Effect of lung volume on helical radiotherapy in esophageal cancer: are there predictive factors to achieve acceptable lung doses?

AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions

PURPOSE

Implementing artificial intelligence technologies allows for the accurate prediction of radiation therapy dose distributions, enhancing treatment planning efficiency. However, esophageal cancers present unique challenges because of tumor complexity and diverse prescription types. Additionally, limited data availability hampers the effectiveness of existing artificial intelligence models. This study developed a deep learning model, trained on a diverse data set of esophageal cancer prescriptions, to improve dose prediction accuracy.

METHODS AND MATERIALS

We retrospectively collected data from 530 patients with esophageal cancer, including single-target and simultaneous integrated boost prescriptions, for model building. The proposed Asymmetric ResNeSt (AS-NeSt) model features novel 3-dimensional (3D) ResNeSt blocks and an asymmetrical architecture. We constructed a loss function targeting global and local doses and validated the model's performance against existing alternatives. Model-assisted experiments were used to validate its clinical benefits.

RESULTS

The AS-NeSt model maintained an absolute prediction error below 5% for each dosimetric metric. The average Dice similarity coefficient for isodose volumes was 0.93. The model achieved an average relative prediction error of 2.02%, statistically lower than Hierarchically Densely Connected U-net (4.17%), DoseNet (2.35%), and Densely Connected Network (3.65%). It also demonstrated significantly fewer parameters and shorter prediction times. Clinically, the AS-NeSt model raised physicians' ability to accurately preassess appropriate treatment methods before planning from 95.24% to 100%, reduced planning time by over 61% for junior dosimetrists and 52% for senior dosimetrists, and decreased both inter- and intra-dosimetrist discrepancies by more than 50%.

CONCLUSIONS

The AS-NeSt model, developed with innovative 3D ResNeSt blocks and an asymmetrical encoder-decoder structure, has been validated using clinical esophageal cancer patient data. It accurately predicts 3D dose distributions for various prescriptions, including simultaneous integrated boost, showing potential to improve the management of esophageal cancer treatment in a clinical setting.

Investigation of Predictors to Achieve Acceptable Lung Dose in T-Shaped Upper and Middle Esophageal Cancer With IMRT and VMAT

Purpose

The purpose of this study is to investigate whether there are predictors and cutoff points that can predict the acceptable lung dose using intensity-modulated radiation therapy (IMRT) and volume-modulated arc therapy (VMAT) in radiotherapy for upper ang middle esophageal cancer.

Material and Methods

Eighty-two patients with T-shaped upper-middle esophageal cancer (UMEC) were enrolled in this retrospective study. Jaw-tracking IMRT plan (JT-IMRT), full-arc VMAT plan (F-VMAT), and pactial-arc VMAT plan (P-VMAT) were generated for each patient. Dosimetric parameters such as MLD and V20 of total lung were compared among the three plannings. Ten factors such as PCTV_inferior length and PCTV_inferior length/total lung length were calculated to find the predictors and cutoff points of the predictors. All patients were divided into two groups according to the cutoff points, and the dosimetric differences between the two groups of the three plans were compared. ANOVA, receiver operating characteristic (ROC) analysis, and Mann–Whitney U-test were performed for comparisons between datasets. A p <0.05 was considered statistically significant.

Result

The quality of the targets of the three plannings was comparable. The total lung dose in P-VMAT was significantly lower than that in JT IMRT and F-VMAT. Monitor unit (MU) of F-VMAT and P-VMAT was significantly lower than that of JT IMRT. ROC analysis showed that among JT IMRT, F-VMAT, and P-VMAT, PCTV_i-L, and PCTV_i-L/TL_L had diagnostic power to predict the suitability of RT plans according to lung dose constraints of our department. For JT IMRT, the cutoff points of PCTV_i-L and PCTV_i-L/TL_L were 16.6 and 0.59. For F-VMAT, the cutoff points of PCTV_i-L and PCTV_i-L/TL_L were 16.75 and 0.62. For P-VMAT, the cutoff points of PCTV_i-L and PCTV_i-L/TL_L were 15.15 and 0.59. After Mann–Whitney U-test analysis, it was found that among the three plannings, the group with lower PCTV_i-L and PCTV_i-L/TL_L could significantly reduce the dose of total lung and heart (p <0.05).

Conclusion

PCTV_{i-L <}16.6 and PCTV_i-L/TL_L <0.59 for JT IMRT, PCTV_{i-L <}16.75 and PCTV_i-L/TL_L <0.62 for F-VMAT and PCTV_{i-L <}15.15, and PCTV_i-L/TL_L <0.59 for P-VMAT can predict whether patients with T-shaped UMEC can meet the lung dose limits of our department.

Regression models for predicting physical and EQD2 plan parameters of two methods of hybrid planning for stage III NSCLC

Background/purpose

To establish regression models of physical and equivalent dose in 2 Gy per fraction (EQD₂) plan parameters of two kinds of hybrid planning for stage III NSCLC.

Methods

Two kinds of hybrid plans named conventional fraction radiotherapy & stereotactic body radiotherapy (C&S) and conventional fraction radiotherapy & simultaneous integrated boost (C&SIB) were retrospectively made for 20 patients with stage III NSCLC. Prescription dose of C&S plans was 2 Gy × 30f for planning target volume of lymph node (PTV_LN) and 12.5 Gy × 4f for planning target volume of primary tumor (PTV_PT), while prescription dose of C&SIB plans was 2 Gy × 26f for PTV_LN and sequential 2 Gy × 4f for PTV_LN combined with 12.5 Gy × 4f for PTV_PT. Regression models of physical and EQD₂ plan parameters were established based on anatomical geometry features for two kinds of hybrid plans. The features were mainly characterized by volume ratio, min distance and overlapping slices thickness of two structures. The possibilities of regression models of EQD₂ plan parameters were verified by spearman’s correlation coefficients between physical and EQD₂ plan parameters, and the influence on the consistence of fitting goodness between physical and EQD₂ models was investigated by the correlations between physical and EQD₂ plan parameters. Finally, physical and EQD₂ models predictions were compared with plan parameters for two new patients.

Results

Physical and EQD₂ plan parameters of PTV_LN CI_60Gy have shown strong positive correlations with PTV_LN volume and min distance_{(PT to LN)}, and strong negative correlations with PTV_PT volume for two kinds of hybrid plans. PTV_(PT+LN) CI_60Gy is not only correlated with above three geometry features, but also negatively correlated with overlapping slices thickness_{(PT and LN)}. When neck lymph node metastasis was excluded from PTV_LN volume, physical and EQD₂ total lung V₂₀ showed a high linear correlation with corrected volume ratio_{(LN to total lung).} Meanwhile, physical total lung mean dose (MLD) had a high linear correlation with corrected volume ratio_{(LN to total lung)}, while EQD₂ total lung MLD was not only affected by corrected volume ratio_{(LN to total lung)} but also volume ratio_{(PT to total lung).} Heart D₅, D₃₀ and mean dose (MHD) would be more susceptible to overlapping structure_{(heart and LN)}. Min distance_{(PT to ESO)} may be an important feature for predicting EQD₂ esophageal max dose for hybrid plans. It’s feasible for regression models of EQD₂ plan parameters, and the consistence of the fitting goodness of physical and EQD₂ models had a positive correlation with spearman’s correlation coefficients between physical and EQD₂ plan parameters. For total lung V₂₀, ipsilateral lung V₂₀, and ipsilateral lung MLD, the models could predict that C&SIB plans were higher than C&S plans for two new patients.

Conclusion

The regression models of physical and EQD₂ plan parameters were established with at least moderate fitting goodness in this work, and the models have a potential to predict physical and EQD₂ plan parameters for two kinds of hybrid planning.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01848-9.