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In this work we develop onconet, novel machine learning algorithm that assesses treatment response from a 1,954 pairs of sequential fdg pet/ct exams through weak supervision using the standard uptake values (suvmax) in associated radiology reports. onconet demonstrates an auroc of 0.86 and 0.84 on internal and external institution test sets respectively for determination of change between scans while also showing strong agreement to clinical scoring systems with a kappa score of 0.8.