Jean-Emmanuel Bibault

Big Data and machine learning in radiation oncology: State of the art and future prospects

Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and imaging performed before each fraction, have generated a considerable flow of data that needs to be integrated. In the same time, Electronic Health Records now provide phenotypic profiles of large cohorts of patients that could be correlated to this information. In this review, we describe methods that could be used to create integrative predictive models in radiation oncology. Potential uses of machine learning methods such as support vector machine, artificial neural networks, and deep learning are also discussed.

Clinical Outcomes of Several IMRT Techniques for Patients With Head and Neck Cancer: A Propensity Score–Weighted Analysis

PURPOSEnThe Advanced Radiotherapy Oto-Rhino-Laryngologie (ART-ORL) study (NCT02024035) was performed to prospectively evaluate the clinical and economic aspects of helical TomoTherapy and volumetric modulated arc therapy (RapidArc, Varian Medical Systems, Palo Alto, CA) for patients with head and neck cancer.nnnMETHODS AND MATERIALSnFourteen centers participated in this prospective comparative study. Randomization was not possible based on the availability of equipment. Patients with epidermoid or undifferentiated nasopharyngeal carcinoma or epidermoid carcinoma of the oropharynx and oral cavity (T1-T4, M0, N0-N3) were included between February 2010 and February 2012. Only the results of the clinical study are presented in this report, as the results of the economic assessment have been published previously. Inverse probability of treatment weighting using the propensity score analysis was undertaken in an effort to adjust for potential bias due to nonrandomization. Locoregional control, cancer-specific survival, and overall survival assessed 18xa0months after treatment, as well as long-term toxicity and salivary function, were evaluated.nnnRESULTSnThe analysis included 166 patients. The following results are given after inverse probability of treatment weighting adjustment. The locoregional control rate at 18xa0months was significantly better in the TomoTherapy group: 83.3% (95% confidence interval [CI], 72.5%-90.2%) versus 72.7% (95% CI, 62.1%-80.8%) in the RapidArc group (P=.025). The cancer-specific survival rate was better in the TomoTherapy group: 97.2% (95% CI, 89.3%-99.3%) versus 85.5% (95% CI, 75.8%-91.5%) in the RapidArc group (P=.014). No significant difference was shown in progression-free or overall survival. TomoTherapy induced fewer acute salivary disorders (P=.012). Posttreatment salivary function degradation was worse in the RapidArc group (P=.012).nnnCONCLUSIONSnTomoTherapy provided better locoregional control and cancer-specific survival than RapidArc treatment, with fewer salivary disorders. No significant difference was shown in progression-free and overall survival. These results should be explored in a randomized trial.

Social media for radiation oncologists: A practical primer

a Hôpital Européen Georges Pompidou, Paris Descartes University, Paris Sorbonne Cité, Paris, France b INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France c Department of Radiation Medicine, Lowell General Hospital, Lowell, Massachusetts d Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey/Robert Wood Johnson Medical School/New Jersey Medical School, New Brunswick, New Jersey

Integrating Multimodal Radiation Therapy Data into i2b2

Background u2003Clinical data warehouses are now widely used to foster clinical and translational research and the Informatics for Integrating Biology and the Bedside (i2b2) platform has become a de facto standard for storing clinical data in many projects. However, to design predictive models and assist in personalized treatment planning in cancer or radiation oncology, all available patient data need to be integrated into i2b2, including radiation therapy data that are currently not addressed in many existing i2b2 sites. Objective u2003To use radiation therapy data in projects related to rectal cancer patients, we assessed the feasibility of integrating radiation oncology data into the i2b2 platform. Methods u2003The Georges Pompidou European Hospital, a hospital from the Assistance Publique – Hôpitaux de Paris group, has developed an i2b2-based clinical data warehouse of various structured and unstructured clinical data for research since 2008. To store and reuse various radiation therapy data—dose details, activities scheduling, and dose-volume histogram (DVH) curves—in this repository, we first extracted raw data by using some reverse engineering techniques and a vendors application programming interface. Then, we implemented a hybrid storage approach by combining the standard i2b2 “Entity-Attribute-Value” storage mechanism with a “JavaScript Object Notation (JSON) document-based” storage mechanism without modifying the i2b2 core tables. Validation was performed using (1) the Business Objects framework for replicating vendors application screens showing dose details and activities scheduling data and (2) the R software for displaying the DVH curves. Results u2003We developed a pipeline to integrate the radiation therapy data into the Georges Pompidou European Hospital i2b2 instance and evaluated it on a cohort of 262 patients. We were able to use the radiation therapy data on a preliminary use case by fetching the DVH curve data from the clinical data warehouse and displaying them in a R chart. Conclusion u2003By adding radiation therapy data into the clinical data warehouse, we were able to analyze radiation therapy response in cancer patients and we have leveraged the i2b2 platform to store radiation therapy data, including detailed information such as the DVH to create new ontology-based modules that provides research investigators with a wider spectrum of clinical data.

Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer

Treatment of locally advanced rectal cancer involves chemoradiation, followed by total mesorectum excision. Complete response after chemoradiation is an accurate surrogate for long-term local control. Predicting complete response from pre-treatment features could represent a major step towards conservative treatment. Patients with a T2-4 N0-1 rectal adenocarcinoma treated between June 2010 and October 2016 with neo-adjuvant chemoradiation from three academic institutions were included. All clinical and treatment data was integrated in our clinical data warehouse, from which we extracted the features. Radiomics features were extracted from the tumor volume from the treatment planning CT Scan. A Deep Neural Network (DNN) was created to predict complete response, as a methodological proof-of-principle. The results were compared to a baseline Linear Regression model using only the TNM stage as a predictor and a second model created with Support Vector Machine on the same features used in the DNN. Ninety-five patients were included in the final analysis. There were 49 males (52%) and 46 females (48%). Median tumour size was 48u2009mm (15–130). Twenty-two patients (23%) had pathologic complete response after chemoradiation. One thousand six hundred eighty-three radiomics features were extracted. The DNN predicted complete response with an 80% accuracy, which was better than the Linear Regression model (69.5%) and the SVM model (71.58%). Our model correctly predicted complete response after neo-adjuvant rectal chemoradiotherapy in 80% of the patients of this multicenter cohort. Our results may help to identify patients who would benefit from a conservative treatment, rather than a radical resection.

Learning radiation oncology in Europe: Results of the ESTRO multidisciplinary survey

Highlights • This survey was performed to understand the RO education systems in Europe.• There were 463 participants from 34 European countries.• The survey showed significant disparities between countries.• A quarter of participants indicated that their national education program is insufficient.

The role of Next-Generation Sequencing in tumoral radiosensitivity prediction

Highlights • Next-Generation Sequencing cost has significantly decreased recently.• NGS is used to comprehensively assess tumoral radiosensitivity.• Personalized and dose-adapted radiotherapy could be achieved through the use of these technologies.

Techniques innovantes en radiothérapie externe

La radiotherapie est une discipline clinique et technique qui repose sur l’innovation. Depuis les annees 1990xa0et l’avenement de l’informatique, les progres se sont significativement acceleres. Ils permettent une meilleure definition de la cible et des organes a risque, une amelioration de la distribution de la dose et une prise en compte des mouvements du patient pendant la seance de traitement. En parallele, les progres en biologie moleculaire et en bio-informatique permettront de personnaliser les traitements pour chaque patient. Cette mise au point presente les progres realises dans le domaine des techniques de radiotherapie externe. Ainsi, la radiotherapie conformationnelle avec modulation d’intensite s’impose progressivement en France depuis les annees 2000, et plus recemment, la radiotherapie en conditions stereotaxiques est en train d’ouvrir tout un champ de nouvelles indications pour de nombreux patients a differents stades de leur maladie. Ces techniques s’appuient en partie sur des systemes d’imagerie de plus en plus sophistiques qui permettent de controler la position et le mouvement du patient avant et pendant le traitement, en conjonction avec les progres realises dans le domaine des collimateurs multilames et de la robotique. Cette mise au point aborde egalement la protontherapie et ses avantages dosimetriques par rapport aux photons.