Histogram analysis of apparent diffusion coefficient for non-small cell lung cancer: correlation with pathological grade.
Mariko Doai1, Naoko Tsuchiya1, Hisao Tonami1, and Katsuo Usuda2

1Radiology, Kanazawa Medical University, Kahoku, Japan, 2Thoracic surgery, Kanazawa Medical Universiy, Kahoku, Japan

### Synopsis

The purpose of this study is to investigate the application of histogram analysis of ADC in determining the pathological grade of non-small cell lung cancer (NSCLC). The study included 54 patients and the histogram parameters were correlated with pathological grades. For the parameters that were significantly different between high- and low-grade tumors, ROC curve analysis was performed. The 95th percentile of ADC was the most beneficial parameter for distinguishing high-from low-grade tumors. ADC histogram analysis on the basis of the entire tumor volume is useful for predicting the pathological grade of NSCLC.

### Objective

Lung cancer is the most common malignant tumor and has become the main cause of cancer mortality. From a clinical point of view, it is important to predict tumor grading before treatment. Until now, several studies have explored the value of histogram analysis of apparent diffusion coefficient (ADC) for preoperative tumor grading in various malignant lesions including glioma, prostate cancer, and cervical cancer (1,2,3 ). The purpose of this study is to investigate the application of histogram analysis of ADC in determining the pathological grade of non-small cell lung cancer (NSCLC).

### Results

Mean, maximum, quartile, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC showed significant differences among the pathological grades (p=0.000, 0.001, 0.001, 0.012, 0.002, 0.000, 0.000, 0.000, 0.000, respectively) (Table 1) . Mean, 50th, 75th, 90th, and 95th percentiles of ADC proved to be significant histogram parameters for differentiating high- from low-grade. ROC analyses of the histogram parameters between high- and low-grade showed that AUC (area under the curve) of the mean, 50th, 75th, 90th, and 95th percentiles of ADC were 0.706, 0.688, 0.713, 0.730, and 0.740, respectively. The 95th percentile of ADC achieved the highest AUC, with a cut-off value of 1634.1×10-6 mm2/sec and sensitivity and specificity of 0.846 and 0.667, respectively. Figure 1 shows the representative case.

### Conclusion

The 95th percentile of ADC is the most beneficial parameter for distinguishing high-from low-grade tumors. ADC histogram analysis on the basis of the entire tumor volume is useful for predicting the pathological grade of NSCLC.

### Acknowledgements

No acknowledgement found.

### References

1.Ryu JY, Choi HS, Park JS, et al. Glioma: Application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heternogenetiy. PLOS one. 2014;9(9):e108335

2.Donati FO, Mazaheri Y, Afag A, et.al.Prostate cancer aggressiveness:Assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology.2014;27(1):143-152

3.Woo S, Cho YJ, Kim YS, et al. Histogram analysis of aaparent effusion coefficient map of diffusion-weighted MRI in endometrial cancer: a preliminary correlation study with histological grade. Acta Radilogica.2013;55(10):1270-1277

### Figures

Table 1. ADC histogram parameters of pathological grade of non-small cell lung cancer. The differences in histogram parameters among the groups were analyzed by Jonkhere-Terpstra test * Statistically significant with p<0.05.

Figure 1. Representative case of adenocarcinoma of right upper lobe (grade 3).

(A) A 3-D VOI was placed on the tumor at ADC map, and extracted tumor image was generated. (B) ADC histogram of the tumor.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
3456