Effectiveness of Apparent Diffusion Coefficient Values in Predicting Pathologic Subtypes and Grade in Non-Small-Cell...

Area of Science:

• Radiology and Medical Imaging
• Oncology
• Biomedical Engineering

Background:

• Non-small-cell lung cancer (NSCLC) diagnosis and staging are critical for treatment planning.
• Accurate prediction of histopathologic subtypes and tumor grade is essential for tailoring therapy.
• Current methods for determining these factors can be invasive or time-consuming.

Purpose of the Study:

• To evaluate the predictive capability of apparent diffusion coefficient (ADC) values.
• To correlate ADC values with histopathologic subtypes of NSCLC.
• To assess the relationship between ADC values and tumor grade in NSCLC.

Main Methods:

• Retrospective analysis of 48 surgically diagnosed NSCLC cases (January 2018-March 2020).
• Construction of ADC maps and measurement of mean ADC values from regions of interest within tumors.
• Consensus evaluation of all MRI scans by two experienced radiologists.

Main Results:

• Mean ADC values differed significantly across NSCLC subtypes: adenocarcinoma (1.51 ± 0.19 × 10^-3 mm²/s), squamous cell carcinoma (1.32 ± 0.15 × 10^-3 mm²/s), and large cell carcinoma (1.39 ± 0.25 × 10^-3 mm²/s).
• Mean ADC values decreased with increasing tumor grade: grade 1 (1.44 ± 0.14 × 10^-3 mm²/s), grade 2 (1.25 ± 0.10 × 10^-3 mm²/s), and grade 3 (1.07 ± 0.15 × 10^-3 mm²/s).
• Established cut-off values demonstrated high sensitivity and specificity for discriminating between tumor grades.

Conclusions:

• Apparent diffusion coefficient (ADC) values serve as a reliable, non-invasive biomarker.
• ADC measurements can accurately predict NSCLC histopathologic subtypes.
• ADC values effectively differentiate tumor grades, aiding in the assessment of NSCLC aggressiveness.