Dataset Information

Radiation Response Prediction Model Based on Integrated Clinical and Genomic Data Analysis.

ABSTRACT:

Purpose

The value of the genomic profiling by targeted gene-sequencing on radiation therapy response prediction was evaluated through integrated analysis including clinical information. Radiation response prediction model was constructed based on the analyzed findings.

Materials and methods

Patients who had the tumor sequenced using institutional cancer panel after informed consent and received radiotherapy for the measurable disease served as the target cohort. Patients with irradiated tumor locally controlled for more than 6 months after radiotherapy were defined as the durable local control (DLC) group, otherwise, non-durable local control (NDLC) group. Significant genomic factors and domain knowledge were used to develop the Bayesian Network model to predict radiotherapy response.

Results

Altogether, 88 patients were collected for analysis. Of those, 41 (43.6%) and 47 (54.4%) patients were classified as the NDLC and DLC group, respectively. Somatic mutations of NOTCH2 and BCL were enriched in the NDLC group, whereas, mutations of CHEK2, MSH2, and NOTCH1 were more frequently found in the DLC group. Altered DNA repair pathway was associated with better local failure-free survival (hazard ratio, 0.40; 95% confidence interval, 0.19 to 0.86; p=0.014). Smoking somatic signature was found more frequently in the DLC group. Area under the receiver operating characteristic curve of the Bayesian network model predicting probability of 6-month local control was 0.83.

Conclusion

Durable radiation response was associated with alterations of DNA repair pathway and smoking somatic signature. Bayesian network model could provide helpful insights for high precision radiotherapy. However, these findings should be verified in prospective cohort for further individualization.

SUBMITTER: Jang BS

PROVIDER: S-EPMC9016297 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Radiation Response Prediction Model Based on Integrated Clinical and Genomic Data Analysis.

Jang Bum-Sup BS Chang Ji-Hyun JH Jeon Seung Hyuck SH Song Myung Geun MG Lee Kyung-Hun KH Im Seock-Ah SA Kim Jong-Il JI Kim Tae-You TY Chie Eui Kyu EK

Cancer research and treatment 20210824 2

<h4>Purpose</h4>The value of the genomic profiling by targeted gene-sequencing on radiation therapy response prediction was evaluated through integrated analysis including clinical information. Radiation response prediction model was constructed based on the analyzed findings.<h4>Materials and methods</h4>Patients who had the tumor sequenced using institutional cancer panel after informed consent and received radiotherapy for the measurable disease served as the target cohort. Patients with irra ...[more]

PMID: 34425668

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Project description:ObjectiveThe 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 methodsThe 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.ResultsCompared 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).ConclusionThe performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.

Dataset Information

Radiation Response Prediction Model Based on Integrated Clinical and Genomic Data Analysis.

Purpose

Materials and methods

Results

Conclusion

Publications

Radiation Response Prediction Model Based on Integrated Clinical and Genomic Data Analysis.

Similar Datasets

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