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Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer

PURPOSE: The benefit of radiation therapy for pancreatic ductal adenocarcinoma (PDAC) remains unclear. We hypothesized that a new mechanistic mathematical model of chemotherapy and radiation response could predict clinical outcomes a priori, using a previously described baseline measurement of perfu...

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Autores principales: Wang, Charles X., Elganainy, Dalia, Zaid, Mohamed M., Butner, Joseph D., Agrawal, Anshuman, Nizzero, Sara, Minsky, Bruce D., Holliday, Emma B., Taniguchi, Cullen M., Smith, Grace L., Koong, Albert C., Herman, Joseph M., Das, Prajnan, Maitra, Anirban, Wang, Huamin, Wolff, Robert A., Katz, Matthew H.G., Crane, Christopher H., Cristini, Vittorio, Koay, Eugene J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042520/
https://www.ncbi.nlm.nih.gov/pubmed/35643254
http://dx.doi.org/10.1016/j.ijrobp.2022.04.044
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author Wang, Charles X.
Elganainy, Dalia
Zaid, Mohamed M.
Butner, Joseph D.
Agrawal, Anshuman
Nizzero, Sara
Minsky, Bruce D.
Holliday, Emma B.
Taniguchi, Cullen M.
Smith, Grace L.
Koong, Albert C.
Herman, Joseph M.
Das, Prajnan
Maitra, Anirban
Wang, Huamin
Wolff, Robert A.
Katz, Matthew H.G.
Crane, Christopher H.
Cristini, Vittorio
Koay, Eugene J.
author_facet Wang, Charles X.
Elganainy, Dalia
Zaid, Mohamed M.
Butner, Joseph D.
Agrawal, Anshuman
Nizzero, Sara
Minsky, Bruce D.
Holliday, Emma B.
Taniguchi, Cullen M.
Smith, Grace L.
Koong, Albert C.
Herman, Joseph M.
Das, Prajnan
Maitra, Anirban
Wang, Huamin
Wolff, Robert A.
Katz, Matthew H.G.
Crane, Christopher H.
Cristini, Vittorio
Koay, Eugene J.
author_sort Wang, Charles X.
collection PubMed
description PURPOSE: The benefit of radiation therapy for pancreatic ductal adenocarcinoma (PDAC) remains unclear. We hypothesized that a new mechanistic mathematical model of chemotherapy and radiation response could predict clinical outcomes a priori, using a previously described baseline measurement of perfusion from computed tomography scans, normalized area under the enhancement curve (nAUC). METHODS AND MATERIALS: We simplified an existing mass transport model that predicted cancer cell death by replacing previously unknown variables with averaged direct measurements from randomly selected pathologic sections of untreated PDAC. This allowed using nAUC as the sole model input to approximate tumor perfusion. We then compared the predicted cancer cell death to the actual cell death measured from corresponding resected tumors treated with neoadjuvant chemoradiation in a calibration cohort (n = 80) and prospective cohort (n = 25). After calibration, we applied the model to 2 separate cohorts for pathologic and clinical associations: targeted therapy cohort (n = 101), cetuximab/bevacizumab + radiosensitizing chemotherapy, and standard chemoradiation cohort (n = 81), radiosensitizing chemotherapy to 50.4 Gy in 28 fractions. RESULTS: We established the relationship between pretreatment computed v nAUC to pathologically verified blood volume fraction of the tumor (r = 0.65; P = .009) and fractional tumor cell death (r = 0.97-0.99; P < .0001) in the calibration and prospective cohorts. On multivariate analyses, accounting for traditional covariates, nAUC independently associated with overall survival in all cohorts (mean hazard ratios, 0.14-0.31). Receiver operator characteristic analyses revealed discrimination of good and bad prognostic groups in the cohorts with area under the curve values of 0.64 to 0.71. CONCLUSIONS: This work presents a new mathematical modeling approach to predict clinical response from chemotherapy and radiation for PDAC. Our findings indicate that oxygen/drug diffusion strongly influences clinical responses and that nAUC is a potential tool to select patients with PDAC for radiation therapy.
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spelling pubmed-100425202023-03-27 Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer Wang, Charles X. Elganainy, Dalia Zaid, Mohamed M. Butner, Joseph D. Agrawal, Anshuman Nizzero, Sara Minsky, Bruce D. Holliday, Emma B. Taniguchi, Cullen M. Smith, Grace L. Koong, Albert C. Herman, Joseph M. Das, Prajnan Maitra, Anirban Wang, Huamin Wolff, Robert A. Katz, Matthew H.G. Crane, Christopher H. Cristini, Vittorio Koay, Eugene J. Int J Radiat Oncol Biol Phys Article PURPOSE: The benefit of radiation therapy for pancreatic ductal adenocarcinoma (PDAC) remains unclear. We hypothesized that a new mechanistic mathematical model of chemotherapy and radiation response could predict clinical outcomes a priori, using a previously described baseline measurement of perfusion from computed tomography scans, normalized area under the enhancement curve (nAUC). METHODS AND MATERIALS: We simplified an existing mass transport model that predicted cancer cell death by replacing previously unknown variables with averaged direct measurements from randomly selected pathologic sections of untreated PDAC. This allowed using nAUC as the sole model input to approximate tumor perfusion. We then compared the predicted cancer cell death to the actual cell death measured from corresponding resected tumors treated with neoadjuvant chemoradiation in a calibration cohort (n = 80) and prospective cohort (n = 25). After calibration, we applied the model to 2 separate cohorts for pathologic and clinical associations: targeted therapy cohort (n = 101), cetuximab/bevacizumab + radiosensitizing chemotherapy, and standard chemoradiation cohort (n = 81), radiosensitizing chemotherapy to 50.4 Gy in 28 fractions. RESULTS: We established the relationship between pretreatment computed v nAUC to pathologically verified blood volume fraction of the tumor (r = 0.65; P = .009) and fractional tumor cell death (r = 0.97-0.99; P < .0001) in the calibration and prospective cohorts. On multivariate analyses, accounting for traditional covariates, nAUC independently associated with overall survival in all cohorts (mean hazard ratios, 0.14-0.31). Receiver operator characteristic analyses revealed discrimination of good and bad prognostic groups in the cohorts with area under the curve values of 0.64 to 0.71. CONCLUSIONS: This work presents a new mathematical modeling approach to predict clinical response from chemotherapy and radiation for PDAC. Our findings indicate that oxygen/drug diffusion strongly influences clinical responses and that nAUC is a potential tool to select patients with PDAC for radiation therapy. 2022-09-01 2022-05-26 /pmc/articles/PMC10042520/ /pubmed/35643254 http://dx.doi.org/10.1016/j.ijrobp.2022.04.044 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Article
Wang, Charles X.
Elganainy, Dalia
Zaid, Mohamed M.
Butner, Joseph D.
Agrawal, Anshuman
Nizzero, Sara
Minsky, Bruce D.
Holliday, Emma B.
Taniguchi, Cullen M.
Smith, Grace L.
Koong, Albert C.
Herman, Joseph M.
Das, Prajnan
Maitra, Anirban
Wang, Huamin
Wolff, Robert A.
Katz, Matthew H.G.
Crane, Christopher H.
Cristini, Vittorio
Koay, Eugene J.
Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer
title Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer
title_full Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer
title_fullStr Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer
title_full_unstemmed Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer
title_short Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer
title_sort mass transport model of radiation response: calibration and application to chemoradiation for pancreatic cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042520/
https://www.ncbi.nlm.nih.gov/pubmed/35643254
http://dx.doi.org/10.1016/j.ijrobp.2022.04.044
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