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ePoster Display

1112P - Modified TGR: A new strong radiological marker to accurately predict early response to PRRT in GEPNETs

Date

16 Sep 2021

Session

ePoster Display

Topics

Tumour Site

Neuroendocrine Neoplasms

Presenters

Federica Scalorbi

Citation

Annals of Oncology (2021) 32 (suppl_5): S906-S920. 10.1016/annonc/annonc678

Authors

F. Scalorbi1, G. Calareso2, E. Garanzini3, A. Marchiano3, E. Seregni1, M. Maccauro1

Author affiliations

  • 1 Nuclear Medicine Department, Istituto Nazionale Tumori, 20133BJ - Milan/IT
  • 2 Radiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan/IT
  • 3 Radiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 - Milan/IT

Resources

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Abstract 1112P

Background

To investigate the value of modified TGR (tumor growth rate) as radiological predictor of early response to PRRT, in GEPNET patients.

Methods

G1-G2 GEPNET patients treated with PRRT (177Lu-Oxodotreotide, 4 administrations, 7.4 GBq) at our centre from 04/2019 to 10/2020 were considered. Three CT/MRI scans per patient were collected: one performed within 3 months before PRRT, one interim evaluation after 2 PRRT and one within 4 months after the end of treatment to assess early response, according to RECIST1.1. All the scans were centrally re-evaluated by 2 dedicated radiologists. TGR was calculated in 2 ways: assuming the volume of lesions can be calculated applying the volume of a sphere formula (TGR_sphere, classical TGR formula, Dromain, BMC 2019) or the volume of an elliptical cylinder (TGR_ elliptical, new model). In both cases, to assess TGR, baseline versus interim evaluations were compared and the values were expressed as % increase/month. Patients were subdivided as responders (CR, PR, SD) and non-responders (PD), according to RECIST. Previous therapy lines were calculated as possible confounders. Fisher and K-Wallis test were applied to assess independence between response to treatment and patient characteristics. Logistic regression was performed to determine predictability of both TGR models, ROC analysis was applied to assess the performance of the 2 models and evaluate optimal TGR cut-off.

Results

Twenty-seven patients (12 males, 15 females, mean age 63.9, range 37-80) were evaluated. 15 (55.6%) were midgut, 12 (44.4%) foregut, 24 (88.8%). PRRT was applied in second line in 18 (66.6%), in third or further in 9 (33.4%). Considering RECIST, 4 (14.8%) were non-responders. Logist regression showed OR equal to 5.9 with AUC 0.95 (Sensitivity 75%, Specificity 95%) for TGR_elliptical model and OR 1.05 with AUC 0.75 (Sensitivity 25%, Specificity 75%), for TGR_spherical. The optimal cut-off value for progression prediction was 5.5% increase/month for TGR_elliptical (Sensitivity 100%, Specificity 86.4%) and 5.3%/month for TGR_sphere (Sensitivity 75%, Specificity 81.8%).

Conclusions

Interim TGR_elliptical is a strong and accurate predictor of early progression of GEPNET disease after PRRT. External validation is on course.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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