Analysis of the early response to chemotherapy in lung cancer using apparent diffusion coefficient single-slice histogram

Jianxing Xu,Xiao Han,Zhengyang Zhou,Jiangang Zheng

doi:10.4314/tjpr.v17i4.17

Abstract

Purpose : To evaluate the application of apparent diffusion coefficient (ADC) values derived from diffusion-weighted imaging (DWI) using single-slice histogram analysis to study the chemotherapy responses in lung cancer. Methods : A total of 22 chemotherapy patients with advanced lung cancer from the Nanjing Drum Tower Hospital (Nanjing, China) were included in the study. We obtained DWI before and during chemotherapy, performed single-slice histogram analysis of ADC values, and assessed responses after 3 months of chemotherapy. Differences in ADC histogram parameters were compared between the responder and non-responder groups. Results : After therapy, we classified 13 as responders and 9 patients as non-responders. The recorded peak ADC value (ADC peak ) and lowest ADC value (ADC lowest ) did not show any significant difference in baseline ADC lowest and ADC peak between responders and non-responders. After chemotherapy, 13 responders had significant increase in ADC lowest and ADC peak compared with pre-treatment values (p < 0.001). ADC lowest significantly increased in 9 non-responders (p < 0.05), although ADC peak did not significantly increase. ADC peak changes were significantly larger in the responder group than in the nonresponder group (p = 0.024). ADC lowest changes after treatment were larger in the responder group than in the non-responder group, though not significantly. Conclusion : ADC values derived from single-slice histogram analysis may provide a useful and clinically feasible method for monitoring early chemotherapy response in patients with lung cancer. Keywords : Lung cancer, Chemotherapy, Apparent diffusion coefficient values, Diffusion-weighted imaging, Single-slice histogram analysis

Highlights

Chemotherapy is the dominant treatment strategy for patients with advanced lung cancer.Response Evaluation Criteria in Solid Tumors (RECIST) is often used as standard for evaluating treatment responses in lung cancer, which analyzes tumor size changes byIn recent years, investigations have reported that diffusion-weighted imaging (DWI) has potential for monitoring the early treatment response in many malignant tumors, including rectal cancer, cervical cancer, breast cancer, head and neck cancers, prostate cancer and brain tumors [3 - 5], and apparent diffusion coefficient (ADC) derived from DWI changes as early as 24 h after therapy [6]
Investigations have reported that diffusion-weighted imaging (DWI) has potential for monitoring the early treatment response in many malignant tumors, including rectal cancer, cervical cancer, breast cancer, head and neck cancers, prostate cancer and brain tumors [3 - 5], and ADC derived from DWI changes as early as 24 h after therapy [6]
Single-slice ADC histograms were generated and ADCpeak and ADClowest were recorded from pretreatment and post-treatment ADC maps

Summary

Introduction

Chemotherapy is the dominant treatment strategy for patients with advanced lung cancer.Response Evaluation Criteria in Solid Tumors (RECIST) is often used as standard for evaluating treatment responses in lung cancer, which analyzes tumor size changes byIn recent years, investigations have reported that diffusion-weighted imaging (DWI) has potential for monitoring the early treatment response in many malignant tumors, including rectal cancer, cervical cancer, breast cancer, head and neck cancers, prostate cancer and brain tumors [3 - 5], and ADC derived from DWI changes as early as 24 h after therapy [6]. Response Evaluation Criteria in Solid Tumors (RECIST) is often used as standard for evaluating treatment responses in lung cancer, which analyzes tumor size changes by. A variety of methods have been applied to measuring mean ADC values on ADC maps from various regions of interest (ROIs) located in the tumor [7,8]. Histogram analysis based on the whole-tumor ROI method is preferred for differentiating or grading tumors or predicting treatment response in various organs [9,10,11,12]. Single-slice histogram analysis can be used to generate the ADC histogram, which represents the pixel frequency of each ADC value for the slice image. Single-slice histogram analysis requires virtually no data manipulation and can be generated in seconds

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