Clinicopathological correlation of radiologic measurement of post-therapy tumor size and tumor volume for pancreatic ductal adenocarcinoma.

OBJECTIVES: Tumor size measurement is critical for accurate tumor staging in patients with pancreatic ductal adenocarcinoma (PDAC). However, accurate tumor size measurement is challenging in patients who received neoadjuvant therapy before resection, due to treatment-induced fibrosis and tumor invasion beyond the grossly identified tumor area. In this study, we evaluated the correlation between the tumor size and tumor volume measured on post-therapy computed tomography (CT) scans and the pathological measurement. Also, we investigated the correlation between these measurements and clinicopathological parameters and survival. MATERIALS AND METHODS: Retrospectively, we evaluated 343 patients with PDAC who received neoadjuvant therapy, followed by pancreaticoduodenectomy and had pre-operative pancreatic protocol CT imaging. We measured the longest tumor diameter (RadL) and the radiological tumor volume (RadV) on the post-therapy CT scan, then we categorized RadL into four radiologic tumor stages (RTS) based on the current AJCC staging (8th edition) protocol and RadV based on the median. Pearson correlation or Spearman's coefficient (δ), T-test and ANOVA was used to test the correlation between the radiological and pathological measurement. Chi-square analysis was used to test the correlation with the tumor pathological response, lymph-node metastasis and margin status and Kaplan-Meier and Cox-proportional hazard for survival analysis. P-value < 0.05 was considered significant. RESULTS: As a continuous variable, RadL showed a positive linear correlation with the post-therapy pathologic tumor size in the overall patient population (Pearson correlation coefficient: 0.72, P < 0.001) and RadV (δ: 0.63, p < 0.0001). However, there was no correlation between RadL and pathologic tumor size in patients with ypT0 and those with pathologic tumor size of ≤1.0 cm. Post-therapy RTS and RadV group correlated with ypT stage, tumor response grades using either CAP or MDA grading system, distance of superior mesenteric artery margin and tumor recurrence/metastasis. CONCLUSION: Although RadL tends to understage ypT in PDAC patients who had no radiologically detectable tumor or small tumors (RTS0 or RTS1), radiologic measurement of post-therapy tumor size may be used as a marker for the pathologic tumor staging and tumor response to neoadjuvant therapy.

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 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.