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PDF file - 391K, Supplementary figure 1: Effect of R1507 (18mg/kg/week), Cisplatin (3mg/kg/week) for a total of 3 injections on day 1, 8 and 15) and combination of both treatments on H526 (A) and H146 (B) xenografts in nude mice Effect of R1507 (18mg/kg/week), ionizing radiation (4 fractions of 2 Gy; days 1-4) and combination of both treatments on H526 xenografts in Nude mice (C).Supplementary figure 2: Kaplan-Meier plots showing the tumor progression of nude mice with H146 xenografts. Supplementary figure 3: Heatmap depicting the mean fold-change for the 94 probe sets that are significantly differentially expressed (2 fold change, P < 0.05, Benjamini Hochberg adjusted) at R1507 day 7 only or that exhibit an inversion of R1507 day gene expression profile. Supplementary figure 4: Effect of short- and prolonged-R1507 exposure on the protein expression of selected receptor tyrosine kinase and intracellular nodes as captured by the PathScan RTK Signaling Antibody Array (see Supplementary Methods). Supplementary figure 5: Heatmap depicting the mean fold-change for the 399 probe sets that are significantly differentially expressed under R1507 + Cisplatin Cisplatin (two-way ANOVA, P

XLSX file - 35K, Effect of R1507, Cisplatin, IR and their combinations on the RTV5.

Insulin-like growth factor receptor-1 (IGF-1R) inhibition could be a relevant therapeutic approach in small cell lung cancer (SCLC) given the importance of an IGF-1R autocrine loop and its role in DNA damage repair processes. We assessed IGF-1R and pAkt protein expression in 83 SCLC human specimens. The efficacy of R1507 (a monoclonal antibody directed against IGF-1R) alone or combined with cisplatin or ionizing radiation (IR) was evaluated in H69, H146, and H526 cells in vitro and in vivo. Innovative genomic and functional approaches were conducted to analyze the molecular behavior under the different treatment conditions. A total of 53% and 37% of human specimens expressed IGF-1R and pAkt, respectively. R1507 showed single-agent activity in H146 and H526 cells but not in H69 cells. R1507 exhibited synergistic effects with both cisplatin and IR in vitro. The triple combination R1507-cisplatin-IR led to a dramatic delay in tumor growth compared with cisplatin-IR in H526 cells. Analyzing the apparent absence of antitumoral effect of R1507 alone in vivo, we observed a transient reduction of IGF-1R staining intensity in vivo, concomitant to the activation of multiple cell surface receptors and intracellular proteins involved in proliferation, angiogenesis, and survival. Finally, we identified that the nucleotide excision repair pathway was mediated after exposure to R1507-CDDP and R1507-IR in vitro and in vivo. In conclusion, adding R1507 to the current standard cisplatin-IR doublet reveals remarkable chemo- and radiosensitizing effects in selected SCLC models and warrants to be investigated in the clinical setting. Mol Cancer Ther; 12(7); 1213–22. ©2013 AACR.

XLSX file - 33K, Supplementary table 2: Overlapping pathways and differentially expressed genes (2 fold change, P

Highlights • Professional quality of life is crucial for oncology professionals. • Alexythymia and empathy affect the individual capacity to cope with stress. • Alexythimia has a negative impact on radiation therapists’ professional quality of life. • Higher empatic concern increases the quality of professional well-being. • This data may help targeting professionals at risk to implement preventive strategies., Background and purpose Physical and mental well-being are crucial for oncology professionals as they affect performance at work. Personality traits, as alexithymia and empathy, may influence professional quality of life. Alexithymia involves diminished skills in emotion processing and awareness. Empathy is pertinent to the ability to understand another’s ‘state of mind/emotion’. The PROject on Burn-Out in RadiatioN Oncology (PRO BONO) investigates professional quality of life amongst radiation oncology professionals, exploring the role of alexithymia and empathy. The present study reports on data pertinent to radiation therapists (RTTs). Material and methods An online survey targeted ESTRO members. Participants were asked to fill out 3 questionnaires for alexithymia, empathy and professional quality of life: (a) Toronto Alexithymia Scale (TAS-20); (b) Interpersonal Reactivity Index (IRI); (c) Professional Quality of Life Scale (ProQoL). The present analysis focuses on RTTS to evaluate compassion satisfaction (CS), secondary traumatic stress (STS) and Burnout and their correlation with alexithymia and empathy, using generalized linear modeling. Covariates found significant at univariate linear regression analysis were included in the multivariate linear regression model. Results A total of 399 RTTs completed all questionnaires. The final model for the burnout scale of ProQoL found, as significal predictors, the TAS-20 total score (β = 0.46, p

Highlights • Burnout is critical for oncology professionals, affecting quality of life and performance at work. • Alexithymia and empathy are personality traits influencing the capacity to cope with stress. • Alexithymia increases burnout among medical physicists with a negative impact at work. • Higher level of empathy are correlated to a better professional quality of life. • This data may help identifying subjects at risk to implement preventive strategies., Background and purpose The professional quality of life of radiation oncology professionals can be influenced by different contributing factors, including personality traits. Alexithymia involves deficits in emotion processing and awareness. Empathy is the ability to understand another’s ‘state of mind/emotion’. We investigated professional quality of life, including burnout, in radiation oncology, exploring the role of alexithymia and empathy and targeting the population of medical physicists (MPs), since this professional category is usually underrepresented in surveys exploring professional well-being in radiation oncology and MPs may experience professional distress given the increasing complexity of multimodal cancer care. Material and methods An online survey was addressed to ESTRO members. Participants filled out three questionnaires to evaluate alexithymia, empathy and professional quality of life: a) Toronto Alexithymia Scale (TAS-20); b) Interpersonal Reactivity Index (IRI); c) Professional Quality of Life Scale (ProQoL). Professional quality of life as per ProQoL was considered as dependent variable. The three domains of the ProQoL, namely compassion satisfaction (CS), secondary traumatic stress (STS) and burnout were correlated with alexithymia (as per TAS-20) and empathy (as per IRI with three subcategories: empathic concern, perspective taking and personal distress) and demographic/professional characteristics as independent variables. Generalized linear modeling was used. Significant covariates on univariate linear regression analysis were included in the multivariate linear regression model. Results A total of 308 medical physicists completed all questionnaires. Alexithymia as per TAS-20 was correlated to decreased CS (β = −0.25, p

Stereotactic radiotherapy of oligometastases, mono- or hypofractionated, represents a fundamental change in the practice of the specialty as it was developed for a century. Despite the great heterogeneity of sites, techniques, and doses, most studies found a high local control rate, around 70 to 90% at 2 years, and reduced toxicity, around 5% of grade 3 at 2 years. Four main phase II and III trials are underway in France. Future research concerns the association of stereotactic radiotherapy with immunotherapy or different conventional chemotherapy protocols, the identification of the best clinical presentations, and optimization of fractionation and biological dose for poor prognosis localizations.

Simple Summary This article presents a gradient-boosted model that can predict 10-year prostate cancer mortality with high accuracy. The model was developed and validated on prospective multicenter data from the PLCO trial. Using XGBoost and Shapley values, it provides interpretability to understand its prediction. It can be used online to provide predictions and support informed decision-making in PCa treatment. Abstract Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality. This article presents a gradient-boosting model to predict the risk of prostate cancer mortality within 10 years after a cancer diagnosis, and to provide an interpretable prediction. This work uses prospective data from the PLCO Cancer Screening and selected patients who were diagnosed with prostate cancer. During follow-up, 8776 patients were diagnosed with prostate cancer. The dataset was randomly split into a training (n = 7021) and testing (n = 1755) dataset. Accuracy was 0.98 (±0.01), and the area under the receiver operating characteristic was 0.80 (±0.04). This model can be used to support informed decision-making in prostate cancer treatment. AI interpretability provides a novel understanding of the predictions to the users.

PurposeLung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients’ care.MethodsA literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020ResultsWe identified several studies at each point of patient’s care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications.ConclusionPromising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.

Background The recent development of endoscopic resection for superficial gastrointestinal cancers could justify the need for a dedicated oncological multidisciplinary meeting (MDM). The aim of our study was to evaluate the impact of the dedicated MDM on the management of superficial cancers of the digestive tract. Methods A dedicated MDM was developed at our tertiary referral center. A retrospective review of the MDM conclusions for all patients referred from March 2015 to March 2017 was performed. Outcomes measurements were the outcomes of endoscopic resection, and the concordance rate between the MDM recommendations, European Society of Gastrointestinal Endoscopy (ESGE) guidelines, and final patient management. Results In total, 153 patients with a median age of 69 years were included. Half of the patients had major comorbidities. The mean lesion size was 25 mm, and R0 and curative resection rate were 73.9 % and 56.9 %, respectively. Forty-three patients had an indication for surgery after endoscopic resection. The concordance rate between ESGE guidelines and MDM recommendation was 92.2 %, and 12 patients did not receive the treatment recommended due to comorbidities. Conclusion A MDM dedicated to superficial tumors helped tailor the ESGE guidelines to each patient in order to avoid unnecessary surgery.

Simple Summary Efficient antitumoral immune response is conditioned by a regulated cell death called immunogenic cell death. Many immunologic cell death inducers are currently used in cancer treatment. Among them, radiation therapy is an adaptable, well tolerated and widely used modality of treatment in modern oncology. Moreover, there is growing evidence for synergistic mechanisms between radiotherapy and immune checkpoint inhibitors. Although pre-clinical concepts are numerous, robust clinical evidence is scarce. Radioimmunology is a rapidly evolving discipline with several ongoing clinical trials. In this review, we (i) explain the rationale behind radiotherapy and immune checkpoint-inhibitor association in the light of the most recent knowledge, (ii) provide the results of the latest clinical trials evaluating radiation therapy and immune checkpoint association and (iii) explore the future directions of radioimmunology research. Abstract The immunogenic cell death (ICD) is defined as a regulated cell death able to induce an adaptive immunity. It depends on different parameters including sufficient antigenicity, adjuvanticity and favorable microenvironment conditions. Radiation therapy (RT), a pillar of modern cancer treatment, is being used in many tumor types in curative, (neo) adjuvant, as well as metastatic settings. The anti-tumor effects of RT have been traditionally attributed to the mitotic cell death resulting from the DNA damages triggered by the release of reactive oxygen species. Recent evidence suggests that RT may also exert its anti-tumor effect by recruiting tumor-specific immunity. RT is able to induce the release of tumor antigens, to act as an immune adjuvant and thus to synergize with the anti-tumor immunity. The advent of new efficient immunotherapeutic agents, such as immune checkpoint inhibitors (ICI), in multiple tumor types sheds new light on the opportunity of combining RT and ICI. Here, we will describe the biological and radiobiological rationale of the RT-induced ICD. We will then focus on the interest to combine RT and ICI, from bench to bedside, and summarize the clinical data existing with this combination. Finally, RT technical adaptations to optimize the ICD induction will be discussed.

To evaluate trimodal conservative treatment as an alternative to radical surgery for urothelial muscle-invasive bladder cancer (MIBC). This retrospective study reported the carcinologic and functional results of patients (pts) presenting a cT2/T3 N0M0 operable MIBC and fit for surgery, treated by a conservative strategy. Treatment consisted of a transurethral resection (TURB) followed by concomitant bi-fractionated split-course radiochemotherapy (RCT) with 5FU-Cisplatine. A control cystoscopy was performed six weeks after the induction RCT (eq45Gy) with systematic biopsies. Patients with complete histologic response achieved RCT protocol. Salvage surgery was proposed to pts with persistent tumor. 313 pts (83% cT2 and 17% cT3) treated between 1988 and 2013 were included, with a median follow-up of 59 months and 67-year mean age. After the induction RCT, the histologic response rate was 83%. After five years, overall, disease-free, and functional bladder-intact survival rates were respectively 69%, 61%, and 69%, significantly better for pts in complete response after induction RCT. Late urinary and digestive toxicities were limited, with respective rates of 4% and 1.5% of grade 3 toxicity. Trimodal strategy with RCT after TURB showed interesting functional and oncologic results and should be considered as an alternative to surgery in well-selected pts.

Background: There is no evidence to support surgery or radiotherapy as the best treatment for resectable oropharyngeal cancers with a negative HPV status. Predictive algorithms may help to decide which strategy to choose, but they will only be accepted by caregivers and European authorities if they are interpretable. As a proof of concept, we developed a predictive and interpretable algorithm to predict locoregional relapse at 18 months for oropharyngeal cancers as a first step towards that goal. Methods: The model was based on clinical and Pyradiomics features extracted from the dosimetric CT scan. Intraclass correlation was used to filter out features dependant on delineation. Correlated redundant features were also removed. An XGBoost model was cross-validated and optimised on the HN1 cohort (79 patients), and performances were assessed on the ART ORL cohort (45 patients). The Shapley Values were used to provide an overall and local explanation of the model. Results: On the ART ORL cohort, the model trained on HN1 yielded a precision&mdash, or predictive positive value&mdash, of 0.92, a recall of 0.42, an area under the curve of the receiver operating characteristic of 0.68 and an accuracy of 0.64. The most contributory features were shape Voxel Volume, grey level size zone matrix Small Area Emphasis (glszmSAE), gldm Dependence Non Uniformity Normalized (gldmDNUN), Sex and Age. Conclusions: We developed an interpretable and generalizable model that could yield a good precision&mdash, positive predictive value&mdash, for relapse at 18 months on a different test cohort.

The worldwide growth of cancer incidence can be explained in part by changes in the prevalence and distribution of risk factors. There are geographical gaps in the estimates of cancer prevalence, which could be filled with innovative methods. We used deep learning (DL) features extracted from satellite images to predict cancer prevalence at the census tract level in seven cities in the United States. We trained the model using detailed cancer prevalence estimates from 2018 available in the CDC (Center for Disease Control) 500 Cities project. Data from 3500 census tracts covering 14,483,366 inhabitants were included. Features were extracted from 170,210 satellite images with deep learning. This method explained up to 64.37% (median = 43.53%) of the variation of cancer prevalence. Satellite features are highly correlated with individual socioeconomic and health measures that are linked to cancer prevalence (age, smoking and drinking status, and obesity). A higher similarity between two environments is associated with better generalization of the model (p = 1.10&ndash, 6). This method can be used to accurately estimate cancer prevalence at a high spatial resolution without using surveys at a fraction of the cost.

Background There are many scales for screening the impact of a disease. These scales are generally used to diagnose or assess the type and severity of a disease and are carried out by doctors. The chatbot helps patients suffering from primary headache disorders through personalized text messages. It could be used to collect patient-reported outcomes. Objective The aims of this study were (1) to study whether the collection and analysis of remote scores, without prior medical intervention, are possible by a chatbot, (2) to perform suggested diagnosis and define the type of headaches, and (3) to assess the patient satisfaction and engagement with the chatbot. Method Voluntary users of the chatbot were recruited online. They had to be over 18 and have a personal history of headaches. A questionnaire was presented (1) by text messages to the participants to evaluate migraines (2) based on the criteria of the International Headache Society. Then, the Likert scale (3) was used to assess overall satisfaction with the use of the chatbot. Results We included 610 participants with primary headache disorders. A total of 89.94% (572/610) participants had fully completed the questionnaire (eight items), 4.72% (30/610) had partially completed it, and 5.41% (33) had refused to complete it. Statistical analysis was performed on 86.01% (547/610) of participants. Auto diagnostic showed that 14.26% (78/547) participants had a tension headache, and 85.74% (469/547) had a probable migraine. In this population, 15.78% (74/469) suffered from migraine without probable aura, and 84.22% (395/469) had migraine without aura. The patient’s age had a significant incidence regarding the auto diagnosis ( P = .008Conclusion The study confirmed that it was possible to obtain such a collection remotely, and quickly (average time of 3.24 min) with a high success rate (89.67% (547/610) participants who had fully completed the IHS questionnaire). Users were strongly engaged through chatbot: out of the total number of participants, we observed a very low number of uncompleted questionnaires (6.23% (38/610)). Conversational agents can be used to remotely collect data on the nature of the symptoms of patients suffering from primary headache disorders. These results are promising regarding patient engagement and trust in the chatbot.

Purpose Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy. Methods A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms “radiotherapy”, “radiation oncology” and “radiomics”. The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review. Results A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n = 5), head and neck (n = 5), esophageal (n = 3), rectal (n = 3), pancreatic (n = 2) cancer and brain metastases (n = 2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic. Conclusion Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.

Chatbots are software that interact with users by using an algorithm, without human back-end intervention...

Background and purpose: Different factors may influence the professional quality of life of oncology professionals. Among them, personality traits, as alexithymia and empathy, are underinvestigated. Alexithymia is about deficits in emotion processing and awareness. Empathy is the ability to understand another's 'state of mind'/emotion. The PROject on BurnOut in RadiatioN Oncology (PRO BONO) assesses professional quality of life, including burnout, in the field of radiation oncology and investigates alexithymia and empathy as contributing factors.Material and methods: An online survey was conducted amongst ESTRO members. Participants completed 3 validated questionnaires for alexithymia, empathy and professional quality of life: (a) Toronto Alexithymia Scale; (b) Interpersonal Reactivity Index; (c) Professional Quality of Life Scale. The present analysis, focusing on radiation/clinical oncologists, evaluates Compassion Satisfaction (CS), Secondary Traumatic Stress (STS) and Burnout and correlates them with alexithymia and empathy (empathic concern, perspective taking and personal distress) with generalized linear modeling. Significant covariates on univariate linear regression analysis were included in the multivariate linear regression model.Results: A total of 825 radiation oncologists completed all questionnaires. A higher level of alexithymia was associated to decreased CS (beta:-0.101; SE: 0.018; p Conclusions: Alexithymic personality trait increased burnout risk, with less professional satisfaction. Empathic concern was associated to increased stress, without leading to burnout, resulting in higher professional fulfillment. These results may be used to benchmark preventing strategies, such as work-hour restrictions, peer support, debriefing sessions, and leadership initiatives for professionals at risk. (c) 2020 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 147 (2020) 162-168

Highlights • A survey to assess the National Societies needs has been developed. • 64 out of 89 National societies from 32 European countries completed the questionnaire. • The highest priority is Optimal patient care to cure cancer & reduce treatment-related toxicity. • High need for more consensus guidelines, exchange of best practices and high quality accredited education. • Networking between National Societies and ESTRO will be further reinforced., Purpose to determine how ESTRO can collaborate with Radiation Oncology National Societies (NS) according to its mission and values, and to define the new roadmap to strengthen the NS network role in the forthcoming years. Materials and methods the ESTRO NS committee launched a survey addressed to all European National Societies, available online from June 5th to October 30th 2018. Questions were divided into three main sections: 1. general information about NS; 2. relevant activities (to understand the landscape of each NS context of action); 3. relevant needs (to understand how ESTRO can support the NS). Eighty-nine European NS were invited to participate. Respondents were asked to rank ESTRO milestones in order of importance, indicating the level of priority to their society. Results a total of 58 out of 89 NS (65.2%) from 31 European countries completed the questionnaire. The majority of NS ranked “Optimal patient care to cure cancer and to reduce treatment-related toxicity” as the highest level of priority. This aligns well with the ESTRO vision 2030 “Optimal health for all together.” NS also indicated a high need for more consensus guidelines and exchange of best practices, access to high quality accredited education, implementation of the ESTRO School Core Curriculum at the national level, and defining quality indicators and standard in Radiation Oncology, improved communication and increased channelling of information. Conclusion The results of this survey will be used to strengthen the relations between ESTRO and European NS to promote and develop initiatives to improve cancer care.

International audience; De nouveaux concepts de consultations sont actuellement en train de profondément changer la façon d’exercer la médecine. L’utilisation de questionnaires standardisés, des questionnaires patients (PRO, patient-reported outcome, et son application informatique, ePRO) ont déjà fait irruption dans nos consultations. En parallèle, la télémédecine, voire l’utilisation d’agents conversationnels automatiques médicaux, permettent d’assurer une consultation à distance, plus accessible. Ces différents outils ont un intérêt majeur en oncologie, notamment dans le contexte de la chronicisation des maladies et du suivi à long terme que les oncologues radiothérapeutes prennent en charge. Dans cet article, nous détaillons chacun de ces nouveaux concepts

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 48 mm (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.

Stereotactic radiotherapy represents a fundamental change in the practice of radiotherapy of lung cancers. Despite the great heterogeneity of sites, techniques, and doses, most studies found a high local control rate, around 70 to 90% at 2 years, and reduced toxicity, around 5% of grade 3 at 2 years. Stereotactic radiotherapy can be realized either by a dedicated accelerator (CyberKnife®) or by a conventional accelerator associated with specific systems. The two modalities deliver a very precise irradiation whose very good results published to date are similar. Some technical characteristics specific to each type of linear accelerator could guide the choice according to the target volume treated.

Introduction: Deep Learning (DL) is a machine learning technique that uses deep neural networks to create a model. The application areas of deep learning in radiation oncology include image segmentation and detection, image phenotyping, and radiomic signature discovery, clinical outcome prediction, image dose quantification, dose-response modeling, radiation adaptation, and image generation. In this review, we explain the methods used in DL and perform a literature review using the Medline database to identify studies using deep learning in radiation oncology. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later. Methods: A literature review was performed using PubMed/Medline in order to identify important recent publications to be synthesized into a review of the current status of Deep Learning in radiation oncology, directed at a clinically-oriented reader. The search strategy included the search terms "radiotherapy" and "deep learning." In addition, reference lists of selected articles were hand searched for further potential hits of relevance to this review. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later. Results: Studies using DL for image segmentation were identified in Brain (n = 2), Head and Neck (n = 3), Lung (n = 6), Abdominal (n = 2), and Pelvic (n = 6) cancers. Use of Deep Learning has also been reported for outcome prediction, such as toxicity modeling (n = 3), treatment response and survival (n = 2), or treatment planning (n = 5). Conclusion: Over the past few years, there has been a significant number of studies assessing the performance of DL techniques in radiation oncology. They demonstrate how DL-based systems can aid clinicians in their daily work, be it by reducing the time required for or the variability in segmentation, or by helping to predict treatment outcomes and toxicities. It still remains to be seen when these techniques will be employed in routine clinical practice.

Background Clinical 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 To 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 The 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 vendor's 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 vendor's application screens showing dose details and activities scheduling data and (2) the R software for displaying the DVH curves. Results We 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 By 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.

Background Little information is currently available concerning young medical students desire to pursue a career in oncology, or their career expectations. Methods This project is a cross-sectional epidemiological study. A voluntary and anonymous questionnaire was distributed to all young oncologists studying in France between the 2nd of October 2013 and the 23rd of February 2014. Results The overall response rate was 75.6%. A total of 505 young oncologists completed the questionnaire. The main determining factors in the decision to practice oncology were the cross-sectional nature of the field (70.8%), the depth and variety of human relations (56.3%) and the multi-disciplinary field of work (50.2%). Most residents would like to complete a rotation outside of their assigned region (59.2%) or abroad (70.2%) in order to acquire additional expertise (67.7%). In addition, most interns would like to undertake a fellowship involving care, teaching and research in order to hone their skills (85.7%) and forge a career in public hospitals (46.4%). Career prospects mainly involve salaried positions in public hospitals. Many young oncologists are concerned about their professional future, due to the shortage of openings (40.8%), the workload (52.8%) and the lack of work-life balance (33.4%). Conclusions This investigation provides a comprehensive profile of the reasons young oncologists chose to pursue a career in oncology, and their career prospects. Electronic supplementary material The online version of this article (10.1186/s12909-018-1117-2) contains supplementary material, which is available to authorized users.

Purpose/Objective(s) To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT). Materials/Methods A DUL model was introduced to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow band mapping to augment the conventional strategy. 251 anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two Groups. Group one contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group two contained 9 CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSC), the Hausdorff distances, and the distances of the center-of-mass. Results The average DSCs between DUL-based prostate contours and reference contours for test data in Group one and Group two-consensus were 0.83 ± 0.04, and 0.85 ± 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm ± 1.15 mm, and 2.98 mm ± 1.42 mm, respectively. Conclusion This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.

Background: The management of prostate cancer (PCa) that has been diagnosed after PSA testing relies on risk stratification. Treatments have significant urinary, digestive and sexual toxicities that can impact the patients’ quality of life. Many patients do not benefit from treatment because their cancer is indolent or because they die from other competitive causes. New models are needed to stratify patients at diagnosis and decide whether treatment could have an interest or not. Artificial Intelligence (AI) techniques can provide solutions in that setting. Methods: We used data from the prospective clinical trial PLCO and selected patients who were diagnosed with PCa during follow-up. We trained two gradient-boosting models to predict 10-year cancer-specific (CSS) and overall survival (OS) with features that described the general health status of the patients and PCa. Using shapley values, we provided a graphical and interpretable prediction, at the individual level. Findings: During follow-up, 8,776 patients were diagnosed with PCa. The dataset was split into a training (n=7,021) and a testing (n=1,755) dataset. Accuracy was 0.87 (± 0.02) and 0.98 (± 0.01) for OS and CSS respectively. The area under the receiver operating characteristic was 0.84 (± 0.02) and 0.81 (± 0.04) and the area under precision-recall was 0.6 (± 0.03) and 0.55 (± 0.07) for OS and CSS respectively. The models are available online. Interpretation: The clinical question of who to treat and who to observe is a major unmet need in prostate cancer. Using prospective data, we trained two models to predict 10-year CSS and OS with high accuracy. These models can be used online to provide predictions and support informed decision-making in PCa treatment. AI interpretability provides a novel understanding of the predictions to the clinicians and the patients using the models. Funding Statement: None. Declaration of Interests: The authors declare no conflict of interest. Ethics Approval Statement: The authors stated that since this was a secondary analysis of a previously approved clinical trial, no ethics committee approval was required.

Resume Contexte En reponse a la crise sanitaire causee par le Sars-CoV-2, de nombreux pays ont pris la decision de confiner leur population. Il a ete demontre que des evenements traumatisants peuvent etre responsables d’alterations de la sante telles qu’une detresse peritraumatique ou un syndrome de stress post-traumatique. Cependant, l’etendue des effets de la pandemie et des confinements sur la sante mentale demeurent encore mal connue. Objectifs L’Objectif de cette etude etait d’evaluer chez des patients a risque la detresse peritraumatique en France, ou la propagation de l’epidemie a impose un confinement generalise. Methodes Des patients ont ete recrutes parmi les utilisateurs de quatre chatbots dedies respectivement au cancer du sein, a l’asthme, a la depression et a la migraine. La detresse psychologique des patients a ete mesuree en utilisant l’inventaire de detresse peritraumatique (PDI), une echelle de reference validee. Une correlation a ensuite ete effectuee entre le risque de detresse peritraumatique et differentes caracteristiques des patients. Resultats Au total, 1771 participants ont ete inclus. Les femmes representaient 91,25 % (n = 1616) avec une moyenne d’âge de 33 ans et 7,96 % (n = 141) etaient des hommes avec une moyenne d’âge de 28 ans ( Tableau 1 ). Le score PDI median etait de 10 chez les patientes atteintes de cancer du sein, 11 chez les patients atteints de migraine, 12 chez les patients asthmatiques et 13 chez les patients depressifs ( Fig. 1 ). Un total de 38,06 % (n = 674) des patients etaient en detresse psychologique (score PDI ≥ 14), definie comme un etat de risque accru de syndrome de stress post-traumatique. La prevalence de detresse psychologique etait de 42 % chez les patients asthmatiques (209/497), 34 % chez les patientes d’un cancer du sein (123/360), 54 % chez les patients depressifs (246/459) et 39 % chez les patients migraineux (178/455). Une analyse de variance a permis de montrer que les femmes, la depression et le non-emploi etaient associes a des scores PDI significativement plus eleves. Chez ces patients a risque, on observe a la fois des emotions dysphoriques et la perception d’un risque vital. Les patients qui utilisent leur smartphone ou leur ordinateur pendant plus d’une heure par jour avaient egalement des scores PDI significativement plus eleves (p = 0,026). Conclusion Cette etude a permis de montrer le tres fort impact de la COVID-19 sur la sante mentale chez plus de 1700 patients. Certaines categories de patients, telles que les femmes, les personnes sans emploi ou depressives, apparaissent plus vulnerables face au risque de troubles psychologiques causes par la pandemie.