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

Charles X. Wang, Dalia Elganainy, Mohamed M. Zaid, Joseph D. Butner, Anshuman Agrawal, Sara Nizzero, Bruce D. Minsky, Emma B. Holliday, Cullen M. Taniguchi, Grace L. Smith, Albert C. Koong, Joseph M. Herman, Prajnan Das, Anirban Maitra, Huamin Wang, Robert A. Wolff, Matthew H.G. Katz, Christopher H. Crane, Vittorio Cristini, Eugene J. Koay

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)163-172
Number of pages10
JournalInternational Journal of Radiation Oncology Biology Physics
Volume114
Issue number1
DOIs
StatePublished - Sep 1 2022

ASJC Scopus subject areas

  • Radiation
  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research

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