title: Volume-based response evaluation with consensual lesion selection: a pilot study by using cloud solutions and comparison to RECIST 1.1.
abstract: OBJECTIVE
  Lesion volume is considered as a promising alternative to Response Evaluation Criteria in Solid Tumors (RECIST) to make tumor measurements more accurate and consistent, which would enable an earlier detection of temporal changes. In this article, we report the results of a pilot study aiming at evaluating the effects of a consensual lesion selection on volume-based response (VBR) assessments.
  
  
  METHODS
  Eleven patients with lung computed tomography scans acquired at three time points were selected from Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) and proprietary databases. Images were analyzed according to RECIST 1.1 and VBR criteria by three readers working in different geographic locations. Cloud solutions were used to connect readers and carry out a consensus process on the selection of lesions used for computing response. Because there are not currently accepted thresholds for computing VBR, we have applied a set of thresholds based on measurement variability (-35% and +55%). The benefit of this consensus was measured in terms of multiobserver agreement by using Fleiss kappa (κfleiss) and corresponding standard errors (SE).
  
  
  RESULTS
  VBR after consensual selection of target lesions allowed to obtain κfleiss = 0.85 (SE = 0.091), which increases up to 0.95 (SE = 0.092), if an extra consensus on new lesions is added. As a reference, the agreement when applying RECIST without consensus was κfleiss = 0.72 (SE = 0.088). These differences were found to be statistically significant according to a z-test.
  
  
  CONCLUSIONS
  An agreement on the selection of lesions allows reducing the inter-reader variability when computing VBR. Cloud solutions showed to be an interesting and feasible strategy for standardizing response evaluations, reducing variability, and increasing consistency of results in multicenter clinical trials.
authors.affiliation: R&D Department, MEDIAN Technologies, Les Deux Arcs B, 1800 Route des Crêtes, Valbonne 06560, France. Electronic address: estanislao.oubel@mediantechnologies.com.
authors.forename: Estanislao
authors.initials: E
authors.lastname: Oubel
authors.affiliation: Radiology Department, Nice University Hospital, Nice, France.
authors.forename: Eric
authors.initials: E
authors.lastname: Bonnard
authors.affiliation: Division of Hematology, Respiratory Medicine and Oncology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan.
authors.forename: Naoko
authors.initials: N
authors.lastname: Sueoka-Aragane
authors.affiliation: Division of Hematology, Respiratory Medicine and Oncology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan.
authors.forename: Naomi
authors.initials: N
authors.lastname: Kobayashi
authors.affiliation: R&D Department, MEDIAN Technologies, Les Deux Arcs B, 1800 Route des Crêtes, Valbonne 06560, France.
authors.forename: Colette
authors.initials: C
authors.lastname: Charbonnier
authors.affiliation: Global Healthcare IT Project, Medical Equipment Group, Canon Inc, Tokyo, Japan.
authors.forename: Junta
authors.initials: J
authors.lastname: Yamamichi
authors.affiliation: Global Healthcare IT Project, Medical Equipment Group, Canon Inc, Tokyo, Japan.
authors.forename: Hideaki
authors.initials: H
authors.lastname: Mizobe
authors.affiliation: Radiology Department, Nice University Hospital, Nice, France.
authors.forename: Shinya
authors.initials: S
authors.lastname: Kimura
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D020407:Internet; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods; D010865:Pilot Projects; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D066066:Response Evaluation Criteria in Solid Tumors; D012680:Sensitivity and Specificity; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*; D016896:Treatment Outcome; D047368:Tumor Burden
keywords: Clinical trials; RECIST; biomarkers; cloud computing; consensus; lesion volume; volume thresholds