Predictive models of tumour response to treatment using functional imaging techniques

Editorial, abstract not included.Loredana G. Marcu, Eva Bezak, Iuliana Toma-Dasu, and Alexandru Das

Clinical oxygen enhancement ratio of tumors in carbon ion radiotherapy: the influence of local oxygenation changes

The effect of carbon ion radiotherapy on hypoxic tumors has recently been questioned because of low linear energy transfer (LET) values in the spread-out Bragg peak (SOBP). The aim of this study was to investigate the role of hypoxia and local oxygenation changes (LOCs) in fractionated carbon ion radiotherapy. Three-dimensional tumors with hypoxic subvolumes were simulated assuming interfraction LOCs. Different fractionations were applied using a clinically relevant treatment plan with a known LET distribution. The surviving fraction was calculated, taking oxygen tension, dose and LET into account, using the repairable–conditionally repairable (RCR) damage model with parameters for human salivary gland tumor cells. The clinical oxygen enhancement ratio (OER) was defined as the ratio of doses required for a tumor control probability of 50% for hypoxic and well-oxygenated tumors. The resulting OER was well above unity for all fractionations. For the hypoxic tumor, the tumor control probability was considerably higher if LOCs were assumed, rather than static oxygenation. The beneficial effect of LOCs increased with the number of fractions. However, for very low fraction doses, the improvement related to LOCs did not compensate for the increase in total dose required for tumor control. In conclusion, our results suggest that hypoxia can influence the outcome of carbon ion radiotherapy because of the non-negligible oxygen effect at the low LETs in the SOBP. However, if LOCs occur, a relatively high level of tumor control probability is achievable with a large range of fractionation schedules for tumors with hypoxic subvolumes, but both hyperfractionation and hypofractionation should be pursued with caution

Radiobiological description of the LET dependence of the cell survival of oxic and anoxic cells irradiated by carbon ions.

Light-ion radiation therapy against hypoxic tumors is highly curative due to reduced dependence on the presence of oxygen in the tumor at elevated linear energy transfer (LET) towards the Bragg peak. Clinical ion beams using spread-out Bragg peak (SOBP) are characterized by a wide spectrum of LET values. Accurate treatment optimization requires a method that can account for influence of the variation in response for a broad range of tumor hypoxia, absorbed doses and LETs. This paper presents a parameterization of the Repairable Conditionally-Repairable (RCR) cell survival model that can describe the survival of oxic and hypoxic cells over a wide range of LET values, and investigates the relationship between hypoxic radiation resistance and LET. The biological response model was tested by fitting cell survival data under oxic and anoxic conditions for V79 cells irradiated with LETs within the range of 30-500 keV/um. The model provides good agreement with experimental cell survival data for the range of LET investigated, confirming the robustness of the parameterization method. This new version of the RCR model is suitable for describing the biological response of mixed populations of oxic and hypoxic cells and at the same time taking into account the distribution of doses and LETs in the incident beam and its variation with depth in tissue. The model offers a versatile tool for the selection of LET and dose required in the optimization of the therapeutic effect, without severely affecting normal tissue in realistic tumors presenting highly heterogeneous oxic and hypoxic regions

A comparative study of radiomics and deep-learning based methods for pulmonary nodule malignancy prediction in low dose CT images

Objectives: Both radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules. Methods: Conventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction. Results: The best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean±standard deviations) of 0.792±0.025, 0.801±0.018, and 0.817±0.032, respectively through 5-fold crossvalidation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921±0.010, 0.824±0.021, and 0.936±0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938±0.010). Conclusion: The end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study

Impact of the Mean Cochlear Biologically Effective Dose on Hearing Preservation After Stereotactic Radiosurgery for Vestibular Schwannoma: A Retrospective Longitudinal Analysis.

Stereotactic radiosurgery (SRS) is a useful alternative for small- to medium-sized vestibular schwannoma. To evaluate whether biologically effective dose (BEDGy2.47), calculated for mean (BEDGy2.47 mean) and maximal (BEDGy2.47 max) cochlear dose, is relevant for hearing preservation. This is a retrospective longitudinal single-center study. Were analyzed 213 patients with useful baseline hearing. Risk of hearing decline was assessed for Gardner-Robertson classes and pure tone average (PTA) loss. The mean follow-up period was 39 months (median 36, 6-84). Hearing decline (Gardner-Robertson class) 3 years after SRS was associated with higher cochlear BEDGy2.47 mean (odds ratio [OR] 1.39, P = .009). Moreover, BEDGy2.47 mean was more relevant as compared with BEDGy2.47 max (OR 1.13, P = .04). Risk of PTA loss (continuous outcome, follow-up minus baseline) was significantly corelated with BEDGy2.47 mean at 24 (beta coefficient 1.55, P = .002) and 36 (beta coefficient 2.01, P = .004) months after SRS. Risk of PTA loss (>20 dB vs ≤) was associated with higher BEDGy2.47 mean at 6 (OR 1.36, P = .002), 12 (OR 1.36, P = .007), and 36 (OR 1.37, P = .02) months. Risk of hearing decline at 36 months for the BEDGy2.47 mean of 7-8, 10, and 12 Gy2.47 was 28%, 57%, and 85%, respectively. Cochlear BEDGy2.47 mean is relevant for hearing decline after SRS and more relevant as compared with BEDGy2.47 max. Three years after SRS, this was sustained for all hearing decline evaluation modalities. Our data suggest the BEDGy2.47 mean cut-off of ≤8 Gy2.47 for better hearing preservation rates

Patterns of practice of image guided particle therapy for cranio-spinal irradiation : A site specific multi-institutional survey of European Particle Therapy Network

Purpose To investigate the current practice patterns in image-guided particle therapy (IGPT) for cranio-spinal irradiation (CSI). Methods A multi-institutional survey was distributed to European particle therapy centres to analyse all aspects of IGPT. Based on the survey results, a Delphi consensus analysis was developed to define minimum requirements and optimal workflow for clinical practice. The centres participating in the institutional survey were invited to join the Delphi process. Results Eleven centres participated in the survey. Imaging for treatment planning was rather similar among the centres with Computed Tomography (CT) being the main modality. For positioning verification, 2D IGPT was more commonly used than 3D IGPT. Two centres performed routinely imaging for plan adaptation, by the rest ad hoc. Eight centres participated in the Delphi consensus analysis. The full consensus was reached on the use of CT imaging without contrast for treatment planning and the role of magnetic resonance imaging (MRI) in target and organs-at-risk delineation. There was an agreement on the necessity to perform patient position verification and correction before each isocentre. The most important outcome was the clear need for standardization and harmonization of the workflow. Conclusion There were differences in CSI IGPT clinical practice among the European particle therapy centres. Moreover, the optimal workflow as identified by experts was not yet reached. There is a strong need for consensus guidelines. The state-of-the-art imaging technology and protocols need to be implemented into clinical practice to improve the quality of IGPT for CSI