Dosimetric Factors and Radiomics Features Within Different Regions of Interest in Planning CT Images for Improving the Prediction of Radiation Pneumonitis

Synergizing the interaction of single nucleotide polymorphisms with dosiomics features to build a dual-omics model for the prediction of radiation pneumonitis

OBJECTIVE

Radiation pneumonitis (RP) is the major dose-limiting toxicity of thoracic radiotherapy. This study aimed to developed a dual-omics (single nucleotide polymorphisms, SNP and dosiomics) prediction model for symptomatic RP.

MATERIALS AND METHODS

The potential SNPs, which are of significant difference between the RP grade ≥ 3 group and the RP grade ≤ 1 group, were selected from the whole exome sequencing SNPs using the Fisher's exact test. Patients with lung cancer who received thoracic radiotherapy at our institution from 2009 to 2016 were enrolled for SNP selection and model construction. The factorization machine (FM) method was used to model the SNP epistasis effect, and to construct the RP prediction model (SNP-FM). The dosiomics features were extracted, and further selected using the minimum redundancy maximum relevance (mRMR) method. The selected dosiomics features were added to the SNP-FM model to construct the dual-omics model.

RESULTS

For SNP screening, peripheral blood samples of 28 patients with RP grade ≥ 3 and the matched 28 patients with RP grade ≤ 1 were sequenced. 81 SNPs were of significant difference (P < 0.015) and considered as potential SNPs. In addition, 21 radiation toxicity related SNPs were also included. For model construction, 400 eligible patients (including 108 RP grade ≥ 2) were enrolled. Single SNP showed no strong correlation with RP. On the other hand, the SNP-SNP interaction (epistasis effect) of 19 SNPs were modeled by the FM method, and achieved an area under the curve (AUC) of 0.76 in the testing group. In addition, 4 dosiomics features were selected and added to the model, and increased the AUC to 0.81.

CONCLUSIONS

A novel dual-omics model by synergizing the SNP epistasis effect with dosiomics features was developed. The enhanced the RP prediction suggested its promising clinical utility in identifying the patients with severe RP during thoracic radiotherapy.

Improvement of Prediction Performance for Radiation Pneumonitis by Using 3-Dimensional Dosiomic Features

INTRODUCTION

Patients with early non-small-cell lung cancer (NSCLC) have a relatively long survival time after stereotactic body radiation therapy (SBRT). Predicting radiation-induced pneumonia (RP) has important clinical and social implications for improving the quality of life of such patients. This study developed an RP prediction model by using 3-dimensional (3D) dosiomic features. The model can be used to guide radiation therapy to reduce toxicity.

METHODS

Radiomic features were extracted from pre-treatment CT, dose-volume histogram (DVH) parameters and dosiomic features were extracted from the 3D dose distribution of 140 lung cancer patients. Four predictive models: (1) CT; (2) CT + DVH; (3) CT + Rtdose; and (4) Hybrid, CT + DVH + Rtdose, were trained to predict symptomatic RP by extremely randomized trees. Accuracy, sensitivity, specificity, and area under the receiver operator characteristic curve were evaluated.

RESULT

Results showed that the fraction regimen was correlated with symptomatic RP (P < .001). The proposed model achieved promising prediction results. The performance metrics for CT, CT + DVH, CT + Rtdose, and Hybrid were as follows: accuracy: 0.786, 0.821, 0.821, and 0.857; sensitivity: 0.625, 1, 0.875, and 1; specificity: 0.8, 0.565, 0.5, and 0.875; and area under the receiver operator characteristic curve: 0.791, 0.809, 0.907, and 0.920, respectively.

CONCLUSION

Dosiomic features can improve the performance of the predictive model for symptomatic RP compared with that obtained with the CT + DVH model. The model proposed in this study can help radiation oncologists individually predict the incidence rate of RP.

Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study

OBJECTIVE

This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients.

MATERIALS AND METHODS

The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value.

RESULTS

The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05).

CONCLUSION

Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients.

CLINICAL RELEVANCE STATEMENT

Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.

Characterisation of quantitative imaging biomarkers for inflammatory and fibrotic radiation-induced lung injuries using preclinical radiomics

BACKGROUND AND PURPOSE

Radiomics is a rapidly evolving area of research that uses medical images to develop prognostic and predictive imaging biomarkers. In this study, we aimed to identify radiomics features correlated with longitudinal biomarkers in preclinical models of acute inflammatory and late fibrotic phenotypes following irradiation.

MATERIALS AND METHODS

Female C3H/HeN and C57BL6 mice were irradiated with 20 Gy targeting the upper lobe of the right lung under cone-beam computed tomography (CBCT) image-guidance. Blood samples and lung tissue were collected at baseline, weeks 1, 10 & 30 to assess changes in serum cytokines and histological biomarkers. The right lung was segmented on longitudinal CBCT scans using ITK-SNAP. Unfiltered and filtered (wavelet) radiomics features (n = 842) were extracted using PyRadiomics. Longitudinal changes were assessed by delta analysis and principal component analysis (PCA) was used to remove redundancy and identify clustering. Prediction of acute (week 1) and late responses (weeks 20 & 30) was performed through deep learning using the Random Forest Classifier (RFC) model.

RESULTS

Radiomics features were identified that correlated with inflammatory and fibrotic phenotypes. Predictive features for fibrosis were detected from PCA at 10 weeks yet overt tissue density was not detectable until 30 weeks. RFC prediction models trained on 5 features were created for inflammation (AUC 0.88), early-detection of fibrosis (AUC 0.79) and established fibrosis (AUC 0.96).

CONCLUSIONS

This study demonstrates the application of deep learning radiomics to establish predictive models of acute and late lung injury. This approach supports the wider application of radiomics as a non-invasive tool for detection of radiation-induced lung complications.

Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation

OBJECTIVES

Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction.

MATERIALS AND METHODS

Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds.

RESULTS

The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose.

CONCLUSIONS

Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.

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.

Distinguishing immune checkpoint inhibitor-related pneumonitis from radiation pneumonitis by CT radiomics features in non-small cell lung cancer

PURPOSE

To develop a CT-based model to classify pneumonitis etiology in patients with non-small cell lung cancer(NSCLC) after radiotherapy(RT) and Immune checkpoint inhibitors(ICIs).

METHODS

We retrospectively identified 130 NSCLC patients who developed pneumonitis after receipt of ICIs only (n = 50), thoracic RT only (n = 50) (ICIs only + thoracic RT only, the training cohort, n = 100), and RT + ICIs (the test cohort, n = 30). Clinical and CT radiomics features were described and compared between different groups. We constructed a random forest (RF) classifier and a linear discriminant analysis (LDA) classifier by CT radiomics to discern pneumonitis etiology.

RESULTS

The patients in RT + ICIs group have more high grade (grade 3-4) pneumonitis compared to patients in ICIs only or RT only group (p < 0.05). Pneumonitis after the combined therapy was not a simple superposition mode of RT-related pneumonitis(RP) and ICI-related pneumonitis(CIP), resulting in the distinct characteristics of both RT and ICIs-related pneumonitis. The RF classifier showed favorable discrimination between RP and CIP with an area under the receiver operating curve (AUC) of 0.859 (95 %CI: 0.788-0.929) in the training cohort and 0.851 (95 % CI: 0.700-1) in the test cohort. The LDA classifier achieved an AUC of 0.881 (95 %CI: 0.815-0.947) in the training cohort and 0.842 (95 %CI: 0.686-0.997) in the test cohort. Our analysis revealed four principal CT-based features shared across both models:original_glrlm_LongRunLowGrayLevelEmphasis, wavelet-HLL_firstorder_Median, wavelet-LLL_ngtdm_Busyness， and wavelet-LLL_glcm_JointAverage.

CONCLUSION

CT radiomics-based classifiers could provide a noninvasive method to identify the predominant etiology in NSCLC patients who developed pneumonitis after RT alone, ICIs alone or RT + ICIs.

Integration of dosimetric parameters, clinical factors, and radiomics to predict symptomatic radiation pneumonitis in lung cancer patients undergoing combined immunotherapy and radiotherapy

PURPOSE

This study aimed to combine clinical/dosimetric factors and handcrafted/deep learning radiomic features to establish a predictive model for symptomatic (grade ≥ 2) radiation pneumonitis (RP) in lung cancer patients who received immunotherapy followed by radiotherapy.

MATERIALS AND METHODS

This study retrospectively collected data of 73 lung cancer patients with prior receipt of ICIs who underwent thoracic radiotherapy (TRT). Of these 73 patients, 41 (56.2 %) developed symptomatic grade ≥ 2 RP. RP was defined per multidisciplinary clinician consensus using CTCAE v5.0. Regions of interest (ROIs) (from radiotherapy planning CT images) utilized herein were gross tumor volume (GTV), planning tumor volume (PTV), and PTV-GTV. Clinical/dosimetric (mean lung dose and V5-V30) parameters were collected, and 107 handcrafted radiomic (HCR) features were extracted from each ROI. Deep learning-based radiomic (DLR) features were also extracted based on pre-trained 3D residual network models. HCR models, Fusion HCR model, Fusion HCR + ResNet models, and Fusion HCR + ResNet + Clinical models were built and compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC). Five-fold cross-validation was performed to avoid model overfitting.

RESULTS

HCR models across various ROIs and the Fusion HCR model showed good predictive ability with AUCs from 0.740 to 0.808 and 0.740-0.802 in the training and testing cohorts, respectively. The addition of DLR features improved the effectiveness of HCR models (AUCs from 0.826 to 0.898 and 0.821-0.898 in both respective cohorts). The best performing prediction model (HCR + ResNet + Clinical) combined HCR & DLR features with 7 clinical/dosimetric characteristics and achieved an average AUC of 0.936 and 0.946 in both respective cohorts.

CONCLUSIONS

In patients undergoing combined immunotherapy/RT for lung cancer, integrating clinical/dosimetric factors and handcrafted/deep learning radiomic features can offer a high predictive capacity for RP, and merits further prospective validation.

Dosiomics and radiomics improve the prediction of post-radiotherapy neutrophil-lymphocyte ratio in locally advanced non-small cell lung cancer

PURPOSE

To develop and validate a dosiomics and radiomics model based on three-dimensional (3D) dose distribution map and computed tomography (CT) images for the prediction of the post-radiotherapy (post-RT) neutrophil-to-lymphocyte ratio (NLR).

METHODS

This work retrospectively collected 242 locally advanced non-small cell lung cancer (LA-NSCLC) patients who were treated with definitive radiotherapy from 2012 to 2016. The NLR collected one month after the completion of RT was defined as the primary outcome. Clinical characteristics and two-dimensional dosimetric factors calculated from the dose-volume histogram (DVH) were included. A total of 4165 dosiomics and radiomics features were extracted from the 3D dose maps and CT images within five different anatomical regions of interest (ROIs), respectively. Then, a three-step feature selection method was proposed to progressively filter features from coarse to fine: (i) model-based ranking according to individual feature's performance, (ii) maximum relevance and minimum redundancy (mRMR), (iii) select from model based on feature importance calculated with an ensemble of several decision trees. The selected feature subsets were utilized to develop the prediction model with GBDT. All patients were divided into a development set and an independent testing set (2:1). Five-fold cross-validation was applied to the development set for both feature selection and model training procedure. Finally, a fusion model combining dosiomics, radiomics and clinical features was constructed to further improve the prediction results. The area under receiver operating characteristic curve (ROC) were used to evaluate the model performance.

RESULTS

The clinical-based and DVH-based models showed limited predictive power with AUCs of 0.632 (95% CI: 0.490-0.773) and 0.634 (95% CI: 0.497-0.771), respectively, in the independent testing set. The 9 feature-based dosiomics and 3 feature-based radiomics models showed improved AUCs of 0.738 (95% CI: 0.628-0.849) and 0.689 (95% CI: 0.566-0.813), respectively. The dosiomics & radiomics & clinical fusion model further improved the model's generalization ability with an AUC of 0.765 (95% CI: 0.656-0.874).

CONCLUSIONS

Dosiomics and radiomics can benefit the prediction of post-RT NLR of LA-NSCLC patients. This can provide a reference for evaluating radiotherapy-related inflammation.

Research for accurate auxiliary diagnosis of lung cancer based on intracellular fluorescent fingerprint information

The distinctions in pathological types and genetic subtypes of lung cancer have a direct impact on the choice of treatment choices and clinical prognosis in clinical practice. This study used pathological histological sections of surgically removed or biopsied tumor tissue from 36 patients. Based on a small sample size, millions of spectral data points were extracted to investigate the feasibility of employing intracellular fluorescent fingerprint information to diagnose the pathological types and mutational status of lung cancer. The intracellular fluorescent fingerprint information revealed the EGFR gene mutation characteristics in lung cancer, and the area under the curve (AUC) value for the optimal model was 0.98. For the classification of lung cancer pathological types, the macro average AUC value for the ensemble-learning model was 0.97. Our research contributes new idea for pathological diagnosis of lung cancer and offers a quick, easy, and accurate auxiliary diagnostic approach.

Radiation hematologic toxicity prediction for locally advanced rectal cancer using dosimetric and radiomics features

BACKGROUND

Hematologic toxicity (HT) is a common adverse tissue reaction during radiotherapy for rectal cancer patients, which may lead to various negative effects such as reduced therapeutic effect, prolonged treatment period and increased treatment cost. Therefore, predicting the occurrence of HT before radiotherapy is necessary but still challenging.

PURPOSE

This study proposes a hybrid machine learning model to predict the symptomatic radiation HT in rectal cancer patients using the combined demographic, clinical, dosimetric, and Radiomics features, and ascertains the most effective regions of interest (ROI) in CT images and predictive feature sets.

METHODS

A discovery dataset of 240 rectal cancer patients, including 145 patients with HT symptoms and a validation dataset of 96 patients (63 patients with HT) with different dose prescription were retrospectively enrolled. Eight ROIs were contoured on patient CT images to derive Radiomics features, which were then, respectively, combined with the demographic, clinical, and dosimetric features to classify patients with HT symptoms. Moreover, the survival analysis was performed on risky patients with HT in order to understand the HT progression.

RESULTS

The classification models in ROIs of bone marrow and femoral head exhibited relatively high accuracies (accuracy = 0.765 and 0.725) in the discovery dataset as well as comparable performances in the validation dataset (accuracy = 0.758 and 0.714). When combining the two ROIs together, the model performance was the best in both discovery and validation datasets (accuracy = 0.843 and 0.802). In the survival analysis test, only the bone marrow ROI achieved statistically significant performance in accessing risky HT (C-index = 0.658, P = 0.03). Most of the discriminative features were Radiomics features, and only gender and the mean dose in Irradvolume was involved in HT.

CONCLUSION

The results reflect that the Radiomics features of bone marrow are significantly correlated with HT occurrence and progression in rectal cancer. The proposed Radiomics-based model may help the early detection of radiotherapy induced HT in rectal cancer patients and thus improve the clinical outcome in future.

Identification and validation of radiomic features from computed tomography for preoperative classification of neuroblastic tumors in children

BACKGROUND

To identify radiomic features that can predict the pathological type of neuroblastic tumor in children.

METHODS

Data on neuroblastic tumors in 104 children were retrospectively analyzed. There were 14 cases of ganglioneuroma, 24 cases of ganglioneuroblastoma, and 65 cases of neuroblastoma. Stratified sampling was used to randomly allocate the cases into the training and validation sets in a ratio of 3:1. The maximum relevance-minimum redundancy algorithm was used to identify the top 10 of two clinical features and 851 radiomic features in portal venous-phase contrast-enhanced computed tomography images. Least absolute shrinkage and selection operator regression was used to classify tumors in two binary steps: first as ganglioneuroma compared to the other two types, then as ganglioneuroblastoma compared to neuroblastoma.

RESULTS

Based on 10 clinical-radiomic features, the classifier identified ganglioneuroma compared to the other two tumor types in the validation dataset with sensitivity of 100.0%, specificity of 81.8%, and an area under the receiver operating characteristic curve (AUC) of 0.875. The classifier identified ganglioneuroblastoma versus neuroblastoma with a sensitivity of 83.3%, a specificity of 87.5%, and an AUC of 0.854. The overall accuracy of the classifier across all three types of tumors was 80.8%.

CONCLUSION

Radiomic features can help predict the pathological type of neuroblastic tumors in children.

A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome

PURPOSE

To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival.

METHODS

This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centres. They were divided into training (n = 175) and validation cohorts (n = 24). The radiomics, dosiomics and clinical features were extracted from planning CT and electronic medical records and were analysed by LASSO Cox regression. A multiomics prediction model was developed by the optimal algorithm. Overall survival (OS) between the RP, non-RP, mild RP, and severe RP groups was analysed by the Kaplan‒Meier method.

RESULTS

Sixteen radiomics features, two dosiomics features, and one clinical feature were selected to build the best multiomics model. The optimal performance for predicting RP was the area under the receiver operating characteristic curve (AUC) of the testing set (0.94) and validation set (0.92). The RP patients were divided into mild (≤ 2 grade) and severe (> 2 grade) RP groups. The median OS was 31 months for the non-RP group compared with 49 months for the RP group (HR = 0.53, p = 0.0022). Among the RP subgroup, the median OS was 57 months for the mild RP group and 25 months for the severe RP group (HR = 3.72, p < 0.0001).

CONCLUSIONS

The multiomics model contributed to improving the accuracy of RP prediction. Compared with the non-RP patients, the RP patients displayed longer OS, especially the mild RP patients.

Virtual clinical trial based on outcome modeling with iteratively redistributed extrapolation data

Virtual clinical trials (VCTs) can potentially simulate clinical trials on a computer, but their application with a limited number of past clinical cases is challenging due to the biased estimation of the statistical population. In this study, we developed ExMixup, a novel training technique based on machine learning, using iteratively redistributed extrapolated data. Information obtained from 100 patients with prostate cancer and 385 patients with oropharyngeal cancer was used to predict the recurrence after radiotherapy. Model performance was evaluated by developing outcome prediction models based on three types of training methods: training with original data (baseline), interpolation data (Mixup), and interpolation + extrapolation data (ExMixup). Two types of VCTs were conducted to predict the treatment response of patients with distinct characteristics compared to the training data obtained from patient cohorts categorized under risk classification or cancer stage. The prediction models developed with ExMixup yielded concordance indices (95% confidence intervals) of 0.751 (0.719-0.818) and 0.752 (0.734-0.785) for VCTs on the prostate and oropharyngeal cancer datasets, respectively, which significantly outperformed the baseline and Mixup models (P < 0.01). The proposed approach could enhance the ability of VCTs to predict treatment results in patients excluded from past clinical trials.

Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition

Introduction

Pneumonitis is a relevant side effect after radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICIs). Since the effect is radiation dose dependent, the risk increases for high fractional doses as applied for stereotactic body radiation therapy (SBRT) and might even be enhanced for the combination of SBRT with ICI therapy. Hence, patient individual pre-treatment prediction of post-treatment pneumonitis (PTP) might be able to support clinical decision making. Dosimetric factors, however, use limited information and, thus, cannot exploit the full potential of pneumonitis prediction.

Methods

We investigated dosiomics and radiomics model based approaches for PTP prediction after thoracic SBRT with and without ICI therapy. To overcome potential influences of different fractionation schemes, we converted physical doses to 2 Gy equivalent doses (EQD2) and compared both results. In total, four single feature models (dosiomics, radiomics, dosimetric, clinical factors) were tested and five combinations of those (dosimetric+clinical factors, dosiomics+radiomics, dosiomics+dosimetric+clinical factors, radiomics+dosimetric+clinical factors, radiomics+dosiomics+dosimetric+clinical factors). After feature extraction, a feature reduction was performed using pearson intercorrelation coefficient and the Boruta algorithm within 1000-fold bootstrapping runs. Four different machine learning models and the combination of those were trained and tested within 100 iterations of 5-fold nested cross validation.

Results

Results were analysed using the area under the receiver operating characteristic curve (AUC). We found the combination of dosiomics and radiomics features to outperform all other models with AUCradiomics+dosiomics, D = 0.79 (95% confidence interval 0.78-0.80) and AUCradiomics+dosiomics, EQD2 = 0.77 (0.76-0.78) for physical dose and EQD2, respectively. ICI therapy did not impact the prediction result (AUC ≤ 0.5). Clinical and dosimetric features for the total lung did not improve the prediction outcome.

Conclusion

Our results suggest that combined dosiomics and radiomics analysis can improve PTP prediction in patients treated with lung SBRT. We conclude that pre-treatment prediction could support clinical decision making on an individual patient basis with or without ICI therapy.

Machine Learning-Based Multiomics Prediction Model for Radiation Pneumonitis

Objective

The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics.

Materials and Methods

The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD₂) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models.

Results

Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant (p < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant (p > 0.05).

Conclusion

The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.

Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy

This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.

Longitudinal analyses and predictive factors of radiation-induced lung toxicity-related parameters after stereotactic radiotherapy for lung cancer

BACKGROUND AND PURPOSE

The purpose of this prospective study was to investigate changes in longitudinal parameters after stereotactic radiotherapy for lung cancer and to identify possible pretreatment factors related to radiation-induced lung toxicity and the decline in pulmonary function after radiotherapy.

MATERIALS AND METHODS

Protocol-specified examinations, including 4-D CT, laboratory tests, pulmonary function tests (PFTs) and body composition measurements, were performed before SRT and at 1 month, 4 months and 12 months after stereotactic radiotherapy. Longitudinal differences were tested by using repeated-measures analysis of variance. Correlations were examined by using the Pearson product-moment correlation coefficient (r).

RESULTS

Sixteen patients were analyzed in this study. During a median follow-up period of 26.6 months, grade 1 and 2 lung toxicity occurred in 11 patients and 1 patient, respectively. The mean Hounsfield units (HU) and standard deviation (SD) of the whole lung, as well as sialylated carbohydrate antigen KL-6 (KL-6) and surfactant protein-D (SP-D), peaked at 4 months after radiotherapy (p = 0.11, p<0.01, p = 0.04 and p<0.01, respectively). At 4 months, lung V20 Gy (%) and V40 Gy (%) were correlated with changes in SP-D, whereas changes in the mean HU of the lung were related to body mass index and lean body mass index (r = 0.54, p = 0.02; r = 0.57, p = 0.01; r = 0.69, p<0.01; and r = 0.69, p<0.01, respectively). The parameters of PFTs gradually declined over time. When regarding the change in PFTs from pretreatment to 12 months, lung V5 Gy (cc) showed significant correlations with diffusion capacity for carbon monoxide (DLCO), DLCO/alveolar volume and the relative change in DLCO (r = -0.72, p<0.01; r = -0.73, p<0.01; and r = -0.63, p = 0.01, respectively).

CONCLUSIONS

The results indicated that some parameters peaked at 4 months, but PFTs were the lowest at 12 months. Significant correlations between lung V5 Gy (cc) and changes in DLCO and DLCO/alveolar volume were observed.

Risk analysis of grade ≥ 2 radiation pneumonitis based on radiotherapy timeline in stage III/IV non-small cell lung cancer treated with volumetric modulated arc therapy: a retrospective study

Background

Radiotherapy is an important treatment for patients with stage III/IV non-small cell lung cancer (NSCLC), and due to its high incidence of radiation pneumonitis, it is essential to identify high-risk people as early as possible. The present work investigates the value of the application of different phase data throughout the radiotherapy process in analyzing risk of grade ≥ 2 radiation pneumonitis in stage III/IV NSCLC. Furthermore, the phase data fusion was gradually performed with the radiotherapy timeline to develop a risk assessment model.

Methods

This study retrospectively collected data from 91 stage III/IV NSCLC cases treated with Volumetric modulated arc therapy (VMAT). Patient data were collected according to the radiotherapy timeline for four phases: clinical characteristics, radiomics features, radiation dosimetry parameters, and hematological indexes during treatment. Risk assessment models for single-phase and stepwise fusion phases were established according to logistic regression. In addition, a nomogram of the final fusion phase model and risk classification system was generated. Receiver operating characteristic (ROC), decision curve, and calibration curve analysis were conducted to internally validate the nomogram to analyze its discrimination.

Results

Smoking status, PTV and lung radiomics feature, lung and esophageal dosimetry parameters, and platelets at the third week of radiotherapy were independent risk factors for the four single-phase models. The ROC result analysis of the risk assessment models created by stepwise phase fusion were: (area under curve [AUC]: 0.67,95% confidence interval [CI]: 0.52–0.81), (AUC: 0.82,95%CI: 0.70–0.94), (AUC: 0.90,95%CI: 0.80–1.00), and (AUC:0.90,95%CI: 0.80–1.00), respectively. The nomogram based on the final fusion phase model was validated using calibration curve analysis and decision curve analysis, demonstrating good consistency and clinical utility. The nomogram-based risk classification system could correctly classify cases into three diverse risk groups: low-(ratio:3.6%; 0 < score < 135), intermediate-(ratio:30.7%, 135 < score < 160) and high-risk group (ratio:80.0%, score > 160).

Conclusions

In our study, the risk assessment model makes it easy for physicians to assess the risk of grade ≥ 2 radiation pneumonitis at various phases in the radiotherapy process, and the risk classification system and nomogram identify the patient’s risk level after completion of radiation therapy.

Lung Subregion Partitioning by Incremental Dose Intervals Improves Omics-Based Prediction for Acute Radiation Pneumonitis in Non-Small-Cell Lung Cancer Patients

Purpose: To evaluate the effectiveness of features obtained from our proposed incremental-dose-interval-based lung subregion segmentation (IDLSS) for predicting grade ≥ 2 acute radiation pneumonitis (ARP) in lung cancer patients upon intensity-modulated radiotherapy (IMRT). (1) Materials and Methods: A total of 126 non-small-cell lung cancer patients treated with IMRT were retrospectively analyzed. Five lung subregions (SRs) were generated by the intersection of the whole lung (WL) and five sub-regions receiving incremental dose intervals. A total of 4610 radiomics features (RF) from pre-treatment planning computed tomographic (CT) and 213 dosiomics features (DF) were extracted. Six feature groups, including WL-RF, WL-DF, SR-RF, SR-DF, and the combined feature sets of WL-RDF and SR-RDF, were generated. Features were selected by using a variance threshold, followed by a Student t-test. Pearson’s correlation test was applied to remove redundant features. Subsequently, Ridge regression was adopted to develop six models for ARP using the six feature groups. Thirty iterations of resampling were implemented to assess overall model performance by using the area under the Receiver-Operating-Characteristic curve (AUC), accuracy, precision, recall, and F1-score. (2) Results: The SR-RDF model achieved the best classification performance and provided significantly better predictability than the WL-RDF model in training cohort (Average AUC: 0.98 ± 0.01 vs. 0.90 ± 0.02, p < 0.001) and testing cohort (Average AUC: 0.88 ± 0.05 vs. 0.80 ± 0.04, p < 0.001). Similarly, predictability of the SR-DF model was significantly stronger than that of the WL-DF model in training cohort (Average AUC: 0.88 ± 0.03 vs. 0.70 ± 0.030, p < 0.001) and in testing cohort (Average AUC: 0.74 ± 0.08 vs. 0.65 ± 0.06, p < 0.001). By contrast, the SR-RF model significantly outperformed the WL-RF model only in the training set (Average AUC: 0.93 ± 0.02 vs. 0.85 ± 0.03, p < 0.001), but not in the testing set (Average AUC: 0.79 ± 0.05 vs. 0.77 ± 0.07, p = 0.13). (3) Conclusions: Our results demonstrated that the IDLSS method improved model performance for classifying ARP with grade ≥ 2 when using dosiomics or combined radiomics-dosiomics features.

Function-Wise Dual-Omics analysis for radiation pneumonitis prediction in lung cancer patients

Purpose: This study investigates the impact of lung function on radiation pneumonitis prediction using a dual-omics analysis method.

Methods: We retrospectively collected data of 126 stage III lung cancer patients treated with chemo-radiotherapy using intensity-modulated radiotherapy, including pre-treatment planning CT images, radiotherapy dose distribution, and contours of organs and structures. Lung perfusion functional images were generated using a previously developed deep learning method. The whole lung (WL) volume was divided into function-wise lung (FWL) regions based on the lung perfusion functional images. A total of 5,474 radiomics features and 213 dose features (including dosiomics features and dose-volume histogram factors) were extracted from the FWL and WL regions, respectively. The radiomics features (R), dose features (D), and combined dual-omics features (RD) were used for the analysis in each lung region of WL and FWL, labeled as WL-R, WL-D, WL-RD, FWL-R, FWL-D, and FWL-RD. The feature selection was carried out using ANOVA, followed by a statistical F-test and Pearson correlation test. Thirty times train-test splits were used to evaluate the predictability of each group. The overall average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and f1-score were calculated to assess the performance of each group.

Results: The FWL-RD achieved a significantly higher average AUC than the WL-RD group in the training (FWL-RD: 0.927 ± 0.031, WL-RD: 0.849 ± 0.064) and testing cohorts (FWL-RD: 0.885 ± 0.028, WL-RD: 0.762 ± 0.053, p < 0.001). When using radiomics features only, the FWL-R group yielded a better classification result than the model trained with WL-R features in the training (FWL-R: 0.919 ± 0.036, WL-R: 0.820 ± 0.052) and testing cohorts (FWL-R: 0.862 ± 0.028, WL-R: 0.750 ± 0.057, p < 0.001). The FWL-D group obtained an average AUC of 0.782 ± 0.032, obtaining a better classification performance than the WL-D feature-based model of 0.740 ± 0.028 in the training cohort, while no significant difference was observed in the testing cohort (FWL-D: 0.725 ± 0.064, WL-D: 0.710 ± 0.068, p = 0.54).

Conclusion: The dual-omics features from different lung functional regions can improve the prediction of radiation pneumonitis for lung cancer patients under IMRT treatment. This function-wise dual-omics analysis method holds great promise to improve the prediction of radiation pneumonitis for lung cancer patients.

Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: A retrospective cohort study

OBJECTIVE

We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in patients with locally advanced cervical cancer (LACC) undergoing intensity-modulated radiation therapy and brachytherapy.

DESIGN

A retrospective cohort study.

SETTING

Xiangya Hospital of Central South University, Changsha, Hunan, China.

POPULATION

Two hundred and fifty-seven LACC patients who were treated with intensity-modulated radiotherapy from 2014 to 2017.

METHODS

Patients were allocated into the training/validation sets (3:1 ratio) using proportional random sampling, resulting in the same proportion of groups in the two sets. We extracted 254 radiomic features from each of the gross target volume, pelvis and sacral vertebrae. The sequentially backward elimination support vector machine algorithm was used for feature selection and end point prediction.

MAIN OUTCOMES AND MEASURES

Clinical end points include tumour complete response (CR), 5-year overall survival (OS), anaemia, and leucopenia.

RESULTS

A combination of ten clinicopathological parameters and 34 radiomic features performed best for predicting CR (validation balanced accuracy: 80.8%). The validation balanced accuracy of 54 radiomic features was 85.8% for OS, and their scores can stratify patients into the low-risk and high-risk groups (5-year OS: 95.5% versus 36.4%, p < 0.001). The clinical and radiomic models were also predictive of anaemia and leucopenia (validation balanced accuracies: 71.0% and 69.9%).

CONCLUSION

This study demonstrated that combining clinicopathological parameters with CT-based radiomics may have value for predicting clinical end points in LACC. If validated, this model may guide therapeutic strategy to optimise the effectiveness and minimise toxicity or treatment for LACC.

Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients

PURPOSE

We aimed to assess radiomics approaches for estimating three pulmonary function test (PFT) results (forced expiratory volume in one second [FEV1], forced vital capacity [FVC], and the ratio of FEV1 to FVC [FEV₁/FVC]) using data extracted from chest computed tomography (CT) images.

METHODS

This retrospective study included 85 lung cancer patients (mean age, 75 years ±8; 69 men) who underwent stereotactic body radiotherapy between 2012 and 2020. Their pretreatment chest breath-hold CT and PFT data before radiotherapy were obtained. A total of 107 radiomics features (Shape: 14, Intensity: 18, Texture: 75) were extracted using two methods: extraction of the lung tissue (<-250 HU) (APPROACH 1), and extraction of small blood vessels and lung tissue (APPROACH 2). The PFT results were estimated using the least absolute shrinkage and selection operator regression. Pearson's correlation coefficients (r) were determined for all PFT results, and the area under the curve (AUC) was calculated for FEV₁/FVC (<70 %). Finally, we compared our approaches with the conventional formula (Conventional).

RESULTS

For the estimated FEV₁/FVC, the Pearson's r were 0.21 (P =.06), 0.69 (P <.01), and 0.73 (P <.01) for Conventional, APPROACH 1, and APPROACH 2, respectively; the AUCs for FEV₁/FVC (<70 %) were 0.67 (95 % confidence interval [CI]: 0.55, 0.79), 0.82 (CI: 0.72, 0.91; P =.047) and 0.86 (CI: 0.78, 0.94; P =.01), respectively.

CONCLUSIONS

The radiomics approach performed better than the conventional equation and may be useful for assessing lung function based on CT images.

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes

Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.

Multi-Organ Omics-Based Prediction for Adaptive Radiation Therapy Eligibility in Nasopharyngeal Carcinoma Patients Undergoing Concurrent Chemoradiotherapy

Purpose

To investigate the role of different multi-organ omics-based prediction models for pre-treatment prediction of Adaptive Radiotherapy (ART) eligibility in patients with nasopharyngeal carcinoma (NPC).

Methods and Materials

Pre-treatment contrast-enhanced computed tomographic and magnetic resonance images, radiotherapy dose and contour data of 135 NPC patients treated at Hong Kong Queen Elizabeth Hospital were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from a total of eight organ structures. During model development, patient cohort was divided into a training set and a hold-out test set in a ratio of 7 to 3 via 20 iterations. Four single-omics models (R, M, D, C) and four multi-omics models (RD, RC, RM, RMDC) were developed on the training data using Ridge and Multi-Kernel Learning (MKL) algorithm, respectively, under 10-fold cross validation, and evaluated on hold-out test data using average area under the receiver-operator-characteristics curve (AUC). The best-performing single-omics model was first determined by comparing the AUC distribution across the 20 iterations among the four single-omics models using two-sided student t-test, which was then retrained using MKL algorithm for a fair comparison with the four multi-omics models.

Results

The R model significantly outperformed all other three single-omics models (all p-value<0.0001), achieving an average AUC of 0.942 (95%CI: 0.938-0.946) and 0.918 (95%CI: 0.903-0.933) in training and hold-out test set, respectively. When trained with MKL, the R model (R_MKL) yielded an increased AUC of 0.984 (95%CI: 0.981-0.988) and 0.927 (95%CI: 0.905-0.948) in training and hold-out test set respectively, while demonstrating no significant difference as compared to all studied multi-omics models in the hold-out test sets. Intriguingly, Radiomic features accounted for the majority of the final selected features, ranging from 64% to 94%, in all the studied multi-omics models.

Conclusions

Among all the studied models, the Radiomic model was found to play a dominant role for ART eligibility in NPC patients, and Radiomic features accounted for the largest proportion of features in all the multi-omics models.

Promising Biomarkers of Radiation-Induced Lung Injury: A Review

Radiation-induced lung injury (RILI) is one of the main dose-limiting side effects in patients with thoracic cancer during radiotherapy. No reliable predictors or accurate risk models are currently available in clinical practice. Severe radiation pneumonitis (RP) or pulmonary fibrosis (PF) will reduce the quality of life, even when the anti-tumor treatment is effective for patients. Thus, precise prediction and early diagnosis of lung toxicity are critical to overcome this longstanding problem. This review summarizes the primary mechanisms and preclinical animal models of RILI reported in recent decades, and analyzes the most promising biomarkers for the early detection of lung complications. In general, ideal integrated models considering individual genetic susceptibility, clinical background parameters, and biological variations are encouraged to be built up, and more prospective investigations are still required to disclose the molecular mechanisms of RILI as well as to discover valuable intervention strategies.

Construction and Verification of a Radiation Pneumonia Prediction Model Based on Multiple Parameters

Objective:

Patients with lung cancer are at risk of radiation pneumonia (RP) after receiving radiotherapy. We established a prediction model according to the critical indicators extracted from radiation pneumonia patients.

Materials and Methods:

74 radiation pneumonia patients were involved in the training set. Firstly, the clinical data, hematological and radiation dose parameters of the 74 patients were screened by Logistics regression univariate analysis according to the level of radiation pneumonia. Next, Stepwise regression analysis was utilized to construct the regression model. Then, the influence of continuous variables on RP was tested by smoothing function. Finally, the model was externally verified by 30 patients in validation set and visualized by R code.

Results:

In the training set, there was 40 patients suffered≥ level 2 acute radiation pneumonia. Clinical data (diabetes), blood indexes (lymphocyte percentage, basophil percentage, platelet count) and radiation dose (V15 > 40%, V20 > 30%, V35 >18%, V40 > 15%) were related to radiation pneumonia (P < 0.05). Particularly, stepwise regression analysis indicated that the history of diabetes, the basophils percentage, platelet count and V20 could be the best combination used for predicting radiation pneumonia. The column chart was obtained by fitting the regression model with the combined indicator. The receiver operating characteristic (ROC) curve showed that the AUC in the development term was 0.853, the AUC was 0.656 in the validation term. And calibration curves of both groups showed the high stability in efficiently diagnostic. Furthermore, the DCA curve showed that the model had a satisfactory positive net benefit.

Conclusion:

The combination of the basophils percentage, platelet count and V20 is available to build a predictive model of radiation pneumonia for patients with advanced lung cancer.