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Appl. cancer res ; 38: 1-5, jan. 30, 2018. ilus
Article in English | LILACS, Inca | ID: biblio-910537

ABSTRACT

Background: Tumour volume at therapy initiation, Vi, is rarely available in cancer patients, and the last pre-treatment tumour volume available is from previous diagnostic imaging (Vd). Therapeutic efficacy is thus evaluated by comparing tumour volume after treatment with Vd, instead of Vi, which results in underestimation of treatment efficacy. Vi, together with Vd, can also be used for estimation of the natural growth rate of tumour valuable for, e.g., screening programs, prognostication and individualised treatment planning such as chemotherapy scheduling. The aim of this work was to study the feasibility of estimating Vi by back-extrapolating the post-therapy regression of tumour volume, based on data from animal model. Methods: Nude mice bearing human neuroendocrine GOT1 tumour cell line were treated with 177Lu-DOTA-TATE. Tumour volumes were measured regularly after therapy and Vi was estimated by back-extrapolation of (a) linear and (b) exponential regression lines of the two earliest post-therapy tumour volumes and (c) the long-term exponential regression of tumour volume. The estimated Vi values (Vest) were compared with the measured volume of tumour at therapy initiation. Results: The linear regression of the two earliest post-therapy tumour volumes gave the best estimate for Vi (Vest = 0.91 Vi, p < 0.00001), compared with the exponential regression models either on short-term (Vest = 2.30 Vi, p < 0.01), or long-term (Vest = 0.93 Vi, non-significant) follow up of tumour volume after therapy. Conclusion: Back-extrapolation of the early linear regression of tumour volume after therapy gave the best estimate for tumour volume at time of therapy initiation. This estimate can be used as baseline for treatment efficacy evaluation or for estimation of the natural growth rate of tumour (together with the measured tumour volume at pre-treatment diagnostic imaging)


Subject(s)
Animals , Rats , Diagnostic Imaging , Carcinoma, Neuroendocrine , Cell Line, Tumor , Neoplasms
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