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SUMMARY:Patient-specific\, organ-scale forecasting of prostate cancer grow
 th in active surveillance - Hector Gomez (Purdue University)
DTSTART:20230801T123000Z
DTEND:20230801T133000Z
UID:TALK202411@talks.cam.ac.uk
DESCRIPTION:Active surveillance (AS) is a well-established clinical strate
 gy for managing prostate cancer (PCa) with low to intermediate risk. Howev
 er\, current AS protocols have limitations\, including delayed detection o
 f tumor progression and a lack of personalized monitoring plans. To overco
 me these issues\, we propose advancing AS towards a predictive patient-spe
 cific paradigm using a computational model.&nbsp\;The proposed model utili
 zes longitudinal imaging and clinical data collected during standard AS fo
 r individual patients. It describes PCa growth in terms of tumor cell dens
 ity dynamics\, considering tumor cell mobility and net proliferation. The 
 model is initialized with the first MRI scan and then parameterized to min
 imize the discrepancy between model predictions and tumor cell density at 
 subsequent scans. The calibrated model is used to forecast tumor progressi
 on up to a future MRI scan.&nbsp\;Preliminary results from an analysis of 
 eight PCa patients enrolled in AS show promising agreement between the mod
 el predictions and actual tumor growth. These findings suggest that the pr
 oposed computational model could serve as a promising tool for predicting 
 tumor progression in AS\, aiding in the early identification of patients w
 ho may require treatment. If successful\, this approach has the potential 
 to improve the effectiveness of AS by providing personalized monitoring pl
 ans and reducing both overtreatment and undertreatment of PCa.\nJoint work
  with: G. Lorenzo\, C. Wu\, J.P. Yung\, J.F. Ward\, H. Gomez\, A. Reali\, 
 T.E. Yankeelov\, A.M. Venkatesan\, T.J.R. Hughes\n&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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