Robotic image-guided reirradiation of lateral pelvic recurrences: preliminary results

<p>Abstract</p> <p>Background</p> <p>The first-line treatment of a pelvic recurrence in a previously irradiated area is surgery. Unfortunately, few patients are deemed operable, often due to the location of the recurrence, usually too close to the iliac vessels, or the associated surgical morbidity. The objective of this study is to test the viability of robotic image-guided radiotherapy as an alternative treatment in inoperable cases.</p> <p>Methods</p> <p>Sixteen patients previously treated with radiotherapy were reirradiated with CyberKnife^®for lateral pelvic lesions. Recurrences of primary rectal cancer (4 patients), anal canal (6), uterine cervix cancer (4), endometrial cancer (1), and bladder carcinoma (1) were treated. The median dose of the previous treatment was 45 Gy (EqD2 range: 20 to 96 Gy). A total dose of 36 Gy in six fractions was delivered with the CyberKnife over three weeks. The responses were evaluated according to RECIST criteria.</p> <p>Results</p> <p>Median follow-up was 10.6 months (1.9 to 20.5 months). The actuarial local control rate was 51.4% at one year. Median disease-free survival was 8.3 months after CyberKnife treatment. The actuarial one-year survival rate was 46%. Acute tolerance was limited to digestive grade 1 and 2 toxicities.</p> <p>Conclusions</p> <p>Robotic stereotactic radiotherapy can offer a short and well-tolerated treatment for lateral pelvic recurrences in previously irradiated areas in patients otherwise not treatable. Efficacy and toxicity need to be evaluated over the long term, but initial results are encouraging.</p

Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example

BACKGROUND AND PURPOSE Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in scoring articles with a specific focus on AI based segmentation and treatment planning, using modified PROBAST and TRIPOD checklists, in order to provide recommendations for future guideline developers and reviewers. MATERIALS AND METHODS The TRIPOD and PROBAST checklist items were discussed and modified using a Delphi process. After consensus was reached, 2 groups of 3 co-authors scored 2 articles to evaluate usability and further optimize the adapted checklists. Finally, 10 articles were scored by all co-authors. Fleiss' kappa was calculated to assess the reliability of agreement between observers. RESULTS Three of the 37 TRIPOD items and 5 of the 32 PROBAST items were deemed irrelevant. General terminology in the items (e.g., multivariable prediction model, predictors) was modified to align with AI-specific terms. After the first scoring round, further improvements of the items were formulated, e.g., by preventing the use of sub-questions or subjective words and adding clarifications on how to score an item. Using the final consensus list to score the 10 articles, only 2 out of the 61 items resulted in a statistically significant kappa of 0.4 or more demonstrating substantial agreement. For 41 items no statistically significant kappa was obtained indicating that the level of agreement among multiple observers is due to chance alone. CONCLUSION Our study showed low reliability scores with the adapted TRIPOD and PROBAST checklists. Although such checklists have shown great value during development and reporting, this raises concerns about the applicability of such checklists to objectively score scientific articles for AI applications. When developing or revising guidelines, it is essential to consider their applicability to score articles without introducing bias

Professional quality of life and burnout amongst radiation oncologists:The impact of alexithymia and empathy

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 <0.001), increased STS (beta: 0.228; SE: 0.018; p <0.001) and burnout (beta: 0.177; SE: 0.016; p <0.001). A higher empathic concern was significantly associated to increased CS (beta: 0.1.287; SE: 0.305; p = 0.001), STS (beta: 0.114; SE: 0.296; p <0.001), with no effect on burnout. Personal distress was associated to decreased CS (beta:-1.423; SE: 0.275; p <0.001), increased STS (beta: 1.871; SE: 0.283; p <0.001) and burnout (beta: 1.504; SE: 0.245; p <0.001). 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) 162-16

Adjuvant radiation therapy in metastatic lymph nodes from melanoma

<p>Abstract</p> <p>Purpose</p> <p>To analyze the outcome after adjuvant radiation therapy with standard fractionation regimen in metastatic lymph nodes (LN) from cutaneous melanoma.</p> <p>Patients and methods</p> <p>86 successive patients (57 men) were treated for locally advanced melanoma in our institution. 60 patients (69%) underwent LN dissection followed by radiation therapy (RT), while 26 patients (31%) had no radiotherapy.</p> <p>Results</p> <p>The median number of resected LN was 12 (1 to 36) with 2 metastases (1 to 28). Median survival after the first relapse was 31.8 months. Extracapsular extension was a significant prognostic factor for regional control (p = 0.019). Median total dose was 50 Gy (30 to 70 Gy). A standard fractionation regimen was used (2 Gy/fraction). Median number of fractions was 25 (10 to 44 fractions). Patients were treated with five fractions/week. Patients with extracapsular extension treated with surgery followed by RT (total dose ≥50 Gy) had a better regional control than patients treated by surgery followed by RT with a total dose <50 Gy (80% vs. 35% at 5-year follow-up; p = 0.004).</p> <p>Conclusion</p> <p>Adjuvant radiotherapy was able to increase regional control in targeted sub-population (LN with extracapsular extension).</p

Deep learning to predict pathologic complete response to neoadjuvant chemoradiation in locally-advanced rectal cancer

L’utilisation de systèmes informatiques pour formaliser, organiser et planifier le traitement des patients a abouti à la création et à l’accumulation de quantité importante de données. Ces informations comprennent des caractéristiques démographiques, socio-économiques, cliniques, biologiques, d’imagerie, et, de plus en plus, génomiques. La médecine et sa pratique, fondées sur la sémiologie et la physiopathologie, vont être profondément transformées par ce phénomène. La complexité et la quantité des informations à intégrer pour prendre une décision médicale pourrait dépasser rapidement les capacités humaines. Les techniques d’intelligence artificielle pourraient assister le médecin et augmenter ses capacités prédictives et décisionnelles. La première partie de ce travail présente les types de données désormais accessibles en routine en oncologie radiothérapie. Elle détaille les données nécessaires à la création d’un modèle prédictif. Nous explorons comment exploiter les données spécifiques à la radiothérapie et présentons le travail d’homogénéisation et de conceptualisation qui a été réalisé sur ces données, notamment via la création d’une ontologie, dans le but de les intégrer à un entrepôt de données. La deuxième partie explore différentes méthodes de machine learning : k-NN, SVM, ANN et sa variante, le Deep Learning. Leurs avantages et inconvénients respectifs sont évalués avant de présenter les études ayant déjà utilisé ces méthodes dans le cadre de la radiothérapie. La troisième partie présente la création d’un modèle prédictif de la réponse complète à la radiochimiothérapie (RTCT) pré-opératoire dans le cancer du rectum localement avancé. Cette preuve de concept utilise des sources de données hétérogènes et un réseau neuronal profond dans le but d’identifier les patients en réponse complète après RTCT qui pourraient ne pas nécessiter de traitement chirurgical radical. Cet exemple, qui pourrait en pratique être intégré aux logiciels de radiothérapie déjà existant, utilise les données collectées en routine et illustre parfaitement le potentiel des approches de prédiction par IA pour la personnalisation des soins.The use of Electronic Health Records is generating vast amount of data. They include demographic, socio-economic, clinical, biological, imaging and genomic features. Medicine, which relied on semiotics and physiopathology, will be permanently disrupted by this phenomenon. The complexity and volume of data that need to be analyzed to guide treatment decision will soon overcome the human cognitive abilities. Artificial Intelligence methods could be used to assist the physicians and guide decision-making. The first part of this work presents the different types of data routinely generated in oncology, which should be considered for modelling a prediction. We also explore which specific data is created in radiation oncology and explain how it can be integrated in a clinical data warehouse through the use of an ontology we created. The second part reports on several types of machine learning methods: k-NN, SVM, ANN and Deep Learning. Their respective advantages and pitfalls are evaluated. The studies using these methods in the field of radiation oncology are also referenced. The third part details the creation of a model predicting pathologic complete response after neoadjuvant chemoradiation for locally-advanced rectal cancer. This proof-of-concept study uses heterogeneous sources of data and a Deep Neural Network in order to find out which patient could potentially avoid radical surgical treatment, in order to significantly reduce the overall adverse effects of the treatment. This example, which could easily be integrated within the existing treatment planning systems, uses routine health data and illustrates the potential of this kind of approach for treatment personalization

Prédiction par Deep Learning de la réponse complète après radiochimiothérapie pré-opératoire du cancer du rectum localement avancé

The use of Electronic Health Records is generating vast amount of data. They include demographic, socio-economic, clinical, biological, imaging and genomic features. Medicine, which relied on semiotics and physiopathology, will be permanently disrupted by this phenomenon. The complexity and volume of data that need to be analyzed to guide treatment decision will soon overcome the human cognitive abilities. Artificial Intelligence methods could be used to assist the physicians and guide decision-making. The first part of this work presents the different types of data routinely generated in oncology, which should be considered for modelling a prediction. We also explore which specific data is created in radiation oncology and explain how it can be integrated in a clinical data warehouse through the use of an ontology we created. The second part reports on several types of machine learning methods: k-NN, SVM, ANN and Deep Learning. Their respective advantages and pitfalls are evaluated. The studies using these methods in the field of radiation oncology are also referenced. The third part details the creation of a model predicting pathologic complete response after neoadjuvant chemoradiation for locally-advanced rectal cancer. This proof-of-concept study uses heterogeneous sources of data and a Deep Neural Network in order to find out which patient could potentially avoid radical surgical treatment, in order to significantly reduce the overall adverse effects of the treatment. This example, which could easily be integrated within the existing treatment planning systems, uses routine health data and illustrates the potential of this kind of approach for treatment personalization.L’utilisation de systèmes informatiques pour formaliser, organiser et planifier le traitement des patients a abouti à la création et à l’accumulation de quantité importante de données. Ces informations comprennent des caractéristiques démographiques, socio-économiques, cliniques, biologiques, d’imagerie, et, de plus en plus, génomiques. La médecine et sa pratique, fondées sur la sémiologie et la physiopathologie, vont être profondément transformées par ce phénomène. La complexité et la quantité des informations à intégrer pour prendre une décision médicale pourrait dépasser rapidement les capacités humaines. Les techniques d’intelligence artificielle pourraient assister le médecin et augmenter ses capacités prédictives et décisionnelles. La première partie de ce travail présente les types de données désormais accessibles en routine en oncologie radiothérapie. Elle détaille les données nécessaires à la création d’un modèle prédictif. Nous explorons comment exploiter les données spécifiques à la radiothérapie et présentons le travail d’homogénéisation et de conceptualisation qui a été réalisé sur ces données, notamment via la création d’une ontologie, dans le but de les intégrer à un entrepôt de données. La deuxième partie explore différentes méthodes de machine learning : k-NN, SVM, ANN et sa variante, le Deep Learning. Leurs avantages et inconvénients respectifs sont évalués avant de présenter les études ayant déjà utilisé ces méthodes dans le cadre de la radiothérapie. La troisième partie présente la création d’un modèle prédictif de la réponse complète à la radiochimiothérapie (RTCT) pré-opératoire dans le cancer du rectum localement avancé. Cette preuve de concept utilise des sources de données hétérogènes et un réseau neuronal profond dans le but d’identifier les patients en réponse complète après RTCT qui pourraient ne pas nécessiter de traitement chirurgical radical. Cet exemple, qui pourrait en pratique être intégré aux logiciels de radiothérapie déjà existant, utilise les données collectées en routine et illustre parfaitement le potentiel des approches de prédiction par IA pour la personnalisation des soins

The role of Next-Generation Sequencing in tumoral radiosensitivity prediction

Technological advances have led to more precise radiation delivery, which has resulted in significant clinical gains. A better understanding of tumoral radiosensitivity is still needed to develop strategies and further personalize radiation treatments. Next-Generation Sequencing (NGS) and system biology have significantly transformed the field of oncology in the last two decades, but have only a few clinical applications in radiation oncology. This review describes the technical aspects and evolutions of NGS and discusses the latest clinical applications of genomics to predict tumoral radiosensitivity

Deep Learning Prediction of Cancer Prevalence from Satellite Imagery

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

Empowering patients for radiation therapy safety: Results of the EMPATHY study

International audienceWith the increase of treatment complexity, enhancing safety is a key concern in radiation oncology. Beyond the involvement of the healthcare professional, patient involvement and empowerment could play a major role in that setting. We explored how patients perceived and fulfilled that role during their radiation treatment. Materials and methods: A voluntary and anonymous questionnaire was administered to all patients treated in our department between November 2013 and May 2014. The following data were collected: sociodemographic profile; information received and initiatives to search for additional information; behavior when an unusual treatment event was perceived; active involvement in the safety of the treatment; nature and perception of their own involvement. A statistical analysis was performed to assess behavioral predictors. Results: A total of 155 patients answered the survey. Most of them were treated for prostate (n=58, 37.4%), lung (n=27, 17.4%), head and neck (n=26, 16.8%) and breast (n=25, 16.1%). Only eight patients (5%) had previously received radiation therapy. Ninety-five percent of the patients estimated they had received enough information about their treatment, but 48% would have wanted more. When patients noticed an unusual event during their treatment session, most of them (61%) reported it to the radiation therapist. Conclusion: Patient participation to radiation therapy safety should be encouraged to ensure a cooperative risk management. Healthcare professionals need to inform the patients on the basic technical processes involved in their treatment. Patient empowerment should be added to the verifications made by the radiation therapists and physicians but should not replace them