Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma

Abstract

Skull-base chordoma (SBC) can be treated with carbon ion radiotherapy (CIRT) to improve local control (LC). The study aimed to explore the role of multi-parametric radiomic, dosiomic and clinical features as prognostic factors for LC in SBC patients undergoing CIRT. Before CIRT, 57 patients underwent MR and CT imaging, from which tumour contours and dose maps were obtained. MRI and CT-based radiomic, and dosiomic features were selected and fed to two survival models, singularly or by combining them with clinical factors. Adverse LC was given by in-field recurrence or tumour progression. The dataset was split in development and test sets and the models’ performance evaluated using the concordance index (C-index). Patients were then assigned a low- or high-risk score. Survival curves were estimated, and risk groups compared through log-rank tests (after Bonferroni correction α = 0.0083). The best performing models were built on features describing tumour shape and dosiomic heterogeneity (median/interquartile range validation C-index: 0.80/024 and 0.79/0.26), followed by combined (0.73/0.30 and 0.75/0.27) and CT-based models (0.77/0.24 and 0.64/0.28). Dosiomic and combined models could consistently stratify patients in two significantly different groups. Dosiomic and multi-parametric radiomic features showed to be promising prognostic factors for LC in SBC treated with CIRT.

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity

“…Dosiomic is increasingly used in clinical studies aiming to improve the prediction of clinical outcomes, e.g., locoregional recurrence after IMRT for head and neck cancer [23] or local control after carbon-ion radiotherapy in skull-base chordoma [21]. Dosiomic features were analysed by machine learning for the prediction of acute-phase weight loss in lung cancer patients treated with radiotherapy [30].…”

“…A second appealing development is introducing dosiomic features into Tumour Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) models, thus overcoming the current limitation of these models [19]. Some authors recently employed dosiomics to improve the prediction of side effects [20][21][22] or local control after radiotherapy [23], including preliminary multicentre experiences [24]. The proposed dosiomic signatures must be highly stable and reproducible and need validation before being used in clinical practice.…”

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

“…In this regard, several research groups have also suggested to incorporate dosimetric deviations in targets and/or OARs (such as parotid glands) as part of the ART regimen (12,(18)(19)(20). Of note, although Dosiomics has recently been studied for prediction of toxicity (32,34,(41)(42)(43) and prognosis (33,44) in cancer patients, its potential in treatment response prediction, in particular on the basis of the RECIST criteria, has not been reported. Future studies in this aspect are recommended to confirm its capability in this regard.…”

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

“…Notably, the dosiomic features are not more powerful than the clinical parameters, but the combination of these two attributes substantially improves performance. Buizza et al (165) extended this method to rare tumors to assess LC after carbon-ion RT (CIRT) for skull-based chordomas (SBC) and noted that dosiomic signatures were the most promising traits compared with clinical variables, the profile of CT and MR, with comparable radiomic and clinical model capabilities. The studies by Murakami et al (164) and Buizza et al (165) yielded different conclusions when assessing the predictive power of dosiomic profiles and clinical factors, which may be associated with variations in radiation treatment regimens.…”

“…Several recent studies have illustrated that dosiomics is also applicable in evaluating prognosis after radiotherapies, such as locoregional recurrences (LR) ( 163 ), biochemical recurrence (BCR) ( 164 ), and local control (LC) ( 165 ). An LR study in intensity-modulated RT (IMRT) for neck tumors indicated that the combined model based on features of CT, PET, and 3D dose distribution maps performed better than radiomics alone, suggesting that dosiomic characteristics are associated with LR and have prognostic value ( 163 ).…”