Abstract Library

#3636 Texture analysis in the prediction of progression-free survival (PFS) in patients with progressive neuroendocrine tumors (NETs) treated with lanreotide autogel (LAN) in the prospective, open-label, non-comparative, phase 2 CLARINET FORTE trial

Introduction: Tumor texture analysis may provide new imaging biomarkers for PFS prediction in patients with NETs.

Conference:

Presenting Author:

Authors: Dromain C, Truong Thanh X, Grandoulier A, Pavel M,

Keywords: texture analysis, progression-free survival, neuroendocrine tumor,

#3520 Impact of a reduced dosing interval of lanreotide autogel (LAN) on tumor growth rate (TGR) in patients with progressive neuroendocrine tumors (NETs) in the prospective, single-arm, phase 2 CLARINET FORTE trial

Introduction: TGR has been investigated as a potential biomarker for antitumor drug activity in NETs.

Conference:

Presenting Author:

Authors: Dromain C, Ćwikła J, Lombard-Bohas C, Borbath I, Shah T,

Keywords: Tumor Growth Rate, Lanreotide Autogel, midgut, pancreatic,

#3234 Dissociated response as a new biomarker of treatment response in neuroendocrine tumors: Results from phase 3 of the RAISE project

Introduction: RAISE aimed to find a surrogate endpoint to early predict treatment efficacy in patients with neuroendocrine tumors (NET). Dissociated response is the inhomogeneous response of the different lesions of a patient. Its prognostic value remains unclear.

Conference: 18th Annual ENETS Concerence (2021)

Presenting Author: Dromain C

Authors: Dromain C, Pavel M, Ronot M, Schaefer N, Mandair D,

Keywords: neuroendocrine tumor, dissociated response, progression-free survival, prediction,

#3229 The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors: Results from phase 3 of the RAISE project

Introduction: Response Evaluation Criteria in Solid Tumors (RECIST) assesses treatment response via tumor progression in patients with neuroendocrine tumors (NET). RAISE combined deep learning models (DLM) with sum of the longest diameter (SLD) of liver lesions and chromogranin A (CgA) to find a surrogate endpoint for RECIST to early predict progression-free survival (PFS).

Conference: 18th Annual ENETS Concerence (2021)

Presenting Author: Pavel M

Authors: Pavel M, Dromain C, Ronot M, Schaefer N, Mandair D,

Keywords: neuroendocrine tumor, deep learning models, progression-free survival, prediction,

#2993 Proposal of New Imaging Criteria for Evaluating the Response of Liver Metastases to Systemic Treatments in Digestive Neuroendocrine Tumors (NET) as an Alternative to RECIST 1.1

Introduction: RECIST 1.1 criteria have been challenged for the evaluation of treatment response in NET.

Conference: 17th Annual ENETSConcerence (2020)

Presenting Author:

Authors: de Mestier L, Dromain C, Lamarca A, La Salvia A, Op de Beeck B,

Keywords: imaging criteria, response evaluation, CT scan,