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1000
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Abstract/Summary
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Background!#!Manual quantification of the metabolic tumor volume (MTV) from whole-body !##!Methods!#!Fifty consecutive breast cancer patients that underwent !##!Results!#!If PERCIST measurable foci were regarded, the neural network displayed high per patient sensitivity and specificity in detecting suspicious !##!Conclusion!#!Although trained on lymphoma and lung cancer, PARS showed good accuracy in the detection of PERCIST measurable lesions. Therefore, the neural network seems not prone to the clever Hans effect. However, the network has poor accuracy if all manually segmented lesions were used as reference standard. Both the whole body and organ-wise MTV were significant prognosticators of overall survival in advanced breast cancer.
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1000
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Hinweis
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DeepGreen-ID: 657ae883ba4b4a4a990c445924f5adc1 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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