A model combining age, equivalent uniform dose and IL-8 may predict radiation esophagitis in patients with non-small cell lung cancer

Esophagitis and Pneumonitis Related to Concurrent Chemoradiation ± Durvalumab Consolidation in Unresectable Stage III Non-Small-Cell Lung Cancer: Risk Assessment and Management Recommendations Based on a Modified Delphi Process

The addition of durvalumab consolidation to concurrent chemoradiation therapy (cCRT) has fundamentally changed the standard of care for patients with unresectable stage III non-small-cell lung cancer (NSCLC). Nevertheless, concerns related to esophagitis and pneumonitis potentially impact the broad application of all regimen components. A Canadian expert working group (EWG) was convened to provide guidance to healthcare professionals (HCPs) managing these adverse events (AEs) and to help optimize the patient experience. Integrating literature review findings and real-world clinical experience, the EWG used a modified Delphi process to develop 12 clinical questions, 30 recommendations, and a risk-stratification guide. The recommendations address risk factors associated with developing esophagitis and pneumonitis, approaches to risk mitigation and optimal management, and considerations related to initiation and re-initiation of durvalumab consolidation therapy. For both AEs, the EWG emphasized the importance of upfront risk assessment to inform the treatment approach, integration of preventative measures, and prompt initiation of suitable therapy in alignment with AE grade. The EWG also underscored the need for timely, effective communication between multidisciplinary team members and clarity on responsibilities. These recommendations will help support HCP decision-making related to esophagitis and pneumonitis arising from cCRT ± durvalumab and improve outcomes for patients with unresectable stage III NSCLC.

Combined deep learning and radiomics in pretreatment radiation esophagitis prediction for patients with esophageal cancer underwent volumetric modulated arc therapy

PURPOSE

To develop a combined radiomics and deep learning (DL) model in predicting radiation esophagitis (RE) of a grade ≥ 2 for patients with esophageal cancer (EC) underwent volumetric modulated arc therapy (VMAT) based on computed tomography (CT) and radiation dose (RD) distribution images.

MATERIALS AND METHODS

A total of 273 EC patients underwent VMAT were retrospectively reviewed and enrolled from two centers and divided into training (n = 152), internal validation (n = 66), and external validation (n = 55) cohorts, respectively. Radiomic and dosiomic features along with DL features using convolutional neural networks were extracted and screened from CT and RD images to predict RE. The performance of these models was evaluated and compared using the area under curve (AUC) of the receiver operating characteristic curves (ROC).

RESULTS

There were 5 and 10 radiomic and dosiomic features were screened, respectively. XGBoost achieved a best AUC of 0.703, 0.694 and 0.801, 0.729 with radiomic and dosiomic features in the internal and external validation cohorts, respectively. ResNet34 achieved a best prediction AUC of 0.642, 0.657 and 0.762, 0.737 for radiomics based DL model (DLR) and RD based DL model (DLD) in the internal and external validation cohorts, respectively. Combined model of DLD + Dosiomics + clinical factors achieved a best AUC of 0.913, 0.821 and 0.805 in the training, internal, and external validation cohorts, respectively.

CONCLUSION

Although the dose was not responsible for the prediction accuracy, the combination of various feature extraction methods was a factor in improving the RE prediction accuracy. Combining DLD with dosiomic features was promising in the pretreatment prediction of RE for EC patients underwent VMAT.

Enhanced prediction of postoperative radiotherapy-induced esophagitis in non-small cell lung cancer: Dosiomic model development in a real-world cohort and validation in the PORT-C randomized controlled trial

BACKGROUND

Radiotherapy-induced esophagitis (RE) diminishes the quality of life and interrupts treatment in patients with non-small cell lung cancer (NSCLC) undergoing postoperative radiotherapy. Dosimetric models showed limited capability in predicting RE. We aimed to develop dosiomic models to predict RE.

METHODS

Models were trained with a real-world cohort and validated with PORT-C randomized controlled trial cohort. Patients with NSCLC undergoing resection followed by postoperative radiotherapy between 2004 and 2015 were enrolled. The endpoint was grade ≥2 RE. Esophageal three-dimensional dose distribution features were extracted using handcrafted and convolutional neural network (CNN) methods, screened using an entropy-based method, and selected using minimum redundancy and maximum relevance. Prediction models were built using logistic regression. The areas under the receiver operating characteristic curve (AUC) and precision-recall curve were used to evaluate prediction model performance. A dosimetric model was built for comparison.

RESULTS

A total of 190 and 103 patients were enrolled in the training and validation sets, respectively. Using handcrafted and CNN methods, 107 and 4096 features were derived, respectively. Three handcrafted, four CNN-extracted and three dosimetric features were selected. AUCs of training and validation sets were 0.737 and 0.655 for the dosimetric features, 0.730 and 0.724 for handcrafted features, and 0.812 and 0.785 for CNN-extracted features, respectively. Precision-recall curves revealed that CNN-extracted features outperformed dosimetric and handcrafted features.

CONCLUSIONS

Prediction models may identify patients at high risk of developing RE. Dosiomic models outperformed the dosimetric-feature model in predicting RE. CNN-extracted features were more predictive but less interpretable than handcrafted features.

Prediction of adaptive strategies based on deformation vector field features for MR-guided adaptive radiotherapy of prostate cancer

BACKGROUND

The selection of adaptive strategies in MR-guided adaptive radiotherapy (MRgART) usually relies on subjective review of anatomical changes. However, this kind of review may lead to improper selection of adaptive strategy for some fractions.

PURPOSE

The purpose of this study was to develop prediction models based on deformation vector field (DVF) features for automatic and accurate strategy selection, using prostate cancer as an example.

METHODS

100 fractions of 20 prostate cancer patients were retrospectively selected in this study. Treatment plans using both adapt to position (ATP) strategy and adapt to shape (ATS) strategy were generated. Optimal adaptive strategy was determined according to dosimetric evaluation. DVFs of the deformable image registration (DIR) of daily MRI and CT simulation scans were extracted. The shape, first order statistics, and spatial features were extracted from the DVFs, subjected to further selection using the minimum redundancy maximum relevance (mRMR) method. The number of features (F_n ) was hyper-tuned using bootstrapping method, and then F_n indicating a peak area under the curve (AUC) value was used to construct three prediction models.

RESULTS

According to subjective review, the ATS strategy was adopted for all 100 fractions. However, the evaluation results showed that the ATP strategy could have met the clinical requirements for 23 (23%) fractions. The three prediction models showed high prediction performance, with the best performing model achieving an AUC value of 0.896, corresponding accuracy (ACC), sensitivity (SEN) and specificity (SPC) of 0.9, 0.958, and 0.667, respectively. The features used to construct prediction models included four features extracted from y direction of DVF (DVF_y ) and mask, one feature from z direction of DVF (DVF_z ). It indicated that the deformation along the anterior-posterior direction had a greater impact on determining the adaptive strategy than other directions.

CONCLUSIONS

DVF-feature-based models could accurately predict the adaptive strategy and avoid unnecessary selection of time-consuming ATS strategy, which consequently improves the efficiency of the MRgART process.

Using inflammatory indexes and clinical parameters to predict radiation esophagitis in patients with small-cell lung cancer undergoing chemoradiotherapy

OBJECTIVE

Radiation esophagitis (RE) is a common adverse effect in small cell lung cancer (SCLC) patients undergoing thoracic radiotherapy. We aim to develop a novel nomogram to predict the acute severe RE (grade≥2) receiving chemoradiation in SCLC patients.

MATERIALS AND METHODS

the risk factors were analyzed by logistic regression, and a nomogram was constructed based on multivariate analysis results. The clinical value of the model was evaluated using the area under the receiver operating curve (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The correlations of inflammation indexes were assessed using Spearman correlation analysis.

RESULTS

Eighty-four of 187 patients (44.9%) developed grade ≥2 RE. Univariate analysis indicated that concurrent chemoradiotherapy (CCRT, p < 0.001), chemotherapy cycle (p = 0.097), system inflammation response index (SIRI, p = 0.048), prognostic-nutrition index (PNI, p = 0.073), platelets-lymphocyte radio (PLR, p = 0.026), platelets-albumin ratio (PAR, p = 0.029) were potential predictors of RE. In multivariate analysis, CCRT [p < 0.001; OR, 3.380; 95% CI, 1.767-6.465], SIRI (p = 0.047; OR, 0.436; 95% CI, 0.192-0.989), and PAR (p = 0.036; OR, 2.907; 95% CI, 1.071-7.891) were independent predictors of grade ≥2 RE. The AUC of nomogram was 0.702 (95% CI, 0.626-0.778), which was greater than each independent predictor (CCRT: 0.645; SIRI: 0.558; PAR: 0.559). Calibration curves showed high coherence between the predicted and actual observation RE, and DCA displayed satisfactory clinical utility.

CONCLUSION

In this study, CCRT, SIRI, and PAR were independent predictors for RE (grade ≥2) in patients with SCLC receiving chemoradiotherapy. We developed and validated a predictive model through these factors. The developed nomogram with superior prediction ability can be used as a quantitative model to predict RE.

Significance of radiation esophagitis: Conditional survival assessment in patients with non-small cell lung cancer

Purpose

This study aimed to examine the effect of radiation esophagitis (RE) and the dynamics of RE on subsequent survival in non-small cell lung cancer (NSCLC) patients who underwent radiotherapy.

Experimental Design

Patients with NSCLC treated with fractionated thoracic radiotherapy enrolled in prospective trials were eligible. RE was graded prospectively according to Common Terminology Criteria for Adverse Events (CTCAE) v3.0 per protocol requirement weekly during-RT and 1 month after RT. This study applied conditional survival assessment which has advantage over traditional survival analysis as it assesses the survival from the event instead of from the baseline. P-value less than 0.05 was considered to be significant. The primary endpoint is overall survival.

Results

A total of 177 patients were eligible, with a median follow-up of 5 years. The presence of RE, the maximum RE grade, the evolution of RE and the onset timing of RE events were all correlated with subsequent survival. At all conditional time points, patients first presented with RE grade1 (initial RE1) had significant inferior subsequent survival (multivariable HRs median: 1.63, all P-values<0.05); meanwhile those with RE progressed had significant inferior subsequent survival than those never develop RE (multivariable HRs median: 2.08, all P-values<0.05). Multivariable Cox proportional-hazards analysis showed significantly higher C-indexes for models with inclusion of RE events than those without (all P-values<0.05).

Conclusion

This study comprehensively evaluated the impact of RE with conditional survival assessment and demonstrated that RE is associated with inferior survival in NSCLC patients treated with RT.

A novel nomogram containing acute radiation esophagitis predicting radiation pneumonitis in thoracic cancer receiving radiotherapy

Background

Radiation-induced pneumonitis (RP) is a non-negligible and sometimes life-threatening complication among patients with thoracic radiation. We initially aimed to ascertain the predictive value of acute radiation-induced esophagitis (SARE, grade ≥ 2) to symptomatic RP (SRP, grade ≥ 2) among thoracic cancer patients receiving radiotherapy. Based on that, we established a novel nomogram model to provide individualized risk assessment for SRP.

Methods

Thoracic cancer patients who were treated with thoracic radiation from Jan 2018 to Jan 2019 in Shandong Cancer Hospital and Institute were enrolled prospectively. All patients were followed up during and after radiotherapy (RT) to observe the development of esophagitis as well as pneumonitis. Variables were analyzed by univariate and multivariate analysis using the logistic regression model, and a nomogram model was established to predict SRP by “R” version 3.6.0.

Results

A total of 123 patients were enrolled (64 esophageal cancer, 57 lung cancer and 2 mediastinal cancer) in this study prospectively. RP grades of 0, 1, 2, 3, 4 and 5 occurred in 29, 57, 31, 0, 3 and 3 patients, respectively. SRP appeared in 37 patients (30.1%). In univariate analysis, SARE was shown to be a significant predictive factor for SRP (P < 0.001), with the sensitivity 91.9% and the negative predictive value 93.5%. The incidence of SRP in different grades of ARE were as follows: Grade 0–1: 6.5%; Grade 2: 36.9%; Grade 3: 80.0%; Grade 4: 100%. Besides that, the dosimetric factors considering total lung mean dose, total lung V5, V20, ipsilateral lung mean dose, ipsilateral lung V5, and mean esophagus dose were correlated with SRP (all P < 0.05) by univariate analysis. The incidence of SRP was significantly higher in patients whose symptoms of RP appeared early. SARE, mean esophagus dose and ipsilateral mean lung dose were still significant in multivariate analysis, and they were included to build a predictive nomogram model for SRP.

Conclusions

As an early index that can reflect the tissue’s radiosensitivity visually, SARE can be used as a predictor for SRP in patients receiving thoracic radiation. And the nomogram containing SARE may be fully applied in future’s clinical work.

X-rays induced IL-8 production in lung cancer cells via p38/MAPK and NF-κB pathway

PURPOSE

It is reported inflammatory cytokine interleukin-8 (IL-8) could predict radiation-induced lung toxicity (RILT). RILT is believed to be a consequence of a cascade of cytokine production. It is considered that vascular endothelial cell and macrophages are the mainly source of cytokines. This study was investigated the production of IL-8 from cancer cells induced by X-rays may involve in the radiation-induced inflammation.

MATERIALS AND METHODS

We analyzed IL-8 in human lung cancer cell lines after expose to X-rays, and we also detect IL-8 in HUVEC cells and THP1 cells as endothelial cell and macrophage model to identify the change in normal cells after expose. Furthermore, we added the inhibitors to the culture with or without radiation to identify the role of MAPK and NF-κB pathways on the radiation-induced secretion of IL-8.

RESULTS

Radiation could induce IL-8 production both in non-lung cancer cells (HUVECs and THP1 cells) and in lung cancer cells (A549 cells, H446 cells, PC-9 cells). Simultaneously, radiation activated p38/MAPK and NF-κB signal pathways in lung cancer cells. Moreover, p38/MAPK inhibitor SB203580 and NF-κB inhibitor BAY11-7082 could block the IL-8 up-regulated by X-rays but JNK inhibitor SP600125, ERK inhibitor U0126, ROS Scavenger NAC could not inhibit this phenomenon.

CONCLUSIONS

X-rays could induce IL-8 production in lung cancer cells, which may be related to the activation of p38/MAPK and NF-κB signaling pathway, providing a new point for elucidating the mechanism of radiation pneumonitis.

Dose-volume predictors of early esophageal toxicity in non-small cell lung cancer patients treated with accelerated-hyperfractionated radiotherapy

BACKGROUND AND PURPOSE

Early radiation-induced esophageal toxicity (RIET) is one of the major side effects in patients with non-small cell lung cancer (NSCLC) and can be a reason for treatment interruptions. As the age of patients with NSCLC and corresponding comorbidities continue to increase, primary radiotherapy alone is a commonly used alternative treatment in these cases. The aim of the present study is to compare dosimetric and clinical parameters from the previously reported CHARTWEL trial for their ability to predict esophagitis and investigate potential differences in the accelerated and conventional fractionation arm.

MATERIAL AND METHODS

146 patients of the Dresden cohort of the randomized phase III CHARTWEL trial were included in this post-hoc analysis. Side effects were prospectively scored weekly during the first 8 weeks from start of radiotherapy. To compare both treatment arms, recorded dose-volume parameters were adjusted for the different fractionation schedules. Logistic regression was performed to predict early RIET for the entire study group as well as for the individual treatment arms. Different dosimetric and clinical parameters were tested.

RESULTS

Patients receiving the accelerated CHARTWEL schedule experienced earlier and more severe esophagitis (e.g. 20.5% vs. 9.6% ≥grade 2 at week 3, respectively). In contrast, the median time period for recovery of grade 1 esophagitis was significantly longer for patients with conventional fractionation compared to the CHARTWEL group (median [range]: 21 [12-49] days vs. 15 [7-84] days, p = 0.028). In univariable logistic regression none of the dose-volume parameters showed a significant correlation with early RIET grade ≥ 2 in the conventional irradiation group. In contrast, for patients receiving CHARTWEL, the physical dose-volumes parameters V₄₀ and V₅₀; and re-scaled values V_EQD2,50 and V_EQD2,60 were significant predictors of early RIET grade ≥ 2. Dose-volume parameters remained different between CHARTWEL and conventional fractionation even after biological rescaling.

CONCLUSION

Our results show a more dominant dose-volume effect in the CHARTWEL arm compared to conventional fractionation, especially for higher esophageal doses. These findings support the notion that dose-volume parameters for radiation esophagitis determined in a specific and time dependent setting of field arrangements can not be easily transferred to another setting. In clinical practice esophageal volumes receiving 40 Gy or more should be strictly limited in hyperfractionated-accelerated fraction schemes.