 30- or 90-day mortality. Achieving pCR was associated with improved overall median survival (59.5 months ± 4.0 versus 30.1 months ± 0.76 for those with persistent disease, p<0.001). On logistic regression, neoadjuvant chemoradiation therapy was independently associated with achieving pCR (Odds Ratio 2.75, 2.01 – 3.77, p<0.001). Despite improvement in pCR rate with neoadjuvant chemoradiation, neoadjuvant therapy type was not independently associated with long-term survival (HR 1.12, 95% CI 0.97 – 1.30, p=0.12). Conclusion While neoadjuvant chemoradiation is more successful in downstaging esophageal cancer prior to esophagectomy, this therapy was not independently prognostic for improved long-term survival. Other factors affecting long-term survival among pathologic complete responders and among patients with persistent disease should be investigated to clarify this association.

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Is cardiac toxicity a relevant issue in the radiation treatment of esophageal cancer?

Cited by 128 publications

References 34 publications

Cardiac substructure segmentation with deep learning for improved cardiac sparing

Cardiac substructure segmentation with deep learning for improved cardiac sparing

Pulmonary Function Changes After Radiotherapy for Lung or Esophageal Cancer: A Systematic Review Focusing on Dose-Volume Parameters

Neoadjuvant Chemotherapy versus Chemoradiation Prior to Esophagectomy: Impact on Rate of Complete Pathologic Response and Survival in Esophageal Cancer Patients

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