Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

Esophageal cancer is a thoracic cancer for which radiotherapy (RT) is an effective treatment [1]. Radiation pneumonitis (RP) is one of the side effects of thoracic radiation therapy, occurring in patients whose lungs have been irradiated during treatments for malignancy. RP usually develops in the first 6 months after irradiation. Depending on its severity, the symptoms of RP include dyspnea, nonproductive cough and hypoxemia requiring supplemental oxygen. Careful consideration of dose to the lung must be made to limit the occurence of RP which affect radiation planning.

Machine learning (ML) models can be developed as normal tissue complication probability (NTCP) models to predict toxicity arising from radiation. Dosimetric factors extracted from DVH and patient factors such as comorbidity, age, and history of chemotherapy are commonly used as features in predictive models for RP after treatment [2‐4]. Recently, texture analysis (TA) approaches, such as radiomics, have emerged that can extract latent information from medical images and improve the performance of predictive models in the field of radiation oncology. TA has also been applied to dose distribution in radiotherapy, referred to as dosiomics. Several researchers have reported improved toxicity prediction performance after radiation therapy by dosiomics [5‐7], including for RP [8‐10]. However, studies on dosiomics as features for the prediction of RP were performed in lung cancer patients. Due to differences in dose distribution to the lung and other confounding factors in lung cancer that are associated with the risk of RP, such as tumor location [11] and changes in gross tumor volume (GTV) during treatment, a model developed for patients lung cancer may not be applicable to esophageal cancer patients.

Fraction size is an important factor in radiation therapy. The impact of fraction size can be illustrated using the well-known linear quadratic (LQ) model. Fraction size has been shown to be a significant factor of RP [12‐14]. However, previous studies using dosiomic features for the prediction of RP did not consider the effect of fraction size.

The purpose of this study was to develop a model with DVH and dosiomic features to predict the risk of RP in esophageal cancer patients treated with radiation therapy and to compare the performance of DVH and dosiomic features with or without consideration of the fraction size effect by correcting the dose distribution to the equivalent dose in 2 Gy (EQD2).

The distribution of RP grade was showed in Additional file 1: Table S3. Due to the RT modality can affect the texture of dose distribution, the association between RT modality and RP for RP grade 0 and grade ≥ 1 was test by cri-square. The result showed that there are no association between RT modality and RP (p value 0.89). This result was consistent to previous study which no benefits of IMRT over 3D conformal technique in reduction of RP [29].

In the model building process, dosiomic features were progressively eliminated due to the high correlation among features. The remaining number of features were 20 and 24 in the DO and DOEQD2, respectively, and these features were used to the train logistic regression model (Additional file 1: Table S2).

The mean of the ROC curve is provided in Additional file 1: Fig. S1. The AUCs of the models with DVH, DVHEQD2, DO and DOEQD2 were 0.66 ± 0.11, 0.66 ± 0.11, 0.70 ± 0.11 and 0.71 ± 0.11, respectively (Table 2). The data showed that there was no significant difference observed in the performance when correcting dose distribution to EQD2 in either model (DVH model and DVHEQD2 model (p value = 0.56), DO model and DOEQD2 model (p value = 0.73)). Both dosiomic models performed better than the dosimetric-based models (p value < 0.01).

Features that were selected in more than 50% of all groups are shown in Fig. 2. The median odds ratios (ORs) with 10th–90th ORs of the features that were selected in more than 50% of the groups are shown in Table 2. Features that were selected in more than 50% in the DVH and DVHEQD2 groups were V40 and V45 and V35 and V40, respectively. Busyness from NTGDM and the 90th percentile were selected in the dosiomic-based group. Other features that were selected for more than 50% in DO were LongRunHighGrayLevelEmphasis (LRHGLE) from GLRLM and LowGrayLevelZoneEmphasis (LGLZE) from GLSZM in DOEQD2. We further investigated the correlation between features with and without correction to EQD2 of the selected features. The correlations among the most selected features in DVH, DVHEQD2, DO and DOEQD2 are shown in Additional file 1: Figs. S2, S3, S4, S5, S6, S7 and S8. Most of the selected features had a high CC (CC ≥ 0.9) between features with and without correction to EQD2. Only LGLZE had a CC of 0.73.

To the best of our knowledge, our study is the first in dosiomics to analyze radiation delivered per fraction instead of the sum of all fractions and to investigate the effect of EQD2 on DVH and dosiomic features. In other studies, the dose per fraction per voxel of the predictive model of IMRT, VMAT and stereotactic body radiation therapy (SBRT) was usually the same for all fractions. If the fraction size is the same for all fractions, the total dose can simply be divided by the number of fractions to obtain the fraction size per voxel. However, our data include 3D conformal techniques that can involve several beam arrangements and treatment phases for different fractions depending on the clinical judgment of the radiation oncologist.

In the DVH and DVHEQD2 models, the most selected features were V35, V40 and V45. The difference in selected features between DVH and DVHEQD2 can be explained by correction of the dose to EQD2, resulting in a lower dose. The overall values of the features are shown in Additional file 1: Figs. S2, S3 and S4. Previous studies usually indicate the importance of V20 [30‐34] and MLD [30, 32, 33, 35‐37] as predictors of RP. However, our study revealed that more in than 50% of the reads, V35, V40 and V45 (Fig. 2) were selected. The results of a previous study of patients treated with CCRT with the 3D conformal technique suggested that V30 is a predictive factor for RP grade ≥ 2 [31] in lung cancer patients. Although V30 was not among the most selected features in our study, this might be because the grade of RP was different, and in [31], Vx other than V20 and V30 were not investigated. Another study also revealed V40 to be an important factor, but the end point was severe RP (RP ≥ 3) [33] in lung cancer patients. In studies of esophageal cancer patients, V30 has been shown to be significantly correlated with acute RP (RP occurs within 3 months post radiation therapy) with 3D conformal techniques [34]. These researchers investigated V50 but did not find a significant correlation with RP; however, V35, V40 and V45 were not investigated. In another study of esophageal cancer patients treated with CCRT by 3D conformal technique, V5–V60 were analyzed and none of these factors were found to be associated with RP grade ≥ 2 [38].

In our study, the two most selected dosiomic features in the DO and DOEQD2 groups were Busyness in the NTGDM and the 90th percentile of dose (Fig. 2). The third most selected feature in DO and DOEQD2 was different (LRHGLE in DO and LGLZE in DOEQD2). This was because in DOEQD2, LRHGLE was eliminated due to its high correlation with other features, while LRHGLE in DO was not eliminated (Additional file 1: Table S1). Two prior studies in dosiomic features and RP reported the most important dosiomic features in GLCM group. Contrast from GLCM was found to be the most predictive feature of RP grade ≥ 2 in non-small cell lung cancer (NSCLC) treated with VMAT [8]. Correlation with GLCM extracted from wavelet transformation of dose imaging in lung regions with a dose greater than 5 Gy was found to be a significant feature in a predictive model of RP grade ≥ 2 in early-stage NSCLC treated with SBRT [9]. Another study in dosiomics were analyzed reported the use of a combination of 8 features in the GLSZM group and 1 feature in the GLCM group, which resulted in the best performance for the prediction of late RP (RP grade ≥ 2 developed 6 months after the start of RT) in lung cancer treated with VMAT. However, we did not find any feature that was selected for more than 50% in the GLCM group and found one only feature in the GLSZM group. These results are different from those of previous studies (Fig. 2). This might be due to the difference in the nature of the dose distribution and disease of the patients studied, since in our dataset, most of the patients were treated by the 3D conformal technique, and the study was performed in esophageal cancer patients.

The most selected feature in the dosiomic-based model was busyness. Busyness indicates the spatial frequency of intensity changes [39]. In dose distribution, busyness can be interpreted as an intensity change in radiation dose. From our result, busyness is negatively correlated with RP. Thus, a low spatial frequency change in radiation dose intensity is correlated with RP. The second most selected feature in the dosiomic-based model is the 90th percentile. By estimating parameters from the Lyman–Kutcher–Burman (LKB) model, the lungs have usually been interpreted as parallel organs [13, 23]. In parallel organs, the 90th percentile of the dose (one “hot” spot) in the dose distribution might not be a good feature. However, in this study, we predicted RP grade 1, which may manifest only in the form of local changes in radiographic images. Therefore, the lungs can be viewed as a series organ in this case.

Previous studies demonstrated that the use of dosiomic features can improve the performance of predictive models in RP [8‐10]. In our study, dosiomic features also showed significant improvement from dosimetric features. We also further investigated the performance of features with and without correction to EQD2. The results showed that no significant improvement from DVH to DVHEQD2 and DO to DOEQD2 could be found. Previous research studies of biological dose accumulation and conventional accumulation dose showed that the difference between biological dose accumulation and conventional accumulation dose increases depending on daily variation of dose in theoretical prediction [15]. Their results indicated that the biological dose would be unlikely to affect the dose–response model, which was based on DVH in standard fractionation. In other words, this indicates there is no impact on the outcome whether we accumulate the dose by biological effect or not. Their results might explain why our DVH and DVHEQD2 models were not different. For dosiomic features, our results did not show a significant difference in performance between DO and DOEQD2. However, the results showed differences in the most selected features (Fig. 2). By looking at the CC of the selected features between DO and DOEQD2, some dosiomic features (LGLZE) had lower a CC. This may lead to different selected features between DO and DOEQD2. Although we did not find any difference in performance between the DO and DOEQD2 models, we recommend correction of dose to biological dose due to potential differences in the selected features. Since the cell survival from the LQ model is nonlinear, correcting the dose to EQD2 (or other biological doses) could improve validity over using dose with linear summation. As previous studies reported a correlation between fraction size and RP [12‐14], we can extract more information about dose distribution for predicting RP if we correct the dose distribution first.

We believe that our data reflect the population of esophageal cancer patients since patient characteristic (age, sex and tumor stage) were similar to other study of esophageal cancer [27, 29, 37]. The incident of RP grade ≥ 2 in esophageal cancer was report by systematic review about 6.6% which similar to our study (5%), although information for grade 1 RP were lacking. Patients with history of interstitial lung disease (ILD) in our dataset were excluded because it was difficult to differentiate between ILD and radiation pneumonitis (RP). The treatment of ILD and RP are also similar (prescription of steroid) which can lead to misclassification. The majority of patient in our dataset were treated with 3D conformal technique which may limit generalization to other RT technique (e.g. IMRT). For patient with dose distribution and consistent number of beams throughout all fraction, the result of the predictive model should not much be difference since the performance of dosiomic model were the same with or without correction of dose distribution to EQD2.

The limitation of this study was the paucity of RP events requiring steroids (5 patients), which corresponded to RP grade ≥ 2. Therefore, the prediction in this study was performed primarily on RP grade ≥ 1, which might limit its usefulness in actual clinical practice, though the model of RP grade ≥ 1 can be used for ruling out RP grade ≥ 2 as well which could aid in some clinical decision. In a previous study, it was suggested that the impact of biological dose on the DVH parameter may be higher with the hypofraction regimen [15]. The second limitation was that in our study, most data were of standard fraction size. The effect of biological dose with the hypofraction regimen on dosiomic features should be investigated.

In this study, we demonstrated the potential of dosiomic features that can improve the performance of a predictive model of radiation pneumonitis grade ≥ 1 in esophageal cancer patients in comparison with that obtained with a DVH-based model. We also confirmed the benefit of dosiomic features in the prediction of RP in 3D conformal RT. Correction of dose per fraction to EQD2 did not improve the performance of the predictive model in standard fractionation. However, we recommend correcting the dose to the biological dose for dose-related analysis. Although this model may not be useful in clinical situations due to the limitation of training data, the insight gained may prove useful in further endeavors to develop a predictive model of RP for use in radiation treatment planning.