An inter-specialty cancer training programme curriculum for Europe.

INTRODUCTION: Multidisciplinary and multi-professional collaboration is vital in providing better outcomes for patients The aim of the INTERACT-EUROPE Project (Wide Ranging Cooperation and Cutting Edge Innovation As A Response To Cancer Training Needs) was to develop an inter-specialty curriculum. A pilot project will enable a pioneer cohort to acquire a sample of the competencies needed. METHODS: A scoping review, qualitative and quantitative surveys were undertaken. The quantitative survey results are reported here. Respondents, including members of education boards, curriculum committees, trainee committees of European specialist societies and the ECO Patient Advisory Committee, were asked to score 127 proposed competencies on a 7-point Likert scale as to their value in achieving the aims of the curriculum. Results were discussed and competencies developed at two stakeholder meetings. A consultative document, shared with stakeholders and available online, requested views regarding the other components of the curriculum. RESULTS: Eleven competencies were revised, three omitted and three added. The competencies were organised according to the CanMEDS framework with 13 Entrustable Professional Activities, 23 competencies and 127 enabling competencies covering all roles in the framework. Recommendations regarding the infrastructure, organisational aspects, eligibility of trainees and training centres, programme contents, assessment and evaluation were developed using the replies to the consultative document. CONCLUSIONS: An Inter-specialty Cancer Training Programme Curriculum and a pilot programme with virtual and face-to-face components have been developed with the aim of improving the care of people affected by cancer.

A glimpse into relapsed refractory multiple myeloma treatment in real-world practice in Spain: the GeminiS study.

OBJECTIVES: To describe the incorporation of monoclonal antibodies (mAb) in real-world (RW) practice for the treatment of patients with relapsed refractory multiple myeloma (RRMM) in a setting with other treatment alternatives. METHODS: This was an observational, multicenter, ambispective study of RRMM treated with or without a mAb. RESULTS: A total of 171 patients were included. For the group treated without mAb, the median (95% CI) progression-free survival (PFS) to relapse was 22.4 (17.8-27.0) months; partial response or better (≥PR) and complete response or better (≥CR) was observed in 74.1% and 24.1% of patients, respectively; and median time to first response in first relapse was 2.0 months and in second relapse was 2.5 months. For the group of patients treated with mAb in first or second relapse, the median PFS was 20.9 (95% CI, could not be evaluated) months; the ≥ PR and ≥ CR rates were 76,2% and 28.6%, respectively; and the median time to first response in first relapse was 1.2 month and in second relapse was 1.0 months. The safety profiles for the combinations were consistent with those expected. CONCLUSIONS: The incorporation of mAb in RW practice for the treatment of RRMM has shown good quality and speed of response with a similar safety profile shown in randomized clinical trials.

Real-World Evidence of Daratumumab Monotherapy in Relapsed/Refractory Multiple Myeloma Patients and Efficacy on Soft-Tissue Plasmacytomas.

INTRODUCTION: Daratumumab is an anti-CD38 agent that was first investigated as single agent in GEN501 and SIRIUS trials in patients with advanced multiple myeloma (MM). Overall response rate (ORR) was 30% with positive impact on progression-free survival (PFS). However, there is a lack of information regarding plasmacytoma response. MATERIALS AND METHODS: Here, we described a heavily pretreated group of 43 patients who received daratumumab monotherapy after EMA approval and focused on plasmacytoma response. RESULTS: After a median follow-up of 26 months, median time to best response was 2.9 months (range 0.8-13.1), median PFS was 5.2 months (95% CI 2.5 - 8.8) and median OS was 11.2 months (95% CI 6.3 - 17.0). Patients who achieved at least partial response had longer median PFS and OS (12.8 and 20.2 months, respectively) than those who achieved minimal response or stable disease (5.3 and 11.2 months, respectively). Ten patients (23%) had plasmacytomas (70% paraskeletal, 30% extramedullary). The clinical benefit for patients with and without plasmacytomas was 20% versus 42%. A dissociation between serological and plasmacytoma response was observed in 40% of the patients. Thus, 50% of the patients with plasmacytomas achieved at least serological minimal response but only 20% had plasmacytoma response. CONCLUSION: This is the first real-world study of daratumumab monotherapy that focuses on efficacy data regarding soft-tissue plasmacytomas in patients with relapsed/refractory mieloma, showing a limited benefit in this patient population.

Streaming cascade-based speech translation leveraged by a direct segmentation model.

The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. Nowadays, state-of-the-art ST systems are populated with deep neural networks that are conceived to work in an offline setup in which the audio input to be translated is fully available in advance. However, a streaming setup defines a completely different picture, in which an unbounded audio input gradually becomes available and at the same time the translation needs to be generated under real-time constraints. In this work, we present a state-of-the-art streaming ST system in which neural-based models integrated in the ASR and MT components are carefully adapted in terms of their training and decoding procedures in order to run under a streaming setup. In addition, a direct segmentation model that adapts the continuous ASR output to the capacity of simultaneous MT systems trained at the sentence level is introduced to guarantee low latency while preserving the translation quality of the complete ST system. The resulting ST system is thoroughly evaluated on the real-life streaming Europarl-ST benchmark to gauge the trade-off between quality and latency for each component individually as well as for the complete ST system.

Computer-aided detection of prostate cancer.

BACKGROUND: Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in developed countries. Detection of prostate carcinoma at an early stage is crucial for successful treatment. MATERIAL AND METHODS: A method for the analysis of transrectal ultrasound images aimed at computer-aided diagnosis of prostate cancer is tested in this paper. First, two classifiers based on k-nearest neighbors and Hidden Markov models are compared. Second, the diagnostic capacity of our system is tested by means of a set of experiments where humans with varying degrees of experience classified a set of ultrasound images with and without the aid of the computer-aided system. The corpus used in this study was specifically acquired for this purpose. It consists of 4944 ultrasound images corresponding to 303 patients, and is publicly available for non-commercial use upon request. RESULTS: The best classification results achieve an area under the receiver operating characteristic curve of 61.6%. However, the diagnostic capacity of an expert urologist using the computer-aided system improves only slightly compared with his/her capacity without the aid of the system. CONCLUSIONS: Despite the difficulty of this task, the obtained results indicate that discrimination between cancerous and non-cancerous tissue is possible to a certain degree. The computer-aided system helps an inexperienced user to make a better diagnosis, however it must be able to perform better in order to be useful in a real-world clinical context.