Single fraction of accelerated partial breast irradiation in the elderly: Early clinical outcome

Abstract Background To analyze the clinical outcome of elderly women with early breast cancer who underwent accelerated partial breast irradiation (APBI) based on a post-operative single fraction of multicatheter interstitial high dose–rate brachytherapy (MIB). Methods A single institution retrospective cohort study was performed focusing on elderly patients (≥ 65 years old) presenting a low-risk breast carcinoma treated by lumpectomy plus axillary evaluation followed by MIB. A single fraction of 16 Gy was prescribed on the 100% isodose. Clinical outcome at 3 years was reported based on local relapse free survival (3-y LRFS), specific survival (SS) and overall survival (OS). Acute (< 180 days after APBI) and late toxicity were evaluated. Cosmetic results were clinically evaluated by the physician. Results Between January 2012 and August 2015, 48 women (51 lesions) were treated. Median age was 77.7 years (range: 65–92) with a median tumor size of 12 mm (range: 3–32). Five patients (pts) presented an axillary lymph node involvement (4 Nmic, 1 N1). Invasive ductal carcinoma was the most frequent histology type (86.3%). With a median follow–up of 40 months (range: 36–42), no local relapse occurred while 1 pt. developed axillary relapse (2.1%). The 3-y LRFS, SS and OS rates were 100%, 100% and 93.1% respectively. Forty-five acute events were remained. The most frequent acute toxicity was grade (G) 1 hyperpigmentation (26.7%), 3 pts. (6.3%) presented G3 acute toxicity (2 breast hematomas, 1 breast abscess). No ≥ G3 late toxicity was observed while 15 late toxicities occurred (G1: 13 events - 86.7%) mainly breast fibrosis). The rate of excellent cosmetic outcome was 76.4%. Conclusion We reported promising and encouraging clinical outcome of a post-operative single fraction of MIB ABPI in the elderly. This approach leads to consider a sfAPBI as an attractive alternative to intra-operative radiation therapy while all the patients will be good candidates for APBI in regards to the post-operative pathological report. More mature results (number of patients and follow-up) are needed

Oral and oropharyngeal cancer surgery with free-flap reconstruction in the elderly: Factors associated with long-term quality of life, patient needs and concerns. A GETTEC cross-sectional study

Objectives: To assess the factors associated with long-term quality of life (QoL) and patient concerns in elderly oral or oropharyngeal cancer (OOPC) patients after oncologic surgery and free-flap reconstruction. Methods: Patients aged over 70 years who were still alive and disease-free at least 1 year after surgery were enrolled in this cross-sectional multicentric study. Patients completed the EORTC QLQ-C30, -H&N35 and -ELD14 QoL questionnaires, and the Hospital Anxiety and Depression Scale (HADS). Patient needs were evaluated using the Patient Concerns Inventory (PCI). Factors associated with these clinical outcomes were determined in univariate and multivariate analysis. Results: Sixty-four patients were included in this study. Long-term QoL, functioning scales and patient autonomy were well-preserved. Main persistent symptoms were fatigue, constipation and oral function-related disorders. Salivary and mastication/swallowing problems were the main patient concerns. The mean number of patient concerns increased with the deterioration of their QoL. Psychological distress (HADS score ≥ 15) and patient frailty (G8 score < 15) were significantly associated with poor QoL outcomes. Conclusions: We found a negative correlation between the number of patient concerns and QoL. Dental rehabilitation and psychological and nutritional supportive measures are of critical importance in the multidisciplinary management of elderly OOPC patients

Application d’algorithmes de machine learning pour l’exploitation de données omiques en oncologie

The development of computer science in medicine and biology has generated a large volume of data. The complexity and the amount of information to be integrated for optimal decision-making in medicine have largely exceeded human capacities. These data includes demographic, clinical and radiological variables, but also biological variables and particularly omics (genomics, proteomics, transcriptomics and metabolomics) characterized by a large number of measured variables relatively to a generally small number of patients. Their analysis represents a real challenge as they are frequently "noisy" and associated with situations of multi-colinearity. Nowadays, computational power makes it possible to identify clinically relevant models within these sets of data by using machine learning algorithms. Through this thesis, our goal is to apply supervised and unsupervised learning methods, to large biological data, in order to participate in the optimization of the classification and therapeutic management of patients with various types of cancer. In the first part of this work a supervised learning method is applied to germline immunogenetic data to predict the efficacy and toxicity of immune checkpoint inhibitor therapy. In the second part, different unsupervised learning methods are compared to evaluate the contribution of metabolomics in the diagnosis and management of breast cancer. Finally, the third part of this work aims to expose the contribution that simulated therapeutic trials can make in biomedical research. The application of machine learning methods in oncology offers new perspectives to clinicians allowing them to make diagnostics faster and more accurately, or to optimize therapeutic management in terms of efficacy and toxicity.Le développement de l’informatique en médecine et en biologie a permis de générer un grand volume de données. La complexité et la quantité d’informations à intégrer lors d’une prise de décision médicale ont largement dépassé les capacités humaines. Ces informations comprennent des variables démographiques, cliniques ou radiologiques mais également des variables biologiques et en particulier omiques (génomique, protéomique, transcriptomique et métabolomique) caractérisées par un grand nombre de variables mesurées relativement au faible nombre de patients. Leur analyse représente un véritable défi dans la mesure où elles sont fréquemment « bruitées » et associées à des situations de multi-colinéarité. De nos jours, la puissance de calcul permet d'identifier des modèles cliniquement pertinents parmi cet ensemble de données en utilisant des algorithmes d’apprentissage automatique. A travers cette thèse, notre objectif est d’appliquer des méthodes d’apprentissage supervisé et non supervisé, à des données biologiques de grande dimension, dans le but de participer à l’optimisation de la classification et de la prise en charge thérapeutique des patients atteints de cancers. La première partie de ce travail consiste à appliquer une méthode d’apprentissage supervisé à des données d’immunogénétique germinale pour prédire l’efficacité thérapeutique et la toxicité d’un traitement par inhibiteur de point de contrôle immunitaire. La deuxième partie compare différentes méthodes d’apprentissage non supervisé permettant d’évaluer l’apport de la métabolomique dans le diagnostic et la prise en charge des cancers du sein en situation adjuvante. Enfin la troisième partie de ce travail a pour but d’exposer l’apport que peuvent présenter les essais thérapeutiques simulés en recherche biomédicale. L’application des méthodes d’apprentissage automatique en oncologie offre de nouvelles perspectives aux cliniciens leur permettant ainsi de poser des diagnostics plus rapidement et plus précisément, ou encore d’optimiser la prise en charge thérapeutique en termes d’efficacité et de toxicité

Application of machine learning algorithms for the exploitation of omic data in oncology

Le développement de l’informatique en médecine et en biologie a permis de générer un grand volume de données. La complexité et la quantité d’informations à intégrer lors d’une prise de décision médicale ont largement dépassé les capacités humaines. Ces informations comprennent des variables démographiques, cliniques ou radiologiques mais également des variables biologiques et en particulier omiques (génomique, protéomique, transcriptomique et métabolomique) caractérisées par un grand nombre de variables mesurées relativement au faible nombre de patients. Leur analyse représente un véritable défi dans la mesure où elles sont fréquemment « bruitées » et associées à des situations de multi-colinéarité. De nos jours, la puissance de calcul permet d'identifier des modèles cliniquement pertinents parmi cet ensemble de données en utilisant des algorithmes d’apprentissage automatique. A travers cette thèse, notre objectif est d’appliquer des méthodes d’apprentissage supervisé et non supervisé, à des données biologiques de grande dimension, dans le but de participer à l’optimisation de la classification et de la prise en charge thérapeutique des patients atteints de cancers. La première partie de ce travail consiste à appliquer une méthode d’apprentissage supervisé à des données d’immunogénétique germinale pour prédire l’efficacité thérapeutique et la toxicité d’un traitement par inhibiteur de point de contrôle immunitaire. La deuxième partie compare différentes méthodes d’apprentissage non supervisé permettant d’évaluer l’apport de la métabolomique dans le diagnostic et la prise en charge des cancers du sein en situation adjuvante. Enfin la troisième partie de ce travail a pour but d’exposer l’apport que peuvent présenter les essais thérapeutiques simulés en recherche biomédicale. L’application des méthodes d’apprentissage automatique en oncologie offre de nouvelles perspectives aux cliniciens leur permettant ainsi de poser des diagnostics plus rapidement et plus précisément, ou encore d’optimiser la prise en charge thérapeutique en termes d’efficacité et de toxicité.The development of computer science in medicine and biology has generated a large volume of data. The complexity and the amount of information to be integrated for optimal decision-making in medicine have largely exceeded human capacities. These data includes demographic, clinical and radiological variables, but also biological variables and particularly omics (genomics, proteomics, transcriptomics and metabolomics) characterized by a large number of measured variables relatively to a generally small number of patients. Their analysis represents a real challenge as they are frequently "noisy" and associated with situations of multi-colinearity. Nowadays, computational power makes it possible to identify clinically relevant models within these sets of data by using machine learning algorithms. Through this thesis, our goal is to apply supervised and unsupervised learning methods, to large biological data, in order to participate in the optimization of the classification and therapeutic management of patients with various types of cancer. In the first part of this work a supervised learning method is applied to germline immunogenetic data to predict the efficacy and toxicity of immune checkpoint inhibitor therapy. In the second part, different unsupervised learning methods are compared to evaluate the contribution of metabolomics in the diagnosis and management of breast cancer. Finally, the third part of this work aims to expose the contribution that simulated therapeutic trials can make in biomedical research. The application of machine learning methods in oncology offers new perspectives to clinicians allowing them to make diagnostics faster and more accurately, or to optimize therapeutic management in terms of efficacy and toxicity

Perceptions d'élèves sur ce qui caractérisent une personne en bonne santé

La plupart des jeunes sont sédentaires, ce qui influence négativement leur condition physique et leur santé. Les conséquences de la sédentarité sur la santé des jeunes ont amené le Ministère de l'Éducation du Québec à intégrer dans les cours d'EPS le volet de l'éducation à la santé. Les enseignants d'ÉPS du Québec s'interrogent : Que doivent-ils enseigner spécifiquement en lien avec la santé ? Quel est le niveau actuel des jeunes en cette matière ? Les élèves ont-ils les connaissances suffisantes pour adopter des habitudes de vie qui contribuent au maintien ou à l'amélioration de leur santé ? Cette étude a donc pour but de décrire les perceptions des élèves sur ce qui caractérise une personne en bonne santé afin de vérifier leur niveau de connaissance en cette matière. Elle vise aussi à identifier les personnes qui contribuent à les éduquer au plan de la santé. Soixante-dix-sept élèves du primaire et 69 élèves du secondaire ont participé à des entrevues de groupe et répondu à un questionnaire. L'analyse des données démontre que les élèves sont en mesure d'identifier les thèmes majeurs qui composent l'univers de la santé ainsi que les principaux comportements à adopter pour être en santé. Cependant, ils sont davantage préoccupés par la dimension physique de la personne en santé au détriment des aspects psychologiques et sociaux. De plus, selon les élèves, les enseignants d'EPS ont peu d'impact sur eux, comparativement aux parents qui sont de loin leurs premiers éducateurs à la sant

Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development

The conventional rules for anti-cancer drug development are no longer sufficient given the relatively limited number of patients available for therapeutic trials. It is thus a real challenge to better design trials in the context of new drug approval for anti-cancer treatment. Artificial intelligence (AI)-based in silico trials can incorporate far fewer but more informative patients and could be conducted faster and at a lower cost. AI can be integrated into in silico clinical trials to improve data analysis, modeling and simulation, personalized medicine approaches, trial design optimization, and virtual patient generation. Health authorities are encouraged to thoroughly review the rules for setting up clinical trials, incorporating AI and in silico methodology once they have been appropriately validated. This article also aims to highlight the limits and challenges related to AI and machine learning

Artificial Intelligence and Anticancer Drug Development—Keep a Cool Head

Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology

Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings

National audienceOver the last decades, molecular signatures have become increasingly important in oncology and are opening up a new area of personalized medicine. Nevertheless, biological relevance and statistical tools necessary for the development of these signatures have been called into question in the literature. Here, we investigate six typical selection methods for high-dimensional settings and survival endpoints, including LASSO and some of its extensions, component-wise boosting, and random survival forests (RSF). A resampling algorithm based on data splitting was used on nine high-dimensional simulated datasets to assess selection stability on training sets and the intersection between selection methods. Prognostic performances were evaluated on respective validation sets. Finally, one application on a real breast cancer dataset has been proposed. The false discovery rate (FDR) was high for each selection method, and the intersection between lists of predictors was very poor. RSF selects many more variables than the other methods and thus becomes less efficient on validation sets. Due to the complex correlation structure in genomic data, stability in the selection procedure is generally poor for selected predictors, but can be improved with a higher training sample size. In a very high-dimensional setting, we recommend the LASSO-pcvl method since it outperforms other methods by reducing the number of selected genes and minimizing FDR in most scenarios. Nevertheless, this method still gives a high rate of false positives. Further work is thus necessary to propose new methods to overcome this issue where numerous predictors are present. Pluridisciplinary discussion between clinicians and statisticians is necessary to ensure both statistical and biological relevance of the predictors included in molecular signatures