A Novel Nomogram and Risk Classification System Predicting Radiation Pneumonitis in Patients With Esophageal Cancer Receiving Radiation Therapy

Prediction of severe radiation-induced oral mucositis in locally advanced nasopharyngeal carcinoma using the combined systemic immune-inflammatory index and prognostic nutritional index

OBJECTIVE

Severe radiation-induced oral mucositis (sRIOM) can seriously affect patients' quality of life and treatment compliance. This study was to investigate the utility of the systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) in predicting sRIOM in patients with locally advanced nasopharyngeal carcinoma (LANPC).

METHODS

295 patients with LANPC were retrospectively screened. The pre-radiotherapy SII and PNI were calculated based on peripheral blood samples. A receiver operating characteristic (ROC) curve was used to determine the cut-off value. Logistic regression was used for univariate and multivariate analyses. Patients were classified into three groups based on the SII-PNI score: score of 2, high SII (> cut-off value) and low PNI (≤ cut-off value); score of 1, either high SII or low PNI; score of 0, neither high SII nor low PNI.

RESULTS

The SII-PNI demonstrated significant predictive ability for sRIOM occurrence, as evidenced by an area under the curve (AUC) of 0.738. The incidence rates of sRIOM with SII-PNI score of 2, 1, and 0 were 73.86%, 44.35%, and 18.07%, respectively. Multivariate analysis confirmed that the SII-PNI score was an independent risk factor for sRIOM.

CONCLUSION

The SII-PNI score is a reliable and convenient indicator for predicting sRIOM in patients with LANPC.

Predictive factors for severe radiation-induced lung injury in patients with lung cancer and coexisting interstitial lung disease

BACKGROUND AND PURPOSE

This study aimed to investigate the predictive factors of severe radiation-induced lung injury (RILI) in patients with lung cancer and coexisting interstitial lung disease (ILD) undergoing conventionally fractionated thoracic radiotherapy.

MATERIALS AND METHODS

The study includes consecutive patients treated with thoracic radiotherapy for lung cancer at two tertiary centers between 2010 and 2021. RILI severity was graded using the National Cancer Institute Common Terminology Criteria version 5.0, with severe RILI defined as toxicity grade ≥4, and symptomatic RILI as grade ≥2. The absolute neutrophil count (ANC), absolute lymphocyte count (ALC), and C-reactive protein were collected within 4 weeks before starting radiotherapy. Neutrophil-lymphocyte ratios (NLR) were calculated as ANC/ALC. The median follow-up was 9 (range, 6-114) months.

RESULTS

Among 54 patients, 22 (40.7 %) had severe RILI. On multivariate logistic regression analysis, high pretreatment ANC (p = 0.030, OR = 4.313), pretreatment NLR (p = 0.007, OR = 5.784), and ILD severity (p = 0.027, OR = 2.416) were significant predictors of severe RILI. Dosimetric factors were not associated with severe RP. Overall survival was significantly worse for patients with severe RILI than those without, with 1-year cumulative overall survival rates of 7.4 % and 62.8 %, respectively.

CONCLUSION

Pretreatment blood NLR, ANC, and ILD severity were associated with severe RILI. Overall survival was dismal for patients with severe RILI.

A dynamic nomogram predicting symptomatic pneumonia in patients with lung cancer receiving thoracic radiation

PURPOSE

The most common and potentially fatal side effect of thoracic radiation therapy is radiation pneumonitis (RP). Due to the lack of effective treatments, predicting radiation pneumonitis is crucial. This study aimed to develop a dynamic nomogram to accurately predict symptomatic pneumonitis (RP ≥ 2) following thoracic radiotherapy for lung cancer patients.

METHODS

Data from patients with pathologically diagnosed lung cancer at the Zhongshan People's Hospital Department of Radiotherapy for Thoracic Cancer between January 2017 and June 2022 were retrospectively analyzed. Risk factors for radiation pneumonitis were identified through multivariate logistic regression analysis and utilized to construct a dynamic nomogram. The predictive performance of the nomogram was validated using a bootstrapped concordance index and calibration plots.

RESULTS

Age, smoking index, chemotherapy, and whole lung V5/MLD were identified as significant factors contributing to the accurate prediction of symptomatic pneumonitis. A dynamic nomogram for symptomatic pneumonitis was developed using these risk factors. The area under the curve was 0.89(95% confidence interval 0.83-0.95). The nomogram demonstrated a concordance index of 0.89(95% confidence interval 0.82-0.95) and was well calibrated. Furthermore, the threshold values for high- risk and low- risk were determined to be 154 using the receiver operating curve.

CONCLUSIONS

The developed dynamic nomogram offers an accurate and convenient tool for clinical application in predicting the risk of symptomatic pneumonitis in patients with lung cancer undergoing thoracic radiation.

Analysis of clinical and physical dosimetric factors that determine the outcome of severe acute radiation pneumonitis in lung cancer patients

OBJECTIVE

We conducted a retrospective statistical analysis of clinical and physical dosimetric factors of lung cancer patients who had previously undergone lung and/or mediastinal radiotherapy and died of or survived severe acute radiation pneumonitis (SARP). Our study was the first to reveal the heterogeneity in clinical factors, physical dosimetric factors, and SARP onset time that determined the clinical outcomes of lung cancer patients who developed SARP.

MATERIALS AND METHODS

The clinical characteristics, physical dosimetry factors, and SARP onset time of deceased and surviving patients were retrospectively analyzed. SPSS 20.0 was used for data analysis. Student's t-test was used for intergroup comparison, and a Mann-Whitney U test was used for data with skewed distribution. Qualitative data were represented using frequencies (%), and Fisher's exact test or χ2 test was used for intergroup comparison of nonparametric data. Binary logistic analysis was used for univariate and multivariate analyses. Differences with a P < 0.05 were considered statistically significant.

RESULTS

Univariate analysis revealed that the potential predictors of SARP death were as follows: ipsilateral lung V5 and V30, contralateral lung V5, V10, and V30, total lung V5, V10, and V30, mean lung dose, mean heart dose, and maximum spinal cord dose. Multivariate analysis showed that ipsilateral lung V5 and total lung V5 were predictors that determined the final outcome of SARP patients. In addition, we analyzed the time from the completion of radiotherapy to SARP onset, and found significant difference between the two groups.

CONCLUSIONS

There was no decisive correlation between clinical characteristics and SARP outcome (i.e., death or survival) in lung radiotherapy patients. Ipsilateral lung V5 and total lung V5 were independent predictors of death in SARP patients.

High versus standard radiation dose of definitive concurrent chemoradiotherapy for esophageal cancer: A systematic review and meta-analysis of randomized clinical trials

OBJECTIVES

Compare the efficacy and safety of high vs standard radiation dose of definitive concurrent chemoradiotherapy (dCCRT) for esophageal cancer (EC).

METHODS AND MATERIALS

This meta-analysis is registered in PROSPERO, and it was followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Eligible randomized clinical trials (RCTs) comparing high dose (HD;≥59.4 Gy/1.8 Gy) and standard doses (SD; 50 Gy/2Gy or 50.4 Gy/1.8 Gy) were identified on electronic databases. STATA16.0 was used for statistical analysis. A meta-analysis was performed to compare treatment effect and toxicity.

RESULTS

Four articles with a total of 1014 patients were finally included. The results showed that the two groups had similar 1-, 2-, and 3-year OS rates (RR = 1.08, 95 % CI = 0.90-1.30, P = 0.395; RR = 1.07, 95 % CI = 0.95-1.20, P = 0.272; RR = 1.06, 95 % CI = 0.97-1.17, P = 0.184; respectively) and 2-, and 3-year locoregional progression-free survival (LRPFS) (RR = 0.95, 95 % CI = 0.81-1.10, P = 0.478; RR = 0.97, 95 % CI = 0.85-1.11, P = 0.674; respectively). The HD-RT group had higher grade ≥ 3 treatment-related toxicities (OR = 1.35, 95 % CI = 1.03-1.77, P = 0.029) and treatment-related deaths rates (OR = 1.85, 95 % CI = 1.04-3.28, P = 0.036) compared with the SD-RT group. Results of subgroup analysis also indicated that HD could not bring benefit compared to SD, even with modern radiotherapy techniques.

CONCLUSION

SD-RT had similar treatment effect but lower Grade ≥ 3 treatment-related toxicities rates compared with the HD-RT. Therefore, SD (50 Gy/2Gy or 50.4 Gy/1.8 Gy) should be considered as the recommended dose in dCCRT for EC. Further RCTs are needed to verify our conclusions.

Computed tomography and radiation dose images-based deep-learning model for predicting radiation pneumonitis in lung cancer patients after radiation therapy

PURPOSE

To develop a deep learning model that combines CT and radiation dose (RD) images to predict the occurrence of radiation pneumonitis (RP) in lung cancer patients who received radical (chemo)radiotherapy.

METHODS

CT, RD images and clinical parameters were obtained from 314 retrospectively-collected patients (training set) and 35 prospectively-collected patients (test-set-1) who were diagnosed with lung cancer and received radical radiotherapy in the dose range of 50 Gy and 70 Gy. Another 194 (60 Gy group, test-set-2) and 158 (74 Gy group, test-set-3) patients from the clinical trial RTOG 0617 were used for external validation. A ResNet architecture was used to develop a prediction model that combines CT and RD features. Thereafter, the CT and RD weights were adjusted by using 40 patients from test-set-2 or 3 to accommodate cohorts with different clinical settings or dose delivery patterns. Visual interpretation was implemented using a gradient-weighted class activation map (grad-CAM) to observe the area of model attention during the prediction process. To improve the usability, ready-to-use online software was developed.

RESULTS

The discriminative ability of a baseline trained model had an AUC of 0.83 for test-set-1, 0.55 for test-set-2, and 0.63 for test-set-3. After adjusting CT and RD weights of the model using a subset of the RTOG-0617 subjects, the discriminatory power of test-set-2 and 3 improved to AUC 0.65 and AUC 0.70, respectively. Grad-CAM showed the regions of interest to the model that contribute to the prediction of RP.

CONCLUSION

A novel deep learning approach combining CT and RD images can effectively and accurately predict the occurrence of RP, and this model can be adjusted easily to fit new cohorts.

Clinical and Dosimetric Predictors for Postoperative Cardiopulmonary Complications in Esophageal Squamous Cell Carcinoma Patients Receiving Neoadjuvant Chemoradiotherapy and Surgery

BACKGROUND

Neoadjuvant chemoradiotherapy followed by esophagectomy is the standard treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). This study explored correlations of clinical factors and dose-volume histogram (DVH) parameters with postoperative cardiopulmonary complications and predicted their risk by establishing a nomogram model.

METHODS

Clinical and DVH parameters of ESCC patients who underwent trimodality treatment from 2002 to 2020 were collected. Postoperative cardiopulmonary complications were recorded. Logistic regression analysis was applied, and a nomogram model was constructed. Area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve analyses were performed to evaluate the performance of the nomogram.

RESULTS

Of the 307 ESCC patients enrolled in this study, 65 (21.2%) experienced pulmonary complications and 57 (18.6%) experienced cardiac complications. The following six risk factors were identified as independent risk factors for pulmonary complications by multivariate logistic regression analyses in the integrated model: male sex (odds ratio [OR], 3.26; 95% confidence interval [CI], 1.27-9.70; P = 0.021), post-radiation therapy (RT) forced expiratory volume in 1 s (FEV1) (OR, 0.51; 95% CI 0.28-0.90; P = 0.023), mean lung dose (MLD) (OR, 1.13; 95% CI 1.01-1.28; P = 0.041), and pre-RT monocyte (OR, 8.36; 95% CI 1.23-11.7; P = 0.03). The AUC of this integrated model was 0.705 (95% CI 0.64-0.77). The paclitaxel and cisplatin (TP) concurrent chemotherapy regimen was the independent predictor of cardiac complication (OR, 2.50; 95% CI 1.22-5.55; P = 0.016).

CONCLUSIONS

For ESCC patients who underwent trimodality treatment, male sex, post-RT FEV1, MLD, and pre-RT monocyte were confirmed as significant predictors of postoperative pulmonary complications. A nomogram model including six risk factors was further established. The independent predictor of cardiac complication was TP concurrent chemotherapy.

Pirfenidone for the prevention of radiation-induced lung injury in patients with locally advanced oesophageal squamous cell carcinoma: a protocol for a randomised controlled trial

INTRODUCTION

Radiation-induced lung injury (RILI) is one of the most clinically-challenging toxicities and dose-limiting factors during and/or after thoracic radiation therapy for oesophageal squamous cell carcinoma (ESCC). With limited effective protective drugs against RILI, the main strategy to reduce the injury is strict adherence to dose-volume restrictions of normal lungs. RILI can manifest as acute radiation pneumonitis with cellular injury, cytokine release and cytokine recruitment to inflammatory infiltrate, and subsequent chronic radiation pulmonary fibrosis. Pirfenidone inhibits the production of inflammatory cytokines, scavenges-free radicals and reduces hydroxyproline and collagen formation. Hence, pirfenidone might be a promising drug for RILI prevention. This study aims to evaluate the efficacy and safety of pirfenidone in preventing RILI in patients with locally advanced ESCC receiving chemoradiotherapy.

METHODS AND ANALYSIS

This study is designed as a randomised, placebo-controlled, double-blinded, single-centre phase 2 trial and will explore whether the addition of pirfenidone during concurrent chemoradiation therapy (CCRT) could prevent RILI in patients with locally advanced ESCC unsuitable for surgery. Eligible participants will be randomised at 1:1 to pirfenidone and placebo groups. The primary endpoint is the incidence of grade >2 RILI. Secondary endpoints include the incidence of any grade other than grade >2 RILI, time to RILI occurrence, changes in pulmonary function after CCRT, completion rate of CCRT, disease-free survival and overall survival. The follow-up period will be 1 year. In case the results meet the primary endpoint of this trial, a phase 3 multicentre trial with a larger sample size will be required to substantiate the evidence of the benefit of pirfenidone in RILI prevention.

ETHICS AND DISSEMINATION

This study was approved by the Ethics Committee of Fujian Union Hospital (No. 2021YF001-02). The findings of the trial will be disseminated through peer-reviewed journals, and national and international conference presentations.

TRIAL REGISTRATION NUMBER

ChiCTR2100043032.

Based on clinical Ki-67 expression and serum infiltrating lymphocytes related nomogram for predicting the diagnosis of glioma-grading

Background

Compelling evidence indicates that elevated peripheral serum lymphocytes are associated with a favorable prognosis in various cancers. However, the association between serum lymphocytes and glioma is contradictory. In this study, a nomogram was established to predict the diagnosis of glioma-grading through Ki-67 expression and serum lymphocytes.

Methods

We performed a retrospective analysis of 239 patients diagnosed with LGG and 178 patients with HGG. Immunohistochemistry was used to determine the Ki-67 expression. Following multivariate logistic regression analysis, a nomogram was established and used to identify the most related factors associated with HGG. The consistency index (C-index), decision curve analysis (DCA), and a calibration curve were used to validate the model.

Results

The number of LGG patients with more IDH1/2 mutations and 1p19q co-deletion was greater than that of HGG patients. The multivariate logistic analysis identified Ki-67 expression, serum lymphocyte count, and serum albumin (ALU) as independent risk factors associated with HGG, and these factors were included in a nomogram in the training cohort. In the validation cohort, the nomogram demonstrated good calibration and high consistency (C-index = 0.794). The Spearman correlation analysis revealed a significant association between HGG and serum lymphocyte count (r = −0.238, P &lt;0.001), ALU (r = −0.232, P &lt;0.001), and Ki-67 expression (r = 0.457, P &lt;0.001). Furthermore, the Ki-67 expression was negatively correlated with the serum lymphocyte count (r = −0.244, P &lt;0.05). LGG patients had lower Ki-67 expression and higher serum lymphocytes compared with HGG patients, and a combination of these two variables was significantly higher in HGG patients.

Conclusion

The constructed nomogram is capable of predicting the diagnosis of glioma-grade. A decrease in the level of serum lymphocyte count and increased Ki-67 expression in HGG patients indicate that their immunological function is diminished and the tumor is more aggressive.

Modeling the risk of radiation pneumonitis in esophageal squamous cell carcinoma treated with definitive chemoradiotherapy

BACKGROUND

To develop and validate a nomogram for the prediction of symptomatic radiation pneumonitis (RP) in patients with esophageal squamous cell carcinoma (ESCC) who received definitive concurrent chemoradiotherapy.

METHODS

Clinical factors, dose-volume histogram parameters, and pulmonary function parameters were collected from 402 ESCC patients between 2010 and 2017, including 321 patients in the primary cohort and 81 in the validation cohort. The end-point was the occurrence of symptomatic RP (grade ≥ 2) within the first 12 months after radiotherapy. Univariate and multivariate logistic regression analyses were applied to evaluate the predictive value of each factor for RP. A prediction model was generated in the primary cohort, which was internally validated to assess its performance.

RESULTS

In the primary cohort, 31 patients (9.7%) experienced symptomatic RP. Based on logistic regression model, patients with larger planning target volumes (PTVs) or higher lung V₂₀ had a higher predictive risk of RP, whereas the overall risk was substantially higher for three-dimensional conformal radiotherapy (3DCRT) than intensity-modulated radiotherapy. On multivariate analysis, independent predictive factors for RP were smoking history (P = 0.035), radiotherapy modality (P < 0.001), PTV (P = 0.039), and lung V₂₀ (P < 0.001), which were incorporated into the nomogram. The areas under the receiver operating characteristic curve of the nomogram in the primary and validation cohorts were 0.772 and 0.900, respectively, which were superior to each predictor alone.

CONCLUSIONS

Non-smoking status, 3DCRT, lung V₂₀ (> 27.5%), and PTV (≥ 713.0 cc) were significantly associated with a higher risk of RP. A nomogram was built with satisfactory prediction ability.

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.

Computed Tomography-Based Delta-Radiomics Analysis for Discriminating Radiation Pneumonitis in Patients With Esophageal Cancer After Radiation Therapy

PURPOSE

Our purpose was to construct a computed tomography (CT)-based delta-radiomics nomogram and corresponding risk classification system for individualized and accurate estimation of severe acute radiation pneumonitis (SARP) in patients with esophageal cancer (EC) after radiation therapy.

METHODS AND MATERIALS

Four hundred patients with EC were enrolled from 2 independent institutions and were divided into the training (n = 200) and validation (n = 200) cohorts. Eight hundred fifty radiomics features of lung were extracted from treatment planning images, including the positioning CT before radiation therapy (CT₁) and the resetting CT after receiving 40 to 45 Gy (CT₂). The longitudinal net changes in radiomics features from CT₁ to CT₂ were calculated and defined as delta-radiomics features. Least absolute shrinkage and selection operator algorithm was performed to features selection and delta-radiomics signature building. Integrating the signature with multidimensional clinicopathologic, dosimetric, and hematological predictors of SARP, a novel CT-based delta-radiomics nomogram was established according to multivariate analysis. The clinical application values of nomogram were both evaluated in the training and validation cohorts by concordance index, calibration curves, and decision curve analysis. Recursive partitioning analysis was used to generate a risk classification system.

RESULTS

The delta-radiomics signature consisting of 24 features was significantly associated with SARP status (P < .001). Incorporating it with other high-risk factors, Subjective Global Assessment score, pulmonary fibrosis score, mean lung dose, and systemic immune inflammation index, the developed delta-radiomics nomogram showed increased improvement in SARP discrimination accuracy with concordance index of 0.975 and 0.921 in the training and validation cohorts, respectively. Calibration curves and decision curve analysis confirmed the satisfactory clinical feasibility and utility of nomogram. The risk classification system displayed excellent performance on identifying SARP occurrence (P < .001).

CONCLUSIONS

The delta-radiomics nomogram and risk classification system as low-cost and noninvasive means exhibited superior predictive accuracy and provided individualized probability of SARP stratification for patients with EC.

Review of MR-Guided Radiotherapy for Esophageal Cancer

In this review, we outline the potential benefits and the future role of MRI and MR-guided radiotherapy (MRgRT) in the management of esophageal cancer. Although not currently used in most clinical practice settings, MRI is a useful non-invasive imaging modality that provides excellent soft tissue contrast and the ability to visualize cancer physiology. Chemoradiation therapy with or without surgery is essential for the management of locally advanced esophageal cancer. MRI can help stage esophageal cancer, delineate the gross tumor volume (GTV), and assess the response to chemoradiotherapy. Integrated MRgRT systems can help overcome the challenge of esophageal motion due to respiratory motion by using real-time imaging and tumor tracking with respiratory gating. With daily on-table MRI, shifts in tumor position and tumor regression can be taken into account for online-adaptation. The combination of accurate GTV visualization, respiratory gating, and online adaptive planning, allows for tighter treatment volumes and improved sparing of the surrounding normal organs. This could lead to a reduction in radiotherapy induced cardiac toxicity, pneumonitis and post-operative complications. Tumor physiology as seen on diffusion weighted imaging or dynamic contrast enhancement can help individualize treatments based on the response to chemoradiotherapy. Patients with a complete response on MRI can be considered for organ preservation while patients with no response can be offered an earlier resection. In patients with a partial response to chemoradiotherapy, areas of residual cancer can be targeted for dose escalation. The tighter and more accurate targeting enabled with MRgRT may enable hypofractionated treatment schedules.

A Novel Nomogram Model Based on Cone-Beam CT Radiomics Analysis Technology for Predicting Radiation Pneumonitis in Esophageal Cancer Patients Undergoing Radiotherapy

Purpose

We quantitatively analyzed the characteristics of cone-beam computed tomography (CBCT) radiomics in different periods during radiotherapy (RT) and then built a novel nomogram model integrating clinical features and dosimetric parameters for predicting radiation pneumonitis (RP) in patients with esophageal squamous cell carcinoma (ESCC).

Methods

At our institute, a retrospective study was conducted on 96 ESCC patients for whom we had complete clinical feature and dosimetric parameter data. CBCT images of each patient in three different periods of RT were obtained, the images were segmented using both lungs as the region of interest (ROI), and 851 image features were extracted. The least absolute shrinkage selection operator (LASSO) was applied to identify candidate radiomics features, and logistic regression analyses were applied to construct the rad-score. The optimal period for the rad-score, clinical features, and dosimetric parameters were selected to construct the nomogram model and then the receiver operating characteristic (ROC) curve was used to evaluate the prediction capacity of the model. Calibration curves and decision curves were used to demonstrate the discriminatory and clinical benefit ratios, respectively.

Results

The relative volume of total lung treated with ≥5 Gy (V5), mean lung dose (MLD), and tumor stage were independent predictors of RP and were finally incorporated into the nomogram. When the three time periods were modeled, the first period was better than the others. In the primary cohort, the area under the ROC curve (AUC) was 0.700 (95% confidence interval (CI) 0.568–0.832), and in the independent validation cohort, the AUC was 0.765 (95% CI 0.588–0.941). In the nomogram model that integrates clinical features and dosimetric parameters, the AUC in the primary cohort was 0.836 (95% CI 0.700–0.918), and the AUC in the validation cohort was 0.905 (95% CI 0.799–1.000). The nomogram model exhibits excellent performance. Calibration curves indicate a favorable consistency between the nomogram prediction and the actual outcomes. The decision curve exhibits satisfactory clinical utility.

Conclusion

The radiomics model based on early lung CBCT is a potentially valuable tool for predicting RP. V5, MLD, and tumor stage have certain predictive effects for RP. The developed nomogram model has a better prediction ability than any of the other predictors and can be used as a quantitative model to predict RP.