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Updated: Jun 8, 2025
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
Published on: October 25, 2011
Whole-volume histogram analysis of spectral-computed tomography iodine maps characterizes HER2 expression in gastric
Wei-Ling Zhang1, Jing Sun1, Rong-Fang Huang2
1Department of Radiology, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China.
Background:
Although surgery remains the primary treatment for gastric cancer (GC), the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance. HER2 overexpression occurs in approximately 15%-20% of advanced GC cases, directly affecting treatment-related decisions. Spectral-computed tomography (sCT) enables the quantification of material compositions, and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth, angiogenesis, and response to systemic chemotherapy. No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps (IMs).
Aim:
To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.
Methods:
This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations. Nineteen parameters were extracted via sCT IM histogram analysis: The minimum, maximum, mean, standard deviation, variance, coefficient of variation, skewness, kurtosis, entropy, percentiles (1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th, and 99th), and lesion volume. Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed. Receiver operating characteristic curves were used to evaluate the parameters' diagnostic performance.
Results:
Values for the histogram parameters of the maximum, mean, standard deviation, variance, entropy, and percentiles were significantly lower in the HER2+ group than in the HER2- group (all P < 0.05). The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue (P = 0.001 and 0.023, respectively). The 99th percentile had the largest area under the curve for GC HER2 status identification (0.740), with 76.2%, sensitivity, 65.0% specificity, and 67.3% accuracy. All sCT IM histogram parameters correlated positively with the GC HER2 status (r = 0.237-0.337, P = 0.001-0.017).
Conclusion:
Whole-lesion histogram parameters derived from sCT IM analysis, and especially the 99th percentile, can serve as imaging biomarkers of HER2 overexpression in GC.
