Proving of a Mathematical Model of Cell Calculation Based on Apparent Diffusion Coefficient

Area of Science:

• Radiology and Oncology
• Medical Imaging Analysis
• Tumor Biology

Background:

• Accurate tumor cellularity assessment is crucial for diagnosis and treatment planning.
• Mathematical models utilizing apparent diffusion coefficient (ADC) parameters offer a non-invasive approach to estimate cellularity.
• Previous research proposed a model based on ADCmean and ADCmin for cellularity calculation.

Purpose of the Study:

• To evaluate the accuracy of a mathematical model for calculating tumor cellularity based on ADC parameters.
• To compare calculated cellularity with histopathology-estimated cell counts across diverse tumor types.

Main Methods:

• Re-analysis of previous data from 134 patients with various tumors.
• Calculation of cellularity using the Atuegwu et al. (2013) formula based on ADCmean and ADCmin.
• Correlation analysis (Pearson's coefficient) between calculated and histopathology-estimated cellularity, with P < .05 indicating statistical significance.

Main Results:

• Strong positive correlation between calculated and estimated cellularity was observed in head and neck squamous cell carcinoma (HNSCC) (r=0.701, P=.016) and lymphomas (r=0.661, P=.001).
• Moderate correlation was found in rectal cancer (r=0.510, P=.036).
• No statistically significant correlations were found for uterine cervical cancer, meningiomas, and thyroid cancer.

Conclusions:

• The ADC-based formula for cellularity calculation is not universally applicable across all tumor types.
• The model demonstrates potential utility for HNSCC, cerebral lymphomas, and rectal cancer.
• Further validation is recommended for its application in other tumor types, including uterine cervical cancer, meningioma, and thyroid cancer.