Description

In recent years, imaging has become essential in the evaluation of treatment efficacy in medical oncology . As a result, the proportion of clinical trials involving imaging has risen sharply, and radiology departments are faced with the problem of work overload in the face of complex specific interpretation methods which have developed further recently with immunotherapies (iRECIST 2017, imRECIST 2018).

The RECIST 1.1 method is the reference method for evaluating treatments of solid cancers because it allows standardization, reproducibility and a common language between all stakeholders.

Interpretation is based on :A pre-treatment examination called Baseline, which defines the lesions to be monitored and evaluation examinations (generally every 6 weeks) to compare measurements and define overall tumor evolution (Complete Response, Partial Response, Stable Disease, Progressive Disease).

It is difficult to concentrate the increasing number of protocol examinations on a small number of radiologists. As a result, the number of radiologists involved in these assessments multiplies the difficulties and risks of error. For the past 2 years, the Institut Paoli Calmettes has been testing cooperative protocol readings between radiologists and medical electroradiology manipulators (MEMs). First impressions are encouraging, and show that both radiologists and investigators are satisfied, with a priori an increase in the quality of radiological reports and a saving in interpretation time that we hope to objectify. This type of organization has already been partly studied in other Cancer Centers, such as the Centre Antoine Lacassagne in Nice and the Centre Henri Becquerel in Rouen, demonstrating the effectiveness of manipulators in measuring target lesions.

Artificial Intelligence solutions are currently being developed in all areas of imaging, opening up the possibility of applications to RECIST assessments). While these solutions appear to be of interest for the reassessment of previously identified lesions, their limitations for the time being relate to the differentiation between malignant lesions and benign tissue. The aim of adding AI to human analyses would be to further improve measurement performance and speed of assessment, without replacing but implementing the contribution that specialized manipulators could already make to radiological interpretations. In a difficult period for the radiology manipulator profession, these proposed new skills may also give new interest to the profession by integrating into an advanced practice pathway for which radiologists remain responsible. The aim of this study is to evaluate the contribution of collaboration with a manipulator specialized in therapeutic trials for the radiologist in charge of interpretation.

It aims to redefine the sharing of activities between delegators (mainly doctors) and other healthcare professionals (receiving delegation, or delegates), by entrusting the latter with certain activities outside their usual remit, in order to free up medical time while making the careers of these healthcare professionals more attractive.

The innovative nature of this cooperation would highlight the value of radiologist/EMM collaboration in intellectual acts.

It is not possible to compare 2 cohorts of patients in order to objectivize the contribution of manipulators to patient care, as the difficulty and time required to interpret RECIST 1.1 vary greatly from one patient to another, and depend mainly on the type of disease and its extent on the scanner.

Thus, 2 groups of patients comparable in terms of difficulty and interpretation time cannot be composed. For this reason, the study is based on the radiologist's assessment of the assistance provided by the MEM.